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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/05/03 21:40:03 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@633fb546147c3da5a805d317a44eb4d67e5b8fa8)
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 26cb84376 deploying docs (apache/tvm@633fb546147c3da5a805d317a44eb4d67e5b8fa8)
26cb84376 is described below
commit 26cb8437634a7fb6ec66181552635f1c52d8f643
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Tue May 3 21:39:57 2022 +0000
deploying docs (apache/tvm@633fb546147c3da5a805d317a44eb4d67e5b8fa8)
---
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.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 | 1147 ++++++++++++++++++--
.../tune_network_cuda.rst.txt | 2 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 76 +-
.../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 | 2 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 9 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 61 +-
.../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 | 45 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 80 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 6 +-
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 | 72 +-
docs/how_to/deploy_models/deploy_prequantized.html | 8 +-
.../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 | 36 +-
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 | 1147 ++++++++++++++++++--
.../tune_with_autoscheduler/tune_network_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autoscheduler/tune_sparse_x86.html | 76 +-
.../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 | 2 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 5 +-
docs/tutorial/autotvm_relay_x86.html | 170 +--
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 | 41 +-
115 files changed, 2987 insertions(+), 1048 deletions(-)
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 a85ba3fed..43942685e 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.zip1ebc153a-6d2e-447e-b6b2-097abde69dac from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipfd9b566b-f71f-4ae9-b955-06c269f95e9f 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 3220a8457..807f5be1b 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_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index f8944d769..2603dc844 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -201,7 +201,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.653 seconds)
+ **Total running time of the script:** ( 1 minutes 7.677 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 b170581af..96893ccfe 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 f4ff85e41..aa23d31e4 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -372,7 +372,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.958 seconds)
+ **Total running time of the script:** ( 1 minutes 3.693 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 6e5253e9a..9fe3e692b 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
=================
-**05:15.919** total execution time for **how_to_compile_models** files:
+**05:24.226** total execution time for **how_to_compile_models** files:
-- **01:05.653**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **01:02.958**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:55.806**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:30.023**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
-- **00:25.222**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:20.987**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:20.902**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:18.652**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:13.210**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.506**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:07.677**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:03.693**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:57.735**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:30.506**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
+- **00:26.309**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:22.016**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:21.160**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:19.146**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:13.386**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.598**: :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 fe46d33aa..677ed4e5d 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
@@ -393,7 +393,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.9410 15.9169 16.2924 15.6807 0.1600
+ 15.9496 15.9255 16.1020 15.8305 0.0803
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 4b0b3e130..22eb8f2fa 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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/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').
@@ -253,7 +253,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 5.767 seconds)
+ **Total running time of the script:** ( 3 minutes 11.334 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 682983eb0..d581fe982 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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+
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55%|#####5 | 7.49M/13.6M [00:00<00:00, 39.4MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 61.0MB/s]
@@ -344,7 +344,7 @@ 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)
- 90.4604 90.3123 91.8797 90.1704 0.3136
+ 90.4632 90.3277 95.3507 90.1518 0.6050
@@ -384,7 +384,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 6.147 seconds)
+ **Total running time of the script:** ( 1 minutes 7.204 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 3160a9462..b7a47bedb 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
@@ -351,7 +351,7 @@ 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)
- 120.3516 120.3294 121.2233 119.5416 0.3531
+ 119.9625 119.8841 121.1679 119.3007 0.4159
@@ -385,7 +385,7 @@ 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 53.586 seconds)
+ **Total running time of the script:** ( 1 minutes 57.081 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 c48527278..86971be71 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -221,7 +221,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 16.533 seconds)
+ **Total running time of the script:** ( 1 minutes 20.213 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 c3d4c264f..55336caa0 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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@@ -202,7 +202,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 24.476 seconds)
+ **Total running time of the script:** ( 2 minutes 26.150 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 13f6c90c0..83888e033 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
=================
-**10:36.167** total execution time for **how_to_deploy_models** files:
+**10:53.949** total execution time for **how_to_deploy_models** files:
-- **03:05.767**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:24.476**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:53.586**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:16.533**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:06.147**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:27.963**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.486**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.208**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:11.334**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:26.150**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:57.081**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:20.213**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:07.204**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:29.578**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:22.190**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.199**: :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 b070bd341..6e6484662 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
@@ -423,7 +423,7 @@ 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.zipab6d22fd-b239-42ed-9c28-b7bd07166807 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip27ce98f0-fcb8-4757-88c0-4838684ab5f7 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 60451ce14..855ecd6bd 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
=================
-**00:38.731** total execution time for **how_to_extend_tvm** files:
+**00:39.626** total execution time for **how_to_extend_tvm** files:
-- **00:35.072**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.391**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.054**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.214**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:35.965**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.332**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.120**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.210**: :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 3187738f9..fec8fec7c 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: 5995us [5995us] (45.49%; 45.49%)
- FoldScaleAxis: 7185us [17us] (54.51%; 54.51%)
- FoldConstant: 7168us [1476us] (54.39%; 99.76%)
- InferType: 5692us [5692us] (43.18%; 79.40%)
+ InferType: 6486us [6486us] (46.18%; 46.18%)
+ FoldScaleAxis: 7559us [2us] (53.82%; 53.82%)
+ FoldConstant: 7557us [1593us] (53.81%; 99.97%)
+ InferType: 5964us [5964us] (42.46%; 78.92%)
@@ -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: 5739us [5739us] (44.46%; 44.46%)
- FoldScaleAxis: 7170us [2us] (55.54%; 55.54%)
- FoldConstant: 7168us [1468us] (55.53%; 99.97%)
- InferType: 5700us [5700us] (44.16%; 79.52%)
+ InferType: 6117us [6117us] (44.68%; 44.68%)
+ FoldScaleAxis: 7574us [2us] (55.32%; 55.32%)
+ FoldConstant: 7572us [1594us] (55.31%; 99.97%)
+ InferType: 5977us [5977us] (43.66%; 78.95%)
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 08039b635..585ccd838 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
@@ -295,7 +295,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 52.770417 ms
+ Convolution: 48.119834 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 18588dfec..4a62cf862 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
@@ -628,7 +628,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 10.204333 ms
+ conv2d with tensor core: 10.983607 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 26b2e9b93..48b0871bd 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018571
- Baseline: 3.262366
+ Numpy running time: 0.019161
+ Baseline: 3.423051
@@ -210,7 +210,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.307721
+ Opt1: 0.297574
@@ -309,7 +309,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.344343
+ Opt2: 0.332082
@@ -401,7 +401,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.119647
+ Opt3: 0.117544
@@ -520,7 +520,7 @@ flattening.
.. code-block:: none
- Opt4: 0.111523
+ Opt4: 0.110347
@@ -638,7 +638,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111944
+ Opt5: 0.110699
@@ -759,7 +759,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.146853
+ Opt6: 0.144545
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 a1533718d..aa21ee78d 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
=================
-**00:35.013** total execution time for **how_to_optimize_operators** files:
+**00:35.251** total execution time for **how_to_optimize_operators** files:
-- **00:32.285**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.480**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.248**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:32.475**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.507**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.269**: :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 a79ba663a..62d4064e5 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
=================
-**04:56.637** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:19.638**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:20.116**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:40.659**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:19.102**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.619**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.504**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**04:58.771** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:22.927**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:21.573**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:40.672**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:15.772**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:09.297**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.531**: :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 3e99065e0..fe9793fb1 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
@@ -222,37 +222,588 @@ 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" = 28;
- allocate(conv2d_nchw: Pointer(local float32), float32, [2]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [108]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [4608]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope="local", align=4)[0] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 8;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [1296]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [9216]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope="local", align=16)[0] = 0f32
+ conv2d_nchw_1[4] = 0f32
+ conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[16] = 0f32
+ conv2d_nchw_1[20] = 0f32
+ conv2d_nchw_1[24] = 0f32
conv2d_nchw_1[1] = 0f32
- for (rc.outer.outer: int32, 0, 128) {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448;
- if @tir.likely((threadIdx.x_1 < 108), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [108], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((1 <= (floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7))) && ((floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 27)*49)) + (floordiv(floormod(threadIdx.x_1, 27), 9)*7)) + (floormod(block [...]
