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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/05/27 00:36:19 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@4a769c1da3fef695bb865a1ade91236bbd28f37a)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 875264b80 deploying docs (apache/tvm@4a769c1da3fef695bb865a1ade91236bbd28f37a)
875264b80 is described below
commit 875264b8018a9b4370a8a8c0dff58110a47f465e
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Fri May 27 00:36:13 2022 +0000
deploying docs (apache/tvm@4a769c1da3fef695bb865a1ade91236bbd28f37a)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_onnx.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 22 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 4 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/deploy_ssd_gluoncv.rst.txt | 4 +-
.../deploy_models/sg_execution_times.rst.txt | 18 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 10 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 16 +-
.../tune_conv2d_layer_cuda.rst.txt | 1768 ++++++++++----------
.../tune_network_cuda.rst.txt | 2 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 465 ++++-
.../tune_with_autotvm/sg_execution_times.rst.txt | 12 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 34 +-
.../work_with_microtvm/micro_autotune.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 12 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../how_to/work_with_schedules/tensorize.rst.txt | 2 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
.../frontend/deploy_classification.rst.txt | 2 +-
.../tutorials/frontend/deploy_detection.rst.txt | 2 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 11 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 56 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 26 +-
.../tutorial/tensor_expr_get_started.rst.txt | 49 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 83 +-
docs/how_to/compile_models/from_onnx.html | 2 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 7 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 22 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 45 +-
docs/how_to/deploy_models/deploy_prequantized.html | 11 +-
.../deploy_models/deploy_prequantized_tflite.html | 4 +-
docs/how_to/deploy_models/deploy_quantized.html | 2 +-
docs/how_to/deploy_models/deploy_ssd_gluoncv.html | 34 +-
docs/how_to/deploy_models/sg_execution_times.html | 18 +-
.../extend_tvm/bring_your_own_datatypes.html | 2 +-
docs/how_to/extend_tvm/sg_execution_times.html | 10 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 1768 ++++++++++----------
.../tune_with_autoscheduler/tune_network_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autoscheduler/tune_sparse_x86.html | 465 ++++-
.../tune_with_autotvm/sg_execution_times.html | 12 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 34 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 16 +-
.../work_with_microtvm/sg_execution_times.html | 12 +-
.../how_to/work_with_relay/sg_execution_times.html | 8 +-
.../work_with_schedules/sg_execution_times.html | 18 +-
docs/how_to/work_with_schedules/tensorize.html | 2 +-
docs/reference/api/python/auto_scheduler.html | 4 +-
.../api/typedoc/classes/bytestreamreader.html | 12 +-
.../api/typedoc/classes/cachedcallstack.html | 34 +-
docs/reference/api/typedoc/classes/dldatatype.html | 12 +-
docs/reference/api/typedoc/classes/dldevice.html | 10 +-
.../reference/api/typedoc/classes/environment.html | 12 +-
docs/reference/api/typedoc/classes/ffilibrary.html | 20 +-
.../api/typedoc/classes/graphexecutor.html | 16 +-
docs/reference/api/typedoc/classes/instance.html | 40 +-
docs/reference/api/typedoc/classes/memory.html | 34 +-
docs/reference/api/typedoc/classes/module.html | 10 +-
docs/reference/api/typedoc/classes/ndarray.html | 22 +-
.../api/typedoc/classes/packedfunccell.html | 6 +-
docs/reference/api/typedoc/classes/rpcserver.html | 14 +-
docs/reference/api/typedoc/classes/scalar.html | 6 +-
.../api/typedoc/classes/webgpucontext.html | 12 +-
docs/reference/api/typedoc/enums/argtypecode.html | 30 +-
.../api/typedoc/enums/aynccallbackcode.html | 4 +-
.../api/typedoc/enums/dldatatypecode.html | 8 +-
.../api/typedoc/enums/rpcserverstate.html | 12 +-
docs/reference/api/typedoc/enums/sizeof.html | 18 +-
docs/reference/api/typedoc/index.html | 112 +-
.../api/typedoc/interfaces/disposable.html | 2 +-
.../api/typedoc/interfaces/functioninfo.html | 6 +-
.../api/typedoc/interfaces/libraryprovider.html | 4 +-
docs/searchindex.js | 2 +-
.../vta/tutorials/autotvm/sg_execution_times.html | 6 +-
.../tutorials/frontend/deploy_classification.html | 2 +-
.../vta/tutorials/frontend/deploy_detection.html | 2 +-
.../vta/tutorials/frontend/sg_execution_times.html | 6 +-
.../vta/tutorials/optimize/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/sg_execution_times.html | 6 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 6 +-
docs/tutorial/autotvm_relay_x86.html | 264 +--
docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 26 +-
docs/tutorial/tensor_expr_get_started.html | 45 +-
119 files changed, 3456 insertions(+), 2693 deletions(-)
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index 9623a72e3..68da28e30 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -289,7 +289,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.409 seconds)
+ **Total running time of the script:** ( 1 minutes 2.451 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index a04b10d85..57544b377 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -98,7 +98,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip91e10873-09c1-4e9e-ab90-833eac612e58 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipa91db0de-31a0-4a62-a78b-f9d973d9753a from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index 61b6015e5..e8ee3fa87 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -100,7 +100,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
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diff --git a/docs/_sources/how_to/compile_models/from_onnx.rst.txt b/docs/_sources/how_to/compile_models/from_onnx.rst.txt
index 0040f2125..10992f76d 100644
--- a/docs/_sources/how_to/compile_models/from_onnx.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_onnx.rst.txt
@@ -128,7 +128,7 @@ provides a static definition of the input size.
.. code-block:: none
- /workspace/python/tvm/relay/frontend/onnx.py:5664: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/python/tvm/relay/frontend/onnx.py:5677: UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec for node. Name: OpType: Conv
warnings.warn(str(e))
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index e459996ae..7165ac2ba 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -210,7 +210,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.092 seconds)
+ **Total running time of the script:** ( 1 minutes 13.840 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 49345e467..030a02500 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -79,7 +79,7 @@ Load a pretrained PyTorch model
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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+
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diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 6476791dd..e0b25d749 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -381,7 +381,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 1.397 seconds)
+ **Total running time of the script:** ( 1 minutes 0.298 seconds)
.. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index a8662151f..e591876d2 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,15 +5,15 @@
Computation times
=================
-**06:18.695** total execution time for **how_to_compile_models** files:
+**05:48.423** total execution time for **how_to_compile_models** files:
-- **01:19.409**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **01:05.092**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **01:01.397**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:40.861**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:37.334**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:29.861**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
-- **00:21.562**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:21.191**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:19.315**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:02.673**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:13.840**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:02.451**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **01:00.298**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:33.552**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:29.681**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
+- **00:24.067**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:21.246**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:21.123**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:19.486**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:02.679**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index c8dfc3d14..6994a1a04 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -402,7 +402,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.2367 16.0712 17.0173 15.9307 0.3384
+ 14.9208 14.8962 15.2210 14.7275 0.1403
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index f5ad27e6e..7e3845bee 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -108,7 +108,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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67%|######6 | 114M/170M [00:00<00:00, 178MB/s]
79%|#######9 | 135M/170M [00:00<00:00, 192MB/s]
93%|#########3| 159M/170M [00:00<00:00, 209MB/s]
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+
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s]
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83%|########3 | 141M/170M [00:03<00:00, 48.9MB/s]
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89%|########9 | 152M/170M [00:03<00:00, 49.3MB/s]
92%|#########2| 157M/170M [00:03<00:00, 47.7MB/s]
95%|#########4| 161M/170M [00:03<00:00, 46.5MB/s]
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100%|##########| 170M/170M [00:03<00:00, 48.5MB/s]
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -262,7 +262,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 7.093 seconds)
+ **Total running time of the script:** ( 3 minutes 2.511 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index 08ee21b9e..a37b91bee 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -187,7 +187,7 @@ training. Other models require a full post training calibration.
.. code-block:: none
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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33%|###3 | 4.54M/13.6M [00:00<00:00, 47.6MB/s]
67%|######6 | 9.08M/13.6M [00:00<00:00, 43.1MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 55.7MB/s]
+
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26%|##6 | 3.55M/13.6M [00:00<00:00, 37.2MB/s]
52%|#####2 | 7.11M/13.6M [00:00<00:00, 37.2MB/s]
91%|######### | 12.3M/13.6M [00:00<00:00, 45.1MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 44.6MB/s]
@@ -353,7 +353,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 90.5619 90.2690 99.1649 90.1398 1.0863
+ 88.2732 88.1940 88.8165 87.9212 0.2529
@@ -393,7 +393,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 6.596 seconds)
+ **Total running time of the script:** ( 1 minutes 4.686 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index d350fe6bc..b2797b6fd 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -360,7 +360,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 120.1172 120.0455 125.7181 119.1990 0.6962
+ 118.3424 118.2984 122.6727 117.5725 0.5796
@@ -394,7 +394,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 51.984 seconds)
+ **Total running time of the script:** ( 1 minutes 52.755 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index 9af17a7ec..ed5279a34 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -223,7 +223,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 14.160 seconds)
+ **Total running time of the script:** ( 1 minutes 51.853 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
index 089008433..2fcb75e79 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -137,7 +137,7 @@ Convert and compile model for CPU.
data: None
input_sym_arg_type = in_param.infer_type()[0]
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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+
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@@ -211,7 +211,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 24.808 seconds)
+ **Total running time of the script:** ( 2 minutes 22.892 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index c781ab534..8dd89f010 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**10:34.814** total execution time for **how_to_deploy_models** files:
+**11:04.158** total execution time for **how_to_deploy_models** files:
-- **03:07.093**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:24.808**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:51.984**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:14.160**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:06.596**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:28.426**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.542**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.204**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:02.511**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:22.892**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:52.755**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:51.853**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:04.686**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:28.016**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:21.243**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.202**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index d183b6d80..0b3355909 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -425,7 +425,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipb13670ac-ec3f-4a42-8f25-47b952f982ad from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip9f51e92b-0a8d-4586-b71c-72ab496afebb from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index b9db8d240..2d762d040 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
Computation times
=================
-**00:38.326** total execution time for **how_to_extend_tvm** files:
+**00:37.579** total execution time for **how_to_extend_tvm** files:
-- **00:34.756**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.294**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.067**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.208**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:34.159**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.211**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.007**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.202**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 33a11f1ad..2da63e13e 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -199,10 +199,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 5943us [5943us] (45.09%; 45.09%)
- FoldScaleAxis: 7237us [2us] (54.91%; 54.91%)
- FoldConstant: 7234us [1478us] (54.89%; 99.97%)
- InferType: 5756us [5756us] (43.67%; 79.56%)
+ InferType: 5738us [5738us] (44.86%; 44.86%)
+ FoldScaleAxis: 7052us [2us] (55.14%; 55.14%)
+ FoldConstant: 7050us [1467us] (55.12%; 99.97%)
+ InferType: 5583us [5583us] (43.65%; 79.20%)
@@ -239,10 +239,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 5820us [5820us] (44.74%; 44.74%)
- FoldScaleAxis: 7188us [2us] (55.26%; 55.26%)
- FoldConstant: 7186us [1500us] (55.24%; 99.98%)
- InferType: 5686us [5686us] (43.71%; 79.12%)
+ InferType: 5614us [5614us] (44.43%; 44.43%)
+ FoldScaleAxis: 7020us [2us] (55.57%; 55.57%)
+ FoldConstant: 7018us [1522us] (55.55%; 99.97%)
+ InferType: 5497us [5497us] (43.51%; 78.32%)
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index 2d1cf4fe2..846a823b6 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -299,7 +299,7 @@ latency of convolution.
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Convolution: 54.153677 ms
+ Convolution: 54.243287 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index 59287ff56..34365c55b 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -632,7 +632,7 @@ be able to run on our build server
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- conv2d with tensor core: 6.848243 ms
+ conv2d with tensor core: 6.618245 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index 42480a32e..ee3c52ecb 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,10 +118,10 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018346
+ Numpy running time: 0.019255
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Baseline: 3.219244
+ Baseline: 3.443456
@@ -212,7 +212,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.302480
+ Opt1: 0.312268
@@ -311,7 +311,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.343368
+ Opt2: 0.344336
@@ -403,7 +403,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.117844
+ Opt3: 0.122771
@@ -522,7 +522,7 @@ flattening.
.. code-block:: none
- Opt4: 0.110319
+ Opt4: 0.110611
@@ -640,7 +640,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111073
+ Opt5: 0.111446
@@ -761,7 +761,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.144650
+ Opt6: 0.146447
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index fc99d3d8e..96e3611f4 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:34.586** total execution time for **how_to_optimize_operators** files:
+**00:35.704** total execution time for **how_to_optimize_operators** files:
-- **00:31.901**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.426**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.259**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:32.956**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.477**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.271**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index effef2d0e..57aba5368 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**04:55.452** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:21.759**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:18.617**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:40.379**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:17.166**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:09.051**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.480**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**04:56.748** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:23.155**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:18.088**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:39.961**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:18.067**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:09.152**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.325**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index a2278e986..f702039f1 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -222,461 +222,469 @@ cooperative fetching, unrolling and operator fusion.
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute}
preflattened_buffer_map = {data_1: data_3: Buffer(data_2, float32, [1, 512, 7, 7], []), kernel_1: kernel_3: Buffer(kernel_2, float32, [512, 512, 3, 3], []), bias_1: bias_3: Buffer(bias_2, float32, [1, 512, 1, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [1, 512, 7, 7], [])} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 56;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 16;
allocate(conv2d_nchw: Pointer(local float32), float32, [4]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=8)[0] = 0f32
- conv2d_nchw_1[2] = 0f32
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [4032]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [6144]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=16)[0] = 0f32
conv2d_nchw_1[1] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[3] = 0f32
- for (rc.outer.outer: int32, 0, 64) {
- let cse_var_1: int32 = (rc.outer.outer*72)
- {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope="shared")[(threadIdx.x_1*6)] = @tir.if_then_else((((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- }
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64512)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129024)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193536)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258048)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1*6)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
+ for (rc.outer.outer: int32, 0, 8) {
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_2: int32 = (rc.outer.outer*3136)
+ let cse_var_1: int32 = (rc.outer.outer*576)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [4032], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((7 <= floormod(threadIdx.x_1, 63)) && (floormod(threadIdx.x_1, 63) < 56)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + (floordiv(threadIdx.x_1, 63)*49)) + rx.outer.outer) + floormod(threadIdx.x_1, 63)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 56), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dt [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 784)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 4), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 4), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 112), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 4), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, d [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1176)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 168), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1568)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 224), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1960)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 1), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 1), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 280), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 2352)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 3), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 3), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 336), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 2744)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 5), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 392), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 3136)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 448), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 3528)] = @tir.if_then_else(((((7 <= floormod(threadIdx.x_1, 63)) && (floormod(threadIdx.x_1, 63) < 56)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + (floordiv(floordiv(threadIdx.x_1, 7), 9)*49)) + rx.outer.outer) + floormod(threadIdx.x_1, 63)) + 2736)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ if @tir.likely((threadIdx.x_1 < 112), dtype=bool) {
+ pad_temp.shared_1[(threadIdx.x_1 + 3920)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 560), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32 [...]
}
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1: Buffer(kernel.shared, float32, [6144], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*147456) + (floordiv(threadIdx.x_2, 192)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 392)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 49), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1176)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 147), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 24), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1568)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 196), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 32), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1960)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 245), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 40), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 2352)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 294), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 48), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 2744)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 343), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 56), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3136)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 392), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 64), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3528)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 441), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 72), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3920)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 490), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 80), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 4312)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 539), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 88), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 4704)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 588), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 96), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 5096)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 637), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 104), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 686), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 112), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ if @tir.likely((threadIdx.x_2 < 264), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 5880)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 735), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 120), 192)*3)) + rx.outer.outer)]
}
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- }
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64513)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129025)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193537)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258049)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1*6)] = @tir.if_then_else((((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
+ for (rc.outer.inner: int32, 0, 2) {
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96))]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 1)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 2)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 3)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 4)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 5)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 6)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 7)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 8)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 9)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 10)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 11)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 12)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 13)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 14)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 15)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 16)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 17)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 18)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 19)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 20)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 21)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 22)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 23)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 24)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 25)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 26)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 27)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 28)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 29)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 30)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 31)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 32)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 33)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 34)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 35)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 36)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 37)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 38)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 39)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 40)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 41)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 42)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 43)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 44)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 45)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 46)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 47)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 48)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 49)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 50)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 51)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 52)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 53)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 54)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 55)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 56)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 57)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 58)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 59)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 60)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 61)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 62)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 63)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 64)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 65)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 66)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 67)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 68)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 69)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 70)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 71)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 72)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 73)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 74)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 75)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 76)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 77)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 78)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 79)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 80)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 81)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 82)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 83)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 84)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 85)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 86)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 87)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 88)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 89)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 90)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 91)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 92)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 93)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 94)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 95)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 192)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 193)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 194)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 195)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 196)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 197)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 198)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 199)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 200)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 201)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 202)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 203)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 204)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 205)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 206)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 207)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 208)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 209)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 210)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 211)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 212)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 213)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 214)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 215)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 216)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 217)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 218)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 219)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 220)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 221)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 222)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 223)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 224)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 225)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 226)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 227)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 228)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 229)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 230)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 231)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 232)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 233)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 234)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 235)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 236)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 237)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 238)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 239)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 240)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 241)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 242)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 243)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 244)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 245)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 246)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 247)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 248)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 249)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 250)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 251)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 252)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 253)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 254)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 255)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 256)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 257)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 258)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 259)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 260)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 261)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 262)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 263)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 264)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 265)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 266)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 267)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 268)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 269)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 270)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 271)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 272)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 273)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 274)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 275)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 276)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 277)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 278)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 279)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 280)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 281)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 282)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 283)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 284)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 285)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 286)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 287)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 384)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 385)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 386)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 387)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 388)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 389)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 390)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 391)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 392)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 393)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 394)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 395)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 396)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 397)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 398)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 399)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 400)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 401)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 402)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 403)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 404)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 405)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 406)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 407)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 408)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 409)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 410)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 411)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 412)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 413)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 414)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 415)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 416)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 417)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 418)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 419)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 420)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 421)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 422)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 423)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 424)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 425)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 426)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 427)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 428)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 429)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 430)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 431)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 432)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 433)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 434)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 435)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 436)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 437)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 438)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 439)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 440)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 441)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 442)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 443)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 444)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 445)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 446)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 447)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 448)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 449)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 450)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 451)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 452)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 453)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 454)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 455)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 456)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 457)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 458)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 459)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 460)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 461)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 462)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 463)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 464)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 465)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 466)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 467)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 468)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 469)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 470)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 471)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 472)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 473)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 474)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 475)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 476)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 477)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 478)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 479)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 576)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 577)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 578)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 579)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 580)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 581)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 582)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 583)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 584)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 585)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 586)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 587)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 588)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 589)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 590)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 591)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 592)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 593)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 594)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 595)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 596)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 597)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 598)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 599)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 600)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 601)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 602)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 603)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 604)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 605)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 606)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 607)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 608)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 609)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 610)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 611)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 612)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 613)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 614)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 615)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 616)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 617)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 618)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 619)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 620)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 621)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 622)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 623)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 624)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 625)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 626)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 627)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 628)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 629)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 630)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 631)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 632)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 633)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 634)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 635)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 636)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 637)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 638)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 639)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 640)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 641)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 642)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 643)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 644)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 645)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 646)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 647)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 648)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 649)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 650)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 651)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 652)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 653)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 654)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 655)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 656)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 657)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 658)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 659)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 660)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 661)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 662)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 663)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 664)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 665)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 666)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 667)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 668)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 669)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 670)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 671)]))
}
}
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64514)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129026)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193538)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258050)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
}
}
- for (i1.inner: int32, 0, 2) {
- compute[(((((floordiv(blockIdx.x, 7)*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + floormod(blockIdx.x, 7))] = max((conv2d_nchw_1[i1.inner] + bias[(((floordiv(blockIdx.x, 7)*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
- compute[((((((floordiv(blockIdx.x, 7)*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + floormod(blockIdx.x, 7)) + 1568)] = max((conv2d_nchw_1[(i1.inner + 2)] + bias[((((floordiv(blockIdx.x, 7)*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner) + 32)]), 0f32)
+ for (i1.inner: int32, 0, 4) {
+ compute[((((blockIdx.x*1568) + (floordiv(threadIdx.x, 49)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 49))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 49)*4)) + i1.inner)]), 0f32)
}
}
}
@@ -733,7 +741,7 @@ We build the binary and check its correctness and performance.