- }
- for (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer: int32, 0, 11) {
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448;
- if @tir.likely((((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*7) + floordiv(threadIdx.x_2, 64)) < 72), dtype=bool) {
- kernel.shared_1: Buffer(kernel.shared, float32, [4608], [], scope="shared")[((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*448) + threadIdx.x_2)] = kernel[((((floordiv(blockIdx.x, 7)*589824) + (floordiv(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + floordiv(threadIdx.x_2, 4)), 9)*4608)) + (rc.outer.outer*36)) + floormod(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*448) + threadIdx.x_2), 36))]
+ conv2d_nchw_1[5] = 0f32
+ conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[13] = 0f32
+ conv2d_nchw_1[17] = 0f32
+ conv2d_nchw_1[21] = 0f32
+ conv2d_nchw_1[25] = 0f32
+ conv2d_nchw_1[2] = 0f32
+ conv2d_nchw_1[6] = 0f32
+ conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[14] = 0f32
+ conv2d_nchw_1[18] = 0f32
+ conv2d_nchw_1[22] = 0f32
+ conv2d_nchw_1[26] = 0f32
+ conv2d_nchw_1[3] = 0f32
+ conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[15] = 0f32
+ conv2d_nchw_1[19] = 0f32
+ conv2d_nchw_1[23] = 0f32
+ conv2d_nchw_1[27] = 0f32
+ for (rc.outer.outer: int32, 0, 32) {
+ let cse_var_2: int32 = (rc.outer.outer*784)
+ let cse_var_1: int32 = (rc.outer.outer*144)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [1296], [], scope="shared")[(threadIdx.x_1*4)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1*4), 81)) && (floormod((threadIdx.x_1*4), 81) < 72)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1*4), 81)*49)) + (floordiv(floormod((threadIdx.x_1*4), 81), 9)*7)) + floormod((threadIdx.x_1*4), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 1)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 1), 81)) && (floormod(((threadIdx.x_1*4) + 1), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 1), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 1), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 2)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 2), 81)) && (floormod(((threadIdx.x_1*4) + 2), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 2), 9))) && (floormod(((threadIdx.x_1*4) + 2), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 2), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 2), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 2), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 3)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 3), 81)) && (floormod(((threadIdx.x_1*4) + 3), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 3), 9))) && (floormod(((threadIdx.x_1*4) + 3), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 3), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 3), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 3), 9)) - 8)], 0f32, dtype=float32)
}
- }
- for (rc.outer.inner: int32, 0, 2) {
- for (ry.outer.inner: int32, 0, 3) {
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 448)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 448), 81)) && (floormod(((threadIdx.x_1*4) + 43), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 7), 9))) && (floormod(((threadIdx.x_1*4) + 7), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 448), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 448), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 7), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 449)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 449), 81)) && (floormod(((threadIdx.x_1*4) + 44), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 8), 9))) && (floormod(((threadIdx.x_1*4) + 8), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 449), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 449), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 8), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 450)] = @tir.if_then_else(((((1 <= floormod((floordiv((threadIdx.x_1*4), 9) + 5), 9)) && (floormod(((threadIdx.x_1*4) + 45), 81) < 72)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 450), 81)*49)) + (floormod((floordiv((threadIdx.x_1*4), 9) + 5), 9)*7)) + floormod((threadIdx.x_1*4), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 451)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 451), 81)) && (floormod(((threadIdx.x_1*4) + 46), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 451), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 451), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype=float32)
+ }
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 896)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 896), 81)) && (floormod(((threadIdx.x_1*4) + 5), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 5), 9))) && (floormod(((threadIdx.x_1*4) + 5), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 896), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 896), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 5), 9)) - 8)], 0f32, dtype=float32)
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 897)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 897), 81)) && (floormod(((threadIdx.x_1*4) + 6), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 6), 9))) && (floormod(((threadIdx.x_1*4) + 6), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 897), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 897), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 6), 9)) - 8)], 0f32, dtype=float32)
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 898)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 898), 81)) && (floormod(((threadIdx.x_1*4) + 7), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 7), 9))) && (floormod(((threadIdx.x_1*4) + 7), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 898), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 898), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 7), 9)) - 8)], 0f32, dtype=float32)
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 899)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 899), 81)) && (floormod(((threadIdx.x_1*4) + 8), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 8), 9))) && (floormod(((threadIdx.x_1*4) + 8), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 899), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 899), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 8), 9)) - 8)], 0f32, dtype=float32)
+ }
+ }
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1: Buffer(kernel.shared, float32, [9216], [], scope="shared")[threadIdx.x_2] = kernel[(((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 14), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 21), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 35), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 42), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 56), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 77), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 84), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1568)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 98), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1680)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 105), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1792)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 112), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1904)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 119), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2016)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 64512)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2128)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 133), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2240)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 140), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2352)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 147), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2464)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 154), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2576)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 161), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2688)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 168), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2800)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 175), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2912)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 182), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3024)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 96768)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3136)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 196), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3248)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 203), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3360)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 210), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3472)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 217), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3584)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 224), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3696)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 231), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3808)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 238), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3920)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 245), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4032)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 129024)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4144)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 259), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4256)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 266), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4368)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 273), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4480)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 280), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4592)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 287), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4704)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 294), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4816)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 301), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4928)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 308), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5040)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 161280)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5152)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 322), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5264)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 329), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5376)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 336), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 343), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5600)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 350), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5712)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 357), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5824)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 364), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5936)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 371), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6048)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 193536)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6160)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 385), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6272)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 392), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6384)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 399), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6496)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 406), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6608)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 413), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6720)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 420), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6832)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 427), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6944)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 434), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7056)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 225792)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7168)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 448), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7280)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 455), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7392)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 462), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7504)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 469), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7616)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 476), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7728)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 483), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7840)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 490), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7952)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 497), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8064)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 258048)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8176)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 511), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8288)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 518), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8400)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 525), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8512)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 532), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8624)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 539), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8736)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 546), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8848)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 553), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8960)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 560), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 9072)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 290304)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_2 < 32), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 9184)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 574), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ }
+ for (rc.outer.inner: int32, 0, 4) {
for (rx.outer.inner: int32, 0, 3) {
- for (rc.inner: int32, 0, 2) {
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((((rc.outer.inner*54) + (rc.inner*27)) + (ry.outer.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7))]*kernel.shared_1[(((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((((rc.outer.inner*54) + (rc.inner*27)) + (ry.outer.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
- }
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
}
}
}
}
- compute[((((floordiv(blockIdx.x, 7)*6272) + (floordiv(threadIdx.x, 7)*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*128) + floordiv(threadIdx.x, 7))]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (floordiv(threadIdx.x, 7)*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7)) + 3136)] = max((conv2d_nchw_1[1] + bias[(((floordiv(blockIdx.x, 7)*128) + floordiv(threadIdx.x, 7)) + 64)]), 0f32)
+ for (i1.inner: int32, 0, 4) {
+ compute[((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[(i1.inner + 16)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[(i1.inner + 20)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[(i1.inner + 24)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ }
}
}
@@ -304,7 +855,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.406 ms
+ Execution time of this operator: 0.337 ms
@@ -348,37 +899,37 @@ 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=1)
- 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=64)
- 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=2)
+ conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=2)
+ 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=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=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=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=2)
- 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=1)
- conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
+ 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_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+ conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=4)
+ conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=4)
+ 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=3)
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=1)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
- 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=16)
+ 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=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=7)
- compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+ compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+ compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
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)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -397,14 +948,14 @@ 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=448)
+ 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)
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=1)
+ 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=4)
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=448)
+ 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)
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", 0)
+ 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:
@@ -422,36 +973,496 @@ 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__(448) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[2];
- __shared__ float pad_temp_shared[108];
- __shared__ float kernel_shared[4608];
+ extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[1296];
+ __shared__ float kernel_shared[9216];
conv2d_nchw[0] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
+ conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[16] = 0.000000e+00f;
+ conv2d_nchw[20] = 0.000000e+00f;
+ conv2d_nchw[24] = 0.000000e+00f;
conv2d_nchw[1] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
+ conv2d_nchw[5] = 0.000000e+00f;
+ conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[13] = 0.000000e+00f;
+ conv2d_nchw[17] = 0.000000e+00f;
+ conv2d_nchw[21] = 0.000000e+00f;
+ conv2d_nchw[25] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
+ conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[14] = 0.000000e+00f;
+ conv2d_nchw[18] = 0.000000e+00f;
+ conv2d_nchw[22] = 0.000000e+00f;
+ conv2d_nchw[26] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
+ conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[15] = 0.000000e+00f;
+ conv2d_nchw[19] = 0.000000e+00f;
+ conv2d_nchw[23] = 0.000000e+00f;
+ conv2d_nchw[27] = 0.000000e+00f;
+ for (int rc_outer_outer = 0; rc_outer_outer < 32; ++rc_outer_outer) {
__syncthreads();
- if (((int)threadIdx.x) < 108) {