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Execution time of this operator: 0.391 ms
+ Execution time of this operator: 0.332 ms
@@ -777,36 +785,36 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_i, factor=1)
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
- conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
- conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
- conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
- conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
+ conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
+ conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=4)
+ conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=8)
+ conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
- conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
+ conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
- conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=1)
- conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=8)
- conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
- conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
+ conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=32)
+ conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=2)
+ conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=3)
+ conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2 [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
- compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
- compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
+ compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=4)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
+ compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
- compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+ compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
@@ -826,14 +834,14 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=392)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=6)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=392)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
- s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 1024)
+ s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -853,415 +861,439 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ extern "C" __global__ void __launch_bounds__(392) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
float conv2d_nchw[4];
- __shared__ float pad_temp_shared[72];
- __shared__ float kernel_shared[1536];
+ __shared__ float pad_temp_shared[4032];
+ __shared__ float kernel_shared[6144];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[1] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[3] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = ((((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = ((((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = ((((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = ((((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = ((((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = ((((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64512)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129024)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193536)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258048)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- }
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = (((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = (((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = (((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = (((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = (((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = (((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64513)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129025)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193537)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258049)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- }
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = ((((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = ((((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = ((((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = ((((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = ((((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = ((((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64514)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129026)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193538)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258050)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
+ for (int rc_outer_outer = 0; rc_outer_outer < 8; ++rc_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
+ __syncthreads();
+ pad_temp_shared[((int)threadIdx.x)] = (((((7 <= (((int)threadIdx.x) % 63)) && ((((int)threadIdx.x) % 63) < 56)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 3136) + ((((int)threadIdx.x) / 63) * 49)) + rx_outer_outer) + (((int)threadIdx.x) % 63)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 392)] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 392) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= (((((int)threadIdx.x) / 7) + 4) % 9)) && ((((((int)threadIdx.x) / 7) + 4) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 784) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 4) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1176)] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1176) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1568)] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1568) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1960)] = (((((1 <= (((((int)threadIdx.x) / 7) + 1) % 9)) && ((((((int)threadIdx.x) / 7) + 1) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1960) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 1) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 2352)] = (((((1 <= (((((int)threadIdx.x) / 7) + 3) % 9)) && ((((((int)threadIdx.x) / 7) + 3) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 2352) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 3) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 2744)] = (((((1 <= (((((int)threadIdx.x) / 7) + 5) % 9)) && ((((((int)threadIdx.x) / 7) + 5) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 2744) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 5) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 3136)] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 3136) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 3528)] = (((((7 <= (((int)threadIdx.x) % 63)) && ((((int)threadIdx.x) % 63) < 56)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 3136) + ((((int)threadIdx.x) / 63) * 49)) + rx_outer_outer) + (((int)threadIdx.x) % 63)) + 2736)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 112) {
+ pad_temp_shared[(((int)threadIdx.x) + 3920)] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 3920) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 392)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 392) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 8) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 16) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1176)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1176) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 24) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1568) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 32) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1960)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1960) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 40) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2352) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 48) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 2744)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2744) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 56) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3136) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 64) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3528)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3528) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 72) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3920) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 80) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 4312)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4312) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 88) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4704) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 96) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 5096)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5096) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 104) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5488) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 112) % 192) * 3)) + rx_outer_outer)];
+ if (((int)threadIdx.x) < 264) {
+ kernel_shared[(((int)threadIdx.x) + 5880)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5880) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 120) % 192) * 3)) + rx_outer_outer)];
+ }
+ __syncthreads();
+ for (int rc_outer_inner = 0; rc_outer_inner < 2; ++rc_outer_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[(((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96))]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 1)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 2)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 3)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 4)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 5)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 6)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 7)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 8)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 9)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 10)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 11)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 12)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 13)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 14)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 15)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 16)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 17)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 18)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 19)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 20)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 21)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 22)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 23)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 24)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 25)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 26)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 27)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 28)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 29)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 30)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 31)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 32)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 33)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 34)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 35)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 36)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 37)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 38)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 39)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 40)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 41)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 42)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 43)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 44)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 45)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 46)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 47)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 48)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 49)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 50)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 51)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 52)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 53)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 54)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 55)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 56)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 57)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 58)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 59)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 60)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 61)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 62)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 63)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 64)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 65)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 66)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 67)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 68)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 69)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 70)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 71)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 72)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 73)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 74)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 75)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 76)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 77)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 78)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 79)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 80)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 81)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 82)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 83)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 84)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 85)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 86)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 87)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 88)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 89)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 90)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 91)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 92)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 93)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 94)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 95)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 192)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 193)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 194)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 195)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 196)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 197)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 198)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 199)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 200)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 201)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 202)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 203)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 204)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 205)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 206)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 207)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 208)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 209)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 210)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 211)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 212)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 213)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 214)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 215)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 216)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 217)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 218)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 219)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 220)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 221)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 222)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 223)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 224)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 225)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 226)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 227)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 228)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 229)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 230)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 231)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 232)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 233)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 234)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 235)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 236)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 237)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 238)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 239)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 240)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 241)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 242)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 243)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 244)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 245)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 246)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 247)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 248)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 249)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 250)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 251)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 252)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 253)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 254)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 255)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 256)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 257)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 258)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 259)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 260)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 261)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 262)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 263)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 264)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 265)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 266)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 267)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 268)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 269)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 270)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 271)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 272)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 273)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 274)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 275)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 276)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 277)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 278)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 279)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 280)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 281)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 282)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 283)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 284)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 285)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 286)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 287)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 384)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 385)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 386)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 387)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 388)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 389)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 390)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 391)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 392)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 393)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 394)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 395)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 396)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 397)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 398)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 399)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 400)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 401)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 402)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 403)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 404)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 405)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 406)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 407)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 408)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 409)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 410)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 411)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 412)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 413)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 414)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 415)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 416)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 417)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 418)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 419)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 420)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 421)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 422)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 423)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 424)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 425)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 426)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 427)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 428)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 429)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 430)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 431)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 432)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 433)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 434)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 435)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 436)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 437)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 438)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 439)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 440)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 441)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 442)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 443)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 444)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 445)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 446)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 447)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 448)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 449)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 450)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 451)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 452)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 453)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 454)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 455)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 456)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 457)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 458)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 459)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 460)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 461)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 462)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 463)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 464)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 465)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 466)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 467)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 468)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 469)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 470)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 471)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 472)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 473)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 474)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 475)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 476)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 477)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 478)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 479)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 576)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 577)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 578)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 579)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 580)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 581)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 582)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 583)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 584)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 585)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 586)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 587)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 588)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 589)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 590)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 591)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 592)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 593)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 594)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 595)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 596)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 597)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 598)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 599)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 600)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 601)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 602)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 603)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 604)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 605)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 606)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 607)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 608)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 609)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 610)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 611)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 612)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 613)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 614)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 615)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 616)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 617)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 618)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 619)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 620)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 621)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 622)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 623)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 624)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 625)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 626)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 627)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 628)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 629)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 630)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 631)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 632)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 633)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 634)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 635)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 636)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 637)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 638)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 639)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 640)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 641)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 642)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 643)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 644)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 645)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 646)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 647)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 648)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 649)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 650)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 651)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 652)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 653)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 654)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 655)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 656)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 657)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 658)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 659)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 660)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 661)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 662)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 663)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 664)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 665)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 666)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 667)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 668)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 669)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 670)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 671)]));
+ }
}
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- compute[((((((((int)blockIdx.x) / 7) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + (((int)blockIdx.x) % 7))] = max((conv2d_nchw[i1_inner] + bias[((((((int)blockIdx.x) / 7) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[(((((((((int)blockIdx.x) / 7) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + (((int)blockIdx.x) % 7)) + 1568)] = max((conv2d_nchw[(i1_inner + 2)] + bias[(((((((int)blockIdx.x) / 7) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner) + 32)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 4; ++i1_inner) {
+ compute[((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 49) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 49))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 49) * 4)) + i1_inner)]), 0.000000e+00f);
}
}
@@ -1320,7 +1352,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 21.759 seconds)
+ **Total running time of the script:** ( 2 minutes 23.155 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 489f32ab0..a5940a27a 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -616,7 +616,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.5837 9.5939 9.6098 9.5473 0.0265
+ 9.7129 9.7105 9.7590 9.6691 0.0367
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index 6efd89e02..196967ed7 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -635,7 +635,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 756.6745 756.4965 760.3231 753.2039 2.9091
+ 736.0303 737.0998 741.1594 729.8317 4.6859
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.617 seconds)
+ **Total running time of the script:** ( 1 minutes 18.088 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
index 23f706607..75774dd61 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -362,76 +362,409 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- let cse_var_1: int32 = ((i.inner.init*32) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
- compute_5[(cse_var_1 + 1)] = 0f32
- compute_5[(cse_var_1 + 2)] = 0f32
- compute_5[(cse_var_1 + 3)] = 0f32
- compute_5[(cse_var_1 + 4)] = 0f32
- compute_5[(cse_var_1 + 5)] = 0f32
- compute_5[(cse_var_1 + 6)] = 0f32
- compute_5[(cse_var_1 + 7)] = 0f32
- compute_5[(cse_var_1 + 8)] = 0f32
- compute_5[(cse_var_1 + 9)] = 0f32
- compute_5[(cse_var_1 + 10)] = 0f32
- compute_5[(cse_var_1 + 11)] = 0f32
- compute_5[(cse_var_1 + 12)] = 0f32
- compute_5[(cse_var_1 + 13)] = 0f32
- compute_5[(cse_var_1 + 14)] = 0f32
- compute_5[(cse_var_1 + 15)] = 0f32
- }
- }
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 64) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i.inner*32) + (nb_j.inner*16))
- let cse_var_19: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_18: int32 = (cse_var_20 + 1)
- let cse_var_17: int32 = (cse_var_20 + 11)
- let cse_var_16: int32 = (cse_var_20 + 12)
- let cse_var_15: int32 = (cse_var_20 + 13)
- let cse_var_14: int32 = (cse_var_20 + 14)
- let cse_var_13: int32 = (cse_var_20 + 15)
- let cse_var_12: int32 = (cse_var_20 + 2)
- let cse_var_11: int32 = (cse_var_20 + 3)
- let cse_var_10: int32 = (cse_var_20 + 4)
- let cse_var_9: int32 = (cse_var_20 + 5)
- let cse_var_8: int32 = (cse_var_20 + 6)
- let cse_var_7: int32 = (cse_var_20 + 7)
- let cse_var_6: int32 = (cse_var_20 + 8)
- let cse_var_5: int32 = (cse_var_20 + 9)
- let cse_var_4: int32 = ((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.inner*256))
- let cse_var_3: int32 = (cse_var_20 + 10)
+ preflattened_buffer_map = {placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_16: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_18: Buffer(placeholder_10, float32, [128, 256], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ let cse_var_1: int32 = (i.outer.inner*128)
+ {
+ compute_5: Buffer(compute_4, float32, [512], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ compute_5[(cse_var_1 + 16)] = 0f32
+ compute_5[(cse_var_1 + 17)] = 0f32
+ compute_5[(cse_var_1 + 18)] = 0f32
+ compute_5[(cse_var_1 + 19)] = 0f32
+ compute_5[(cse_var_1 + 20)] = 0f32
+ compute_5[(cse_var_1 + 21)] = 0f32
+ compute_5[(cse_var_1 + 22)] = 0f32
+ compute_5[(cse_var_1 + 23)] = 0f32
+ compute_5[(cse_var_1 + 24)] = 0f32
+ compute_5[(cse_var_1 + 25)] = 0f32
+ compute_5[(cse_var_1 + 26)] = 0f32
+ compute_5[(cse_var_1 + 27)] = 0f32
+ compute_5[(cse_var_1 + 28)] = 0f32
+ compute_5[(cse_var_1 + 29)] = 0f32
+ compute_5[(cse_var_1 + 30)] = 0f32
+ compute_5[(cse_var_1 + 31)] = 0f32
+ compute_5[(cse_var_1 + 32)] = 0f32
+ compute_5[(cse_var_1 + 33)] = 0f32
+ compute_5[(cse_var_1 + 34)] = 0f32
+ compute_5[(cse_var_1 + 35)] = 0f32
+ compute_5[(cse_var_1 + 36)] = 0f32
+ compute_5[(cse_var_1 + 37)] = 0f32
+ compute_5[(cse_var_1 + 38)] = 0f32
+ compute_5[(cse_var_1 + 39)] = 0f32
+ compute_5[(cse_var_1 + 40)] = 0f32
+ compute_5[(cse_var_1 + 41)] = 0f32
+ compute_5[(cse_var_1 + 42)] = 0f32
+ compute_5[(cse_var_1 + 43)] = 0f32
+ compute_5[(cse_var_1 + 44)] = 0f32
+ compute_5[(cse_var_1 + 45)] = 0f32
+ compute_5[(cse_var_1 + 46)] = 0f32
+ compute_5[(cse_var_1 + 47)] = 0f32
+ compute_5[(cse_var_1 + 48)] = 0f32
+ compute_5[(cse_var_1 + 49)] = 0f32
+ compute_5[(cse_var_1 + 50)] = 0f32
+ compute_5[(cse_var_1 + 51)] = 0f32
+ compute_5[(cse_var_1 + 52)] = 0f32
+ compute_5[(cse_var_1 + 53)] = 0f32
+ compute_5[(cse_var_1 + 54)] = 0f32
+ compute_5[(cse_var_1 + 55)] = 0f32
+ compute_5[(cse_var_1 + 56)] = 0f32
+ compute_5[(cse_var_1 + 57)] = 0f32
+ compute_5[(cse_var_1 + 58)] = 0f32
+ compute_5[(cse_var_1 + 59)] = 0f32
+ compute_5[(cse_var_1 + 60)] = 0f32
+ compute_5[(cse_var_1 + 61)] = 0f32
+ compute_5[(cse_var_1 + 62)] = 0f32
+ compute_5[(cse_var_1 + 63)] = 0f32
+ compute_5[(cse_var_1 + 64)] = 0f32
+ compute_5[(cse_var_1 + 65)] = 0f32
+ compute_5[(cse_var_1 + 66)] = 0f32
+ compute_5[(cse_var_1 + 67)] = 0f32
+ compute_5[(cse_var_1 + 68)] = 0f32
+ compute_5[(cse_var_1 + 69)] = 0f32
+ compute_5[(cse_var_1 + 70)] = 0f32
+ compute_5[(cse_var_1 + 71)] = 0f32
+ compute_5[(cse_var_1 + 72)] = 0f32
+ compute_5[(cse_var_1 + 73)] = 0f32
+ compute_5[(cse_var_1 + 74)] = 0f32
+ compute_5[(cse_var_1 + 75)] = 0f32
+ compute_5[(cse_var_1 + 76)] = 0f32
+ compute_5[(cse_var_1 + 77)] = 0f32
+ compute_5[(cse_var_1 + 78)] = 0f32
+ compute_5[(cse_var_1 + 79)] = 0f32
+ compute_5[(cse_var_1 + 80)] = 0f32
+ compute_5[(cse_var_1 + 81)] = 0f32
+ compute_5[(cse_var_1 + 82)] = 0f32
+ compute_5[(cse_var_1 + 83)] = 0f32
+ compute_5[(cse_var_1 + 84)] = 0f32
+ compute_5[(cse_var_1 + 85)] = 0f32
+ compute_5[(cse_var_1 + 86)] = 0f32
+ compute_5[(cse_var_1 + 87)] = 0f32
+ compute_5[(cse_var_1 + 88)] = 0f32
+ compute_5[(cse_var_1 + 89)] = 0f32
+ compute_5[(cse_var_1 + 90)] = 0f32
+ compute_5[(cse_var_1 + 91)] = 0f32
+ compute_5[(cse_var_1 + 92)] = 0f32
+ compute_5[(cse_var_1 + 93)] = 0f32
+ compute_5[(cse_var_1 + 94)] = 0f32
+ compute_5[(cse_var_1 + 95)] = 0f32
+ compute_5[(cse_var_1 + 96)] = 0f32
+ compute_5[(cse_var_1 + 97)] = 0f32
+ compute_5[(cse_var_1 + 98)] = 0f32
+ compute_5[(cse_var_1 + 99)] = 0f32
+ compute_5[(cse_var_1 + 100)] = 0f32
+ compute_5[(cse_var_1 + 101)] = 0f32
+ compute_5[(cse_var_1 + 102)] = 0f32
+ compute_5[(cse_var_1 + 103)] = 0f32
+ compute_5[(cse_var_1 + 104)] = 0f32
+ compute_5[(cse_var_1 + 105)] = 0f32
+ compute_5[(cse_var_1 + 106)] = 0f32
+ compute_5[(cse_var_1 + 107)] = 0f32
+ compute_5[(cse_var_1 + 108)] = 0f32
+ compute_5[(cse_var_1 + 109)] = 0f32
+ compute_5[(cse_var_1 + 110)] = 0f32
+ compute_5[(cse_var_1 + 111)] = 0f32
+ compute_5[(cse_var_1 + 112)] = 0f32
+ compute_5[(cse_var_1 + 113)] = 0f32
+ compute_5[(cse_var_1 + 114)] = 0f32
+ compute_5[(cse_var_1 + 115)] = 0f32
+ compute_5[(cse_var_1 + 116)] = 0f32
+ compute_5[(cse_var_1 + 117)] = 0f32
+ compute_5[(cse_var_1 + 118)] = 0f32
+ compute_5[(cse_var_1 + 119)] = 0f32
+ compute_5[(cse_var_1 + 120)] = 0f32
+ compute_5[(cse_var_1 + 121)] = 0f32
+ compute_5[(cse_var_1 + 122)] = 0f32
+ compute_5[(cse_var_1 + 123)] = 0f32
+ compute_5[(cse_var_1 + 124)] = 0f32
+ compute_5[(cse_var_1 + 125)] = 0f32
+ compute_5[(cse_var_1 + 126)] = 0f32
+ compute_5[(cse_var_1 + 127)] = 0f32
+ for (elem_idx: int32, 0, (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ let cse_var_131: int32 = (cse_var_1 + 13)
+ let cse_var_130: int32 = (cse_var_1 + 14)
+ let cse_var_129: int32 = (cse_var_1 + 15)
+ let cse_var_128: int32 = (cse_var_1 + 16)
+ let cse_var_127: int32 = (cse_var_1 + 17)
+ let cse_var_126: int32 = (cse_var_1 + 18)
+ let cse_var_125: int32 = (cse_var_1 + 19)
+ let cse_var_124: int32 = (cse_var_1 + 2)
+ let cse_var_123: int32 = (cse_var_1 + 20)
+ let cse_var_122: int32 = (cse_var_1 + 21)
+ let cse_var_121: int32 = (cse_var_1 + 22)
+ let cse_var_120: int32 = (cse_var_1 + 23)
+ let cse_var_119: int32 = (cse_var_1 + 24)
+ let cse_var_118: int32 = (cse_var_1 + 25)
+ let cse_var_117: int32 = (cse_var_1 + 26)
+ let cse_var_116: int32 = (cse_var_1 + 42)
+ let cse_var_115: int32 = (cse_var_1 + 28)
+ let cse_var_114: int32 = (cse_var_1 + 29)
+ let cse_var_113: int32 = (cse_var_1 + 3)
+ let cse_var_112: int32 = (cse_var_1 + 30)
+ let cse_var_111: int32 = (cse_var_1 + 31)
+ let cse_var_110: int32 = (cse_var_1 + 32)
+ let cse_var_109: int32 = (cse_var_1 + 33)
+ let cse_var_108: int32 = (cse_var_1 + 34)
+ let cse_var_107: int32 = (cse_var_1 + 35)
+ let cse_var_106: int32 = (cse_var_1 + 36)
+ let cse_var_105: int32 = (cse_var_1 + 37)
+ let cse_var_104: int32 = (cse_var_1 + 38)
+ let cse_var_103: int32 = (cse_var_1 + 39)
+ let cse_var_102: int32 = (cse_var_1 + 4)
+ let cse_var_101: int32 = (cse_var_1 + 40)
+ let cse_var_100: int32 = (cse_var_1 + 27)
+ let cse_var_99: int32 = (cse_var_1 + 1)
+ let cse_var_98: int32 = (cse_var_1 + 10)
+ let cse_var_97: int32 = (cse_var_1 + 100)
+ let cse_var_96: int32 = (cse_var_1 + 101)
+ let cse_var_95: int32 = (cse_var_1 + 102)
+ let cse_var_94: int32 = (cse_var_1 + 103)
+ let cse_var_93: int32 = (cse_var_1 + 104)
+ let cse_var_92: int32 = (cse_var_1 + 105)
+ let cse_var_91: int32 = (cse_var_1 + 106)
+ let cse_var_90: int32 = (cse_var_1 + 107)
+ let cse_var_89: int32 = (cse_var_1 + 108)
+ let cse_var_88: int32 = (cse_var_1 + 109)
+ let cse_var_87: int32 = (cse_var_1 + 11)
+ let cse_var_86: int32 = (cse_var_1 + 110)
+ let cse_var_85: int32 = (cse_var_1 + 111)
+ let cse_var_84: int32 = (cse_var_1 + 127)
+ let cse_var_83: int32 = (cse_var_1 + 113)
+ let cse_var_82: int32 = (cse_var_1 + 114)
+ let cse_var_81: int32 = (cse_var_1 + 115)
+ let cse_var_80: int32 = (cse_var_1 + 116)
+ let cse_var_79: int32 = (cse_var_1 + 117)
+ let cse_var_78: int32 = (cse_var_1 + 118)
+ let cse_var_77: int32 = (cse_var_1 + 119)
+ let cse_var_76: int32 = (cse_var_1 + 12)
+ let cse_var_75: int32 = (cse_var_1 + 120)
+ let cse_var_74: int32 = (cse_var_1 + 121)
+ let cse_var_73: int32 = (cse_var_1 + 122)
+ let cse_var_72: int32 = (cse_var_1 + 123)
+ let cse_var_71: int32 = (cse_var_1 + 124)
+ let cse_var_70: int32 = (cse_var_1 + 125)
+ let cse_var_69: int32 = (cse_var_1 + 126)
+ let cse_var_68: int32 = (cse_var_1 + 112)
+ let cse_var_67: int32 = (cse_var_1 + 72)
+ let cse_var_66: int32 = (cse_var_1 + 73)
+ let cse_var_65: int32 = (cse_var_1 + 74)
+ let cse_var_64: int32 = (cse_var_1 + 75)
+ let cse_var_63: int32 = (cse_var_1 + 76)
+ let cse_var_62: int32 = (cse_var_1 + 77)
+ let cse_var_61: int32 = (cse_var_1 + 78)
+ let cse_var_60: int32 = (cse_var_1 + 79)
+ let cse_var_59: int32 = (cse_var_1 + 8)
+ let cse_var_58: int32 = (cse_var_1 + 80)
+ let cse_var_57: int32 = (cse_var_1 + 81)
+ let cse_var_56: int32 = (cse_var_1 + 82)
+ let cse_var_55: int32 = (cse_var_1 + 83)
+ let cse_var_54: int32 = (cse_var_1 + 84)
+ let cse_var_53: int32 = (cse_var_1 + 85)
+ let cse_var_52: int32 = (cse_var_1 + 41)
+ let cse_var_51: int32 = (cse_var_1 + 87)
+ let cse_var_50: int32 = (cse_var_1 + 88)
+ let cse_var_49: int32 = (cse_var_1 + 89)
+ let cse_var_48: int32 = (cse_var_1 + 9)
+ let cse_var_47: int32 = (cse_var_1 + 90)
+ let cse_var_46: int32 = (cse_var_1 + 91)
+ let cse_var_45: int32 = (cse_var_1 + 92)
+ let cse_var_44: int32 = (cse_var_1 + 93)
+ let cse_var_43: int32 = (cse_var_1 + 94)
+ let cse_var_42: int32 = (cse_var_1 + 95)
+ let cse_var_41: int32 = (cse_var_1 + 96)
+ let cse_var_40: int32 = (cse_var_1 + 97)
+ let cse_var_39: int32 = (cse_var_1 + 98)
+ let cse_var_38: int32 = (cse_var_1 + 99)
+ let cse_var_37: int32 = (elem_idx*16)
+ let cse_var_36: int32 = (cse_var_1 + 86)
+ let cse_var_35: int32 = (cse_var_1 + 43)
+ let cse_var_34: int32 = (cse_var_1 + 44)
+ let cse_var_33: int32 = (cse_var_1 + 45)
+ let cse_var_32: int32 = (cse_var_1 + 46)
+ let cse_var_31: int32 = (cse_var_1 + 47)
+ let cse_var_30: int32 = (cse_var_1 + 48)
+ let cse_var_29: int32 = (cse_var_1 + 49)
+ let cse_var_28: int32 = (cse_var_1 + 5)
+ let cse_var_27: int32 = (cse_var_1 + 50)
+ let cse_var_26: int32 = (cse_var_1 + 51)
+ let cse_var_25: int32 = (cse_var_1 + 52)
+ let cse_var_24: int32 = (cse_var_1 + 53)
+ let cse_var_23: int32 = (cse_var_1 + 54)
+ let cse_var_22: int32 = (cse_var_1 + 55)
+ let cse_var_21: int32 = (cse_var_1 + 56)
+ let cse_var_20: int32 = (cse_var_1 + 57)
+ let cse_var_19: int32 = (cse_var_1 + 71)
+ let cse_var_18: int32 = (cse_var_1 + 7)
+ let cse_var_17: int32 = (cse_var_1 + 69)
+ let cse_var_16: int32 = (cse_var_1 + 68)
+ let cse_var_15: int32 = (cse_var_1 + 67)
+ let cse_var_14: int32 = (cse_var_1 + 66)
+ let cse_var_13: int32 = (cse_var_1 + 65)
+ let cse_var_12: int32 = (cse_var_1 + 64)
+ let cse_var_11: int32 = (cse_var_1 + 63)
+ let cse_var_10: int32 = (cse_var_1 + 62)
+ let cse_var_9: int32 = (cse_var_1 + 61)
+ let cse_var_8: int32 = (cse_var_1 + 60)
+ let cse_var_7: int32 = (cse_var_1 + 6)
+ let cse_var_6: int32 = (cse_var_1 + 59)
+ let cse_var_5: int32 = (cse_var_1 + 70)
+ let cse_var_4: int32 = (cse_var_1 + 58)
+ let cse_var_3: int32 = ((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*2048))
{
- compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[((placeholder_3[cse_var_19]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
+ compute_5[cse_var_1] = (compute_5[cse_var_1] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_99] = (compute_5[cse_var_99] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_124] = (compute_5[cse_var_124] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_113] = (compute_5[cse_var_113] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_102] = (compute_5[cse_var_102] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_28] = (compute_5[cse_var_28] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_59] = (compute_5[cse_var_59] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_48] = (compute_5[cse_var_48] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_98] = (compute_5[cse_var_98] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_87] = (compute_5[cse_var_87] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_76] = (compute_5[cse_var_76] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_131] = (compute_5[cse_var_131] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_130] = (compute_5[cse_var_130] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_129] = (compute_5[cse_var_129] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_128] = (compute_5[cse_var_128] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_127] = (compute_5[cse_var_127] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_126] = (compute_5[cse_var_126] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_125] = (compute_5[cse_var_125] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_123] = (compute_5[cse_var_123] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_122] = (compute_5[cse_var_122] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_121] = (compute_5[cse_var_121] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_120] = (compute_5[cse_var_120] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_119] = (compute_5[cse_var_119] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_118] = (compute_5[cse_var_118] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_117] = (compute_5[cse_var_117] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_100] = (compute_5[cse_var_100] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_115] = (compute_5[cse_var_115] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_114] = (compute_5[cse_var_114] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_112] = (compute_5[cse_var_112] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_111] = (compute_5[cse_var_111] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_110] = (compute_5[cse_var_110] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_109] = (compute_5[cse_var_109] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_108] = (compute_5[cse_var_108] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_107] = (compute_5[cse_var_107] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_106] = (compute_5[cse_var_106] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_105] = (compute_5[cse_var_105] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_104] = (compute_5[cse_var_104] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_103] = (compute_5[cse_var_103] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_101] = (compute_5[cse_var_101] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_52] = (compute_5[cse_var_52] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_116] = (compute_5[cse_var_116] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_35] = (compute_5[cse_var_35] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_34] = (compute_5[cse_var_34] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_33] = (compute_5[cse_var_33] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_32] = (compute_5[cse_var_32] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_31] = (compute_5[cse_var_31] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_30] = (compute_5[cse_var_30] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_29] = (compute_5[cse_var_29] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_27] = (compute_5[cse_var_27] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_26] = (compute_5[cse_var_26] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_25] = (compute_5[cse_var_25] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_24] = (compute_5[cse_var_24] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_23] = (compute_5[cse_var_23] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_22] = (compute_5[cse_var_22] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_21] = (compute_5[cse_var_21] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_67] = (compute_5[cse_var_67] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_66] = (compute_5[cse_var_66] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_65] = (compute_5[cse_var_65] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_64] = (compute_5[cse_var_64] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_63] = (compute_5[cse_var_63] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_62] = (compute_5[cse_var_62] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_61] = (compute_5[cse_var_61] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_60] = (compute_5[cse_var_60] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_58] = (compute_5[cse_var_58] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_57] = (compute_5[cse_var_57] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_56] = (compute_5[cse_var_56] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_55] = (compute_5[cse_var_55] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_54] = (compute_5[cse_var_54] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_53] = (compute_5[cse_var_53] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_36] = (compute_5[cse_var_36] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_51] = (compute_5[cse_var_51] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_50] = (compute_5[cse_var_50] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_49] = (compute_5[cse_var_49] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_47] = (compute_5[cse_var_47] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_46] = (compute_5[cse_var_46] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_45] = (compute_5[cse_var_45] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_44] = (compute_5[cse_var_44] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_43] = (compute_5[cse_var_43] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_42] = (compute_5[cse_var_42] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_41] = (compute_5[cse_var_41] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_40] = (compute_5[cse_var_40] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_39] = (compute_5[cse_var_39] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_38] = (compute_5[cse_var_38] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_97] = (compute_5[cse_var_97] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_96] = (compute_5[cse_var_96] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_95] = (compute_5[cse_var_95] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_94] = (compute_5[cse_var_94] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_93] = (compute_5[cse_var_93] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_92] = (compute_5[cse_var_92] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_91] = (compute_5[cse_var_91] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_90] = (compute_5[cse_var_90] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_89] = (compute_5[cse_var_89] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_88] = (compute_5[cse_var_88] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_86] = (compute_5[cse_var_86] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_85] = (compute_5[cse_var_85] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_68] = (compute_5[cse_var_68] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_83] = (compute_5[cse_var_83] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_82] = (compute_5[cse_var_82] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_81] = (compute_5[cse_var_81] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_80] = (compute_5[cse_var_80] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_79] = (compute_5[cse_var_79] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_78] = (compute_5[cse_var_78] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_77] = (compute_5[cse_var_77] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_75] = (compute_5[cse_var_75] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_74] = (compute_5[cse_var_74] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_73] = (compute_5[cse_var_73] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_72] = (compute_5[cse_var_72] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_71] = (compute_5[cse_var_71] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_70] = (compute_5[cse_var_70] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_69] = (compute_5[cse_var_69] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_84] = (compute_5[cse_var_84] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 64) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
- compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
+ for (i0.inner: int32, 0, 32) {
+ for (i1.inner: int32, 0, 16) {
+ let cse_var_132: int32 = ((((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16)) + i1.inner)
+ compute[cse_var_132] = max((compute_5[((i0.inner*16) + i1.inner)] + placeholder_4[cse_var_132]), 0f32)
+ }
}
}
}
@@ -487,7 +820,7 @@ We build the binary and check its correctness and performance.