- pad_temp_shared[((int)threadIdx.x)] = (((((1 <= (((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7))) && ((((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 27) * 49)) + (((((int)threadIdx.x) % 27) / 9) * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) * 4)] = (((((9 <= ((((int)threadIdx.x) * 4) % 81)) && (((((int)threadIdx.x) * 4) % 81) < 72)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) * 4) / 81) * 49)) + ((((((int)threadIdx.x) * 4) % 81) / 9) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 1)] = (((((9 <= (((((int)threadIdx.x) * 4) + 1) % 81)) && ((((((int)threadIdx.x) * 4) + 1) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 1) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 1) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 2)] = (((((9 <= (((((int)threadIdx.x) * 4) + 2) % 81)) && ((((((int)threadIdx.x) * 4) + 2) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 2) % 9))) && ((((((int)threadIdx.x) * 4) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 2) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 2) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 2) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 3)] = (((((9 <= (((((int)threadIdx.x) * 4) + 3) % 81)) && ((((((int)threadIdx.x) * 4) + 3) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 3) % 9))) && ((((((int)threadIdx.x) * 4) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 3) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 3) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 448)] = (((((9 <= (((((int)threadIdx.x) * 4) + 43) % 81)) && ((((((int)threadIdx.x) * 4) + 43) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 7) % 9))) && ((((((int)threadIdx.x) * 4) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 448) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 43) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 7) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 449)] = (((((9 <= (((((int)threadIdx.x) * 4) + 44) % 81)) && ((((((int)threadIdx.x) * 4) + 44) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 8) % 9))) && ((((((int)threadIdx.x) * 4) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 449) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 44) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 450)] = (((((1 <= ((((((int)threadIdx.x) * 4) / 9) + 5) % 9)) && ((((((int)threadIdx.x) * 4) + 45) % 81) < 72)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 450) / 81) * 49)) + (((((((int)threadIdx.x) * 4) / 9) + 5) % 9) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 451)] = (((((9 <= (((((int)threadIdx.x) * 4) + 46) % 81)) && ((((((int)threadIdx.x) * 4) + 46) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 451) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 46) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 896)] = (((((9 <= (((((int)threadIdx.x) * 4) + 5) % 81)) && ((((((int)threadIdx.x) * 4) + 5) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 5) % 9))) && ((((((int)threadIdx.x) * 4) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 896) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 5) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 5) % 9)) - 8)] : 0.000000e+00f);
}
- for (int ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer = 0; ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer < 11; ++ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer) {
- if (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 7) + (((int)threadIdx.x) >> 6)) < 72) {
- kernel_shared[((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 448) + ((int)threadIdx.x))] = kernel[(((((((int)blockIdx.x) / 7) * 589824) + ((((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 112) + (((int)threadIdx.x) >> 2)) / 9) * 4608)) + (rc_outer_outer * 36)) + (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 448) + ((int)threadIdx.x)) % 36))];
- }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 897)] = (((((9 <= (((((int)threadIdx.x) * 4) + 6) % 81)) && ((((((int)threadIdx.x) * 4) + 6) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 6) % 9))) && ((((((int)threadIdx.x) * 4) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 897) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 6) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 6) % 9)) - 8)] : 0.000000e+00f);
+ }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 898)] = (((((9 <= (((((int)threadIdx.x) * 4) + 7) % 81)) && ((((((int)threadIdx.x) * 4) + 7) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 7) % 9))) && ((((((int)threadIdx.x) * 4) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 898) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 7) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 7) % 9)) - 8)] : 0.000000e+00f);
+ }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 899)] = (((((9 <= (((((int)threadIdx.x) * 4) + 8) % 81)) && ((((((int)threadIdx.x) * 4) + 8) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 8) % 9))) && ((((((int)threadIdx.x) * 4) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 899) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 8) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 8) % 9)) - 8)] : 0.000000e+00f);
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x))];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 112) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 224) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 336) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 448) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 560) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 672) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 784) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 896) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1120) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1232) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1344) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1456) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1568) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1680)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1680) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1792) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1904)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1904) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 2016)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 64512)];
+ kernel_shared[(((int)threadIdx.x) + 2128)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2128) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2240) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2352) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2464)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2464) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 2576)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2576) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2688) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2800)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2800) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2912)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2912) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 3024)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 96768)];
+ kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3136) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3248)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3248) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3360)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3360) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3472)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3472) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 3584)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3584) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3696)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3696) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3808)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3808) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3920) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 4032)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 129024)];
+ kernel_shared[(((int)threadIdx.x) + 4144)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4144) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4256)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4256) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4368)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4368) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4480)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4480) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 4592)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4592) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4704) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4816)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4816) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4928)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4928) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 5040)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 161280)];
+ kernel_shared[(((int)threadIdx.x) + 5152)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5152) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5264)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5264) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5376)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5376) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5488) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 5600)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5600) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5712)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5712) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5824)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5824) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5936)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5936) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 6048)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 193536)];
+ kernel_shared[(((int)threadIdx.x) + 6160)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6160) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6272)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6272) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6384)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6384) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6496)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6496) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 6608)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6608) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6720)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6720) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6832)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6832) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6944)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6944) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 7056)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 225792)];
+ kernel_shared[(((int)threadIdx.x) + 7168)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7168) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7280)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7280) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7392)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7392) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7504)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7504) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 7616)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7616) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7728)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7728) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7840)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7840) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7952)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7952) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 8064)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 258048)];
+ kernel_shared[(((int)threadIdx.x) + 8176)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8176) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8288)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8288) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8400)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8400) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8512)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8512) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 8624)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8624) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8736)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8736) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8848)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8848) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8960)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8960) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 9072)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 290304)];
+ if (((int)threadIdx.x) < 32) {
+ kernel_shared[(((int)threadIdx.x) + 9184)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 9184) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 112))];
}
__syncthreads();
- for (int rc_outer_inner = 0; rc_outer_inner < 2; ++rc_outer_inner) {
- for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
- for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
- for (int rc_inner = 0; rc_inner < 2; ++rc_inner) {
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((((rc_outer_inner * 54) + (rc_inner * 27)) + (ry_outer_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((((rc_outer_inner * 54) + (rc_inner * 27)) + (ry_outer_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
- }
- }
+ for (int rc_outer_inner = 0; rc_outer_inner < 4; ++rc_outer_inner) {
+ for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
}
}
}
- compute[(((((((int)blockIdx.x) / 7) * 6272) + ((((int)threadIdx.x) / 7) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) / 7))]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + ((((int)threadIdx.x) / 7) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7)) + 3136)] = max((conv2d_nchw[1] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) / 7)) + 64)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 4; ++i1_inner) {
+ compute[((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[(i1_inner + 4)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[(i1_inner + 8)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[(i1_inner + 12)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[(i1_inner + 16)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[(i1_inner + 20)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[(i1_inner + 24)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ }
}
@@ -509,7 +1520,7 @@ 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 19.638 seconds)
+ **Total running time of the script:** ( 2 minutes 22.927 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 68fcf5c6c..eb7645684 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
@@ -614,7 +614,7 @@ 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)
- 9.6652 9.6713 9.7034 9.6209 0.0340
+ 9.9857 9.9803 10.0105 9.9663 0.0185
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 ae42c20e5..da65d9c53 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
@@ -633,7 +633,7 @@ 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)
- 755.8455 755.8360 760.4283 751.2723 3.7379
+ 767.0790 765.1935 774.2340 761.8095 5.2446
@@ -658,7 +658,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.116 seconds)
+ **Total running time of the script:** ( 1 minutes 21.573 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 b9fdee9b3..99e09c613 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 +362,28 @@ 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_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_7: placeholder_19: Buffer(placeholder_12, int32, [4916], [])} {
+ 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_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
allocate(compute_4: Pointer(global float32), float32, [256]), storage_scope = global {
- for (i.outer.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 8) {
- let cse_var_1: int32 = ((i.outer.inner*128) + (i.inner.init*16))
- {
- compute_5: Buffer(compute_4, float32, [256], [])[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 (i.outer.inner: int32, 0, 4) {
+ for (i.inner.init: int32, 0, 4) {
+ for (j.init: int32, 0, 16) {
+ compute_5: Buffer(compute_4, float32, [256], [])[(((i.outer.inner*64) + (i.inner.init*16)) + j.init)] = 0f32
}
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 8) {
- let cse_var_21: int32 = floormod(i0.outer.i1.outer.fused, 32)
- let cse_var_20: int32 = (elem_idx*16)
- let cse_var_19: int32 = ((i.outer.inner*128) + (i.inner*16))
- let cse_var_18: int32 = (cse_var_19 + 10)
- let cse_var_17: int32 = (cse_var_19 + 11)
- let cse_var_16: int32 = (cse_var_19 + 12)
- let cse_var_15: int32 = (cse_var_19 + 13)
- let cse_var_14: int32 = (cse_var_19 + 14)
- let cse_var_13: int32 = (cse_var_19 + 15)
- let cse_var_12: int32 = (cse_var_19 + 2)
- let cse_var_11: int32 = (cse_var_19 + 3)
- let cse_var_10: int32 = (cse_var_19 + 4)
- let cse_var_9: int32 = (cse_var_19 + 5)
- let cse_var_8: int32 = (cse_var_19 + 6)
- let cse_var_7: int32 = (cse_var_19 + 7)
- let cse_var_6: int32 = (cse_var_19 + 8)
- let cse_var_5: int32 = (cse_var_19 + 9)
- let cse_var_4: int32 = (cse_var_19 + 1)
- let cse_var_3: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*4096) + (i.outer.inner*2048)) + (i.inner*256))
- {
- compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_21]*16) + cse_var_20)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 1)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 2)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 3)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 4)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 5)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 6)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 7)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 8)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 9)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 10)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 11)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 12)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 13)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 14)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 15)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_1: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
+ for (i.inner: int32, 0, 4) {
+ for (j: int32, 0, 16) {
+ let cse_var_3: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ let cse_var_2: int32 = (((i.outer.inner*64) + (i.inner*16)) + j)
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*4096) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 16) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
- compute[ramp(cse_var_22, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_22, 1, 16)]), broadcast(0f32, 16))
+ let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
+ compute[ramp(cse_var_4, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_4, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -485,7 +437,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.814 ms
+ Execution time of this operator: 1.477 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 22f177160..a6c5f2a8e 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
=================
-**00:44.451** total execution time for **how_to_tune_with_autotvm** files:
+**00:44.715** total execution time for **how_to_tune_with_autotvm** files:
-- **00:43.538**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.234**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.228**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.226**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.225**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:43.793**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.233**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.231**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.229**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.229**: :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 484b34a38..76d58fd74 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: 66.20/66.20 result: MeasureResult(costs=(0.0034970898333333334,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5783522129058838, timestamp=1651604888.775878) [('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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 109.84/109.84 result: MeasureResult(costs=(0.0021076316874999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6486032009124756, timestamp=1651611068.0073724) [('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: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/109.84 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: 0x00007fa8985eafa2
+ 12: 0x00007f47684b0fa2
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: 144.46/144.46 result: MeasureResult(costs=(0.00160254381,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4126038551330566, timestamp=1651604915.2084603) [('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: 143.05/143.05 result: MeasureResult(costs=(0.0016183370322580643,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.144789695739746, timestamp=1651611093.6134984) [('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
@@ -2437,7 +2437,7 @@ and measure running time.
Best config:
[('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
- Time cost of this operator: 0.002042
+ Time cost of this operator: 0.001996
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 64fade054..ea970df53 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
@@ -292,10 +292,10 @@ 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 313.0 98.738 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.968 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.931 0.294 (1, 1, 10, 10, 3) 1 1
- Total_time - 317.0 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 316.7 98.757 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.064 0.955 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.288 (1, 1, 10, 10, 3) 1 1
+ Total_time - 320.687 - - - -
@@ -357,10 +357,10 @@ 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 138.4 98.1 (1, 6, 10, 10, 1) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.756 1.245 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.924 0.655 (1, 1, 10, 10, 3) 1 1
- Total_time - 141.08 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 219.1 98.691 (1, 1, 10, 10, 6) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.976 0.89 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.93 0.419 (1, 3, 10, 10, 1) 1 1
+ Total_time - 222.006 - - - -
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 a08fa43b3..f6e8ed456 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
=================
-**00:44.019** total execution time for **how_to_work_with_microtvm** files:
+**00:45.447** total execution time for **how_to_work_with_microtvm** files:
-- **00:39.990**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.429**: :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_tvmc.py` (``micro_tvmc.py``)
-- **00:00.199**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:41.237**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.582**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.219**: :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_reference_vm.py` (``micro_reference_vm.py``)
+- **00:00.204**: :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 e05a43f13..e9bc9b864 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
=================
-**00:08.886** total execution time for **how_to_work_with_relay** files:
+**00:08.679** total execution time for **how_to_work_with_relay** files:
-- **00:06.998**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.665**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.224**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:06.803**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.663**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.213**: :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 0ec39b783..351dd670e 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
=================
-**00:05.757** total execution time for **how_to_work_with_schedules** files:
+**00:05.677** total execution time for **how_to_work_with_schedules** files:
-- **00:02.105**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.146**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.740**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.725**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.313**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.251**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.247**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.230**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:02.050**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.164**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.716**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.712**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.308**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.255**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.240**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:00.232**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.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 3e55d8fd7..1ef28012f 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/tmprmbn71_z/input0.cc'\nsource_filename = \"/tmp/tmprmbn71_z/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/tmpiwdso78j/input0.cc'\nsource_filename = \"/tmp/tmpiwdso78j/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 eaf364fbc..b76662d2b 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
=================
-**00:20.347** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:21.374** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:20.138**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.209**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:21.149**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.225**: :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 f626bf4b5..059e1c77d 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -265,7 +265,7 @@ 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 21.75s!
+ resnet18_v1 inference graph built in 22.54s!
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 f71a8c462..453de1843 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -301,7 +301,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 15.24s!
+ yolov3-tiny inference graph built in 15.26s!
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 dd99501de..6eba55d98 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
=================
-**01:29.123** total execution time for **topic_vta_tutorials_frontend** files:
+**01:29.992** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:47.257**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:41.866**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:47.290**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:42.702**: :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 e449a4e7d..b346c675d 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
=================
-**00:03.526** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.630** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:02.987**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.539**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.057**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.573**: :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 c2556f2dd..7d74233f4 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -7,5 +7,5 @@ Computation times
=================
**00:00.997** total execution time for **topic_vta_tutorials** files:
-- **00:00.509**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.508**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
- **00:00.488**: :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 d93e75c76..a54a06982 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
-
+ .T
@@ -306,7 +306,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 91.859 ms
+ Execution time of this operator: 93.925 ms
@@ -415,6 +415,11 @@ Expression (TE) language that demonstrates how TVM can optimize computational
operations.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 5.133 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 3824b0f9b..5c4c26f10 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -268,7 +268,7 @@ standard deviation.
.. code-block:: none
- {'mean': 485.61423607999586, 'median': 485.6955056000061, 'std': 0.5002350431385256}
+ {'mean': 495.9099344000015, 'median': 494.7740689999989, 'std': 3.001615037945105}
@@ -482,32 +482,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 23.79/ 23.79 GFLOPS | Progress: (4/10) | 5.12 s
[Task 1/25] Current/Best: 13.17/ 23.81 GFLOPS | Progress: (8/10) | 7.22 s
[Task 1/25] Current/Best: 5.80/ 23.81 GFLOPS | Progress: (10/10) | 9.60 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 9.24/ 22.42 GFLOPS | Progress: (4/10) | 2.28 s
[Task 2/25] Current/Best: 8.61/ 22.42 GFLOPS | Progress: (8/10) | 3.33 s
[Task 2/25] Current/Best: 6.22/ 22.42 GFLOPS | Progress: (10/10) | 4.12 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 9.26/ 18.05 GFLOPS | Progress: (4/10) | 2.96 s
[Task 3/25] Current/Best: 24.29/ 24.29 GFLOPS | Progress: (8/10) | 4.43 s
[Task 3/25] Current/Best: 11.96/ 24.29 GFLOPS | Progress: (10/10) | 5.44 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 5.05/ 21.54 GFLOPS | Progress: (4/10) | 5.66 s
[Task 4/25] Current/Best: 5.95/ 21.54 GFLOPS | Progress: (8/10) | 8.62 s
[Task 4/25] Current/Best: 16.69/ 21.54 GFLOPS | Progress: (10/10) | 9.32 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 16.64/ 16.64 GFLOPS | Progress: (4/10) | 3.50 s
[Task 5/25] Current/Best: 16.58/ 16.64 GFLOPS | Progress: (8/10) | 5.29 s
[Task 5/25] Current/Best: 19.44/ 19.44 GFLOPS | Progress: (10/10) | 5.99 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 21.95/ 21.95 GFLOPS | Progress: (4/10) | 2.86 s
[Task 6/25] Current/Best: 3.25/ 21.95 GFLOPS | Progress: (8/10) | 5.94 s
[Task 6/25] Current/Best: 15.89/ 21.95 GFLOPS | Progress: (10/10) | 7.02 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 14.12/ 23.29 GFLOPS | Progress: (4/10) | 3.05 s
[Task 7/25] Current/Best: 19.55/ 23.29 GFLOPS | Progress: (8/10) | 5.79 s
[Task 7/25] Current/Best: 18.51/ 23.29 GFLOPS | Progress: (10/10) | 6.95 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 15.27/ 15.27 GFLOPS | Progress: (4/10) | 3.89 s
[Task 8/25] Current/Best: 11.12/ 16.18 GFLOPS | Progress: (8/10) | 6.15 s
[Task 8/25] Current/Best: 4.17/ 16.18 GFLOPS | Progress: (10/10) | 7.43 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 9.91/ 15.94 GFLOPS | Progress: (4/10) | 8.08 s
[Task 9/25] Current/Best: 4.82/ 17.36 GFLOPS | Progress: (8/10) | 9.67 s
[Task 9/25] Current/Best: 7.85/ 17.36 GFLOPS | Progress: (10/10) | 14.78 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 14.88/ 22.61 GFLOPS | Progress: (4/10) | 2.63 s
[Task 10/25] Current/Best: 10.15/ 22.61 GFLOPS | Progress: (8/10) | 4.59 s