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Execution time of this operator: 1.844 ms
+ Execution time of this operator: 2.502 ms
diff --git a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
index 5600aab0a..a161cc91f 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:44.904** total execution time for **how_to_tune_with_autotvm** files:
+**00:44.935** total execution time for **how_to_tune_with_autotvm** files:
-- **00:43.992**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.235**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.230**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.224**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.222**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:44.053**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.232**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.218**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 75a4e937a..13eccde37 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -859,8 +859,8 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
- No: 6 GFLOPS: 43.38/43.38 result: MeasureResult(costs=(0.0053362214210526315,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6534135341644287, timestamp=1653590200.8923168) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
- No: 7 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 42.41/42.41 result: MeasureResult(costs=(0.0054580131578947375,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.575484037399292, timestamp=1653605049.7827628) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+ No: 7 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -983,7 +983,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
- No: 8 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1106,7 +1106,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
- No: 9 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1229,7 +1229,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
- No: 10 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1247,7 +1247,7 @@ for this template
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
- No: 11 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1370,7 +1370,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
- No: 12 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1493,7 +1493,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
- No: 13 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1616,7 +1616,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
- No: 14 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1739,7 +1739,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
- No: 15 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1862,7 +1862,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
- No: 16 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1985,7 +1985,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
- No: 17 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2108,7 +2108,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
- No: 18 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2231,7 +2231,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
- No: 19 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2319,7 +2319,7 @@ for this template
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007f048739bfa2
+ 12: 0x00007f6b8eee2fa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2384,7 +2384,7 @@ for this template
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
- No: 20 GFLOPS: 143.84/143.84 result: MeasureResult(costs=(0.00160947861,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4207580089569092, timestamp=1653590227.352538) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+ No: 20 GFLOPS: 143.76/143.76 result: MeasureResult(costs=(0.00161030436,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3760154247283936, timestamp=1653605076.1585646) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
@@ -2441,7 +2441,7 @@ and measure running time.
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Time cost of this operator: 0.001987
+ Time cost of this operator: 0.001955
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
index ed4554b49..7281f32f9 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -294,10 +294,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 313.7 98.743 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.073 0.967 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.922 0.29 (1, 1, 10, 10, 3) 1 1
- Total_time - 317.695 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 312.0 98.728 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.953 0.934 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.067 0.338 (1, 1, 10, 10, 3) 1 1
+ Total_time - 316.02 - - - -
@@ -359,10 +359,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 197.1 98.729 (1, 6, 10, 10, 1) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 0.871 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.8 0.401 (1, 3, 10, 10, 1) 1 1
- Total_time - 199.638 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 96.65 97.409 (1, 6, 10, 10, 1) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.758 1.772 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.813 0.819 (1, 3, 10, 10, 1) 1 1
+ Total_time - 99.221 - - - -
diff --git a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
index 53dadfeaf..92b510fbc 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:46.386** total execution time for **how_to_work_with_microtvm** files:
+**00:45.882** total execution time for **how_to_work_with_microtvm** files:
-- **00:42.044**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.707**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.226**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
-- **00:00.206**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
-- **00:00.203**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:41.651**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.632**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+- **00:00.200**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:00.199**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 7e76f4ca8..d0e9c305b 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:12.441** total execution time for **how_to_work_with_relay** files:
+**00:12.157** total execution time for **how_to_work_with_relay** files:
-- **00:10.446**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.769**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.226**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:09.980**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.959**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.219**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
index e72495fce..27db43c1e 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**00:05.916** total execution time for **how_to_work_with_schedules** files:
+**00:05.732** total execution time for **how_to_work_with_schedules** files:
-- **00:02.116**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.247**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.747**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.738**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.328**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.264**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.244**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.232**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:02.102**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.142**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.737**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.727**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.315**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.243**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.241**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.225**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 8a019c44a..76392d66b 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -318,7 +318,7 @@ The importing needs to happen before the tensorized GEMV being executed.
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [1024, 64], []), B_1: B_3: Buffer(B_2, float32, [512, 64], []), C_1: C_3: Buffer(C_2, float32, [1024, 512], [])} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp9fztjaat/input0.cc'\nsource_filename = \"/tmp/tmp9fztjaat/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmplfm8mwyy/input0.cc'\nsource_filename = \"/tmp/tmplfm8mwyy/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index ee26cfb98..bc775c0d4 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:20.774** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:20.664** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:20.558**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.216**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:20.457**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.206**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 332f59fd8..341a1599a 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -267,7 +267,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
- resnet18_v1 inference graph built in 21.86s!
+ resnet18_v1 inference graph built in 21.29s!
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 8396fafd2..7bb18845c 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -303,7 +303,7 @@ The compilation steps are:
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py:389: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 15.21s!
+ yolov3-tiny inference graph built in 14.81s!
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 551fbe744..2a3cbadd8 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**01:29.898** total execution time for **topic_vta_tutorials_frontend** files:
+**01:28.363** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:47.714**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:42.184**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:46.952**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:41.411**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index e9a873387..9c2007c63 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.577** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.488** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:03.013**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.565**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.934**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.554**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
index 10219c234..48452c9e4 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:01.029** total execution time for **topic_vta_tutorials** files:
+**00:00.999** total execution time for **topic_vta_tutorials** files:
-- **00:00.524**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.505**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.506**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.493**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index ff887721b..27ffefc85 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -184,7 +184,7 @@ trials, we can load the best schedule from the log file and apply it.
.. code-block:: none
-
+ *E
@@ -308,7 +308,7 @@ We build the binary and check its correctness and performance.
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Execution time of this operator: 93.665 ms
+ Execution time of this operator: 93.862 ms
@@ -404,7 +404,7 @@ resume the status and do more 5 trials.
Resume search:
/usr/local/lib/python3.7/dist-packages/xgboost/training.py:17: UserWarning: Old style callback is deprecated. See: https://xgboost.readthedocs.io/en/latest/python/callbacks.html
warnings.warn(f'Old style callback is deprecated. See: {link}', UserWarning)
-
+ *E
@@ -417,6 +417,11 @@ Expression (TE) language that demonstrates how TVM can optimize computational
operations.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 23.258 seconds)
+
+
.. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 1b882ef59..11e8cb73e 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -280,7 +280,7 @@ standard deviation.
.. code-block:: none
- {'mean': 495.0658949700005, 'median': 494.72848735000525, 'std': 0.8381999189590658}
+ {'mean': 498.91625751000447, 'median': 498.82358639999893, 'std': 0.3731100177629365}
@@ -494,31 +494,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.51/ 17.51 GFLOPS | Progress: (4/20) | 6.50 s
[Task 1/25] Current/Best: 6.16/ 17.51 GFLOPS | Progress: (8/20) | 8.96 s
[Task 1/25] Current/Best: 11.51/ 22.73 GFLOPS | Progress: (12/20) | 11.38 s
[Task 1/25] Current/Best: 16.78/ 22.84 GFLOPS | Progress: (16/20) | 13.07 s
[Task 1/25] Current/Best: 11.59/ 23.88 GFLOPS | Progress: (20/20) | 14.82 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 11.80/ 13.10 GFLOPS | Progress: (4/20) | 3.74 s
[Task 2/25] Current/Best: 14.35/ 18.38 GFLOPS | Progress: (8/20) | 5.05 s
[Task 2/25] Current/Best: 20.76/ 20.76 GFLOPS | Progress: (12/20) | 6.37 s
[Task 2/25] Current/Best: 12.87/ 20.76 GFLOPS | Progress: (16/20) | 7.66 s
[Task 2/25] Current/Best: 19.50/ 20.76 GFLOPS | Progress: (20/20) | 9.21 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.50 GFLOPS | Progress: (4/20) | 5.79 s
[Task 3/25] Current/Best: 15.58/ 16.80 GFLOPS | Progress: (8/20) | 7.71 s
[Task 3/25] Current/Best: 14.86/ 16.80 GFLOPS | Progress: (12/20) | 9.44 s
[Task 3/25] Current/Best: 7.15/ 23.68 GFLOPS | Progress: (16/20) | 11.37 s
[Task 3/25] Current/Best: 12.60/ 23.68 GFLOPS | Progress: (20/20) | 15.92 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.57/ 20.26 GFLOPS | Progress: (4/20) | 2.34 s
[Task 4/25] Current/Best: 6.88/ 20.26 GFLOPS | Progress: (8/20) | 6.69 s
[Task 4/25] Current/Best: 21.58/ 21.58 GFLOPS | Progress: (12/20) | 11.22 s
[Task 4/25] Current/Best: 16.12/ 21.68 GFLOPS | Progress: (16/20) | 13.46 s
[Task 4/25] Current/Best: 12.67/ 21.68 GFLOPS | Progress: (20/20) | 15.45 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.60/ 10.46 GFLOPS | Progress: (4/20) | 2.52 s
[Task 5/25] Current/Best: 11.81/ 11.81 GFLOPS | Progress: (8/20) | 4.61 s
[Task 5/25] Current/Best: 11.17/ 18.07 GFLOPS | Progress: (12/20) | 7.71 s
[Task 5/25] Current/Best: 11.65/ 22.63 GFLOPS | Progress: (16/20) | 9.13 s
[Task 5/25] Current/Best: 11.75/ 22.63 GFLOPS | Progress: (20/20) | 11.02 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.17/ 20.65 GFLOPS | Progress: (4/20) | 3.90 s
[Task 6/25] Current/Best: 18.99/ 20.65 GFLOPS | Progress: (8/20) | 5.64 s
[Task 6/25] Current/Best: 13.07/ 20.65 GFLOPS | Progress: (12/20) | 7.58 s
[Task 6/25] Current/Best: 19.99/ 20.65 GFLOPS | Progress: (16/20) | 9.81 s
[Task 6/25] Current/Best: 3.74/ 20.65 GFLOPS | Progress: (20/20) | 12.35 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.56/ 12.81 GFLOPS | Progress: (4/20) | 3.58 s
[Task 7/25] Current/Best: 20.03/ 21.06 GFLOPS | Progress: (8/20) | 5.09 s
[Task 7/25] Current/Best: 16.18/ 21.06 GFLOPS | Progress: (12/20) | 6.98 s
[Task 7/25] Current/Best: 12.17/ 21.06 GFLOPS | Progress: (16/20) | 9.02 s
[Task 7/25] Current/Best: 6.22/ 21.73 GFLOPS | Progress: (20/20) | 11.50 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.12/ 14.32 GFLOPS | Progress: (4/20) | 2.84 s
[Task 8/25] Current/Best: 10.13/ 14.32 GFLOPS | Progress: (8/20) | 7.60 s
[Task 8/25] Current/Best: 12.88/ 14.32 GFLOPS | Progress: (12/20) | 13.75 s
[Task 8/25] Current/Best: 19.09/ 19.09 GFLOPS | Progress: (16/20) | 15.87 s
[Task 8/25] Current/Best: 20.39/ 20.39 GFLOPS | Progress: (20/20) | 22.29 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.25/ 15.81 GFLOPS | Progress: (4/20) | 17.48 s
[Task 9/25] Current/Best: 23.39/ 23.39 GFLOPS | Progress: (8/20) | 19.35 s
[Task 9/25] Current/Best: 8.23/ 23.39 GFLOPS | Progress: (12/20) | 21.70 s
[Task 9/25] Current/Best: 17.64/ 23.39 GFLOPS | Progress: (16/20) | 24.37 s
[Task 9/25] Current/Best: 8.97/ 23.39 GFLOPS | Progress: (20/20) | 32.11 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 17.98/ 17.98 GFLOPS | Progress: (4/20) | 2.52 s
[Task 10/25] Current/Best: 15.62/ 17.98 GFLOPS | Progress: (8/20) | 4.09 s
[Task 10/25] Current/Best: 12.37/ 18.92 GFLOPS | Progress: (12/20) | 5.63 s
[Task 10/25] Current/Best: 19.18/ 20.49 GFLOPS | Progress: (16/20) | 6.75 s
[Task 10/25] Current/Best: 8.93/ 20.49 GFLOPS | Progress: (20/20
) | 8.28 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.07/ 18.13 GFLOPS | Progress: (4/20) | 3.22 s
[Task 11/25] Current/Best: 16.67/ 18.13 GFLOPS | Progress: (8/20) | 5.95 s
[Task 11/25] Current/Best: 18.16/ 18.16 GFLOPS | Progress: (12/20) | 7.96 s
[Task 11/25] Current/Best: 12.19/ 21.09 GFLOPS | Progress: (16/20) | 10.75 s
[Task 11/25] Current/Best: 19.29/ 21.58 GFLOPS | Progress: (20/20) | 12.79 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.72/ 18.29 GFLOPS | Progress: (4/20) | 5.33 s
[Task 12/25] Current/Best: 5.32/ 18.29 GFLOPS | Progress: (8/20) | 9.02 s
[Task 12/25] Current/Best: 19.17/ 19.17 GFLOPS | Progress: (12/20) | 11.00 s
[Task 12/25] Current/Best: 15.38/ 19.17 GFLOPS | Progress: (16/20) | 13.75 s
[Task 12/25] Current/Best: 13.47/ 19.17 GFLOPS | Progress: (20/20) | 15.69 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.67/ 17.27 GFLOPS | Progress: (4/20) | 3.61 s
[Task 13/25] Current/Best: 15.06/ 20.81 GFLOPS | Progress: (8/20) | 6.05 s
[Task 13/25] Current/Best: 19.47/ 21.30 GFLOPS | Progress: (12/20) | 9.00 s
[Task 13/25] Current/Best: 12.22/ 21.30 GFLOPS | Progress: (16/20) | 12.40 s
[Task 13/25] Current/Best: 18.82/ 21.30 GFLOPS | Progress: (20/20) | 14.63 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.59/ 13.59 GFLOPS | Progress: (4/20) | 3.22 s
[Task 14/25] Current/Best: 6.07/ 13.59 GFLOPS | Progress: (8/20) | 5.41 s
[Task 14/25] Current/Best: 20.23/ 20.23 GFLOPS | Progress: (12/20) | 7.97 s
[Task 14/25] Current/Best: 16.85/ 20.23 GFLOPS | Progress: (16/20) | 9.88 s
[Task 14/25] Current/Best: 16.75/ 20.23 GFLOPS | Progress: (20/20) | 11.68 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.50/ 17.50 GFLOPS | Progress: (4/20) | 6.60 s
[Task 1/25] Current/Best: 6.14/ 17.50 GFLOPS | Progress: (8/20) | 9.06 s
[Task 1/25] Current/Best: 11.55/ 22.75 GFLOPS | Progress: (12/20) | 11.52 s
[Task 1/25] Current/Best: 16.70/ 22.78 GFLOPS | Progress: (16/20) | 13.20 s
[Task 1/25] Current/Best: 10.88/ 23.87 GFLOPS | Progress: (20/20) | 14.94 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.26/ 13.05 GFLOPS | Progress: (4/20) | 3.58 s
[Task 2/25] Current/Best: 13.76/ 17.34 GFLOPS | Progress: (8/20) | 4.89 s
[Task 2/25] Current/Best: 21.17/ 21.17 GFLOPS | Progress: (12/20) | 6.24 s
[Task 2/25] Current/Best: 12.04/ 21.17 GFLOPS | Progress: (16/20) | 7.52 s
[Task 2/25] Current/Best: 20.02/ 21.17 GFLOPS | Progress: (20/20) | 9.10 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.54 GFLOPS | Progress: (4/20) | 5.80 s
[Task 3/25] Current/Best: 15.58/ 16.89 GFLOPS | Progress: (8/20) | 7.71 s
[Task 3/25] Current/Best: 14.83/ 16.89 GFLOPS | Progress: (12/20) | 9.45 s
[Task 3/25] Current/Best: 7.21/ 23.73 GFLOPS | Progress: (16/20) | 11.36 s
[Task 3/25] Current/Best: 12.12/ 23.73 GFLOPS | Progress: (20/20) | 15.87 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.54/ 19.15 GFLOPS | Progress: (4/20) | 2.34 s
[Task 4/25] Current/Best: 6.80/ 19.15 GFLOPS | Progress: (8/20) | 6.64 s
[Task 4/25] Current/Best: 22.45/ 22.45 GFLOPS | Progress: (12/20) | 11.18 s
[Task 4/25] Current/Best: 16.92/ 22.45 GFLOPS | Progress: (16/20) | 13.43 s
[Task 4/25] Current/Best: 13.38/ 22.45 GFLOPS | Progress: (20/20) | 15.33 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.59/ 10.25 GFLOPS | Progress: (4/20) | 2.56 s
[Task 5/25] Current/Best: 11.85/ 12.76 GFLOPS | Progress: (8/20) | 4.63 s
[Task 5/25] Current/Best: 10.64/ 18.03 GFLOPS | Progress: (12/20) | 7.72 s
[Task 5/25] Current/Best: 11.89/ 22.82 GFLOPS | Progress: (16/20) | 9.14 s
[Task 5/25] Current/Best: 12.07/ 22.82 GFLOPS | Progress: (20/20) | 11.01 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.22/ 20.71 GFLOPS | Progress: (4/20) | 3.91 s
[Task 6/25] Current/Best: 19.00/ 20.71 GFLOPS | Progress: (8/20) | 5.68 s
[Task 6/25] Current/Best: 13.17/ 20.71 GFLOPS | Progress: (12/20) | 7.62 s
[Task 6/25] Current/Best: 19.92/ 20.71 GFLOPS | Progress: (16/20) | 9.86 s
[Task 6/25] Current/Best: 3.70/ 20.71 GFLOPS | Progress: (20/20) | 12.43 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.45/ 12.84 GFLOPS | Progress: (4/20) | 3.60 s
[Task 7/25] Current/Best: 19.93/ 20.30 GFLOPS | Progress: (8/20) | 5.15 s
[Task 7/25] Current/Best: 15.22/ 20.30 GFLOPS | Progress: (12/20) | 7.08 s
[Task 7/25] Current/Best: 12.22/ 20.54 GFLOPS | Progress: (16/20) | 9.16 s
[Task 7/25] Current/Best: 6.29/ 21.54 GFLOPS | Progress: (20/20) | 11.65 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 8.46/ 13.37 GFLOPS | Progress: (4/20) | 3.06 s
[Task 8/25] Current/Best: 9.43/ 13.37 GFLOPS | Progress: (8/20) | 8.26 s
[Task 8/25] Current/Best: 12.97/ 13.63 GFLOPS | Progress: (12/20) | 14.67 s
[Task 8/25] Current/Best: 18.69/ 18.69 GFLOPS | Progress: (16/20) | 16.79 s
[Task 8/25] Current/Best: 20.01/ 20.01 GFLOPS | Progress: (20/20) | 23.52 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.17/ 15.55 GFLOPS | Progress: (4/20) | 18.31 s
[Task 9/25] Current/Best: 22.02/ 22.02 GFLOPS | Progress: (8/20) | 20.15 s
[Task 9/25] Current/Best: 8.21/ 22.02 GFLOPS | Progress: (12/20) | 22.60 s
[Task 9/25] Current/Best: 17.75/ 22.02 GFLOPS | Progress: (16/20) | 25.38 s
[Task 9/25] Current/Best: 8.91/ 22.02 GFLOPS | Progress: (20/20) | 33.45 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.19/ 18.19 GFLOPS | Progress: (4/20) | 2.65 s
[Task 10/25] Current/Best: 15.52/ 18.19 GFLOPS | Progress: (8/20) | 4.23 s
[Task 10/25] Current/Best: 12.33/ 19.12 GFLOPS | Progress: (12/20) | 5.75 s
[Task 10/25] Current/Best: 19.14/ 20.08 GFLOPS | Progress: (16/20) | 6.86 s
[Task 10/25] Current/Best: 8.89/ 20.08 GFLOPS | Progress: (20/20
) | 8.41 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.12/ 18.12 GFLOPS | Progress: (4/20) | 3.24 s
[Task 11/25] Current/Best: 16.68/ 18.12 GFLOPS | Progress: (8/20) | 5.95 s
[Task 11/25] Current/Best: 18.21/ 18.21 GFLOPS | Progress: (12/20) | 7.98 s
[Task 11/25] Current/Best: 13.36/ 21.06 GFLOPS | Progress: (16/20) | 10.71 s
[Task 11/25] Current/Best: 19.46/ 21.37 GFLOPS | Progress: (20/20) | 12.73 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.77/ 18.15 GFLOPS | Progress: (4/20) | 5.32 s
[Task 12/25] Current/Best: 5.10/ 18.15 GFLOPS | Progress: (8/20) | 9.00 s
[Task 12/25] Current/Best: 18.86/ 18.94 GFLOPS | Progress: (12/20) | 10.98 s
[Task 12/25] Current/Best: 15.56/ 18.94 GFLOPS | Progress: (16/20) | 13.72 s
[Task 12/25] Current/Best: 15.15/ 18.94 GFLOPS | Progress: (20/20) | 15.65 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 9.05/ 17.26 GFLOPS | Progress: (4/20) | 3.58 s
[Task 13/25] Current/Best: 16.00/ 20.84 GFLOPS | Progress: (8/20) | 6.02 s
[Task 13/25] Current/Best: 19.72/ 21.56 GFLOPS | Progress: (12/20) | 8.89 s
[Task 13/25] Current/Best: 12.21/ 21.56 GFLOPS | Progress: (16/20) | 12.31 s
[Task 13/25] Current/Best: 18.59/ 21.56 GFLOPS | Progress: (20/20) | 14.56 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 12.56/ 13.38 GFLOPS | Progress: (4/20) | 3.29 s
[Task 14/25] Current/Best: 6.11/ 13.38 GFLOPS | Progress: (8/20) | 5.50 s
[Task 14/25] Current/Best: 20.95/ 20.95 GFLOPS | Progress: (12/20) | 8.05 s
[Task 14/25] Current/Best: 16.30/ 20.95 GFLOPS | Progress: (16/20) | 9.98 s
[Task 14/25] Current/Best: 16.94/ 20.95 GFLOPS | Progress: (20/20) | 11.75 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
-
[Task 15/25] Current/Best: 16.15/ 17.67 GFLOPS | Progress: (4/20) | 2.61 s
[Task 15/25] Current/Best: 14.18/ 18.08 GFLOPS | Progress: (8/20) | 4.12 s
[Task 15/25] Current/Best: 10.26/ 22.37 GFLOPS | Progress: (12/20) | 6.38 s
[Task 15/25] Current/Best: 20.17/ 22.37 GFLOPS | Progress: (16/20) | 9.25 s
[Task 15/25] Current/Best: 9.70/ 22.37 GFLOPS | Progress: (20/20) | 10.40 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.48/ 20.48 GFLOPS | Progress: (4/20) | 2.98 s
[Task 16/25] Current/Best: 2.97/ 20.48 GFLOPS | Progress: (8/20) | 4.60 s
[Task 16/25] Current/Best: 19.09/ 20.48 GFLOPS | Progress: (12/20) | 5.83 s
[Task 16/25] Current/Best: 18.11/ 20.48 GFLOPS | Progress: (16/20) | 7.18 s
[Task 16/25] Current/Best: 9.19/ 21.68 GFLOPS | Progress: (20/20) | 9.24 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 13.51/ 18.64 GFLOPS | Progress: (4/20) | 4.68 s
[Task 17/25] Current/Best: 14.39/ 22.94 GFLOPS | Progress: (8/20) | 7.56 s
[Task 17/25] Current/Best: 16.73/ 22.94 GFLOPS | Progress: (12/20) | 9.60 s
[Task 17/25] Current/Best: 16.44/ 22.94 GFLOPS | Progress: (16/20) | 11.73 s
[Task 17/25] Current/Best: 9.98/ 22.94 GFLOPS | Progress: (20/20) | 13.85 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.60/ 17.21 GFLOPS | Progress: (4/20) | 3.65 s
[Task 18/25] Current/Best: 10.51/ 19.77 GFLOPS | Progress: (8/20) | 7.07 s
[Task 18/25] Current/Best: 19.55/ 19.77 GFLOPS | Progress: (12/20) | 9.01 s
[Task 18/25] Current/Best: 10.00/ 19.77 GFLOPS | Progress: (16/20) | 12.63 s
[Task 18/25] Current/Best: 20.46/ 20.46 GFLOPS | Progress: (20/20) | 14.15 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.06/ 20.21 GFLOPS | Progress: (4/20) | 5.98 s
[Task 19/25] Current/Best: 2.60/ 20.21 GFLOPS | Progress: (8/20) | 9.23 s
[Task 19/25] Current/Best: 19.24/ 21.50 GFLOPS | Progress: (12/20) | 12.04 s
[Task 19/25] Current/Best: 15.41/ 21.60 GFLOPS | Progress: (16/20) | 14.90 s
[Task 19/25] Current/Best: 2.70/ 23.41 GFLOPS | Progress: (20/20) | 17.66 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.41/ 15.25 GFLOPS | Progress: (4/20) | 3.32 s
[Task 20/25] Current/Best: 10.55/ 15.25 GFLOPS | Progress: (8/20) | 6.82 s
[Task 20/25] Current/Best: 2.31/ 16.32 GFLOPS | Progress: (12/20) | 10.80 s
[Task 20/25] Current/Best: 12.36/ 16.32 GFLOPS | Progress: (16/20) | 14.38 s Done.