[Task 10/25] Current/Best: 10.66/ 22.61 GFLOPS | Progress: (10/10) | 6.29 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 16.01/ 21.00 GFLOPS | Progress: (4/10) | 3.02 s
[Task 11/25] Current/Best: 17.59/ 21.00 GFLOPS | Progress: (8/10) | 4.79 s
[Task 11/25] Current/Best: 19.71/ 21.00 GFLOPS | Progress: (10/10) | 5.63 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 9.42/ 18.73 GFLOPS | Progress: (4/10) | 9.51 s
[Task 12/25] Current/Best: 5.75/ 22.09 GFLOPS | Progress: (8/10) | 14.41 s
[Task 12/25] Current/Best: 19.08/ 22.09 GFLOPS | Progress: (10/10) | 15.12 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 13.11/ 13.11 GFLOPS | Progress: (4/10) | 3.89 s
[Task 13/25] Current/Best: 6.59/ 18.34 GFLOPS | Progress: (8/10) | 6.88 s
[Task 13/25] Current/Best: 6.15/ 21.58 GFLOPS | Progress: (10/10) | 8.24 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 10.75/ 10.75 GFLOPS | Progress: (4/10) | 7.11 s
[Task 14/25] Current/Best: 13.97/ 17.11 GFLOPS | Progress: (8/10) | 8.88 s
[Task 14/25] Current/Best: 9.99/ 17.11 GFLOPS | Progress: (10/10) | 9.84 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
-
[Task 15/25] Current/Best: 12.06/ 22.14 GFLOPS | Progress: (4/10) | 3.65 s
[Task 15/25] Current/Best: 21.31/ 22.14 GFLOPS | Progress: (8/10) | 5.32 s
[Task 15/25] Current/Best: 17.66/ 22.14 GFLOPS | Progress: (10/10) | 5.91 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 17.85/ 17.85 GFLOPS | Progress: (4/10) | 3.30 s
[Task 16/25] Current/Best: 6.57/ 17.85 GFLOPS | Progress: (8/10) | 4.73 s
[Task 16/25] Current/Best: 6.92/ 17.85 GFLOPS | Progress: (10/10) | 5.39 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 12.16/ 22.24 GFLOPS | Progress: (4/10) | 3.14 s
[Task 17/25] Current/Best: 10.09/ 22.24 GFLOPS | Progress: (8/10) | 5.29 s
[Task 17/25] Current/Best: 11.54/ 22.24 GFLOPS | Progress: (10/10) | 6.54 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 13.82/ 17.13 GFLOPS | Progress: (4/10) | 3.28 s
[Task 18/25] Current/Best: 10.32/ 21.82 GFLOPS | Progress: (8/10) | 7.37 s
[Task 18/25] Current/Best: 9.78/ 21.82 GFLOPS | Progress: (10/10) | 10.99 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 10.56/ 22.50 GFLOPS | Progress: (4/10) | 3.73 s
[Task 19/25] Current/Best: 12.20/ 22.50 GFLOPS | Progress: (8/10) | 6.71 s
[Task 19/25] Current/Best: 5.37/ 22.50 GFLOPS | Progress: (10/10) | 7.93 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 9.91/ 15.24 GFLOPS | Progress: (4/10) | 2.78 s
[Task 20/25] Current/Best: 9.22/ 17.51 GFLOPS | Progress: (8/10) | 6.48 s
[Task 20/25] Current/Best: 21.52/ 21.52 GFLOPS | Progress: (10/10) | 7.27 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 21.97/ 21.97 GFLOPS | Progress: (4/10) | 2.40 s
[Task 21/25] Current/Best: 6.54/ 21.97 GFLOPS | Progress: (8/10) | 4.23 s
[Task 21/25] Current/Best: 9.35/ 21.97 GFLOPS | Progress: (10/10) | 4.86 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 23.32/ 23.32 GFLOPS | Progress: (4/10) | 2.31 s
[Task 22/25] Current/Best: 8.08/ 23.32 GFLOPS | Progress: (8/10) | 4.89 s
[Task 22/25] Current/Best: 11.45/ 23.32 GFLOPS | Progress: (10/10) | 6.24 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 6.57/ 12.00 GFLOPS | Progress: (4/10) | 4.54 s
[Task 23/25] Current/Best: 10.81/ 12.00 GFLOPS | Progress: (8/10) | 8.53 s
[Task 23/25] Current/Best: 20.63/ 20.63 GFLOPS | Progress: (10/10) | 10.52 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 3.99/ 9.70 GFLOPS | Progress: (4/10) | 15.95 s
[Task 24/25] Current/Best: 3.65/ 9.70 GFLOPS | Progress: (8/10) | 21.80 s
[Task 24/25] Current/Best: 3.77/ 10.59 GFLOPS | Progress: (10/10) | 24.75 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 14.01/ 14.77 GFLOPS | Progress: (4/10) | 5.87 s
[Task 1/25] Current/Best: 5.41/ 16.53 GFLOPS | Progress: (8/10) | 9.73 s
[Task 1/25] Current/Best: 1.93/ 16.53 GFLOPS | Progress: (10/10) | 11.83 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 6.82/ 19.62 GFLOPS | Progress: (4/10) | 2.18 s
[Task 2/25] Current/Best: 12.71/ 19.62 GFLOPS | Progress: (8/10) | 3.93 s
[Task 2/25] Current/Best: 12.82/ 19.62 GFLOPS | Progress: (10/10) | 4.65 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 23.29/ 23.29 GFLOPS | Progress: (4/10) | 2.73 s
[Task 3/25] Current/Best: 14.45/ 24.37 GFLOPS | Progress: (8/10) | 4.69 s
[Task 3/25] Current/Best: 8.39/ 24.37 GFLOPS | Progress: (10/10) | 5.98 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 4.15/ 18.09 GFLOPS | Progress: (4/10) | 2.45 s
[Task 4/25] Current/Best: 17.96/ 22.71 GFLOPS | Progress: (8/10) | 3.86 s
[Task 4/25] Current/Best: 14.60/ 22.71 GFLOPS | Progress: (10/10) | 4.65 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 14.94/ 21.31 GFLOPS | Progress: (4/10) | 2.72 s
[Task 5/25] Current/Best: 8.15/ 21.31 GFLOPS | Progress: (8/10) | 4.45 s
[Task 5/25] Current/Best: 17.51/ 21.31 GFLOPS | Progress: (10/10) | 5.08 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 3.25/ 10.13 GFLOPS | Progress: (4/10) | 3.91 s
[Task 6/25] Current/Best: 20.88/ 20.88 GFLOPS | Progress: (8/10) | 6.90 s
[Task 6/25] Current/Best: 10.76/ 20.88 GFLOPS | Progress: (10/10) | 8.59 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 6.08/ 22.59 GFLOPS | Progress: (4/10) | 2.88 s
[Task 7/25] Current/Best: 14.56/ 22.59 GFLOPS | Progress: (8/10) | 5.03 s
[Task 7/25] Current/Best: 18.15/ 22.59 GFLOPS | Progress: (10/10) | 5.85 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 9.29/ 15.44 GFLOPS | Progress: (4/10) | 2.79 s
[Task 8/25] Current/Best: 17.89/ 17.89 GFLOPS | Progress: (8/10) | 5.74 s
[Task 8/25] Current/Best: 16.73/ 17.89 GFLOPS | Progress: (10/10) | 26.87 s
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 20.59/ 20.59 GFLOPS | Progress: (4/10) | 3.31 s
[Task 9/25] Current/Best: 10.44/ 20.59 GFLOPS | Progress: (8/10) | 5.00 s
[Task 9/25] Current/Best: 23.02/ 23.02 GFLOPS | Progress: (10/10) | 5.72 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 6.40/ 14.51 GFLOPS | Progress: (4/10) | 2.55 s
[Task 10/25] Current/Best: 21.95/ 21.95 GFLOPS | Progress: (8/10) | 4.02 s
[Task 10/25] Current/Best: 3.18/ 21.95 GFLOPS | Progress: (10/10) | 5.25 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 11.03/ 20.00 GFLOPS | Progress: (4/10) | 3.27 s
[Task 11/25] Current/Best: 22.52/ 22.52 GFLOPS | Progress: (8/10) | 4.96 s
[Task 11/25] Current/Best: 10.11/ 22.52 GFLOPS | Progress: (10/10) | 7.30 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 18.60/ 18.60 GFLOPS | Progress: (4/10) | 3.81 s
[Task 12/25] Current/Best: 10.19/ 18.60 GFLOPS | Progress: (8/10) | 7.90 s
[Task 12/25] Current/Best: 22.10/ 22.10 GFLOPS | Progress: (10/10) | 8.69 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 11.21/ 18.41 GFLOPS | Progress: (4/10) | 4.42 s
[Task 13/25] Current/Best: 12.12/ 19.75 GFLOPS | Progress: (8/10) | 6.60 s
[Task 13/25] Current/Best: 12.19/ 19.75 GFLOPS | Progress: (10/10) | 9.30 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 13.06/ 19.06 GFLOPS | Progress: (4/10) | 2.79 s
[Task 14/25] Current/Best: 3.13/ 19.06 GFLOPS | Progress: (8/10) | 6.23 s
[Task 14/25] Current/Best: 10.45/ 19.06 GFLOPS | Progress: (10/10) | 9.38 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 12.96/ 13.51 GFLOPS | Progress: (4/10) | 4.18 s
[Task 15/25] Current/Best: 10.34/ 16.11 GFLOPS | Progress: (8/10) | 8.63 s
[Task 15/25] Current/Best: 16.22/ 16.22 GFLOPS | Progress: (10/10) | 9.28 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 17.73/ 17.73 GFLOPS | Progress: (4/10) | 2.53 s
[Task 16/25] Current/Best: 12.55/ 18.17 GFLOPS | Progress: (8/10) | 5.81 s
[Task 16/25] Current/Best: 20.76/ 20.76 GFLOPS | Progress: (10/10) | 6.36 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 19.77/ 19.77 GFLOPS | Progress: (4/10) | 3.73 s Done.
Done.
-
[Task 25/25] Current/Best: 1.51/ 8.96 GFLOPS | Progress: (4/10) | 6.22 s
[Task 25/25] Current/Best: 8.31/ 8.96 GFLOPS | Progress: (8/10) | 11.77 s
[Task 25/25] Current/Best: 8.83/ 9.81 GFLOPS | Progress: (10/10) | 15.94 s Done.
-
+
[Task 17/25] Current/Best: 9.00/ 19.77 GFLOPS | Progress: (8/10) | 5.49 s
[Task 17/25] Current/Best: 7.47/ 19.77 GFLOPS | Progress: (10/10) | 7.17 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 21.64/ 21.64 GFLOPS | Progress: (4/10) | 2.67 s
[Task 18/25] Current/Best: 9.42/ 21.64 GFLOPS | Progress: (8/10) | 4.97 s
[Task 18/25] Current/Best: 11.04/ 21.64 GFLOPS | Progress: (10/10) | 6.81 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 14.09/ 14.09 GFLOPS | Progress: (4/10) | 4.34 s
[Task 19/25] Current/Best: 16.68/ 20.82 GFLOPS | Progress: (8/10) | 6.21 s
[Task 19/25] Current/Best: 15.84/ 20.82 GFLOPS | Progress: (10/10) | 8.30 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 19.14/ 19.14 GFLOPS | Progress: (4/10) | 4.84 s
[Task 20/25] Current/Best: 9.63/ 19.14 GFLOPS | Progress: (8/10) | 6.68 s
[Task 20/25] Current/Best: 9.03/ 19.14 GFLOPS | Progress: (10/10) | 9.39 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 16.76/ 20.61 GFLOPS | Progress: (4/10) | 2.88 s
[Task 21/25] Current/Best: 4.93/ 20.61 GFLOPS | Progress: (8/10) | 4.50 s
[Task 21/25] Current/Best: 17.35/ 20.71 GFLOPS | Progress: (10/10) | 5.12 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 19.99/ 20.54 GFLOPS | Progress: (4/10) | 2.73 s
[Task 22/25] Current/Best: 1.56/ 21.76 GFLOPS | Progress: (8/10) | 4.59 s
[Task 22/25] Current/Best: 19.29/ 21.76 GFLOPS | Progress: (10/10) | 5.20
s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 18.71/ 19.39 GFLOPS | Progress: (4/10) | 3.11 s
[Task 23/25] Current/Best: 8.94/ 19.98 GFLOPS | Progress: (8/10) | 8.42 s
[Task 23/25] Current/Best: 23.39/ 23.39 GFLOPS | Progress: (10/10) | 9.97 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 8.22/ 9.53 GFLOPS | Progress: (4/10) | 25.16 s
[Task 24/25] Current/Best: 8.43/ 10.19 GFLOPS | Progress: (8/10) | 36.91 s
[Task 24/25] Current/Best: 1.60/ 10.19 GFLOPS | Progress: (10/10) | 47.28 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+ Done.