-
[Task 20/25] Current/Best: 13.67/ 21.77 GFLOPS | Progress: (20/20) | 16.50 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.37/ 17.42 GFLOPS | Progress: (4/20) | 3.17 s
[Task 21/25] Current/Best: 14.40/ 17.42 GFLOPS | Progress: (8/20) | 4.73 s
[Task 21/25] Current/Best: 1.61/ 17.42 GFLOPS | Progress: (12/20) | 6.84 s
[Task 21/25] Current/Best: 18.31/ 18.31 GFLOPS | Progress: (16/20) | 10.31 s
[Task 21/25] Current/Best: 4.44/ 18.31 GFLOPS | Progress: (20/20) | 17.49 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 17.12 GFLOPS | Progress: (4/20) | 2.62 s
[Task 22/25] Current/Best: 9.11/ 21.51 GFLOPS | Progress: (8/20) | 4.60 s
[Task 22/25] Current/Best: 19.78/ 21.51 GFLOPS | Progress: (12/20) | 6.89 s
[Task 22/25] Current/Best: 15.02/ 21.51 GFLOPS | Progress: (16/20) | 8.93 s
[Task 22/25] Current/Best: 15.06/ 21.51 GFLOPS | Progress: (20/20) |
10.61 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.34/ 20.32 GFLOPS | Progress: (4/20) | 3.19 s
[Task 23/25] Current/Best: 14.49/ 20.32 GFLOPS | Progress: (8/20) | 6.57 s
[Task 23/25] Current/Best: 20.66/ 21.45 GFLOPS | Progress: (12/20) | 8.41 s
[Task 23/25] Current/Best: 6.17/ 21.45 GFLOPS | Progress: (16/20) | 15.52 s
[Task 23/25] Current/Best: 7.74/ 21.45 GFLOPS | Progress: (20/20) | 19.75 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.52/ 8.52 GFLOPS | Progress: (4/20) | 13.53 s
[Task 24/25] Current/Best: 2.15/ 8.52 GFLOPS | Progress: (8/20) | 30.34 s
[Task 24/25] Current/Best: 3.84/ 8.52 GFLOPS | Progress: (12/20) | 53.48 s
[Task 24/25] Current/Best: 7.22/ 8.52 GFLOPS | Progress: (16/20) | 58.90 s Done.
-
[Task 24/25] Current/Best: 3.34/ 8.84 GFLOPS | Progress: (20/20) | 64.96 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.91 GFLOPS | Progress: (4/20) | 31.00 s
[Task 25/25] Current/Best: 5.78/ 7.94 GFLOPS | Progress: (8/20) | 61.66 s
[Task 25/25] Current/Best: 5.90/ 7.94 GFLOPS | Progress: (12/20) | 90.14 s
[Task 25/25] Current/Best: 5.84/ 8.88 GFLOPS | Progress: (16/20) | 91.88 s
[Task 25/25] Current/Best: 2.92/ 8.88 GFLOPS | Progress: (20/20) | 112.05 s
+
[Task 15/25] Current/Best: 16.03/ 17.59 GFLOPS | Progress: (4/20) | 2.62 s
[Task 15/25] Current/Best: 14.39/ 18.08 GFLOPS | Progress: (8/20) | 4.14 s
[Task 15/25] Current/Best: 10.37/ 22.21 GFLOPS | Progress: (12/20) | 6.27 s
[Task 15/25] Current/Best: 20.10/ 22.21 GFLOPS | Progress: (16/20) | 9.20 s
[Task 15/25] Current/Best: 9.70/ 22.21 GFLOPS | Progress: (20/20) | 10.36 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.55/ 20.55 GFLOPS | Progress: (4/20) | 2.86 s
[Task 16/25] Current/Best: 3.03/ 20.55 GFLOPS | Progress: (8/20) | 4.46 s
[Task 16/25] Current/Best: 19.35/ 20.55 GFLOPS | Progress: (12/20) | 5.67 s
[Task 16/25] Current/Best: 18.32/ 20.55 GFLOPS | Progress: (16/20) | 7.00 s
[Task 16/25] Current/Best: 10.03/ 22.26 GFLOPS | Progress: (20/20) | 9.03 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.75/ 18.41 GFLOPS | Progress: (4/20) | 4.64 s
[Task 17/25] Current/Best: 14.47/ 23.41 GFLOPS | Progress: (8/20) | 7.40 s
[Task 17/25] Current/Best: 16.68/ 23.41 GFLOPS | Progress: (12/20) | 9.46 s
[Task 17/25] Current/Best: 16.54/ 23.41 GFLOPS | Progress: (16/20) | 11.58 s
[Task 17/25] Current/Best: 10.06/ 23.41 GFLOPS | Progress: (20/20) | 13.70 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.17/ 17.92 GFLOPS | Progress: (4/20) | 3.64 s
[Task 18/25] Current/Best: 10.55/ 19.43 GFLOPS | Progress: (8/20) | 7.06 s
[Task 18/25] Current/Best: 19.54/ 19.54 GFLOPS | Progress: (12/20) | 8.97 s
[Task 18/25] Current/Best: 10.02/ 19.54 GFLOPS | Progress: (16/20) | 12.54 s
[Task 18/25] Current/Best: 20.53/ 20.53 GFLOPS | Progress: (20/20) | 14.07 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 6.70/ 20.13 GFLOPS | Progress: (4/20) | 6.02 s
[Task 19/25] Current/Best: 2.59/ 20.13 GFLOPS | Progress: (8/20) | 9.36 s
[Task 19/25] Current/Best: 14.66/ 21.01 GFLOPS | Progress: (12/20) | 12.24 s
[Task 19/25] Current/Best: 15.51/ 21.01 GFLOPS | Progress: (16/20) | 15.11 s
[Task 19/25] Current/Best: 2.68/ 23.16 GFLOPS | Progress: (20/20) | 17.92 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.44/ 15.34 GFLOPS | Progress: (4/20) | 3.22 s
[Task 20/25] Current/Best: 10.08/ 15.34 GFLOPS | Progress: (8/20) | 6.64 s
[Task 20/25] Current/Best: 2.28/ 15.81 GFLOPS | Progress: (12/20) | 10.57 s
[Task 20/25] Current/Best: 12.43/ 15.81 GFLOPS | Progress: (16/20) | 14.16 s Done.
+
[Task 20/25] Current/Best: 11.63/ 22.10 GFLOPS | Progress: (20/20) | 16.27 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.40/ 17.62 GFLOPS | Progress: (4/20) | 3.16 s
[Task 21/25] Current/Best: 14.58/ 17.62 GFLOPS | Progress: (8/20) | 4.72 s
[Task 21/25] Current/Best: 1.61/ 17.62 GFLOPS | Progress: (12/20) | 6.83 s
[Task 21/25] Current/Best: 18.03/ 18.03 GFLOPS | Progress: (16/20) | 10.25 s
[Task 21/25] Current/Best: 4.45/ 18.03 GFLOPS | Progress: (20/20) | 17.41 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 17.02 GFLOPS | Progress: (4/20) | 2.63 s
[Task 22/25] Current/Best: 8.72/ 21.16 GFLOPS | Progress: (8/20) | 4.61 s
[Task 22/25] Current/Best: 20.02/ 21.16 GFLOPS | Progress: (12/20) | 6.90 s
[Task 22/25] Current/Best: 15.22/ 21.16 GFLOPS | Progress: (16/20) | 8.95 s
[Task 22/25] Current/Best: 13.75/ 21.16 GFLOPS | Progress: (20/20) |
10.62 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.47/ 20.60 GFLOPS | Progress: (4/20) | 3.16 s
[Task 23/25] Current/Best: 15.91/ 20.60 GFLOPS | Progress: (8/20) | 6.53 s
[Task 23/25] Current/Best: 20.84/ 21.58 GFLOPS | Progress: (12/20) | 8.33 s
[Task 23/25] Current/Best: 5.97/ 21.58 GFLOPS | Progress: (16/20) | 15.41 s
[Task 23/25] Current/Best: 7.66/ 21.58 GFLOPS | Progress: (20/20) | 19.64 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.57/ 8.57 GFLOPS | Progress: (4/20) | 13.63 s
[Task 24/25] Current/Best: 3.59/ 8.57 GFLOPS | Progress: (8/20) | 29.14 s
[Task 24/25] Current/Best: 3.04/ 8.57 GFLOPS | Progress: (12/20) | 52.16 s
[Task 24/25] Current/Best: 6.85/ 8.57 GFLOPS | Progress: (16/20) | 57.79 s
[Task 24/25] Current/Best: 3.08/ 8.69 GFLOPS | Progress: (20/20) | 63.97 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ Done.
+
[Task 25/25] Current/Best: 1.53/ 2.74 GFLOPS | Progress: (4/20) | 32.88 s
[Task 25/25] Current/Best: 5.87/ 8.51 GFLOPS | Progress: (8/20) | 358.28 s
[Task 25/25] Current/Best: 6.07/ 8.51 GFLOPS | Progress: (12/20) | 386.21 s
[Task 25/25] Current/Best: 5.87/ 9.40 GFLOPS | Progress: (16/20) | 387.90 s
[Task 25/25] Current/Best: 2.94/ 9.40 GFLOPS | Progress: (20/20) | 407.98 s
The output from this tuning process will look something like this:
@@ -660,8 +660,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 416.4164155199978, 'median': 416.02200489999177, 'std': 2.440451882223344}
- unoptimized: {'mean': 495.0658949700005, 'median': 494.72848735000525, 'std': 0.8381999189590658}
+ optimized: {'mean': 409.9049065999998, 'median': 409.9493076500039, 'std': 1.4443281286747947}
+ unoptimized: {'mean': 498.91625751000447, 'median': 498.82358639999893, 'std': 0.3731100177629365}
@@ -681,7 +681,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 11 minutes 44.801 seconds)
+ **Total running time of the script:** ( 16 minutes 43.297 seconds)
.. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index e131dd53a..d37d34ea1 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -244,7 +244,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.265e-07 secs/op
+ 1.274e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 8dfd59c16..33d95a3de 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -233,7 +233,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x2030ff70)), stage(b, placeholder(b, 0x96f1120)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
+ [stage(a, placeholder(a, 0xd3bdf80)), stage(b, placeholder(b, 0x2a8c09f0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 828b069e7..4b71f98d7 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,17 +5,17 @@
Computation times
=================
-**14:27.212** total execution time for **tutorial** files:
+**19:59.963** total execution time for **tutorial** files:
-- **11:44.801**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **00:58.674**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:51.645**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:26.111**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:23.837**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:01.063**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.720**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.211**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.048**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
-- **00:00.036**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
-- **00:00.034**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.033**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **16:43.297**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:23.258**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **01:01.446**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:26.021**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:23.530**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:01.293**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.714**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.219**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.048**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
+- **00:00.047**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.046**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.044**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index c174f45f8..4d7c9017a 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -253,7 +253,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
Numpy running time: 0.000008
- naive: 0.000009
+ naive: 0.000008
@@ -344,7 +344,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000006
+ parallel: 0.000007
@@ -397,7 +397,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000026
+ vector: 0.000025
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [(stride: int32*n: int32)], [], type="auto"),
@@ -447,10 +447,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.313210000778782e-06 1.0
- naive 8.886e-06 1.068901182475549
- parallel 6.0883e-06 0.7323645137593839
- vector 2.5569199999999996e-05 3.075731275596872
+ numpy 7.820949999768345e-06 1.0
+ naive 8.443799999999999e-06 1.07963866285427
+ parallel 7.0503e-06 0.9014633772379095
+ vector 2.4644499999999998e-05 3.151087783546751
@@ -839,7 +839,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018502
+ Numpy running time: 0.019042
@@ -897,7 +897,7 @@ optimizations.
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- none: 3.231061
+ none: 3.432912
@@ -996,7 +996,7 @@ schedule.
.. code-block:: none
- blocking: 0.307505
+ blocking: 0.308167
@@ -1088,7 +1088,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.337483
+ vectorization: 0.339106
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1160,7 +1160,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.120127
+ loop permutation: 0.119706
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1257,7 +1257,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.110463
+ array packing: 0.110581
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1348,7 +1348,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.110256
+ block caching: 0.113189
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1432,7 +1432,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.143347
+ parallelization: 0.145316
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1511,13 +1511,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.2310612047999996 1.0
- blocking 0.30750462630000003 0.09517140246157434
- vectorization 0.33748344510000006 0.10444972215278418
- loop permutation 0.12012712519999999 0.037178845458433765
- array packing 0.110462519 0.03418769004929374
- block caching 0.11025640510000001 0.034123898654784164
- parallelization 0.1433468549 0.044365255194499804
+ none 3.4329122497999998 1.0
+ blocking 0.30816739330000004 0.08976850291409394
+ vectorization 0.3391055713 0.09878072803048088
+ loop permutation 0.1197055175 0.03486996135918534
+ array packing 0.1105813771 0.03221211876488903
+ block caching 0.11318945949999999 0.03297184759284027
+ parallelization 0.14531579109999998 0.042330179313050026
@@ -1552,6 +1552,11 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.446 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index 0eb5c64ae..65126e122 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-52df2e84141b34cda2b1e723c22d38b22796d6a7
+4a769c1da3fef695bb865a1ade91236bbd28f37a
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 1d1e60c26..8def54396 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -551,7 +551,7 @@ class:['truck 0.9266'] left:471 right:83 top:689 bottom:169
class:['bicycle 0.9984'] left:111 right:113 top:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.409 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.451 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index a768fba00..585aae1a2 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -401,7 +401,7 @@
</div>
<img alt="../../_images/sphx_glr_from_mxnet_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_from_mxnet_001.png" />
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip91e10873-09c1-4e9e-ab90-833eac612e58 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipa91db0de-31a0-4a62-a78b-f9d973d9753a from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index df80f60c0..76acfa304 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -407,51 +407,46 @@ python3 -m pip install -f https://release.oneflow.info <span class="nv">oneflow<
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diff --git a/docs/how_to/compile_models/from_onnx.html b/docs/how_to/compile_models/from_onnx.html
index 90332fe40..2339a584a 100644
--- a/docs/how_to/compile_models/from_onnx.html
+++ b/docs/how_to/compile_models/from_onnx.html
@@ -420,7 +420,7 @@ provides a static definition of the input size.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/frontend/onnx.py:5664: UserWarning: Mismatched attribute type in ' : kernel_shape'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/frontend/onnx.py:5677: UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec for node. Name: OpType: Conv
warnings.warn(str(e))
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index b9ce2232e..001703794 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -469,7 +469,7 @@ A quick solution is</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 5.092 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 13.840 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
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--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -387,9 +387,10 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 4a349890c..e7c23d814 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -612,7 +612,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.397 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 0.298 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 2cce52f11..5cfee2ab5 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -300,18 +300,18 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:18.695</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:48.423</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:19.409</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
-<li><p><strong>01:05.092</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
-<li><p><strong>01:01.397</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
-<li><p><strong>00:40.861</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
-<li><p><strong>00:37.334</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
-<li><p><strong>00:29.861</strong>: <a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></li>
-<li><p><strong>00:21.562</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
-<li><p><strong>00:21.191</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
-<li><p><strong>00:19.315</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
-<li><p><strong>00:02.673</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
+<li><p><strong>01:13.840</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
+<li><p><strong>01:02.451</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
+<li><p><strong>01:00.298</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
+<li><p><strong>00:33.552</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
+<li><p><strong>00:29.681</strong>: <a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></li>
+<li><p><strong>00:24.067</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
+<li><p><strong>00:21.246</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
+<li><p><strong>00:21.123</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
+<li><p><strong>00:19.486</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
+<li><p><strong>00:02.679</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 93dcf0951..c4d502288 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -627,7 +627,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.2367 16.0712 17.0173 15.9307 0.3384
+ 14.9208 14.8962 15.2210 14.7275 0.1403
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 722ae4eb6..801609178 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,16 +409,39 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -516,7 +539,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 7.093 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 2.511 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index 25662814d..13351f37b 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,9 +450,10 @@ training. Other models require a full post training calibration.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
</div>
</div>
@@ -546,7 +547,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 90.5619 90.2690 99.1649 90.1398 1.0863
+ 88.2732 88.1940 88.8165 87.9212 0.2529
</pre></div>
</div>
<div class="admonition note">
@@ -585,7 +586,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 6.596 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.686 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index 0b0a914ef..36e4ef6ba 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -545,7 +545,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 120.1172 120.0455 125.7181 119.1990 0.6962
+ 118.3424 118.2984 122.6727 117.5725 0.5796
</pre></div>
</div>
<div class="admonition note">
@@ -573,7 +573,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 51.984 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 52.755 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index b7061e92b..53435dde4 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -482,7 +482,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.160 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 51.853 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index 6790a02fb..a94a5b2b6 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,22 +415,22 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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</pre></div>
</div>
<p>Create TVM runtime and do inference
@@ -475,7 +475,7 @@ Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from h
</pre></div>
</div>
<img alt="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 24.808 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 22.892 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_ssd_gluoncv.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 9c7ba969d..d47042f41 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:34.814</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:04.158</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>03:07.093</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
-<li><p><strong>02:24.808</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
-<li><p><strong>01:51.984</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
-<li><p><strong>01:14.160</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
-<li><p><strong>01:06.596</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
-<li><p><strong>00:28.426</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
-<li><p><strong>00:21.542</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
-<li><p><strong>00:00.204</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
+<li><p><strong>03:02.511</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
+<li><p><strong>02:22.892</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
+<li><p><strong>01:52.755</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
+<li><p><strong>01:51.853</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
+<li><p><strong>01:04.686</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
+<li><p><strong>00:28.016</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
+<li><p><strong>00:21.243</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
+<li><p><strong>00:00.202</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index 155934548..96708500b 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -590,7 +590,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipb13670ac-ec3f-4a42-8f25-47b952f982ad from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip9f51e92b-0a8d-4586-b71c-72ab496afebb from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 60180a8e0..bb659b3f5 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -300,12 +300,12 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:38.326</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:37.579</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:34.756</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
-<li><p><strong>00:02.294</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
-<li><p><strong>00:01.067</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
-<li><p><strong>00:00.208</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
+<li><p><strong>00:34.159</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
+<li><p><strong>00:02.211</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
+<li><p><strong>00:01.007</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
+<li><p><strong>00:00.202</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 571d3c8ab..4d7382e93 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -486,10 +486,10 @@ profile the execution time of each passes.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 5943us [5943us] (45.09%; 45.09%)
-FoldScaleAxis: 7237us [2us] (54.91%; 54.91%)
- FoldConstant: 7234us [1478us] (54.89%; 99.97%)
- InferType: 5756us [5756us] (43.67%; 79.56%)
+InferType: 5738us [5738us] (44.86%; 44.86%)
+FoldScaleAxis: 7052us [2us] (55.14%; 55.14%)
+ FoldConstant: 7050us [1467us] (55.12%; 99.97%)
+ InferType: 5583us [5583us] (43.65%; 79.20%)
</pre></div>
</div>
</div>
@@ -512,10 +512,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 5820us [5820us] (44.74%; 44.74%)
-FoldScaleAxis: 7188us [2us] (55.26%; 55.26%)
- FoldConstant: 7186us [1500us] (55.24%; 99.98%)
- InferType: 5686us [5686us] (43.71%; 79.12%)
+InferType: 5614us [5614us] (44.43%; 44.43%)
+FoldScaleAxis: 7020us [2us] (55.57%; 55.57%)
+ FoldConstant: 7018us [1522us] (55.55%; 99.97%)
+ InferType: 5497us [5497us] (43.51%; 78.32%)
</pre></div>
</div>
<p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index ba9535f92..02accd7c9 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -538,7 +538,7 @@ latency of convolution.</p>
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Convolution: 54.153677 ms
+Convolution: 54.243287 ms
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-conv-cuda-py">
diff --git a/docs/how_to/optimize_operators/opt_conv_tensorcore.html b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
index 1096459ec..a1b979fcc 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -882,7 +882,7 @@ be able to run on our build server</p>
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-conv2d with tensor core: 6.848243 ms
+conv2d with tensor core: 6.618245 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/optimize_operators/opt_gemm.html b/docs/how_to/optimize_operators/opt_gemm.html
index 5b97160da..796105f5f 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,10 +431,10 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018346
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019255
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Baseline: 3.219244
+Baseline: 3.443456
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -496,7 +496,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.302480
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.312268
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -565,7 +565,7 @@ vastly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.343368
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.344336
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -628,7 +628,7 @@ the access pattern for A matrix is more cache friendly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117844
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.122771
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -713,7 +713,7 @@ flattening.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110319
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110611
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -801,7 +801,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111073
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111446
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -893,7 +893,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.144650
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.146447
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 7565c5fa1..e712ce23c 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.586</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.704</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:31.901</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
-<li><p><strong>00:01.426</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
-<li><p><strong>00:01.259</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
+<li><p><strong>00:32.956</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
+<li><p><strong>00:01.477</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
+<li><p><strong>00:01.271</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 4afa07db1..d9d22048d 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -300,14 +300,14 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>04:55.452</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>04:56.748</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:21.759</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
-<li><p><strong>01:18.617</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
-<li><p><strong>00:40.379</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
-<li><p><strong>00:17.166</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
-<li><p><strong>00:09.051</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
-<li><p><strong>00:08.480</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
+<li><p><strong>02:23.155</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
+<li><p><strong>01:18.088</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
+<li><p><strong>00:39.961</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
+<li><p><strong>00:18.067</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
+<li><p><strong>00:09.152</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
+<li><p><strong>00:08.325</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index f1f74e75c..862ff8efa 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -470,461 +470,469 @@ cooperative fetching, unrolling and operator fusion.</p>
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute}
preflattened_buffer_map = {data_1: data_3: Buffer(data_2, float32, [1, 512, 7, 7], []), kernel_1: kernel_3: Buffer(kernel_2, float32, [512, 512, 3, 3], []), bias_1: bias_3: Buffer(bias_2, float32, [1, 512, 1, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [1, 512, 7, 7], [])} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 56;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 16;
allocate(conv2d_nchw: Pointer(local float32), float32, [4]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=8)[0] = 0f32
- conv2d_nchw_1[2] = 0f32
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [4032]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [6144]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=16)[0] = 0f32
conv2d_nchw_1[1] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[3] = 0f32
- for (rc.outer.outer: int32, 0, 64) {
- let cse_var_1: int32 = (rc.outer.outer*72)
- {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope="shared")[(threadIdx.x_1*6)] = @tir.if_then_else((((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)) && (1 <= floormod(blockIdx.x, 7))), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 8)], 0f32, dtype=float32)
- }
- }
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64512)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129024)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193536)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258048)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3))]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1*6)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
+ for (rc.outer.outer: int32, 0, 8) {
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_2: int32 = (rc.outer.outer*3136)
+ let cse_var_1: int32 = (rc.outer.outer*576)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [4032], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((7 <= floormod(threadIdx.x_1, 63)) && (floormod(threadIdx.x_1, 63) < 56)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + (floordiv(threadIdx.x_1, 63)*49)) + rx.outer.outer) + floormod(threadIdx.x_1, 63)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 56), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(th [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 784)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 4), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 4), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 112), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 4), 9)*7)) + rx.outer.outer) + floormod(t [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1176)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 168), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1568)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 224), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 1960)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 1), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 1), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 280), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 2352)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 3), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 3), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 336), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 2744)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 5), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 392), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 3136)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 448), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod( [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ pad_temp.shared_1[(threadIdx.x_1 + 3528)] = @tir.if_then_else(((((7 <= floormod(threadIdx.x_1, 63)) && (floormod(threadIdx.x_1, 63) < 56)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + (floordiv(floordiv(threadIdx.x_1, 7), 9)*49)) + rx.outer.outer) + floormod(threadIdx.x_1, 63)) + 2736)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ if @tir.likely((threadIdx.x_1 < 112), dtype=bool) {
+ pad_temp.shared_1[(threadIdx.x_1 + 3920)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 560), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormo [...]
}
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1: Buffer(kernel.shared, float32, [6144], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*147456) + (floordiv(threadIdx.x_2, 192)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 392)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 49), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1176)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 147), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 24), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1568)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 196), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 32), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 1960)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 245), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 40), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 2352)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 294), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 48), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 2744)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 343), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 56), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3136)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 392), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 64), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3528)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 441), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 72), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 3920)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 490), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 80), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 4312)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 539), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 88), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 4704)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 588), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 96), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 5096)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 637), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 104), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 686), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 112), 192)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 392;
+ if @tir.likely((threadIdx.x_2 < 264), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 5880)] = kernel[(((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 8) + 735), 24)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 120), 192)*3)) + rx.outer.outer)]
}
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else(((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 7)], 0f32, dtype=float32)
- }
- }
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64513)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129025)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193537)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 1)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258049)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 1)]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1*6)] = @tir.if_then_else((((1 <= floormod((threadIdx.x_1*6), 9)) && (floormod((threadIdx.x_1*6), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod((threadIdx.x_1*6), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 1)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 1), 9)) && (floormod(((threadIdx.x_1*6) + 1), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 1), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 2)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 2), 9)) && (floormod(((threadIdx.x_1*6) + 2), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*2), 3)*49)) + (floormod(((threadIdx.x_1*6) + 2), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 3)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 3), 9)) && (floormod(((threadIdx.x_1*6) + 3), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 3), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 4)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 4), 9)) && (floormod(((threadIdx.x_1*6) + 4), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 4), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*6) + 5)] = @tir.if_then_else((((1 <= floormod(((threadIdx.x_1*6) + 5), 9)) && (floormod(((threadIdx.x_1*6) + 5), 9) < 8)) && (floormod(blockIdx.x, 7) < 6)), data[(((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*2) + 1), 3)*49)) + (floormod(((threadIdx.x_1*6) + 5), 9)*7)) + floormod(blockIdx.x, 7)) - 6)], 0f32, dtype=float32)
+ for (rc.outer.inner: int32, 0, 2) {
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96))]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 1)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 2)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 3)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 4)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 5)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 6)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 7)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 8)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 9)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 10)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 11)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 12)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 13)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 14)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 15)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 16)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 17)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 18)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 19)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 20)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 21)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 22)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 23)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 24)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 25)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 26)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 27)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 28)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 29)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 30)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 31)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 32)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 33)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 34)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 35)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 36)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 37)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 38)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 39)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 40)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 41)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 42)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 43)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 44)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 45)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 46)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 47)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 48)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 49)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 50)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 51)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 52)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 53)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 54)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 55)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 56)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 57)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 58)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 59)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 60)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 61)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 62)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 63)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 64)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 65)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 66)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 67)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 68)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 69)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 70)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 71)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 72)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 73)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 74)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 75)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 76)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 77)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 78)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 79)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 80)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 81)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 82)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 83)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 84)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 85)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 86)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 87)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 88)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 89)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 90)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 91)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 92)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 93)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 94)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 95)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 192)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 193)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 194)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 195)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 196)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 197)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 198)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 199)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 200)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 201)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 202)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 203)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 204)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 205)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 206)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 207)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 208)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 209)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 210)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 211)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 212)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 213)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 214)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 215)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 216)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 217)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 218)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 219)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 220)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 221)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 222)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 223)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 224)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 225)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 226)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 227)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 228)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 229)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 230)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 231)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 232)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 233)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 234)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 235)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 236)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 237)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 238)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 239)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 240)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 241)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 242)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 243)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 244)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 245)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 246)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 247)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 248)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 249)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 250)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 251)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 252)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 253)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 254)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 255)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 256)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 257)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 258)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 259)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 260)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 261)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 262)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 263)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 264)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 265)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 266)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 267)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 268)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 269)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 270)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 271)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 272)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 273)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 274)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 275)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 276)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 277)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 278)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 279)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 280)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 281)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 282)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 283)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 284)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 285)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 286)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 287)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 384)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 385)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 386)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 387)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 388)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 389)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 390)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 391)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 392)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 393)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 394)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 395)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 396)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 397)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 398)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 399)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 400)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 401)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 402)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 403)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 404)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 405)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 406)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 407)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 408)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 409)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 410)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 411)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 412)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 413)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 414)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 415)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 416)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 417)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 418)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 419)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 420)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 421)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 422)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 423)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 424)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 425)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 426)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 427)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 428)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 429)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 430)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 431)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 432)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 433)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 434)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 435)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 436)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 437)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 438)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 439)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 440)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 441)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 442)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 443)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 444)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 445)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 446)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 447)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 448)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 449)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 450)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 451)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 452)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 453)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 454)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 455)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 456)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 457)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 458)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 459)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 460)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 461)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 462)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 463)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 464)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 465)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 466)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 467)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 468)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 469)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 470)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 471)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 472)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 473)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 474)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 475)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 476)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 477)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 478)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 479)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rc.outer.inner*2016) + floormod(threadIdx.x, 49))]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 576)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 577)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 578)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 579)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 70)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 580)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 77)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 581)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 582)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 133)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 583)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 140)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 584)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 585)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 196)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 586)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 203)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 587)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 588)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 259)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 589)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 266)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 590)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 591)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 322)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 592)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 329)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 593)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 594)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 385)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 595)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 392)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 596)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 597)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 448)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 598)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 455)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 599)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 600)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 511)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 601)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 518)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 602)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 603)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 574)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 604)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 581)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 605)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 606)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 637)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 607)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 644)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 608)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 609)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 700)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 610)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 707)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 611)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 612)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 763)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 613)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 770)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 614)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 615)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 826)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 616)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 833)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 617)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 618)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 889)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 619)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 896)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 620)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 621)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 952)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 622)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 959)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 623)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 624)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1015)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 625)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1022)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 626)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 627)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1078)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 628)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1085)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 629)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 630)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1141)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 631)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1148)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 632)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 633)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1204)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 634)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1211)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 635)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 636)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1267)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 637)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1274)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 638)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 639)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1330)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 640)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1337)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 641)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 642)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1393)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 643)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1400)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 644)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 645)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1456)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 646)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1463)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 647)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 648)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1519)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 649)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1526)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 650)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 651)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1582)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 652)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1589)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 653)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 654)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1645)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 655)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1652)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 656)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 657)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1708)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 658)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1715)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 659)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 660)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1771)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 661)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1778)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 662)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 663)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1834)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 664)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1841)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 665)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 666)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1897)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 667)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1904)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 668)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 669)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1960)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 670)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*2016) + floormod(threadIdx.x, 49)) + 1967)]*kernel.shared_1[(((floordiv(threadIdx.x, 49)*768) + (rc.outer.inner*96)) + 671)]))
}
}
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 64514)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 56), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 70), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 129026)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 98), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 112), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 193538)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 140), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 154), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 24)*3)) + 2)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 8), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 24)*3)) + 258050)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 8) + 182), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 16), 24)*3)) + 2)]
- }
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[(floordiv(threadIdx.x, 7)*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 768)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 24)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[floormod(threadIdx.x, 7)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 792)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 769)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 25)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 793)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 770)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 26)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 794)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 771)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 27)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 795)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 772)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 28)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 10)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 796)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 773)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 29)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 11)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 797)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 774)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 30)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 798)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 775)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 31)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 19)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 799)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 776)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 32)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 20)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 800)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 777)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 33)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 801)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 778)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 34)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 28)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 802)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 779)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 35)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 29)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 803)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 780)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 36)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 804)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 781)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 37)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 37)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 805)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 782)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 38)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 38)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 806)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 783)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 39)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 807)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 784)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 40)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 46)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 808)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 785)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 41)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 47)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 809)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 786)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 42)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 810)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 787)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 43)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 55)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 811)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 788)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 44)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 56)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 812)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 789)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 45)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 813)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 790)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 46)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 814)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 791)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 47)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(floormod(threadIdx.x, 7) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*48) + 815)]))
}
}
- for (i1.inner: int32, 0, 2) {
- compute[(((((floordiv(blockIdx.x, 7)*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + floormod(blockIdx.x, 7))] = max((conv2d_nchw_1[i1.inner] + bias[(((floordiv(blockIdx.x, 7)*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
- compute[((((((floordiv(blockIdx.x, 7)*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + floormod(blockIdx.x, 7)) + 1568)] = max((conv2d_nchw_1[(i1.inner + 2)] + bias[((((floordiv(blockIdx.x, 7)*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner) + 32)]), 0f32)
+ for (i1.inner: int32, 0, 4) {
+ compute[((((blockIdx.x*1568) + (floordiv(threadIdx.x, 49)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 49))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 49)*4)) + i1.inner)]), 0f32)
}
}
}
@@ -966,7 +974,7 @@ cooperative fetching, unrolling and operator fusion.</p>
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Execution time of this operator: 0.391 ms
+Execution time of this operator: 0.332 ms
</pre></div>
</div>
</div>
@@ -996,36 +1004,36 @@ conv2d_nchw_nn_o_i, conv2d_nchw_nn_i = s[conv2d_nchw].split(conv2d_nchw_nn, fact
conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_i, factor=1)
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
-conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
-conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
-conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
-conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
+conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
+conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=4)
+conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=8)
+conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
-conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
+conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
-conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=1)
-conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=8)
-conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
-conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
+conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=32)
+conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=2)
+conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=3)
+conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2d_nc [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
-compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
+compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=4)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
+compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
-compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
@@ -1045,14 +1053,14 @@ s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=392)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=6)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=392)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
-s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 1024)
+s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -1072,415 +1080,439 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+extern "C" __global__ void __launch_bounds__(392) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
float conv2d_nchw[4];
- __shared__ float pad_temp_shared[72];
- __shared__ float kernel_shared[1536];
+ __shared__ float pad_temp_shared[4032];
+ __shared__ float kernel_shared[6144];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[1] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[3] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = ((((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = ((((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = ((((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = ((((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = ((((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = ((((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) && (1 <= (((int)blockIdx.x) % 7))) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 8)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64512)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129024)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193536)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258048)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[(((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3))];
- }
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = (((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = (((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = (((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = (((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = (((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = (((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 7)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64513)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129025)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193537)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 1)];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258049)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 1)];
- }
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
- __syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 6)] = ((((1 <= ((((int)threadIdx.x) * 6) % 9)) && (((((int)threadIdx.x) * 6) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + (((((int)threadIdx.x) * 6) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 1)] = ((((1 <= (((((int)threadIdx.x) * 6) + 1) % 9)) && ((((((int)threadIdx.x) * 6) + 1) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 1) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 2)] = ((((1 <= (((((int)threadIdx.x) * 6) + 2) % 9)) && ((((((int)threadIdx.x) * 6) + 2) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 2) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 2) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 3)] = ((((1 <= (((((int)threadIdx.x) * 6) + 3) % 9)) && ((((((int)threadIdx.x) * 6) + 3) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 3) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 4)] = ((((1 <= (((((int)threadIdx.x) * 6) + 4) % 9)) && ((((((int)threadIdx.x) * 6) + 4) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 4) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[((((int)threadIdx.x) * 6) + 5)] = ((((1 <= (((((int)threadIdx.x) * 6) + 5) % 9)) && ((((((int)threadIdx.x) * 6) + 5) % 9) < 8)) && ((((int)blockIdx.x) % 7) < 6)) ? data[(((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 2) + 1) / 3) * 49)) + ((((((int)threadIdx.x) * 6) + 5) % 9) * 7)) + (((int)blockIdx.x) % 7)) - 6)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 64514)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 560) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 129026)];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 784) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 193538)];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1120) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1232) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) % 24) * 3)) + 2)];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) % 24) * 3)) + 258050)];
- if (((int)threadIdx.x) < 80) {
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 1456) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) % 24) * 3)) + 2)];
+ for (int rc_outer_outer = 0; rc_outer_outer < 8; ++rc_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
+ __syncthreads();
+ pad_temp_shared[((int)threadIdx.x)] = (((((7 <= (((int)threadIdx.x) % 63)) && ((((int)threadIdx.x) % 63) < 56)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 3136) + ((((int)threadIdx.x) / 63) * 49)) + rx_outer_outer) + (((int)threadIdx.x) % 63)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 392)] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 392) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= (((((int)threadIdx.x) / 7) + 4) % 9)) && ((((((int)threadIdx.x) / 7) + 4) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 784) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 4) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1176)] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1176) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1568)] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1568) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 1960)] = (((((1 <= (((((int)threadIdx.x) / 7) + 1) % 9)) && ((((((int)threadIdx.x) / 7) + 1) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 1960) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 1) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 2352)] = (((((1 <= (((((int)threadIdx.x) / 7) + 3) % 9)) && ((((((int)threadIdx.x) / 7) + 3) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 2352) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 3) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 2744)] = (((((1 <= (((((int)threadIdx.x) / 7) + 5) % 9)) && ((((((int)threadIdx.x) / 7) + 5) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 2744) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 5) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 3136)] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 3136) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 3528)] = (((((7 <= (((int)threadIdx.x) % 63)) && ((((int)threadIdx.x) % 63) < 56)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 3136) + ((((int)threadIdx.x) / 63) * 49)) + rx_outer_outer) + (((int)threadIdx.x) % 63)) + 2736)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 112) {
+ pad_temp_shared[(((int)threadIdx.x) + 3920)] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 3136) + (((((int)threadIdx.x) + 3920) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 392)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 392) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 8) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 16) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1176)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1176) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 24) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1568) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 32) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 1960)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1960) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 40) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2352) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 48) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 2744)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2744) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 56) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3136) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 64) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3528)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3528) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 72) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3920) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 80) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 4312)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4312) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 88) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4704) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 96) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 5096)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5096) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 104) % 192) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5488) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 112) % 192) * 3)) + rx_outer_outer)];
+ if (((int)threadIdx.x) < 264) {
+ kernel_shared[(((int)threadIdx.x) + 5880)] = kernel[(((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5880) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) + 120) % 192) * 3)) + rx_outer_outer)];
+ }
+ __syncthreads();
+ for (int rc_outer_inner = 0; rc_outer_inner < 2; ++rc_outer_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[(((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96))]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 1)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 2)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 3)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 4)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 5)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 6)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 7)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 8)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 9)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 10)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 11)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 12)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 13)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 14)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 15)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 16)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 17)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 18)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 19)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 20)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 21)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 22)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 23)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 24)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 25)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 26)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 27)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 28)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 29)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 30)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 31)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 32)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 33)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 34)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 35)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 36)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 37)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 38)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 39)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 40)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 41)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 42)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 43)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 44)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 45)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 46)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 47)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 48)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 49)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 50)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 51)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 52)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 53)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 54)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 55)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 56)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 57)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 58)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 59)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 60)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 61)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 62)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 63)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 64)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 65)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 66)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 67)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 68)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 69)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 70)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 71)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 72)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 73)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 74)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 75)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 76)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 77)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 78)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 79)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 80)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 81)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 82)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 83)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 84)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 85)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 86)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 87)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 88)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 89)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 90)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 91)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 92)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 93)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 94)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 95)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 192)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 193)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 194)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 195)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 196)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 197)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 198)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 199)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 200)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 201)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 202)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 203)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 204)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 205)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 206)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 207)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 208)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 209)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 210)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 211)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 212)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 213)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 214)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 215)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 216)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 217)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 218)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 219)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 220)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 221)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 222)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 223)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 224)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 225)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 226)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 227)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 228)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 229)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 230)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 231)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 232)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 233)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 234)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 235)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 236)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 237)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 238)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 239)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 240)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 241)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 242)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 243)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 244)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 245)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 246)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 247)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 248)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 249)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 250)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 251)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 252)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 253)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 254)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 255)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 256)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 257)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 258)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 259)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 260)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 261)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 262)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 263)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 264)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 265)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 266)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 267)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 268)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 269)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 270)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 271)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 272)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 273)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 274)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 275)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 276)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 277)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 278)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 279)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 280)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 281)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 282)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 283)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 284)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 285)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 286)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 287)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 384)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 385)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 386)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 387)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 388)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 389)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 390)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 391)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 392)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 393)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 394)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 395)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 396)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 397)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 398)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 399)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 400)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 401)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 402)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 403)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 404)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 405)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 406)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 407)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 408)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 409)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 410)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 411)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 412)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 413)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 414)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 415)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 416)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 417)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 418)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 419)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 420)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 421)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 422)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 423)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 424)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 425)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 426)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 427)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 428)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 429)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 430)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 431)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 432)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 433)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 434)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 435)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 436)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 437)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 438)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 439)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 440)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 441)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 442)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 443)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 444)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 445)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 446)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 447)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 448)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 449)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 450)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 451)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 452)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 453)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 454)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 455)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 456)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 457)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 458)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 459)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 460)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 461)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 462)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 463)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 464)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 465)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 466)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 467)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 468)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 469)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 470)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 471)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 472)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 473)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 474)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 475)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 476)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 477)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 478)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 479)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49))] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 576)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 577)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 578)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 579)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 70)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 580)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 77)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 581)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 582)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 133)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 583)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 140)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 584)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 585)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 196)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 586)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 203)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 587)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 588)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 259)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 589)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 266)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 590)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 591)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 322)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 592)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 329)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 593)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 594)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 385)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 595)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 392)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 596)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 597)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 448)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 598)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 455)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 599)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 504)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 600)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 511)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 601)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 518)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 602)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 567)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 603)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 574)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 604)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 581)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 605)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 630)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 606)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 637)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 607)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 644)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 608)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 693)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 609)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 700)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 610)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 707)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 611)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 756)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 612)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 763)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 613)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 770)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 614)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 819)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 615)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 826)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 616)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 833)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 617)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 882)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 618)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 889)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 619)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 896)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 620)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 945)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 621)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 952)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 622)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 959)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 623)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 624)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1015)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 625)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1022)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 626)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 627)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1078)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 628)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1085)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 629)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 630)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1141)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 631)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1148)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 632)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 633)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1204)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 634)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1211)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 635)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 636)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1267)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 637)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1274)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 638)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 639)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1330)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 640)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1337)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 641)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 642)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1393)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 643)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1400)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 644)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 645)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1456)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 646)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1463)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 647)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 648)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1519)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 649)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1526)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 650)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 651)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1582)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 652)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1589)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 653)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 654)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1645)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 655)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1652)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 656)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 657)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1708)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 658)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1715)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 659)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 660)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1771)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 661)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1778)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 662)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 663)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1834)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 664)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1841)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 665)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 666)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1897)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 667)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1904)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 668)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 669)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1960)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 670)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 2016) + (((int)threadIdx.x) % 49)) + 1967)] * kernel_shared[((((((int)threadIdx.x) / 49) * 768) + (rc_outer_inner * 96)) + 671)]));
+ }
}
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[((((int)threadIdx.x) / 7) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 768)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 24)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((int)threadIdx.x) % 7)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 792)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 769)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 25)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 793)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 770)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 26)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 794)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 771)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 27)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 795)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 772)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 28)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 10)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 796)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 773)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 29)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 11)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 797)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 774)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 30)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 798)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 775)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 31)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 19)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 799)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 776)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 32)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 20)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 800)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 777)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 33)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 801)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 778)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 34)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 28)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 802)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 779)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 35)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 29)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 803)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 780)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 36)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 804)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 781)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 37)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 37)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 805)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 782)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 38)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 38)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 806)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 783)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 39)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 807)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 784)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 40)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 46)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 808)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 785)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 41)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 47)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 809)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 786)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 42)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 810)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 787)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 43)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 55)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 811)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 788)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 44)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 56)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 812)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 789)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 45)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 813)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 790)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 46)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 814)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 791)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 47)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((int)threadIdx.x) % 7) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 48) + 815)]));
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- compute[((((((((int)blockIdx.x) / 7) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + (((int)blockIdx.x) % 7))] = max((conv2d_nchw[i1_inner] + bias[((((((int)blockIdx.x) / 7) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[(((((((((int)blockIdx.x) / 7) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + (((int)blockIdx.x) % 7)) + 1568)] = max((conv2d_nchw[(i1_inner + 2)] + bias[(((((((int)blockIdx.x) / 7) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner) + 32)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 4; ++i1_inner) {
+ compute[((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 49) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 49))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 49) * 4)) + i1_inner)]), 0.000000e+00f);
}
}
</pre></div>
@@ -1518,7 +1550,7 @@ In the example below we resume the status and do more 5 trials.</p>
Get devices for measurement successfully!