+ Done.
+
[Task 25/25] Current/Best: 9.89/ 9.89 GFLOPS | Progress: (4/10) | 5.82 s
[Task 25/25] Current/Best: 2.94/ 9.89 GFLOPS | Progress: (8/10) | 41.60 s
[Task 25/25] Current/Best: 1.56/ 9.89 GFLOPS | Progress: (10/10) | 57.76 s
The output from this tuning process will look something like this:
@@ -595,8 +594,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621104
- class='n02123159 tiger cat' with probability=0.356378
+ class='n02123045 tabby, tabby cat' with probability=0.621102
+ class='n02123159 tiger cat' with probability=0.356379
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -649,8 +648,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 423.85818206000295, 'median': 423.7159835000057, 'std': 0.8548689843661389}
- unoptimized: {'mean': 485.61423607999586, 'median': 485.6955056000061, 'std': 0.5002350431385256}
+ optimized: {'mean': 429.88311026000247, 'median': 429.5241749499951, 'std': 2.345377712720502}
+ unoptimized: {'mean': 495.9099344000015, 'median': 494.7740689999989, 'std': 3.001615037945105}
@@ -670,7 +669,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 6 minutes 46.086 seconds)
+ **Total running time of the script:** ( 7 minutes 59.447 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 07e6d55e0..423661420 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.271e-07 secs/op
+ 1.256e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 6dd2e4d98..5e0caaec1 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, 0x223dd2b0)), stage(b, placeholder(b, 0x21173260)), 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(mi [...]
+ [stage(a, placeholder(a, 0x22418200)), stage(b, placeholder(b, 0x57b0b50)), 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 5e1ec80c8..bb11b70e1 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
=================
-**09:25.084** total execution time for **tutorial** files:
+**10:48.210** total execution time for **tutorial** files:
-- **06:46.086**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **00:59.062**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:52.052**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:26.227**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:19.467**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:01.159**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.695**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.193**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.040**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
-- **00:00.037**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
-- **00:00.035**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
-- **00:00.031**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
+- **07:59.447**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:05.133**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **01:01.623**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:26.525**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:13.161**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:01.195**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.704**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.198**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.057**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
+- **00:00.056**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.056**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.056**: :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 548a8d054..acf208075 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -243,7 +243,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000008
+ Numpy running time: 0.000007
naive: 0.000006
@@ -438,10 +438,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.589809999899444e-06 1.0
- naive 5.9376e-06 0.7823120736986388
- parallel 6.2355e-06 0.8215620680995457
- vector 2.4689900000000002e-05 3.2530326846557576
+ numpy 7.073049998780334e-06 1.0
+ naive 5.8333000000000005e-06 0.8247220083282156
+ parallel 6.0573000000000004e-06 0.8563915144166253
+ vector 2.4640200000000002e-05 3.4836739460697888
@@ -830,7 +830,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017953
+ Numpy running time: 0.019796
@@ -886,7 +886,7 @@ optimizations.
.. code-block:: none
- none: 3.264705
+ none: 3.442003
@@ -985,7 +985,7 @@ schedule.
.. code-block:: none
- blocking: 0.313538
+ blocking: 0.306660
@@ -1077,7 +1077,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.341040
+ vectorization: 0.335407
@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], []),
@@ -1149,7 +1149,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.114859
+ loop permutation: 0.118970
@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], []),
@@ -1246,7 +1246,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.108777
+ array packing: 0.110969
@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], []),
@@ -1337,7 +1337,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.110779
+ block caching: 0.112964
@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], []),
@@ -1421,7 +1421,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.143930
+ parallelization: 0.147856
@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], []),
@@ -1500,13 +1500,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.264705298 1.0
- blocking 0.31353826199999996 0.09603876410899247
- vectorization 0.34103952530000003 0.10446257599695911
- loop permutation 0.1148587813 0.03518197534410348
- array packing 0.1087768657 0.03331904590795319
- block caching 0.11077945 0.03393245021774704
- parallelization 0.1439299752 0.04408666696138648
+ none 3.4420034504 1.0
+ blocking 0.3066602424 0.08909353137468894
+ vectorization 0.3354069085 0.09744525632623113
+ loop permutation 0.1189697054 0.03456408661826715
+ array packing 0.11096905990000001 0.03223967131325919
+ block caching 0.1129637478 0.032819184939187764
+ parallelization 0.1478555552 0.04295624839737377
@@ -1541,6 +1541,11 @@ 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 1.623 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index f3b634856..d2bd3c6c0 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-0fb155c3c955a9ca388802d46328dfa907b18fe1
+633fb546147c3da5a805d317a44eb4d67e5b8fa8
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index 18fca83e2..f188d7119 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.zip1ebc153a-6d2e-447e-b6b2-097abde69dac 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.zipfd9b566b-f71f-4ae9-b955-06c269f95e9f 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 fb7e4f4fa..669030a9e 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -406,43 +406,49 @@ python3 -m pip install -f https://release.oneflow.info <span class="nv">oneflow<
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>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/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 79f0f0cdf..98a74ea58 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -464,7 +464,7 @@ 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 5.653 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.677 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
index 626dbc63a..894904351 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -387,9 +387,9 @@ 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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</pre></div>
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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 dfe28ceb3..1c2db2799 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -607,7 +607,7 @@ 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 2.958 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.693 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 0428b9109..2339d22af 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>05:15.919</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:24.226</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:05.653</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>01:02.958</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>00:55.806</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>00:30.023</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>00:25.222</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>00:20.987</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>00:20.902</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>00:18.652</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>00:13.210</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>00:02.506</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>01:07.677</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>01:03.693</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>00:57.735</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>00:30.506</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>00:26.309</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>00:22.016</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>00:21.160</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>00:19.146</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>00:13.386</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>00:02.598</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 2c49c3017..d618f882a 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -622,7 +622,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.9410 15.9169 16.2924 15.6807 0.1600
+ 15.9496 15.9255 16.1020 15.8305 0.0803
</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 49e47e0f3..23049a0ac 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,42 +409,40 @@ 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').
@@ -537,7 +535,7 @@ 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 5.767 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 11.334 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">
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<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 6b0756f80..c7f2369dd 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,7 +450,9 @@ 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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@@ -539,7 +541,7 @@ 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)
- 90.4604 90.3123 91.8797 90.1704 0.3136
+ 90.4632 90.3277 95.3507 90.1518 0.6050
</pre></div>
</div>
<div class="admonition note">
@@ -578,7 +580,7 @@ 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>
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<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 3d7332c26..81a22d2d5 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -540,7 +540,7 @@ 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)
- 120.3516 120.3294 121.2233 119.5416 0.3531
+ 119.9625 119.8841 121.1679 119.3007 0.4159
</pre></div>
</div>
<div class="admonition note">
@@ -568,7 +568,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
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<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 3cd50d28e..c4c1c4bd7 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -480,7 +480,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
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<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 83c6eee79..49efd7e37 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,25 +415,21 @@ 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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<p>Create TVM runtime and do inference
@@ -473,7 +469,7 @@ 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 24.476 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 26.150 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 4c0a3d59a..5488ed8bb 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
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@@ -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>10:36.167</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:53.949</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>03:05.767</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>02:24.476</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>01:53.586</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>01:16.533</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>01:06.147</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>00:27.963</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>00:21.486</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>00:00.208</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>03:11.334</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>02:26.150</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>01:57.081</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>01:20.213</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>01:07.204</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>00:29.578</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>00:22.190</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>00:00.199</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 281043fd1..b49f79589 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -588,7 +588,7 @@ 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.zipab6d22fd-b239-42ed-9c28-b7bd07166807 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.zip27ce98f0-fcb8-4757-88c0-4838684ab5f7 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 ef3698911..bb73e40a7 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>00:38.731</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:39.626</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:35.072</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>00:02.391</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>00:01.054</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>00:00.214</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>00:35.965</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>00:02.332</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>00:01.120</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>00:00.210</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 d7fbd98ea..e89d54dfa 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: 5995us [5995us] (45.49%; 45.49%)
-FoldScaleAxis: 7185us [17us] (54.51%; 54.51%)
- FoldConstant: 7168us [1476us] (54.39%; 99.76%)
- InferType: 5692us [5692us] (43.18%; 79.40%)
+InferType: 6486us [6486us] (46.18%; 46.18%)
+FoldScaleAxis: 7559us [2us] (53.82%; 53.82%)
+ FoldConstant: 7557us [1593us] (53.81%; 99.97%)
+ InferType: 5964us [5964us] (42.46%; 78.92%)
</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: 5739us [5739us] (44.46%; 44.46%)
-FoldScaleAxis: 7170us [2us] (55.54%; 55.54%)
- FoldConstant: 7168us [1468us] (55.53%; 99.97%)
- InferType: 5700us [5700us] (44.16%; 79.52%)
+InferType: 6117us [6117us] (44.68%; 44.68%)
+FoldScaleAxis: 7574us [2us] (55.32%; 55.32%)
+ FoldConstant: 7572us [1594us] (55.31%; 99.97%)
+ InferType: 5977us [5977us] (43.66%; 78.95%)
</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 e0ed791df..0e5de3afd 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -534,7 +534,7 @@ latency of convolution.</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>Convolution: 52.770417 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 48.119834 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 d63854df3..4405568a3 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -878,7 +878,7 @@ be able to run on our build server</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>conv2d with tensor core: 10.204333 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 10.983607 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 c2fac2e94..18d6d745a 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,8 +431,8 @@ 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: 0.018571
-Baseline: 3.262366
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019161
+Baseline: 3.423051
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -494,7 +494,7 @@ 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: 0.307721
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.297574
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -563,7 +563,7 @@ 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: 0.344343
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.332082
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -626,7 +626,7 @@ 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: 0.119647
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117544
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -711,7 +711,7 @@ 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: 0.111523
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110347
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -799,7 +799,7 @@ 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: 0.111944
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110699
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -891,7 +891,7 @@ 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: 0.146853
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.144545
</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 fa863ec41..759db74a6 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>00:35.013</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.251</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:32.285</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>00:01.480</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>00:01.248</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>00:32.475</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>00:01.507</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>00:01.269</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 88e6fc75e..54926c2a2 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>04:56.637</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>04:58.771</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:19.638</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>01:20.116</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>00:40.659</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>00:19.102</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>00:08.619</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>00:08.504</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>02:22.927</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>01:21.573</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>00:40.672</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>00:15.772</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>00:09.297</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>00:08.531</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 089d7c8a5..4229d4c8a 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
@@ -470,37 +470,588 @@ 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" = 28;
- allocate(conv2d_nchw: Pointer(local float32), float32, [2]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [108]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [4608]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope="local", align=4)[0] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 8;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [1296]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [9216]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope="local", align=16)[0] = 0f32
+ conv2d_nchw_1[4] = 0f32
+ conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[16] = 0f32
+ conv2d_nchw_1[20] = 0f32
+ conv2d_nchw_1[24] = 0f32
conv2d_nchw_1[1] = 0f32
- for (rc.outer.outer: int32, 0, 128) {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448;
- if @tir.likely((threadIdx.x_1 < 108), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [108], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((1 <= (floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7))) && ((floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 27)*49)) + (floordiv(floormod(th [...]
- }
- for (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer: int32, 0, 11) {
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 448;
- if @tir.likely((((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*7) + floordiv(threadIdx.x_2, 64)) < 72), dtype=bool) {
- kernel.shared_1: Buffer(kernel.shared, float32, [4608], [], scope="shared")[((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*448) + threadIdx.x_2)] = kernel[((((floordiv(blockIdx.x, 7)*589824) + (floordiv(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + floordiv(threadIdx.x_2, 4)), 9)*4608)) + (rc.outer.outer*36)) + floormod(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*448) + threadIdx.x_2), 36))]
+ conv2d_nchw_1[5] = 0f32
+ conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[13] = 0f32
+ conv2d_nchw_1[17] = 0f32
+ conv2d_nchw_1[21] = 0f32
+ conv2d_nchw_1[25] = 0f32
+ conv2d_nchw_1[2] = 0f32
+ conv2d_nchw_1[6] = 0f32
+ conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[14] = 0f32
+ conv2d_nchw_1[18] = 0f32
+ conv2d_nchw_1[22] = 0f32
+ conv2d_nchw_1[26] = 0f32
+ conv2d_nchw_1[3] = 0f32
+ conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[15] = 0f32
+ conv2d_nchw_1[19] = 0f32
+ conv2d_nchw_1[23] = 0f32
+ conv2d_nchw_1[27] = 0f32
+ for (rc.outer.outer: int32, 0, 32) {
+ let cse_var_2: int32 = (rc.outer.outer*784)
+ let cse_var_1: int32 = (rc.outer.outer*144)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [1296], [], scope="shared")[(threadIdx.x_1*4)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1*4), 81)) && (floormod((threadIdx.x_1*4), 81) < 72)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1*4), 81)*49)) + (floordiv(floormod((threadIdx.x_1*4), 81), 9)*7)) + floormod((threadIdx.x_1*4), 9)) - 8 [...]
+ pad_temp.shared_1[((threadIdx.x_1*4) + 1)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 1), 81)) && (floormod(((threadIdx.x_1*4) + 1), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 1), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 1), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 2)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 2), 81)) && (floormod(((threadIdx.x_1*4) + 2), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 2), 9))) && (floormod(((threadIdx.x_1*4) + 2), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 2), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 2), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 2), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 3)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 3), 81)) && (floormod(((threadIdx.x_1*4) + 3), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 3), 9))) && (floormod(((threadIdx.x_1*4) + 3), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 3), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 3), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 3), 9)) - 8)], 0f32, dtype=float32)
}
- }
- for (rc.outer.inner: int32, 0, 2) {
- for (ry.outer.inner: int32, 0, 3) {
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 448)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 448), 81)) && (floormod(((threadIdx.x_1*4) + 43), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 7), 9))) && (floormod(((threadIdx.x_1*4) + 7), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 448), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 448), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 7), 9)) - 8)], 0f32, dtype [...]
+ pad_temp.shared_1[((threadIdx.x_1*4) + 449)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 449), 81)) && (floormod(((threadIdx.x_1*4) + 44), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 8), 9))) && (floormod(((threadIdx.x_1*4) + 8), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 449), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 449), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 8), 9)) - 8)], 0f32, dtype [...]
+ pad_temp.shared_1[((threadIdx.x_1*4) + 450)] = @tir.if_then_else(((((1 <= floormod((floordiv((threadIdx.x_1*4), 9) + 5), 9)) && (floormod(((threadIdx.x_1*4) + 45), 81) < 72)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 450), 81)*49)) + (floormod((floordiv((threadIdx.x_1*4), 9) + 5), 9)*7)) + floormod((threadIdx.x_1*4), 9)) - 8)], 0f32, dtype=float32)
+ pad_temp.shared_1[((threadIdx.x_1*4) + 451)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 451), 81)) && (floormod(((threadIdx.x_1*4) + 46), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 451), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 451), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype [...]
+ }
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 896)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 896), 81)) && (floormod(((threadIdx.x_1*4) + 5), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 5), 9))) && (floormod(((threadIdx.x_1*4) + 5), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 896), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 896), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 5), 9)) - 8)], 0f32, dtyp [...]
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 897)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 897), 81)) && (floormod(((threadIdx.x_1*4) + 6), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 6), 9))) && (floormod(((threadIdx.x_1*4) + 6), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 897), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 897), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 6), 9)) - 8)], 0f32, dtyp [...]
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 898)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 898), 81)) && (floormod(((threadIdx.x_1*4) + 7), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 7), 9))) && (floormod(((threadIdx.x_1*4) + 7), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 898), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 898), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 7), 9)) - 8)], 0f32, dtyp [...]
+ }
+ if @tir.likely((threadIdx.x_1 < 100), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*4) + 899)] = @tir.if_then_else(((((9 <= floormod(((threadIdx.x_1*4) + 899), 81)) && (floormod(((threadIdx.x_1*4) + 8), 81) < 72)) && (1 <= floormod(((threadIdx.x_1*4) + 8), 9))) && (floormod(((threadIdx.x_1*4) + 8), 9) < 8)), data[((((cse_var_2 + (floordiv(((threadIdx.x_1*4) + 899), 81)*49)) + (floordiv(floormod(((threadIdx.x_1*4) + 899), 81), 9)*7)) + floormod(((threadIdx.x_1*4) + 8), 9)) - 8)], 0f32, dtyp [...]