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 21.759 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 23.155 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_conv2d_layer_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index 39e2e8df0..cc99e7292 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -878,7 +878,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.5837 9.5939 9.6098 9.5473 0.0265
+ 9.7129 9.7105 9.7590 9.6691 0.0367
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 0f88d7f0b..f98c9d431 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -897,7 +897,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 756.6745 756.4965 760.3231 753.2039 2.9091
+ 736.0303 737.0998 741.1594 729.8317 4.6859
</pre></div>
</div>
</div>
@@ -919,7 +919,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.617 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.088 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
index 48921777e..505762040 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,76 +600,409 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- let cse_var_1: int32 = ((i.inner.init*32) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
- compute_5[(cse_var_1 + 1)] = 0f32
- compute_5[(cse_var_1 + 2)] = 0f32
- compute_5[(cse_var_1 + 3)] = 0f32
- compute_5[(cse_var_1 + 4)] = 0f32
- compute_5[(cse_var_1 + 5)] = 0f32
- compute_5[(cse_var_1 + 6)] = 0f32
- compute_5[(cse_var_1 + 7)] = 0f32
- compute_5[(cse_var_1 + 8)] = 0f32
- compute_5[(cse_var_1 + 9)] = 0f32
- compute_5[(cse_var_1 + 10)] = 0f32
- compute_5[(cse_var_1 + 11)] = 0f32
- compute_5[(cse_var_1 + 12)] = 0f32
- compute_5[(cse_var_1 + 13)] = 0f32
- compute_5[(cse_var_1 + 14)] = 0f32
- compute_5[(cse_var_1 + 15)] = 0f32
- }
- }
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 64) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i.inner*32) + (nb_j.inner*16))
- let cse_var_19: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_18: int32 = (cse_var_20 + 1)
- let cse_var_17: int32 = (cse_var_20 + 11)
- let cse_var_16: int32 = (cse_var_20 + 12)
- let cse_var_15: int32 = (cse_var_20 + 13)
- let cse_var_14: int32 = (cse_var_20 + 14)
- let cse_var_13: int32 = (cse_var_20 + 15)
- let cse_var_12: int32 = (cse_var_20 + 2)
- let cse_var_11: int32 = (cse_var_20 + 3)
- let cse_var_10: int32 = (cse_var_20 + 4)
- let cse_var_9: int32 = (cse_var_20 + 5)
- let cse_var_8: int32 = (cse_var_20 + 6)
- let cse_var_7: int32 = (cse_var_20 + 7)
- let cse_var_6: int32 = (cse_var_20 + 8)
- let cse_var_5: int32 = (cse_var_20 + 9)
- let cse_var_4: int32 = ((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.inner*256))
- let cse_var_3: int32 = (cse_var_20 + 10)
+ preflattened_buffer_map = {placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_16: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_18: Buffer(placeholder_10, float32, [128, 256], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ let cse_var_1: int32 = (i.outer.inner*128)
+ {
+ compute_5: Buffer(compute_4, float32, [512], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ compute_5[(cse_var_1 + 16)] = 0f32
+ compute_5[(cse_var_1 + 17)] = 0f32
+ compute_5[(cse_var_1 + 18)] = 0f32
+ compute_5[(cse_var_1 + 19)] = 0f32
+ compute_5[(cse_var_1 + 20)] = 0f32
+ compute_5[(cse_var_1 + 21)] = 0f32
+ compute_5[(cse_var_1 + 22)] = 0f32
+ compute_5[(cse_var_1 + 23)] = 0f32
+ compute_5[(cse_var_1 + 24)] = 0f32
+ compute_5[(cse_var_1 + 25)] = 0f32
+ compute_5[(cse_var_1 + 26)] = 0f32
+ compute_5[(cse_var_1 + 27)] = 0f32
+ compute_5[(cse_var_1 + 28)] = 0f32
+ compute_5[(cse_var_1 + 29)] = 0f32
+ compute_5[(cse_var_1 + 30)] = 0f32
+ compute_5[(cse_var_1 + 31)] = 0f32
+ compute_5[(cse_var_1 + 32)] = 0f32
+ compute_5[(cse_var_1 + 33)] = 0f32
+ compute_5[(cse_var_1 + 34)] = 0f32
+ compute_5[(cse_var_1 + 35)] = 0f32
+ compute_5[(cse_var_1 + 36)] = 0f32
+ compute_5[(cse_var_1 + 37)] = 0f32
+ compute_5[(cse_var_1 + 38)] = 0f32
+ compute_5[(cse_var_1 + 39)] = 0f32
+ compute_5[(cse_var_1 + 40)] = 0f32
+ compute_5[(cse_var_1 + 41)] = 0f32
+ compute_5[(cse_var_1 + 42)] = 0f32
+ compute_5[(cse_var_1 + 43)] = 0f32
+ compute_5[(cse_var_1 + 44)] = 0f32
+ compute_5[(cse_var_1 + 45)] = 0f32
+ compute_5[(cse_var_1 + 46)] = 0f32
+ compute_5[(cse_var_1 + 47)] = 0f32
+ compute_5[(cse_var_1 + 48)] = 0f32
+ compute_5[(cse_var_1 + 49)] = 0f32
+ compute_5[(cse_var_1 + 50)] = 0f32
+ compute_5[(cse_var_1 + 51)] = 0f32
+ compute_5[(cse_var_1 + 52)] = 0f32
+ compute_5[(cse_var_1 + 53)] = 0f32
+ compute_5[(cse_var_1 + 54)] = 0f32
+ compute_5[(cse_var_1 + 55)] = 0f32
+ compute_5[(cse_var_1 + 56)] = 0f32
+ compute_5[(cse_var_1 + 57)] = 0f32
+ compute_5[(cse_var_1 + 58)] = 0f32
+ compute_5[(cse_var_1 + 59)] = 0f32
+ compute_5[(cse_var_1 + 60)] = 0f32
+ compute_5[(cse_var_1 + 61)] = 0f32
+ compute_5[(cse_var_1 + 62)] = 0f32
+ compute_5[(cse_var_1 + 63)] = 0f32
+ compute_5[(cse_var_1 + 64)] = 0f32
+ compute_5[(cse_var_1 + 65)] = 0f32
+ compute_5[(cse_var_1 + 66)] = 0f32
+ compute_5[(cse_var_1 + 67)] = 0f32
+ compute_5[(cse_var_1 + 68)] = 0f32
+ compute_5[(cse_var_1 + 69)] = 0f32
+ compute_5[(cse_var_1 + 70)] = 0f32
+ compute_5[(cse_var_1 + 71)] = 0f32
+ compute_5[(cse_var_1 + 72)] = 0f32
+ compute_5[(cse_var_1 + 73)] = 0f32
+ compute_5[(cse_var_1 + 74)] = 0f32
+ compute_5[(cse_var_1 + 75)] = 0f32
+ compute_5[(cse_var_1 + 76)] = 0f32
+ compute_5[(cse_var_1 + 77)] = 0f32
+ compute_5[(cse_var_1 + 78)] = 0f32
+ compute_5[(cse_var_1 + 79)] = 0f32
+ compute_5[(cse_var_1 + 80)] = 0f32
+ compute_5[(cse_var_1 + 81)] = 0f32
+ compute_5[(cse_var_1 + 82)] = 0f32
+ compute_5[(cse_var_1 + 83)] = 0f32
+ compute_5[(cse_var_1 + 84)] = 0f32
+ compute_5[(cse_var_1 + 85)] = 0f32
+ compute_5[(cse_var_1 + 86)] = 0f32
+ compute_5[(cse_var_1 + 87)] = 0f32
+ compute_5[(cse_var_1 + 88)] = 0f32
+ compute_5[(cse_var_1 + 89)] = 0f32
+ compute_5[(cse_var_1 + 90)] = 0f32
+ compute_5[(cse_var_1 + 91)] = 0f32
+ compute_5[(cse_var_1 + 92)] = 0f32
+ compute_5[(cse_var_1 + 93)] = 0f32
+ compute_5[(cse_var_1 + 94)] = 0f32
+ compute_5[(cse_var_1 + 95)] = 0f32
+ compute_5[(cse_var_1 + 96)] = 0f32
+ compute_5[(cse_var_1 + 97)] = 0f32
+ compute_5[(cse_var_1 + 98)] = 0f32
+ compute_5[(cse_var_1 + 99)] = 0f32
+ compute_5[(cse_var_1 + 100)] = 0f32
+ compute_5[(cse_var_1 + 101)] = 0f32
+ compute_5[(cse_var_1 + 102)] = 0f32
+ compute_5[(cse_var_1 + 103)] = 0f32
+ compute_5[(cse_var_1 + 104)] = 0f32
+ compute_5[(cse_var_1 + 105)] = 0f32
+ compute_5[(cse_var_1 + 106)] = 0f32
+ compute_5[(cse_var_1 + 107)] = 0f32
+ compute_5[(cse_var_1 + 108)] = 0f32
+ compute_5[(cse_var_1 + 109)] = 0f32
+ compute_5[(cse_var_1 + 110)] = 0f32
+ compute_5[(cse_var_1 + 111)] = 0f32
+ compute_5[(cse_var_1 + 112)] = 0f32
+ compute_5[(cse_var_1 + 113)] = 0f32
+ compute_5[(cse_var_1 + 114)] = 0f32
+ compute_5[(cse_var_1 + 115)] = 0f32
+ compute_5[(cse_var_1 + 116)] = 0f32
+ compute_5[(cse_var_1 + 117)] = 0f32
+ compute_5[(cse_var_1 + 118)] = 0f32
+ compute_5[(cse_var_1 + 119)] = 0f32
+ compute_5[(cse_var_1 + 120)] = 0f32
+ compute_5[(cse_var_1 + 121)] = 0f32
+ compute_5[(cse_var_1 + 122)] = 0f32
+ compute_5[(cse_var_1 + 123)] = 0f32
+ compute_5[(cse_var_1 + 124)] = 0f32
+ compute_5[(cse_var_1 + 125)] = 0f32
+ compute_5[(cse_var_1 + 126)] = 0f32
+ compute_5[(cse_var_1 + 127)] = 0f32
+ for (elem_idx: int32, 0, (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ let cse_var_131: int32 = (cse_var_1 + 13)
+ let cse_var_130: int32 = (cse_var_1 + 14)
+ let cse_var_129: int32 = (cse_var_1 + 15)
+ let cse_var_128: int32 = (cse_var_1 + 16)
+ let cse_var_127: int32 = (cse_var_1 + 17)
+ let cse_var_126: int32 = (cse_var_1 + 18)
+ let cse_var_125: int32 = (cse_var_1 + 19)
+ let cse_var_124: int32 = (cse_var_1 + 2)
+ let cse_var_123: int32 = (cse_var_1 + 20)
+ let cse_var_122: int32 = (cse_var_1 + 21)
+ let cse_var_121: int32 = (cse_var_1 + 22)
+ let cse_var_120: int32 = (cse_var_1 + 23)
+ let cse_var_119: int32 = (cse_var_1 + 24)
+ let cse_var_118: int32 = (cse_var_1 + 25)
+ let cse_var_117: int32 = (cse_var_1 + 26)
+ let cse_var_116: int32 = (cse_var_1 + 42)
+ let cse_var_115: int32 = (cse_var_1 + 28)
+ let cse_var_114: int32 = (cse_var_1 + 29)
+ let cse_var_113: int32 = (cse_var_1 + 3)
+ let cse_var_112: int32 = (cse_var_1 + 30)
+ let cse_var_111: int32 = (cse_var_1 + 31)
+ let cse_var_110: int32 = (cse_var_1 + 32)
+ let cse_var_109: int32 = (cse_var_1 + 33)
+ let cse_var_108: int32 = (cse_var_1 + 34)
+ let cse_var_107: int32 = (cse_var_1 + 35)
+ let cse_var_106: int32 = (cse_var_1 + 36)
+ let cse_var_105: int32 = (cse_var_1 + 37)
+ let cse_var_104: int32 = (cse_var_1 + 38)
+ let cse_var_103: int32 = (cse_var_1 + 39)
+ let cse_var_102: int32 = (cse_var_1 + 4)
+ let cse_var_101: int32 = (cse_var_1 + 40)
+ let cse_var_100: int32 = (cse_var_1 + 27)
+ let cse_var_99: int32 = (cse_var_1 + 1)
+ let cse_var_98: int32 = (cse_var_1 + 10)
+ let cse_var_97: int32 = (cse_var_1 + 100)
+ let cse_var_96: int32 = (cse_var_1 + 101)
+ let cse_var_95: int32 = (cse_var_1 + 102)
+ let cse_var_94: int32 = (cse_var_1 + 103)
+ let cse_var_93: int32 = (cse_var_1 + 104)
+ let cse_var_92: int32 = (cse_var_1 + 105)
+ let cse_var_91: int32 = (cse_var_1 + 106)
+ let cse_var_90: int32 = (cse_var_1 + 107)
+ let cse_var_89: int32 = (cse_var_1 + 108)
+ let cse_var_88: int32 = (cse_var_1 + 109)
+ let cse_var_87: int32 = (cse_var_1 + 11)
+ let cse_var_86: int32 = (cse_var_1 + 110)
+ let cse_var_85: int32 = (cse_var_1 + 111)
+ let cse_var_84: int32 = (cse_var_1 + 127)
+ let cse_var_83: int32 = (cse_var_1 + 113)
+ let cse_var_82: int32 = (cse_var_1 + 114)
+ let cse_var_81: int32 = (cse_var_1 + 115)
+ let cse_var_80: int32 = (cse_var_1 + 116)
+ let cse_var_79: int32 = (cse_var_1 + 117)
+ let cse_var_78: int32 = (cse_var_1 + 118)
+ let cse_var_77: int32 = (cse_var_1 + 119)
+ let cse_var_76: int32 = (cse_var_1 + 12)
+ let cse_var_75: int32 = (cse_var_1 + 120)
+ let cse_var_74: int32 = (cse_var_1 + 121)
+ let cse_var_73: int32 = (cse_var_1 + 122)
+ let cse_var_72: int32 = (cse_var_1 + 123)
+ let cse_var_71: int32 = (cse_var_1 + 124)
+ let cse_var_70: int32 = (cse_var_1 + 125)
+ let cse_var_69: int32 = (cse_var_1 + 126)
+ let cse_var_68: int32 = (cse_var_1 + 112)
+ let cse_var_67: int32 = (cse_var_1 + 72)
+ let cse_var_66: int32 = (cse_var_1 + 73)
+ let cse_var_65: int32 = (cse_var_1 + 74)
+ let cse_var_64: int32 = (cse_var_1 + 75)
+ let cse_var_63: int32 = (cse_var_1 + 76)
+ let cse_var_62: int32 = (cse_var_1 + 77)
+ let cse_var_61: int32 = (cse_var_1 + 78)
+ let cse_var_60: int32 = (cse_var_1 + 79)
+ let cse_var_59: int32 = (cse_var_1 + 8)
+ let cse_var_58: int32 = (cse_var_1 + 80)
+ let cse_var_57: int32 = (cse_var_1 + 81)
+ let cse_var_56: int32 = (cse_var_1 + 82)
+ let cse_var_55: int32 = (cse_var_1 + 83)
+ let cse_var_54: int32 = (cse_var_1 + 84)
+ let cse_var_53: int32 = (cse_var_1 + 85)
+ let cse_var_52: int32 = (cse_var_1 + 41)
+ let cse_var_51: int32 = (cse_var_1 + 87)
+ let cse_var_50: int32 = (cse_var_1 + 88)
+ let cse_var_49: int32 = (cse_var_1 + 89)
+ let cse_var_48: int32 = (cse_var_1 + 9)
+ let cse_var_47: int32 = (cse_var_1 + 90)
+ let cse_var_46: int32 = (cse_var_1 + 91)
+ let cse_var_45: int32 = (cse_var_1 + 92)
+ let cse_var_44: int32 = (cse_var_1 + 93)
+ let cse_var_43: int32 = (cse_var_1 + 94)
+ let cse_var_42: int32 = (cse_var_1 + 95)
+ let cse_var_41: int32 = (cse_var_1 + 96)
+ let cse_var_40: int32 = (cse_var_1 + 97)
+ let cse_var_39: int32 = (cse_var_1 + 98)
+ let cse_var_38: int32 = (cse_var_1 + 99)
+ let cse_var_37: int32 = (elem_idx*16)
+ let cse_var_36: int32 = (cse_var_1 + 86)
+ let cse_var_35: int32 = (cse_var_1 + 43)
+ let cse_var_34: int32 = (cse_var_1 + 44)
+ let cse_var_33: int32 = (cse_var_1 + 45)
+ let cse_var_32: int32 = (cse_var_1 + 46)
+ let cse_var_31: int32 = (cse_var_1 + 47)
+ let cse_var_30: int32 = (cse_var_1 + 48)
+ let cse_var_29: int32 = (cse_var_1 + 49)
+ let cse_var_28: int32 = (cse_var_1 + 5)
+ let cse_var_27: int32 = (cse_var_1 + 50)
+ let cse_var_26: int32 = (cse_var_1 + 51)
+ let cse_var_25: int32 = (cse_var_1 + 52)
+ let cse_var_24: int32 = (cse_var_1 + 53)
+ let cse_var_23: int32 = (cse_var_1 + 54)
+ let cse_var_22: int32 = (cse_var_1 + 55)
+ let cse_var_21: int32 = (cse_var_1 + 56)
+ let cse_var_20: int32 = (cse_var_1 + 57)
+ let cse_var_19: int32 = (cse_var_1 + 71)
+ let cse_var_18: int32 = (cse_var_1 + 7)
+ let cse_var_17: int32 = (cse_var_1 + 69)
+ let cse_var_16: int32 = (cse_var_1 + 68)
+ let cse_var_15: int32 = (cse_var_1 + 67)
+ let cse_var_14: int32 = (cse_var_1 + 66)
+ let cse_var_13: int32 = (cse_var_1 + 65)
+ let cse_var_12: int32 = (cse_var_1 + 64)
+ let cse_var_11: int32 = (cse_var_1 + 63)
+ let cse_var_10: int32 = (cse_var_1 + 62)
+ let cse_var_9: int32 = (cse_var_1 + 61)
+ let cse_var_8: int32 = (cse_var_1 + 60)
+ let cse_var_7: int32 = (cse_var_1 + 6)
+ let cse_var_6: int32 = (cse_var_1 + 59)
+ let cse_var_5: int32 = (cse_var_1 + 70)
+ let cse_var_4: int32 = (cse_var_1 + 58)
+ let cse_var_3: int32 = ((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*2048))
{
- compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[((placeholder_3[cse_var_19]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
+ compute_5[cse_var_1] = (compute_5[cse_var_1] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_99] = (compute_5[cse_var_99] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_124] = (compute_5[cse_var_124] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_113] = (compute_5[cse_var_113] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_102] = (compute_5[cse_var_102] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_28] = (compute_5[cse_var_28] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_59] = (compute_5[cse_var_59] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_48] = (compute_5[cse_var_48] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_98] = (compute_5[cse_var_98] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_87] = (compute_5[cse_var_87] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_76] = (compute_5[cse_var_76] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_131] = (compute_5[cse_var_131] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_130] = (compute_5[cse_var_130] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_129] = (compute_5[cse_var_129] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)])], 0f32)))
+ compute_5[cse_var_128] = (compute_5[cse_var_128] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_127] = (compute_5[cse_var_127] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_126] = (compute_5[cse_var_126] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_125] = (compute_5[cse_var_125] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_123] = (compute_5[cse_var_123] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_122] = (compute_5[cse_var_122] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_121] = (compute_5[cse_var_121] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_120] = (compute_5[cse_var_120] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_119] = (compute_5[cse_var_119] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_118] = (compute_5[cse_var_118] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_117] = (compute_5[cse_var_117] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_100] = (compute_5[cse_var_100] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_115] = (compute_5[cse_var_115] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_114] = (compute_5[cse_var_114] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_112] = (compute_5[cse_var_112] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_111] = (compute_5[cse_var_111] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 256)], 0f32)))
+ compute_5[cse_var_110] = (compute_5[cse_var_110] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_109] = (compute_5[cse_var_109] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_108] = (compute_5[cse_var_108] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_107] = (compute_5[cse_var_107] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_106] = (compute_5[cse_var_106] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_105] = (compute_5[cse_var_105] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_104] = (compute_5[cse_var_104] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_103] = (compute_5[cse_var_103] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_101] = (compute_5[cse_var_101] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_52] = (compute_5[cse_var_52] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_116] = (compute_5[cse_var_116] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_35] = (compute_5[cse_var_35] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_34] = (compute_5[cse_var_34] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_33] = (compute_5[cse_var_33] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_32] = (compute_5[cse_var_32] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_31] = (compute_5[cse_var_31] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 512)], 0f32)))
+ compute_5[cse_var_30] = (compute_5[cse_var_30] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_29] = (compute_5[cse_var_29] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_27] = (compute_5[cse_var_27] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_26] = (compute_5[cse_var_26] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_25] = (compute_5[cse_var_25] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_24] = (compute_5[cse_var_24] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_23] = (compute_5[cse_var_23] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_22] = (compute_5[cse_var_22] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_21] = (compute_5[cse_var_21] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 768)], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_67] = (compute_5[cse_var_67] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_66] = (compute_5[cse_var_66] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_65] = (compute_5[cse_var_65] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_64] = (compute_5[cse_var_64] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_63] = (compute_5[cse_var_63] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_62] = (compute_5[cse_var_62] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_61] = (compute_5[cse_var_61] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_60] = (compute_5[cse_var_60] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1024)], 0f32)))
+ compute_5[cse_var_58] = (compute_5[cse_var_58] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_57] = (compute_5[cse_var_57] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_56] = (compute_5[cse_var_56] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_55] = (compute_5[cse_var_55] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_54] = (compute_5[cse_var_54] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_53] = (compute_5[cse_var_53] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_36] = (compute_5[cse_var_36] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_51] = (compute_5[cse_var_51] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_50] = (compute_5[cse_var_50] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_49] = (compute_5[cse_var_49] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_47] = (compute_5[cse_var_47] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_46] = (compute_5[cse_var_46] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_45] = (compute_5[cse_var_45] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_44] = (compute_5[cse_var_44] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_43] = (compute_5[cse_var_43] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_42] = (compute_5[cse_var_42] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1280)], 0f32)))
+ compute_5[cse_var_41] = (compute_5[cse_var_41] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_40] = (compute_5[cse_var_40] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_39] = (compute_5[cse_var_39] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_38] = (compute_5[cse_var_38] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_97] = (compute_5[cse_var_97] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_96] = (compute_5[cse_var_96] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_95] = (compute_5[cse_var_95] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_94] = (compute_5[cse_var_94] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_93] = (compute_5[cse_var_93] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_92] = (compute_5[cse_var_92] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_91] = (compute_5[cse_var_91] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_90] = (compute_5[cse_var_90] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_89] = (compute_5[cse_var_89] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_88] = (compute_5[cse_var_88] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_86] = (compute_5[cse_var_86] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_85] = (compute_5[cse_var_85] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1536)], 0f32)))
+ compute_5[cse_var_68] = (compute_5[cse_var_68] + (placeholder_1[((placeholder_3[cse_var_2]*16) + cse_var_37)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_83] = (compute_5[cse_var_83] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 1)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_82] = (compute_5[cse_var_82] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 2)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_81] = (compute_5[cse_var_81] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 3)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_80] = (compute_5[cse_var_80] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 4)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_79] = (compute_5[cse_var_79] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 5)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_78] = (compute_5[cse_var_78] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 6)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_77] = (compute_5[cse_var_77] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 7)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_75] = (compute_5[cse_var_75] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 8)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_74] = (compute_5[cse_var_74] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 9)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_73] = (compute_5[cse_var_73] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 10)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_72] = (compute_5[cse_var_72] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 11)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_71] = (compute_5[cse_var_71] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 12)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_70] = (compute_5[cse_var_70] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 13)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_69] = (compute_5[cse_var_69] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 14)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
+ compute_5[cse_var_84] = (compute_5[cse_var_84] + (placeholder_1[(((placeholder_3[cse_var_2]*16) + cse_var_37) + 15)]*max(placeholder[((cse_var_3 + placeholder_2[(placeholder_3[cse_var_2] + elem_idx)]) + 1792)], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 64) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
- compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
+ for (i0.inner: int32, 0, 32) {
+ for (i1.inner: int32, 0, 16) {
+ let cse_var_132: int32 = ((((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16)) + i1.inner)
+ compute[cse_var_132] = max((compute_5[((i0.inner*16) + i1.inner)] + placeholder_4[cse_var_132]), 0f32)
+ }
}
}
}
@@ -710,7 +1043,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Execution time of this operator: 1.844 ms
+Execution time of this operator: 2.502 ms
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index c799eaef6..fc33b6abc 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:44.904</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:44.935</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:43.992</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
-<li><p><strong>00:00.235</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
-<li><p><strong>00:00.230</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
-<li><p><strong>00:00.224</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
-<li><p><strong>00:00.222</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
+<li><p><strong>00:44.053</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.232</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
+<li><p><strong>00:00.218</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.216</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
+<li><p><strong>00:00.216</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 3c4aeb0e1..7604579ac 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -1142,8 +1142,8 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
-No: 6 GFLOPS: 43.38/43.38 result: MeasureResult(costs=(0.0053362214210526315,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6534135341644287, timestamp=1653590200.8923168) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-No: 7 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 6 GFLOPS: 42.41/42.41 result: MeasureResult(costs=(0.0054580131578947375,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.575484037399292, timestamp=1653605049.7827628) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+No: 7 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1266,7 +1266,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
-No: 8 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1389,7 +1389,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
-No: 9 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1512,7 +1512,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
-No: 10 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1530,7 +1530,7 @@ No: 10 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
-No: 11 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1653,7 +1653,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
-No: 12 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1776,7 +1776,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
-No: 13 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1899,7 +1899,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
-No: 14 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2022,7 +2022,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
-No: 15 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2145,7 +2145,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
-No: 16 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2268,7 +2268,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
-No: 17 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2391,7 +2391,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
-No: 18 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2514,7 +2514,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
-No: 19 GFLOPS: 0.00/43.38 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/42.41 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2602,7 +2602,7 @@ tvm._ffi.base.TVMError: Traceback (most recent call last):
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007f048739bfa2
+ 12: 0x00007f6b8eee2fa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2667,7 +2667,7 @@ Traceback (most recent call last):
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
-No: 20 GFLOPS: 143.84/143.84 result: MeasureResult(costs=(0.00160947861,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4207580089569092, timestamp=1653590227.352538) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+No: 20 GFLOPS: 143.76/143.76 result: MeasureResult(costs=(0.00161030436,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3760154247283936, timestamp=1653605076.1585646) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
</pre></div>
</div>
<p>Finally we can inspect the best config from log file, check correctness,
@@ -2710,7 +2710,7 @@ and measure running time.</p>
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Time cost of this operator: 0.001987
+Time cost of this operator: 0.001955
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 52393a195..ff0838af5 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -555,10 +555,10 @@ the tuned operator.</p>
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 313.7 98.743 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.073 0.967 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.922 0.29 (1, 1, 10, 10, 3) 1 1
-Total_time - 317.695 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 312.0 98.728 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.953 0.934 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.067 0.338 (1, 1, 10, 10, 3) 1 1
+Total_time - 316.02 - - - -
</pre></div>
</div>
</div>
@@ -610,10 +610,10 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 197.1 98.729 (1, 6, 10, 10, 1) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 0.871 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.8 0.401 (1, 3, 10, 10, 1) 1 1
-Total_time - 199.638 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 96.65 97.409 (1, 6, 10, 10, 1) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.758 1.772 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.813 0.819 (1, 3, 10, 10, 1) 1 1
+Total_time - 99.221 - - - -
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 6708597b2..d53c6aed6 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:46.386</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:45.882</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:42.044</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
-<li><p><strong>00:03.707</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
-<li><p><strong>00:00.226</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
-<li><p><strong>00:00.206</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
-<li><p><strong>00:00.203</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:41.651</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
+<li><p><strong>00:03.632</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
+<li><p><strong>00:00.201</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
+<li><p><strong>00:00.200</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:00.199</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 113b9f8d9..93653088a 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:12.441</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:12.157</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:10.446</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
-<li><p><strong>00:01.769</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
-<li><p><strong>00:00.226</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
+<li><p><strong>00:09.980</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
+<li><p><strong>00:01.959</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
+<li><p><strong>00:00.219</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index 6c742f321..343d43302 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:05.916</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.732</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.116</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
-<li><p><strong>00:01.247</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
-<li><p><strong>00:00.747</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
-<li><p><strong>00:00.738</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
-<li><p><strong>00:00.328</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
-<li><p><strong>00:00.264</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
-<li><p><strong>00:00.244</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
-<li><p><strong>00:00.232</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
+<li><p><strong>00:02.102</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
+<li><p><strong>00:01.142</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
+<li><p><strong>00:00.737</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
+<li><p><strong>00:00.727</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
+<li><p><strong>00:00.315</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
+<li><p><strong>00:00.243</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
+<li><p><strong>00:00.241</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
+<li><p><strong>00:00.225</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index d9f990b3e..c631e18fd 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -552,7 +552,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [1024, 64], []), B_1: B_3: Buffer(B_2, float32, [512, 64], []), C_1: C_3: Buffer(C_2, float32, [1024, 512], [])} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp9fztjaat/input0.cc'\nsource_filename = \"/tmp/tmp9fztjaat/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmplfm8mwyy/input0.cc'\nsource_filename = \"/tmp/tmplfm8mwyy/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/reference/api/python/auto_scheduler.html b/docs/reference/api/python/auto_scheduler.html
index d2bf2a712..787c70f76 100644
--- a/docs/reference/api/python/auto_scheduler.html
+++ b/docs/reference/api/python/auto_scheduler.html
@@ -1715,7 +1715,7 @@ Can be the a function or the function name.</p></li>
<dl class="py function">
<dt class="sig sig-object py" id="tvm.auto_scheduler.auto_schedule">
-<span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">auto_schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em clas [...]
+<span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">auto_schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em clas [...]
<dd><p>THIS API IS DEPRECATED.</p>
<p>Run auto scheduling search for a task.</p>
<dl class="field-list simple">
@@ -1752,7 +1752,7 @@ the initial naive schedule (state).</p>
<dl class="py class">
<dt class="sig sig-object py" id="tvm.auto_scheduler.SketchPolicy">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
+<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
<dd><p>The search policy that searches in a hierarchical search space defined by sketches.
The policy randomly samples programs from the space defined by sketches and use evolutionary
search to fine-tune them.</p>
diff --git a/docs/reference/api/typedoc/classes/bytestreamreader.html b/docs/reference/api/typedoc/classes/bytestreamreader.html
index 2c4ebe54c..fb41b779b 100644
--- a/docs/reference/api/typedoc/classes/bytestreamreader.html
+++ b/docs/reference/api/typedoc/classes/bytestreamreader.html
@@ -119,7 +119,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
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@@ -141,7 +141,7 @@
<div class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Uint8Array</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
</ul>
</aside>
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@@ -151,7 +151,7 @@
<div class="tsd-signature tsd-kind-icon">offset<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 0</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Uint8Array</span></h4>
@@ -185,7 +185,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
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@@ -202,7 +202,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
diff --git a/docs/reference/api/typedoc/classes/cachedcallstack.html b/docs/reference/api/typedoc/classes/cachedcallstack.html
index 8cec92014..0b27fc705 100644
--- a/docs/reference/api/typedoc/classes/cachedcallstack.html
+++ b/docs/reference/api/typedoc/classes/cachedcallstack.html
@@ -144,7 +144,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L223">memory.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L223">memory.ts:223</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L208">memory.ts:208</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L208">memory.ts:208</a></li>
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L312">memory.ts:312</a></li>
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<div class="tsd-comment tsd-typography">
@@ -226,7 +226,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L284">memory.ts:284</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L284">memory.ts:284</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -262,7 +262,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L388">memory.ts:388</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L388">memory.ts:388</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -300,7 +300,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L376">memory.ts:376</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L376">memory.ts:376</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -340,7 +340,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L267">memory.ts:267</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L267">memory.ts:267</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -373,7 +373,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L243">memory.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L243">memory.ts:243</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -390,7 +390,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L321">memory.ts:321</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L321">memory.ts:321</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -422,7 +422,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L252">memory.ts:252</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L252">memory.ts:252</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -444,7 +444,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L359">memory.ts:359</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L359">memory.ts:359</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -470,7 +470,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L342">memory.ts:342</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L342">memory.ts:342</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -496,7 +496,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L350">memory.ts:350</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L350">memory.ts:350</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -522,7 +522,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L326">memory.ts:326</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L326">memory.ts:326</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -548,7 +548,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L363">memory.ts:363</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L363">memory.ts:363</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -574,7 +574,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L346">memory.ts:346</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L346">memory.ts:346</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -600,7 +600,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L334">memory.ts:334</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L334">memory.ts:334</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
diff --git a/docs/reference/api/typedoc/classes/dldatatype.html b/docs/reference/api/typedoc/classes/dldatatype.html
index ae48e0c8f..a7de472a7 100644
--- a/docs/reference/api/typedoc/classes/dldatatype.html
+++ b/docs/reference/api/typedoc/classes/dldatatype.html
@@ -119,7 +119,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L262">runtime.ts:262</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
<div class="tsd-signature tsd-kind-icon">bits<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L260">runtime.ts:260</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L260">runtime.ts:260</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">code<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L258">runtime.ts:258</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L258">runtime.ts:258</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -177,7 +177,7 @@
<div class="tsd-signature tsd-kind-icon">lanes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L262">runtime.ts:262</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -199,7 +199,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L279">runtime.ts:279</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L279">runtime.ts:279</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -216,7 +216,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L270">runtime.ts:270</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L270">runtime.ts:270</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/dldevice.html b/docs/reference/api/typedoc/classes/dldevice.html
index 17744c3a6..cfd34597e 100644
--- a/docs/reference/api/typedoc/classes/dldevice.html
+++ b/docs/reference/api/typedoc/classes/dldevice.html
@@ -118,7 +118,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L202">runtime.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L202">runtime.ts:202</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -146,7 +146,7 @@
<div class="tsd-signature tsd-kind-icon">device<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L200">runtime.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L200">runtime.ts:200</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -161,7 +161,7 @@
<div class="tsd-signature tsd-kind-icon">device<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L198">runtime.ts:198</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L198">runtime.ts:198</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -183,7 +183,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L223">runtime.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L223">runtime.ts:223</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -205,7 +205,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L230">runtime.ts:230</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L230">runtime.ts:230</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/environment.html b/docs/reference/api/typedoc/classes/environment.html
index 23f2ef9a3..d649cf8b1 100644
--- a/docs/reference/api/typedoc/classes/environment.html
+++ b/docs/reference/api/typedoc/classes/environment.html
@@ -125,7 +125,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L86">environment.ts:86</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L86">environment.ts:86</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -169,7 +169,7 @@
<aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/libraryprovider.html">LibraryProvider</a>.<a href="../interfaces/libraryprovider.html#imports">imports</a></p>
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L70">environment.ts:70</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L70">environment.ts:70</a></li>
</ul>
</aside>
</section>
@@ -179,7 +179,7 @@
<div class="tsd-signature tsd-kind-icon">logger<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L69">environment.ts:69</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L69">environment.ts:69</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -210,7 +210,7 @@
<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">ctypes.FTVMWasmPackedCFunc</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = [undefined,]</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L78">environment.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L78">environment.ts:78</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -228,7 +228,7 @@
<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<wbr>Free<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = []</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L84">environment.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L84">environment.ts:84</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -250,7 +250,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/environment.ts#L105">environment.ts:105</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/environment.ts#L105">environment.ts:105</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/ffilibrary.html b/docs/reference/api/typedoc/classes/ffilibrary.html
index bc54e5224..84a4c129c 100644
--- a/docs/reference/api/typedoc/classes/ffilibrary.html
+++ b/docs/reference/api/typedoc/classes/ffilibrary.html
@@ -131,7 +131,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L49">runtime.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L49">runtime.ts:49</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -156,7 +156,7 @@
<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L46">runtime.ts:46</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L46">runtime.ts:46</a></li>
</ul>
</aside>
</section>
@@ -166,7 +166,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L45">runtime.ts:45</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L45">runtime.ts:45</a></li>
</ul>
</aside>
</section>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L44">runtime.ts:44</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L44">runtime.ts:44</a></li>
</ul>
</aside>
</section>
@@ -186,7 +186,7 @@
<div class="tsd-signature tsd-kind-icon">webGPUContext<span class="tsd-signature-symbol">:</span> <a href="webgpucontext.html" class="tsd-signature-type">WebGPUContext</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L47">runtime.ts:47</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L47">runtime.ts:47</a></li>
</ul>
</aside>
</section>
@@ -203,7 +203,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L76">runtime.ts:76</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L76">runtime.ts:76</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -226,7 +226,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L66">runtime.ts:66</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L66">runtime.ts:66</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -243,7 +243,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L84">runtime.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L84">runtime.ts:84</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <a href="cachedcallstack.html" class="tsd-signature-type">CachedCallStack</a></h4>
@@ -260,7 +260,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L95">runtime.ts:95</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L95">runtime.ts:95</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -283,7 +283,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L72">runtime.ts:72</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L72">runtime.ts:72</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
diff --git a/docs/reference/api/typedoc/classes/graphexecutor.html b/docs/reference/api/typedoc/classes/graphexecutor.html
index 681c9ca1d..eae7e86c9 100644
--- a/docs/reference/api/typedoc/classes/graphexecutor.html
+++ b/docs/reference/api/typedoc/classes/graphexecutor.html
@@ -130,7 +130,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L583">runtime.ts:583</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L583">runtime.ts:583</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">module<span class="tsd-signature-symbol">:</span> <a href="module.html" class="tsd-signature-type">Module</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L579">runtime.ts:579</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L579">runtime.ts:579</a></li>
</ul>
</aside>
</section>
@@ -179,7 +179,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L654">runtime.ts:654</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L654">runtime.ts:654</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -224,7 +224,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L597">runtime.ts:597</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L597">runtime.ts:597</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -241,7 +241,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L631">runtime.ts:631</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L631">runtime.ts:631</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -279,7 +279,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L644">runtime.ts:644</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L644">runtime.ts:644</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -310,7 +310,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L621">runtime.ts:621</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L621">runtime.ts:621</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -332,7 +332,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L609">runtime.ts:609</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L609">runtime.ts:609</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/instance.html b/docs/reference/api/typedoc/classes/instance.html
index e676b140e..e8ece5c29 100644
--- a/docs/reference/api/typedoc/classes/instance.html
+++ b/docs/reference/api/typedoc/classes/instance.html
@@ -139,7 +139,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L692">runtime.ts:692</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L692">runtime.ts:692</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -202,7 +202,7 @@
<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L684">runtime.ts:684</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L684">runtime.ts:684</a></li>
</ul>
</aside>
</section>
@@ -212,7 +212,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L683">runtime.ts:683</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L683">runtime.ts:683</a></li>
</ul>
</aside>
</section>
@@ -229,7 +229,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L932">runtime.ts:932</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L932">runtime.ts:932</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -260,7 +260,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L994">runtime.ts:994</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L994">runtime.ts:994</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -303,7 +303,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L924">runtime.ts:924</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L924">runtime.ts:924</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -341,7 +341,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L732">runtime.ts:732</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L732">runtime.ts:732</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -358,7 +358,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L952">runtime.ts:952</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L952">runtime.ts:952</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -402,7 +402,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L816">runtime.ts:816</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L816">runtime.ts:816</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -434,7 +434,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -465,7 +465,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L846">runtime.ts:846</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L846">runtime.ts:846</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -497,7 +497,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L750">runtime.ts:750</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L750">runtime.ts:750</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -520,7 +520,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -568,7 +568,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L789">runtime.ts:789</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L789">runtime.ts:789</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -608,7 +608,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L914">runtime.ts:914</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L914">runtime.ts:914</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -646,7 +646,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -698,7 +698,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L740">runtime.ts:740</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L740">runtime.ts:740</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -722,7 +722,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L868">runtime.ts:868</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L868">runtime.ts:868</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -754,7 +754,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L857">runtime.ts:857</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L857">runtime.ts:857</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -786,7 +786,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L940">runtime.ts:940</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L940">runtime.ts:940</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/memory.html b/docs/reference/api/typedoc/classes/memory.html
index 7e57132b7..7ccbf8166 100644
--- a/docs/reference/api/typedoc/classes/memory.html
+++ b/docs/reference/api/typedoc/classes/memory.html
@@ -130,7 +130,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L40">memory.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L40">memory.ts:40</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -152,7 +152,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Memory</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L32">memory.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L32">memory.ts:32</a></li>
</ul>
</aside>
</section>
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L33">memory.ts:33</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L33">memory.ts:33</a></li>
</ul>
</aside>
</section>
@@ -179,7 +179,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L154">memory.ts:154</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L154">memory.ts:154</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -210,7 +210,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L90">memory.ts:90</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L90">memory.ts:90</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -233,7 +233,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L97">memory.ts:97</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L97">memory.ts:97</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -256,7 +256,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L74">memory.ts:74</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L74">memory.ts:74</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -279,7 +279,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L81">memory.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L81">memory.ts:81</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -302,7 +302,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L104">memory.ts:104</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L104">memory.ts:104</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -325,7 +325,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L132">memory.ts:132</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L132">memory.ts:132</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -362,7 +362,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L145">memory.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L145">memory.ts:145</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -393,7 +393,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L60">memory.ts:60</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L60">memory.ts:60</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -416,7 +416,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L67">memory.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L67">memory.ts:67</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -439,7 +439,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L53">memory.ts:53</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L53">memory.ts:53</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -462,7 +462,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L114">memory.ts:114</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L114">memory.ts:114</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -485,7 +485,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L124">memory.ts:124</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L124">memory.ts:124</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -502,7 +502,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/memory.ts#L175">memory.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/memory.ts#L175">memory.ts:175</a></li>
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diff --git a/docs/reference/api/typedoc/classes/module.html b/docs/reference/api/typedoc/classes/module.html
index b748d189b..4157d464a 100644
--- a/docs/reference/api/typedoc/classes/module.html
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@@ -124,7 +124,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L504">runtime.ts:504</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L504">runtime.ts:504</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -170,7 +170,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L502">runtime.ts:502</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L502">runtime.ts:502</a></li>
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@@ -187,7 +187,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L516">runtime.ts:516</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L516">runtime.ts:516</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -204,7 +204,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L530">runtime.ts:530</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L530">runtime.ts:530</a></li>
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<div class="tsd-comment tsd-typography">
@@ -236,7 +236,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L561">runtime.ts:561</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L561">runtime.ts:561</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/ndarray.html b/docs/reference/api/typedoc/classes/ndarray.html
index 60c6d50e3..c3095074b 100644
--- a/docs/reference/api/typedoc/classes/ndarray.html
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@@ -130,7 +130,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L304">runtime.ts:304</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L304">runtime.ts:304</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -158,7 +158,7 @@
<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <a href="dldevice.html" class="tsd-signature-type">DLDevice</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L297">runtime.ts:297</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L297">runtime.ts:297</a></li>
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<div class="tsd-comment tsd-typography">
@@ -173,7 +173,7 @@
<div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L293">runtime.ts:293</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L293">runtime.ts:293</a></li>
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<div class="tsd-comment tsd-typography">
@@ -188,7 +188,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L289">runtime.ts:289</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L289">runtime.ts:289</a></li>
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<div class="tsd-comment tsd-typography">
@@ -203,7 +203,7 @@
<div class="tsd-signature tsd-kind-icon">ndim<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L291">runtime.ts:291</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L291">runtime.ts:291</a></li>
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<div class="tsd-comment tsd-typography">
@@ -218,7 +218,7 @@
<div class="tsd-signature tsd-kind-icon">shape<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L295">runtime.ts:295</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L295">runtime.ts:295</a></li>
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<div class="tsd-comment tsd-typography">
@@ -240,7 +240,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L370">runtime.ts:370</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L370">runtime.ts:370</a></li>
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<div class="tsd-comment tsd-typography">
@@ -273,7 +273,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L414">runtime.ts:414</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L414">runtime.ts:414</a></li>
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<div class="tsd-comment tsd-typography">
@@ -305,7 +305,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L355">runtime.ts:355</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L355">runtime.ts:355</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -322,7 +322,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L474">runtime.ts:474</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L474">runtime.ts:474</a></li>
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<div class="tsd-comment tsd-typography">
@@ -346,7 +346,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L443">runtime.ts:443</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L443">runtime.ts:443</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/packedfunccell.html b/docs/reference/api/typedoc/classes/packedfunccell.html
index 52f9c99ee..f8bbc90c1 100644
--- a/docs/reference/api/typedoc/classes/packedfunccell.html
+++ b/docs/reference/api/typedoc/classes/packedfunccell.html
@@ -122,7 +122,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L158">runtime.ts:158</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L158">runtime.ts:158</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L157">runtime.ts:157</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L157">runtime.ts:157</a></li>
</ul>
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@@ -164,7 +164,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L165">runtime.ts:165</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L165">runtime.ts:165</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
diff --git a/docs/reference/api/typedoc/classes/rpcserver.html b/docs/reference/api/typedoc/classes/rpcserver.html
index 526e35929..b5db983a2 100644
--- a/docs/reference/api/typedoc/classes/rpcserver.html
+++ b/docs/reference/api/typedoc/classes/rpcserver.html
@@ -115,7 +115,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">get<wbr>Imports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signat [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -201,7 +201,7 @@
<div class="tsd-signature tsd-kind-icon">key<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
</ul>
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@@ -211,7 +211,7 @@
<div class="tsd-signature tsd-kind-icon">logger<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -242,7 +242,7 @@
<div class="tsd-signature tsd-kind-icon">socket<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">WebSocket</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
</ul>
</aside>
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@@ -252,7 +252,7 @@
<div class="tsd-signature tsd-kind-icon">state<span class="tsd-signature-symbol">:</span> <a href="../enums/rpcserverstate.html" class="tsd-signature-type">RPCServerState</a><span class="tsd-signature-symbol"> = RPCServerState.InitHeader</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
</ul>
</aside>
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@@ -262,7 +262,7 @@
<div class="tsd-signature tsd-kind-icon">url<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
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diff --git a/docs/reference/api/typedoc/classes/scalar.html b/docs/reference/api/typedoc/classes/scalar.html
index 7725dbd1c..fa35aaa12 100644
--- a/docs/reference/api/typedoc/classes/scalar.html
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@@ -112,7 +112,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -137,7 +137,7 @@
<div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -152,7 +152,7 @@
<div class="tsd-signature tsd-kind-icon">value<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L143">runtime.ts:143</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L143">runtime.ts:143</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/webgpucontext.html b/docs/reference/api/typedoc/classes/webgpucontext.html
index a740889f5..46d2da2cf 100644
--- a/docs/reference/api/typedoc/classes/webgpucontext.html
+++ b/docs/reference/api/typedoc/classes/webgpucontext.html
@@ -120,7 +120,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -145,7 +145,7 @@
<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">GPUDevice</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
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@@ -155,7 +155,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
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@@ -172,7 +172,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
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<div class="tsd-comment tsd-typography">
@@ -209,7 +209,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
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<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/enums/argtypecode.html b/docs/reference/api/typedoc/enums/argtypecode.html
index ff53d824a..cd3fcbd56 100644
--- a/docs/reference/api/typedoc/enums/argtypecode.html
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@@ -106,7 +106,7 @@
<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 6</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
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</section>
@@ -116,7 +116,7 @@
<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
</ul>
</aside>
</section>
@@ -126,7 +126,7 @@
<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
</ul>
</aside>
</section>
@@ -136,7 +136,7 @@
<div class="tsd-signature tsd-kind-icon">Null<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
</ul>
</aside>
</section>
@@ -146,7 +146,7 @@
<div class="tsd-signature tsd-kind-icon">TVMBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 12</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
</ul>
</aside>
</section>
@@ -156,7 +156,7 @@
<div class="tsd-signature tsd-kind-icon">TVMDLTensor<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 7</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
</ul>
</aside>
</section>
@@ -166,7 +166,7 @@
<div class="tsd-signature tsd-kind-icon">TVMData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
</ul>
</aside>
</section>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">TVMModule<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 9</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
</ul>
</aside>
</section>
@@ -186,7 +186,7 @@
<div class="tsd-signature tsd-kind-icon">TVMNDArray<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 13</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
</ul>
</aside>
</section>
@@ -196,7 +196,7 @@
<div class="tsd-signature tsd-kind-icon">TVMObject<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
</ul>
</aside>
</section>
@@ -206,7 +206,7 @@
<div class="tsd-signature tsd-kind-icon">TVMObjectRValue<wbr>Ref<wbr>Arg<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 14</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
</ul>
</aside>
</section>
@@ -216,7 +216,7 @@
<div class="tsd-signature tsd-kind-icon">TVMOpaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
</ul>
</aside>
</section>
@@ -226,7 +226,7 @@
<div class="tsd-signature tsd-kind-icon">TVMPacked<wbr>Func<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 10</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
</ul>
</aside>
</section>
@@ -236,7 +236,7 @@
<div class="tsd-signature tsd-kind-icon">TVMStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 11</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
</ul>
</aside>
</section>
@@ -246,7 +246,7 @@
<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/aynccallbackcode.html b/docs/reference/api/typedoc/enums/aynccallbackcode.html
index dc248f3bd..0a98f41c8 100644
--- a/docs/reference/api/typedoc/enums/aynccallbackcode.html
+++ b/docs/reference/api/typedoc/enums/aynccallbackcode.html
@@ -93,7 +93,7 @@
<div class="tsd-signature tsd-kind-icon">k<wbr>Exception<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L676">runtime.ts:676</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L676">runtime.ts:676</a></li>
</ul>
</aside>
</section>
@@ -103,7 +103,7 @@
<div class="tsd-signature tsd-kind-icon">k<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L675">runtime.ts:675</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L675">runtime.ts:675</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/dldatatypecode.html b/docs/reference/api/typedoc/enums/dldatatypecode.html
index 3e782902e..013c19e77 100644
--- a/docs/reference/api/typedoc/enums/dldatatypecode.html
+++ b/docs/reference/api/typedoc/enums/dldatatypecode.html
@@ -95,7 +95,7 @@
<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L242">runtime.ts:242</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L242">runtime.ts:242</a></li>
</ul>
</aside>
</section>
@@ -105,7 +105,7 @@
<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L240">runtime.ts:240</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L240">runtime.ts:240</a></li>
</ul>
</aside>
</section>
@@ -115,7 +115,7 @@
<div class="tsd-signature tsd-kind-icon">Opaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L243">runtime.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L243">runtime.ts:243</a></li>
</ul>
</aside>
</section>
@@ -125,7 +125,7 @@
<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L241">runtime.ts:241</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L241">runtime.ts:241</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/rpcserverstate.html b/docs/reference/api/typedoc/enums/rpcserverstate.html
index 37ea64746..64e3a5103 100644
--- a/docs/reference/api/typedoc/enums/rpcserverstate.html
+++ b/docs/reference/api/typedoc/enums/rpcserverstate.html
@@ -90,7 +90,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
</ul>
</aside>
</section>
@@ -100,7 +100,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<wbr>Key<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
</ul>
</aside>
</section>
@@ -110,7 +110,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Server<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
</ul>
</aside>
</section>
@@ -120,7 +120,7 @@
<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Body<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
</ul>
</aside>
</section>
@@ -130,7 +130,7 @@
<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Header<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
</ul>
</aside>
</section>
@@ -140,7 +140,7 @@
<div class="tsd-signature tsd-kind-icon">Wait<wbr>For<wbr>Callback<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/sizeof.html b/docs/reference/api/typedoc/enums/sizeof.html
index 2f54cb361..0454160eb 100644
--- a/docs/reference/api/typedoc/enums/sizeof.html
+++ b/docs/reference/api/typedoc/enums/sizeof.html
@@ -100,7 +100,7 @@
<div class="tsd-signature tsd-kind-icon">DLData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
</ul>
</aside>
</section>
@@ -110,7 +110,7 @@
<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32 + I32</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
</ul>
</aside>
</section>
@@ -120,7 +120,7 @@
<div class="tsd-signature tsd-kind-icon">F32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
</ul>
</aside>
</section>
@@ -130,7 +130,7 @@
<div class="tsd-signature tsd-kind-icon">F64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
</ul>
</aside>
</section>
@@ -140,7 +140,7 @@
<div class="tsd-signature tsd-kind-icon">I32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
</ul>
</aside>
</section>
@@ -150,7 +150,7 @@
<div class="tsd-signature tsd-kind-icon">I64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
</ul>
</aside>
</section>
@@ -160,7 +160,7 @@
<div class="tsd-signature tsd-kind-icon">TVMValue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
</ul>
</aside>
</section>
@@ -170,7 +170,7 @@
<div class="tsd-signature tsd-kind-icon">U16<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
</ul>
</aside>
</section>
@@ -180,7 +180,7 @@
<div class="tsd-signature tsd-kind-icon">U8<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/index.html b/docs/reference/api/typedoc/index.html
index 969e9b794..da78a1e37 100644
--- a/docs/reference/api/typedoc/index.html
+++ b/docs/reference/api/typedoc/index.html
@@ -174,7 +174,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Alloc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, ndim<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeCode<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeBits<span class="tsd [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>Bytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">num [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -282,7 +282,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>To<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>from<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, to<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-sig [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -326,7 +326,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>ToBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</sp [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -370,7 +370,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -406,7 +406,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMBackend<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number< [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -458,7 +458,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMCFunc<wbr>Set<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ret<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signa [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -506,7 +506,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMCb<wbr>Arg<wbr>ToReturn<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, code<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span c [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -545,7 +545,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Call<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-t [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -601,7 +601,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -637,7 +637,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Get<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span cla [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -676,7 +676,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>List<wbr>Global<wbr>Names<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>outSize<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, outArray<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -715,7 +715,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Register<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, f<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, override<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</spa [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -758,7 +758,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMGet<wbr>Last<wbr>Error<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -788,7 +788,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -824,7 +824,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Get<wbr>Function<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, funcName<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, queryImports<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">numbe [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -872,7 +872,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Import<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, dep<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-si [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -912,7 +912,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMSynchronize<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>deviceType<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, deviceId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signatur [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -954,7 +954,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Alloc<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>size<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -990,7 +990,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Free<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ptr<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1026,7 +1026,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Func<wbr>Create<wbr>FromCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resource<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1066,7 +1066,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>args<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1118,7 +1118,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<wbr>Finalizer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resourceHandle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1154,7 +1154,7 @@
<div class="tsd-signature tsd-kind-icon">GPUPointer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1169,7 +1169,7 @@
<div class="tsd-signature tsd-kind-icon">Packed<wbr>Func<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>args<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol"> & </span><a href="interfaces/disp [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L36">runtime.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L36">runtime.ts:36</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1184,7 +1184,7 @@
<div class="tsd-signature tsd-kind-icon">Pointer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1199,7 +1199,7 @@
<div class="tsd-signature tsd-kind-icon">Ptr<wbr>Offset<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1217,7 +1217,7 @@
<div class="tsd-signature tsd-kind-icon">RPC_<wbr>MAGIC<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">1045105</span><span class="tsd-signature-symbol"> = 1045105</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
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@@ -1508,7 +1508,7 @@
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@@ -1539,7 +1539,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L247">runtime.ts:247</a></li>
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@@ -1549,7 +1549,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L248">runtime.ts:248</a></li>
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<ul>
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<div class="tsd-signature tsd-kind-icon">3<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "handle"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L250">runtime.ts:250</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L175">runtime.ts:175</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L176">runtime.ts:176</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L180">runtime.ts:180</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L177">runtime.ts:177</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L178">runtime.ts:178</a></li>
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<div class="tsd-signature tsd-kind-icon">8<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "metal"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L179">runtime.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L179">runtime.ts:179</a></li>
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@@ -1640,7 +1640,7 @@
<div class="tsd-signature tsd-kind-icon">Device<wbr>Str<wbr>ToEnum<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L183">runtime.ts:183</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L183">runtime.ts:183</a></li>
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<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1649,7 +1649,7 @@
<div class="tsd-signature tsd-kind-icon">cl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 4</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L186">runtime.ts:186</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L186">runtime.ts:186</a></li>
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@@ -1659,7 +1659,7 @@
<div class="tsd-signature tsd-kind-icon">cpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L184">runtime.ts:184</a></li>
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@@ -1669,7 +1669,7 @@
<div class="tsd-signature tsd-kind-icon">cuda<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L185">runtime.ts:185</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L185">runtime.ts:185</a></li>
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@@ -1679,7 +1679,7 @@
<div class="tsd-signature tsd-kind-icon">metal<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L189">runtime.ts:189</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L189">runtime.ts:189</a></li>
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@@ -1689,7 +1689,7 @@
<div class="tsd-signature tsd-kind-icon">opencl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L187">runtime.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L187">runtime.ts:187</a></li>
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@@ -1699,7 +1699,7 @@
<div class="tsd-signature tsd-kind-icon">vulkan<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 7</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L188">runtime.ts:188</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L188">runtime.ts:188</a></li>
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@@ -1709,7 +1709,7 @@
<div class="tsd-signature tsd-kind-icon">webgpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 15</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/runtime.ts#L190">runtime.ts:190</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/runtime.ts#L190">runtime.ts:190</a></li>
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index e2c636daf..f311b23d1 100644
--- a/docs/reference/api/typedoc/interfaces/disposable.html
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@@ -113,7 +113,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/types.ts#L52">types.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/types.ts#L52">types.ts:52</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/interfaces/functioninfo.html b/docs/reference/api/typedoc/interfaces/functioninfo.html
index 557deaaba..894905a63 100644
--- a/docs/reference/api/typedoc/interfaces/functioninfo.html
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@@ -95,7 +95,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
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@@ -115,7 +115,7 @@
<div class="tsd-signature tsd-kind-icon">name<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
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index 425e17977..603c439d5 100644
--- a/docs/reference/api/typedoc/interfaces/libraryprovider.html
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@@ -112,7 +112,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/types.ts#L34">types.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/types.ts#L34">types.ts:34</a></li>
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<div class="tsd-comment tsd-typography">
@@ -127,7 +127,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/52df2e841/web/src/types.ts#L39">types.ts:39</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/4a769c1da/web/src/types.ts#L39">types.ts:39</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 46b11fe7b..ddcd0a519 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
+Search.setIndex({docnames:["arch/benchmark","arch/convert_layout","arch/debugger","arch/device_target_interactions","arch/frontend/tensorflow","arch/hybrid_script","arch/index","arch/inferbound","arch/introduction_to_module_serialization","arch/microtvm_design","arch/microtvm_project_api","arch/model_library_format","arch/pass_infra","arch/relay_intro","arch/relay_op_strategy","arch/runtime","arch/runtimes/vulkan","arch/security","arch/virtual_machine","contribute/ci","contribute/code_gu [...]
\ No newline at end of file
diff --git a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
index 28a26c1e5..2d6922b00 100644
--- a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
@@ -300,10 +300,10 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:20.774</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
+<p><strong>00:20.664</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:20.558</strong>: <a class="reference internal" href="tune_relay_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-relay-vta-py"><span class="std std-ref">Auto-tuning a convolutional network on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_vta.py</span></code>)</p></li>
-<li><p><strong>00:00.216</strong>: <a class="reference internal" href="tune_alu_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-alu-vta-py"><span class="std std-ref">Auto-tuning a ALU fused op on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_alu_vta.py</span></code>)</p></li>
+<li><p><strong>00:20.457</strong>: <a class="reference internal" href="tune_relay_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-relay-vta-py"><span class="std std-ref">Auto-tuning a convolutional network on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_vta.py</span></code>)</p></li>
+<li><p><strong>00:00.206</strong>: <a class="reference internal" href="tune_alu_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-alu-vta-py"><span class="std std-ref">Auto-tuning a ALU fused op on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_alu_vta.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_classification.html b/docs/topic/vta/tutorials/frontend/deploy_classification.html
index aacd9ee4e..14fdf97d1 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_classification.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_classification.html
@@ -541,7 +541,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
-resnet18_v1 inference graph built in 21.86s!
+resnet18_v1 inference graph built in 21.29s!
</pre></div>
</div>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_detection.html b/docs/topic/vta/tutorials/frontend/deploy_detection.html
index f2ad85645..2eeab870d 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -559,7 +559,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py:389: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
-yolov3-tiny inference graph built in 15.21s!
+yolov3-tiny inference graph built in 14.81s!
</pre></div>
</div>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/sg_execution_times.html b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
index d24d3e39a..ac9726a93 100644
--- a/docs/topic/vta/tutorials/frontend/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
@@ -300,10 +300,10 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-frontend-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>01:29.898</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:28.363</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:47.714</strong>: <a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></li>
-<li><p><strong>00:42.184</strong>: <a class="reference internal" href="deploy_classification.html#sphx-glr-topic-vta-tutorials-frontend-deploy-classification-py"><span class="std std-ref">Deploy Pretrained Vision Model from MxNet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_classification.py</span></code>)</p></li>
+<li><p><strong>00:46.952</strong>: <a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></li>
+<li><p><strong>00:41.411</strong>: <a class="reference internal" href="deploy_classification.html#sphx-glr-topic-vta-tutorials-frontend-deploy-classification-py"><span class="std std-ref">Deploy Pretrained Vision Model from MxNet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_classification.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/optimize/sg_execution_times.html b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
index e6b2c52d5..13df7b6e0 100644
--- a/docs/topic/vta/tutorials/optimize/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
@@ -300,10 +300,10 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-optimize-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.577</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.488</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:03.013</strong>: <a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></li>
-<li><p><strong>00:00.565</strong>: <a class="reference internal" href="matrix_multiply_opt.html#sphx-glr-topic-vta-tutorials-optimize-matrix-multiply-opt-py"><span class="std std-ref">Matrix Multiply Blocking</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply_opt.py</span></code>)</p></li>
+<li><p><strong>00:02.934</strong>: <a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></li>
+<li><p><strong>00:00.554</strong>: <a class="reference internal" href="matrix_multiply_opt.html#sphx-glr-topic-vta-tutorials-optimize-matrix-multiply-opt-py"><span class="std std-ref">Matrix Multiply Blocking</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply_opt.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/sg_execution_times.html b/docs/topic/vta/tutorials/sg_execution_times.html
index ced6fdb89..6faf19962 100644
--- a/docs/topic/vta/tutorials/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -300,10 +300,10 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:01.029</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
+<p><strong>00:00.999</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:00.524</strong>: <a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></li>
-<li><p><strong>00:00.505</strong>: <a class="reference internal" href="vta_get_started.html#sphx-glr-topic-vta-tutorials-vta-get-started-py"><span class="std std-ref">Get Started with VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">vta_get_started.py</span></code>)</p></li>
+<li><p><strong>00:00.506</strong>: <a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></li>
+<li><p><strong>00:00.493</strong>: <a class="reference internal" href="vta_get_started.html#sphx-glr-topic-vta-tutorials-vta-get-started-py"><span class="std std-ref">Get Started with VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">vta_get_started.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/tutorial/auto_scheduler_matmul_x86.html b/docs/tutorial/auto_scheduler_matmul_x86.html
index b398ab80e..093c9abb8 100644
--- a/docs/tutorial/auto_scheduler_matmul_x86.html
+++ b/docs/tutorial/auto_scheduler_matmul_x86.html
@@ -453,7 +453,7 @@ trials, we can load the best schedule from the log file and apply it.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>*E
</pre></div>
</div>
</div>
@@ -547,7 +547,7 @@ operator fusion.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/target/target.py:317: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-Execution time of this operator: 93.665 ms
+Execution time of this operator: 93.862 ms
</pre></div>
</div>
</div>
@@ -613,6 +613,7 @@ resume the status and do more 5 trials.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Resume search:
/usr/local/lib/python3.7/dist-packages/xgboost/training.py:17: UserWarning: Old style callback is deprecated. See: https://xgboost.readthedocs.io/en/latest/python/callbacks.html
warnings.warn(f'Old style callback is deprecated. See: {link}', UserWarning)
+*E
</pre></div>
</div>
</div>
@@ -623,6 +624,7 @@ automatically optimize a matrix multiplication, without the need to specify a
search template. It ends a series of examples that starts from the Tensor
Expression (TE) language that demonstrates how TVM can optimize computational
operations.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.258 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-auto-scheduler-matmul-x86-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_scheduler_matmul_x86.py</span></code></a></p>
diff --git a/docs/tutorial/autotvm_relay_x86.html b/docs/tutorial/autotvm_relay_x86.html
index ce40d358e..7f64ee7d2 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -521,7 +521,7 @@ standard deviation.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 495.0658949700005, 'median': 494.72848735000525, 'std': 0.8381999189590658}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 498.91625751000447, 'median': 498.82358639999893, 'std': 0.3731100177629365}
</pre></div>
</div>
</div>
@@ -675,179 +675,179 @@ depending on the specifics of the model and the target platform.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 1/25] Current/Best: 17.51/ 17.51 GFLOPS | Progress: (4/20) | 6.50 s
-[Task 1/25] Current/Best: 6.16/ 17.51 GFLOPS | Progress: (8/20) | 8.96 s
-[Task 1/25] Current/Best: 11.51/ 22.73 GFLOPS | Progress: (12/20) | 11.38 s
-[Task 1/25] Current/Best: 16.78/ 22.84 GFLOPS | Progress: (16/20) | 13.07 s
-[Task 1/25] Current/Best: 11.59/ 23.88 GFLOPS | Progress: (20/20) | 14.82 s Done.
+[Task 1/25] Current/Best: 17.50/ 17.50 GFLOPS | Progress: (4/20) | 6.60 s
+[Task 1/25] Current/Best: 6.14/ 17.50 GFLOPS | Progress: (8/20) | 9.06 s
+[Task 1/25] Current/Best: 11.55/ 22.75 GFLOPS | Progress: (12/20) | 11.52 s
+[Task 1/25] Current/Best: 16.70/ 22.78 GFLOPS | Progress: (16/20) | 13.20 s
+[Task 1/25] Current/Best: 10.88/ 23.87 GFLOPS | Progress: (20/20) | 14.94 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 2/25] Current/Best: 11.80/ 13.10 GFLOPS | Progress: (4/20) | 3.74 s
-[Task 2/25] Current/Best: 14.35/ 18.38 GFLOPS | Progress: (8/20) | 5.05 s
-[Task 2/25] Current/Best: 20.76/ 20.76 GFLOPS | Progress: (12/20) | 6.37 s
-[Task 2/25] Current/Best: 12.87/ 20.76 GFLOPS | Progress: (16/20) | 7.66 s
-[Task 2/25] Current/Best: 19.50/ 20.76 GFLOPS | Progress: (20/20) | 9.21 s Done.
+[Task 2/25] Current/Best: 12.26/ 13.05 GFLOPS | Progress: (4/20) | 3.58 s
+[Task 2/25] Current/Best: 13.76/ 17.34 GFLOPS | Progress: (8/20) | 4.89 s
+[Task 2/25] Current/Best: 21.17/ 21.17 GFLOPS | Progress: (12/20) | 6.24 s
+[Task 2/25] Current/Best: 12.04/ 21.17 GFLOPS | Progress: (16/20) | 7.52 s
+[Task 2/25] Current/Best: 20.02/ 21.17 GFLOPS | Progress: (20/20) | 9.10 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 3/25] Current/Best: 1.63/ 10.50 GFLOPS | Progress: (4/20) | 5.79 s
-[Task 3/25] Current/Best: 15.58/ 16.80 GFLOPS | Progress: (8/20) | 7.71 s
-[Task 3/25] Current/Best: 14.86/ 16.80 GFLOPS | Progress: (12/20) | 9.44 s
-[Task 3/25] Current/Best: 7.15/ 23.68 GFLOPS | Progress: (16/20) | 11.37 s
-[Task 3/25] Current/Best: 12.60/ 23.68 GFLOPS | Progress: (20/20) | 15.92 s Done.
+[Task 3/25] Current/Best: 1.63/ 10.54 GFLOPS | Progress: (4/20) | 5.80 s
+[Task 3/25] Current/Best: 15.58/ 16.89 GFLOPS | Progress: (8/20) | 7.71 s
+[Task 3/25] Current/Best: 14.83/ 16.89 GFLOPS | Progress: (12/20) | 9.45 s
+[Task 3/25] Current/Best: 7.21/ 23.73 GFLOPS | Progress: (16/20) | 11.36 s
+[Task 3/25] Current/Best: 12.12/ 23.73 GFLOPS | Progress: (20/20) | 15.87 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 4/25] Current/Best: 9.57/ 20.26 GFLOPS | Progress: (4/20) | 2.34 s
-[Task 4/25] Current/Best: 6.88/ 20.26 GFLOPS | Progress: (8/20) | 6.69 s
-[Task 4/25] Current/Best: 21.58/ 21.58 GFLOPS | Progress: (12/20) | 11.22 s
-[Task 4/25] Current/Best: 16.12/ 21.68 GFLOPS | Progress: (16/20) | 13.46 s
-[Task 4/25] Current/Best: 12.67/ 21.68 GFLOPS | Progress: (20/20) | 15.45 s Done.
+[Task 4/25] Current/Best: 9.54/ 19.15 GFLOPS | Progress: (4/20) | 2.34 s
+[Task 4/25] Current/Best: 6.80/ 19.15 GFLOPS | Progress: (8/20) | 6.64 s
+[Task 4/25] Current/Best: 22.45/ 22.45 GFLOPS | Progress: (12/20) | 11.18 s
+[Task 4/25] Current/Best: 16.92/ 22.45 GFLOPS | Progress: (16/20) | 13.43 s
+[Task 4/25] Current/Best: 13.38/ 22.45 GFLOPS | Progress: (20/20) | 15.33 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 5/25] Current/Best: 9.60/ 10.46 GFLOPS | Progress: (4/20) | 2.52 s
-[Task 5/25] Current/Best: 11.81/ 11.81 GFLOPS | Progress: (8/20) | 4.61 s
-[Task 5/25] Current/Best: 11.17/ 18.07 GFLOPS | Progress: (12/20) | 7.71 s
-[Task 5/25] Current/Best: 11.65/ 22.63 GFLOPS | Progress: (16/20) | 9.13 s
-[Task 5/25] Current/Best: 11.75/ 22.63 GFLOPS | Progress: (20/20) | 11.02 s Done.
+[Task 5/25] Current/Best: 9.59/ 10.25 GFLOPS | Progress: (4/20) | 2.56 s
+[Task 5/25] Current/Best: 11.85/ 12.76 GFLOPS | Progress: (8/20) | 4.63 s
+[Task 5/25] Current/Best: 10.64/ 18.03 GFLOPS | Progress: (12/20) | 7.72 s
+[Task 5/25] Current/Best: 11.89/ 22.82 GFLOPS | Progress: (16/20) | 9.14 s
+[Task 5/25] Current/Best: 12.07/ 22.82 GFLOPS | Progress: (20/20) | 11.01 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 6/25] Current/Best: 12.17/ 20.65 GFLOPS | Progress: (4/20) | 3.90 s
-[Task 6/25] Current/Best: 18.99/ 20.65 GFLOPS | Progress: (8/20) | 5.64 s
-[Task 6/25] Current/Best: 13.07/ 20.65 GFLOPS | Progress: (12/20) | 7.58 s
-[Task 6/25] Current/Best: 19.99/ 20.65 GFLOPS | Progress: (16/20) | 9.81 s
-[Task 6/25] Current/Best: 3.74/ 20.65 GFLOPS | Progress: (20/20) | 12.35 s Done.
+[Task 6/25] Current/Best: 12.22/ 20.71 GFLOPS | Progress: (4/20) | 3.91 s
+[Task 6/25] Current/Best: 19.00/ 20.71 GFLOPS | Progress: (8/20) | 5.68 s
+[Task 6/25] Current/Best: 13.17/ 20.71 GFLOPS | Progress: (12/20) | 7.62 s
+[Task 6/25] Current/Best: 19.92/ 20.71 GFLOPS | Progress: (16/20) | 9.86 s
+[Task 6/25] Current/Best: 3.70/ 20.71 GFLOPS | Progress: (20/20) | 12.43 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 7/25] Current/Best: 10.56/ 12.81 GFLOPS | Progress: (4/20) | 3.58 s
-[Task 7/25] Current/Best: 20.03/ 21.06 GFLOPS | Progress: (8/20) | 5.09 s
-[Task 7/25] Current/Best: 16.18/ 21.06 GFLOPS | Progress: (12/20) | 6.98 s
-[Task 7/25] Current/Best: 12.17/ 21.06 GFLOPS | Progress: (16/20) | 9.02 s
-[Task 7/25] Current/Best: 6.22/ 21.73 GFLOPS | Progress: (20/20) | 11.50 s Done.
+[Task 7/25] Current/Best: 10.45/ 12.84 GFLOPS | Progress: (4/20) | 3.60 s
+[Task 7/25] Current/Best: 19.93/ 20.30 GFLOPS | Progress: (8/20) | 5.15 s
+[Task 7/25] Current/Best: 15.22/ 20.30 GFLOPS | Progress: (12/20) | 7.08 s
+[Task 7/25] Current/Best: 12.22/ 20.54 GFLOPS | Progress: (16/20) | 9.16 s
+[Task 7/25] Current/Best: 6.29/ 21.54 GFLOPS | Progress: (20/20) | 11.65 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 8/25] Current/Best: 10.12/ 14.32 GFLOPS | Progress: (4/20) | 2.84 s
-[Task 8/25] Current/Best: 10.13/ 14.32 GFLOPS | Progress: (8/20) | 7.60 s
-[Task 8/25] Current/Best: 12.88/ 14.32 GFLOPS | Progress: (12/20) | 13.75 s
-[Task 8/25] Current/Best: 19.09/ 19.09 GFLOPS | Progress: (16/20) | 15.87 s
-[Task 8/25] Current/Best: 20.39/ 20.39 GFLOPS | Progress: (20/20) | 22.29 s Done.
+[Task 8/25] Current/Best: 8.46/ 13.37 GFLOPS | Progress: (4/20) | 3.06 s
+[Task 8/25] Current/Best: 9.43/ 13.37 GFLOPS | Progress: (8/20) | 8.26 s
+[Task 8/25] Current/Best: 12.97/ 13.63 GFLOPS | Progress: (12/20) | 14.67 s
+[Task 8/25] Current/Best: 18.69/ 18.69 GFLOPS | Progress: (16/20) | 16.79 s
+[Task 8/25] Current/Best: 20.01/ 20.01 GFLOPS | Progress: (20/20) | 23.52 s Done.
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 9/25] Current/Best: 14.25/ 15.81 GFLOPS | Progress: (4/20) | 17.48 s
-[Task 9/25] Current/Best: 23.39/ 23.39 GFLOPS | Progress: (8/20) | 19.35 s
-[Task 9/25] Current/Best: 8.23/ 23.39 GFLOPS | Progress: (12/20) | 21.70 s
-[Task 9/25] Current/Best: 17.64/ 23.39 GFLOPS | Progress: (16/20) | 24.37 s
-[Task 9/25] Current/Best: 8.97/ 23.39 GFLOPS | Progress: (20/20) | 32.11 s
+[Task 9/25] Current/Best: 14.17/ 15.55 GFLOPS | Progress: (4/20) | 18.31 s
+[Task 9/25] Current/Best: 22.02/ 22.02 GFLOPS | Progress: (8/20) | 20.15 s
+[Task 9/25] Current/Best: 8.21/ 22.02 GFLOPS | Progress: (12/20) | 22.60 s
+[Task 9/25] Current/Best: 17.75/ 22.02 GFLOPS | Progress: (16/20) | 25.38 s
+[Task 9/25] Current/Best: 8.91/ 22.02 GFLOPS | Progress: (20/20) | 33.45 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 10/25] Current/Best: 17.98/ 17.98 GFLOPS | Progress: (4/20) | 2.52 s
-[Task 10/25] Current/Best: 15.62/ 17.98 GFLOPS | Progress: (8/20) | 4.09 s
-[Task 10/25] Current/Best: 12.37/ 18.92 GFLOPS | Progress: (12/20) | 5.63 s
-[Task 10/25] Current/Best: 19.18/ 20.49 GFLOPS | Progress: (16/20) | 6.75 s
-[Task 10/25] Current/Best: 8.93/ 20.49 GFLOPS | Progress: (20/20) | 8.28 s Done.
+[Task 10/25] Current/Best: 18.19/ 18.19 GFLOPS | Progress: (4/20) | 2.65 s
+[Task 10/25] Current/Best: 15.52/ 18.19 GFLOPS | Progress: (8/20) | 4.23 s
+[Task 10/25] Current/Best: 12.33/ 19.12 GFLOPS | Progress: (12/20) | 5.75 s
+[Task 10/25] Current/Best: 19.14/ 20.08 GFLOPS | Progress: (16/20) | 6.86 s
+[Task 10/25] Current/Best: 8.89/ 20.08 GFLOPS | Progress: (20/20) | 8.41 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 11/25] Current/Best: 12.07/ 18.13 GFLOPS | Progress: (4/20) | 3.22 s
-[Task 11/25] Current/Best: 16.67/ 18.13 GFLOPS | Progress: (8/20) | 5.95 s
-[Task 11/25] Current/Best: 18.16/ 18.16 GFLOPS | Progress: (12/20) | 7.96 s
-[Task 11/25] Current/Best: 12.19/ 21.09 GFLOPS | Progress: (16/20) | 10.75 s
-[Task 11/25] Current/Best: 19.29/ 21.58 GFLOPS | Progress: (20/20) | 12.79 s Done.
+[Task 11/25] Current/Best: 12.12/ 18.12 GFLOPS | Progress: (4/20) | 3.24 s
+[Task 11/25] Current/Best: 16.68/ 18.12 GFLOPS | Progress: (8/20) | 5.95 s
+[Task 11/25] Current/Best: 18.21/ 18.21 GFLOPS | Progress: (12/20) | 7.98 s
+[Task 11/25] Current/Best: 13.36/ 21.06 GFLOPS | Progress: (16/20) | 10.71 s
+[Task 11/25] Current/Best: 19.46/ 21.37 GFLOPS | Progress: (20/20) | 12.73 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 12/25] Current/Best: 7.72/ 18.29 GFLOPS | Progress: (4/20) | 5.33 s
-[Task 12/25] Current/Best: 5.32/ 18.29 GFLOPS | Progress: (8/20) | 9.02 s
-[Task 12/25] Current/Best: 19.17/ 19.17 GFLOPS | Progress: (12/20) | 11.00 s
-[Task 12/25] Current/Best: 15.38/ 19.17 GFLOPS | Progress: (16/20) | 13.75 s
-[Task 12/25] Current/Best: 13.47/ 19.17 GFLOPS | Progress: (20/20) | 15.69 s Done.
+[Task 12/25] Current/Best: 7.77/ 18.15 GFLOPS | Progress: (4/20) | 5.32 s
+[Task 12/25] Current/Best: 5.10/ 18.15 GFLOPS | Progress: (8/20) | 9.00 s
+[Task 12/25] Current/Best: 18.86/ 18.94 GFLOPS | Progress: (12/20) | 10.98 s
+[Task 12/25] Current/Best: 15.56/ 18.94 GFLOPS | Progress: (16/20) | 13.72 s
+[Task 12/25] Current/Best: 15.15/ 18.94 GFLOPS | Progress: (20/20) | 15.65 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 13/25] Current/Best: 8.67/ 17.27 GFLOPS | Progress: (4/20) | 3.61 s
-[Task 13/25] Current/Best: 15.06/ 20.81 GFLOPS | Progress: (8/20) | 6.05 s
-[Task 13/25] Current/Best: 19.47/ 21.30 GFLOPS | Progress: (12/20) | 9.00 s
-[Task 13/25] Current/Best: 12.22/ 21.30 GFLOPS | Progress: (16/20) | 12.40 s
-[Task 13/25] Current/Best: 18.82/ 21.30 GFLOPS | Progress: (20/20) | 14.63 s Done.
+[Task 13/25] Current/Best: 9.05/ 17.26 GFLOPS | Progress: (4/20) | 3.58 s
+[Task 13/25] Current/Best: 16.00/ 20.84 GFLOPS | Progress: (8/20) | 6.02 s
+[Task 13/25] Current/Best: 19.72/ 21.56 GFLOPS | Progress: (12/20) | 8.89 s
+[Task 13/25] Current/Best: 12.21/ 21.56 GFLOPS | Progress: (16/20) | 12.31 s
+[Task 13/25] Current/Best: 18.59/ 21.56 GFLOPS | Progress: (20/20) | 14.56 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 14/25] Current/Best: 13.59/ 13.59 GFLOPS | Progress: (4/20) | 3.22 s
-[Task 14/25] Current/Best: 6.07/ 13.59 GFLOPS | Progress: (8/20) | 5.41 s
-[Task 14/25] Current/Best: 20.23/ 20.23 GFLOPS | Progress: (12/20) | 7.97 s
-[Task 14/25] Current/Best: 16.85/ 20.23 GFLOPS | Progress: (16/20) | 9.88 s
-[Task 14/25] Current/Best: 16.75/ 20.23 GFLOPS | Progress: (20/20) | 11.68 s
+[Task 14/25] Current/Best: 12.56/ 13.38 GFLOPS | Progress: (4/20) | 3.29 s
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