+ }
+ }
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1: Buffer(kernel.shared, float32, [9216], [], scope="shared")[threadIdx.x_2] = kernel[(((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 14), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 21), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 35), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 42), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 56), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 77), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 84), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1568)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 98), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1680)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 105), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1792)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 112), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1904)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 119), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2016)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 64512)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2128)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 133), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2240)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 140), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2352)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 147), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2464)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 154), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2576)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 161), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2688)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 168), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2800)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 175), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 2912)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 182), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3024)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 96768)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3136)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 196), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3248)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 203), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3360)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 210), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3472)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 217), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3584)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 224), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3696)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 231), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3808)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 238), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 3920)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 245), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4032)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 129024)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4144)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 259), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4256)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 266), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4368)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 273), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4480)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 280), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4592)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 287), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4704)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 294), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4816)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 301), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 4928)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 308), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5040)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 161280)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5152)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 322), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5264)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 329), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5376)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 336), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 343), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5600)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 350), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5712)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 357), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5824)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 364), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 5936)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 371), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6048)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 193536)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6160)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 385), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6272)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 392), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6384)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 399), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6496)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 406), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6608)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 413), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6720)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 420), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6832)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 427), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 6944)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 434), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7056)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 225792)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7168)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 448), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7280)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 455), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7392)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 462), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7504)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 469), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7616)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 476), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7728)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 483), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7840)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 490), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 7952)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 497), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8064)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 258048)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8176)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 511), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8288)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 518), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 80), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8400)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 525), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 48), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8512)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 532), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 16), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8624)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 539), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 128), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8736)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 546), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 96), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8848)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 553), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 64), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 8960)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 560), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 32), 144))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 9072)] = kernel[((((blockIdx.x*294912) + cse_var_1) + threadIdx.x_2) + 290304)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_2 < 32), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 9184)] = kernel[((((blockIdx.x*294912) + (floordiv((floordiv(threadIdx.x_2, 16) + 574), 9)*4608)) + cse_var_1) + floormod((threadIdx.x_2 + 112), 144))]
+ }
+ for (rc.outer.inner: int32, 0, 4) {
for (rx.outer.inner: int32, 0, 3) {
- for (rc.inner: int32, 0, 2) {
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((((rc.outer.inner*54) + (rc.inner*27)) + (ry.outer.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7))]*kernel.shared_1[(((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((((rc.outer.inner*54) + (rc.inner*27)) + (ry.outer.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
- }
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 144)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 3)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 147)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 6)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 150)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 9)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 153)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 12)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 156)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 15)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 159)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 18)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 162)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 21)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 165)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 24)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 168)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 27)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 171)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 30)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 174)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 33)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 177)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 288)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 432)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 291)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 9)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 10)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 11)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 12)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 13)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 15)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 435)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 294)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 18)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 19)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 20)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 22)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 23)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 24)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 438)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 297)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 81)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 82)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 83)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 84)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 85)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 86)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 87)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 441)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 300)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 90)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 91)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 92)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 93)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 94)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 95)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 96)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 444)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 303)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 99)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 100)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 101)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 102)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 103)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 104)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 105)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 447)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 306)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 162)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 163)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 164)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 165)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 166)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 167)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 168)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 450)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 309)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 171)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 172)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 173)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 174)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 175)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 176)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 177)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 453)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 312)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 180)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 181)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 182)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 183)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 184)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 185)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 186)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 456)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 315)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 243)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 244)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 245)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 246)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 247)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 248)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 249)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 459)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 318)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 252)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 253)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 254)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 255)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 256)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 257)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 258)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 462)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 321)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 261)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 262)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 263)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 264)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 265)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 266)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.outer.inner*324) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 267)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*576) + (rc.outer.inner*36)) + rx.outer.inner) + 465)]))
}
}
}
}
- compute[((((floordiv(blockIdx.x, 7)*6272) + (floordiv(threadIdx.x, 7)*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*128) + floordiv(threadIdx.x, 7))]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (floordiv(threadIdx.x, 7)*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7)) + 3136)] = max((conv2d_nchw_1[1] + bias[(((floordiv(blockIdx.x, 7)*128) + floordiv(threadIdx.x, 7)) + 64)]), 0f32)
+ for (i1.inner: int32, 0, 4) {
+ compute[((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[(i1.inner + 16)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[(i1.inner + 20)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[(i1.inner + 24)] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
+ }
}
}
</pre></div>
@@ -537,7 +1088,7 @@ cooperative fetching, unrolling and operator fusion.</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>Execution time of this operator: 0.406 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.337 ms
</pre></div>
</div>
</div>
@@ -567,37 +1118,37 @@ 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=1)
-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=64)
-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=2)
+conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=2)
+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=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=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=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=2)
-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=1)
-conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
+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_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=4)
+conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=4)
+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=3)
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=1)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
-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=16)
+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=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=7)
-compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
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)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -616,14 +1167,14 @@ 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=448)
+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)
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=1)
+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=4)
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=448)
+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)
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", 0)
+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:
@@ -641,36 +1192,496 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(448) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[2];
- __shared__ float pad_temp_shared[108];
- __shared__ float kernel_shared[4608];
+extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[1296];
+ __shared__ float kernel_shared[9216];
conv2d_nchw[0] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
+ conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[16] = 0.000000e+00f;
+ conv2d_nchw[20] = 0.000000e+00f;
+ conv2d_nchw[24] = 0.000000e+00f;
conv2d_nchw[1] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
+ conv2d_nchw[5] = 0.000000e+00f;
+ conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[13] = 0.000000e+00f;
+ conv2d_nchw[17] = 0.000000e+00f;
+ conv2d_nchw[21] = 0.000000e+00f;
+ conv2d_nchw[25] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
+ conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[14] = 0.000000e+00f;
+ conv2d_nchw[18] = 0.000000e+00f;
+ conv2d_nchw[22] = 0.000000e+00f;
+ conv2d_nchw[26] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
+ conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[15] = 0.000000e+00f;
+ conv2d_nchw[19] = 0.000000e+00f;
+ conv2d_nchw[23] = 0.000000e+00f;
+ conv2d_nchw[27] = 0.000000e+00f;
+ for (int rc_outer_outer = 0; rc_outer_outer < 32; ++rc_outer_outer) {
__syncthreads();
- if (((int)threadIdx.x) < 108) {
- pad_temp_shared[((int)threadIdx.x)] = (((((1 <= (((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7))) && ((((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 27) * 49)) + (((((int)threadIdx.x) % 27) / 9) * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) * 4)] = (((((9 <= ((((int)threadIdx.x) * 4) % 81)) && (((((int)threadIdx.x) * 4) % 81) < 72)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) * 4) / 81) * 49)) + ((((((int)threadIdx.x) * 4) % 81) / 9) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 1)] = (((((9 <= (((((int)threadIdx.x) * 4) + 1) % 81)) && ((((((int)threadIdx.x) * 4) + 1) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 1) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 1) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 2)] = (((((9 <= (((((int)threadIdx.x) * 4) + 2) % 81)) && ((((((int)threadIdx.x) * 4) + 2) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 2) % 9))) && ((((((int)threadIdx.x) * 4) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 2) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 2) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 2) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 3)] = (((((9 <= (((((int)threadIdx.x) * 4) + 3) % 81)) && ((((((int)threadIdx.x) * 4) + 3) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 3) % 9))) && ((((((int)threadIdx.x) * 4) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 3) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 3) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 448)] = (((((9 <= (((((int)threadIdx.x) * 4) + 43) % 81)) && ((((((int)threadIdx.x) * 4) + 43) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 7) % 9))) && ((((((int)threadIdx.x) * 4) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 448) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 43) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 7) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 449)] = (((((9 <= (((((int)threadIdx.x) * 4) + 44) % 81)) && ((((((int)threadIdx.x) * 4) + 44) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 8) % 9))) && ((((((int)threadIdx.x) * 4) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 449) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 44) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 450)] = (((((1 <= ((((((int)threadIdx.x) * 4) / 9) + 5) % 9)) && ((((((int)threadIdx.x) * 4) + 45) % 81) < 72)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 450) / 81) * 49)) + (((((((int)threadIdx.x) * 4) / 9) + 5) % 9) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 451)] = (((((9 <= (((((int)threadIdx.x) * 4) + 46) % 81)) && ((((((int)threadIdx.x) * 4) + 46) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 451) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 46) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 896)] = (((((9 <= (((((int)threadIdx.x) * 4) + 5) % 81)) && ((((((int)threadIdx.x) * 4) + 5) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 5) % 9))) && ((((((int)threadIdx.x) * 4) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 896) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 5) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 5) % 9)) - 8)] : 0.000000e+00f);
}
- for (int ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer = 0; ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer < 11; ++ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer) {
- if (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 7) + (((int)threadIdx.x) >> 6)) < 72) {
- kernel_shared[((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 448) + ((int)threadIdx.x))] = kernel[(((((((int)blockIdx.x) / 7) * 589824) + ((((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 112) + (((int)threadIdx.x) >> 2)) / 9) * 4608)) + (rc_outer_outer * 36)) + (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 448) + ((int)threadIdx.x)) % 36))];
- }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 897)] = (((((9 <= (((((int)threadIdx.x) * 4) + 6) % 81)) && ((((((int)threadIdx.x) * 4) + 6) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 6) % 9))) && ((((((int)threadIdx.x) * 4) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 897) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 6) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 6) % 9)) - 8)] : 0.000000e+00f);
+ }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 898)] = (((((9 <= (((((int)threadIdx.x) * 4) + 7) % 81)) && ((((((int)threadIdx.x) * 4) + 7) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 7) % 9))) && ((((((int)threadIdx.x) * 4) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 898) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 7) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 7) % 9)) - 8)] : 0.000000e+00f);
+ }
+ if (((int)threadIdx.x) < 100) {
+ pad_temp_shared[((((int)threadIdx.x) * 4) + 899)] = (((((9 <= (((((int)threadIdx.x) * 4) + 8) % 81)) && ((((((int)threadIdx.x) * 4) + 8) % 81) < 72)) && (1 <= (((((int)threadIdx.x) * 4) + 8) % 9))) && ((((((int)threadIdx.x) * 4) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((((int)threadIdx.x) * 4) + 899) / 81) * 49)) + (((((((int)threadIdx.x) * 4) + 8) % 81) / 9) * 7)) + (((((int)threadIdx.x) * 4) + 8) % 9)) - 8)] : 0.000000e+00f);
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x))];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 112) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 224) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 336)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 336) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 448) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 560) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 672)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 672) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 784) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 896) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1120) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1232) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1344) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1456) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1568) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1680)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1680) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1792) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 1904)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 1904) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 2016)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 64512)];
+ kernel_shared[(((int)threadIdx.x) + 2128)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2128) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2240) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2352) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2464)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2464) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 2576)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2576) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2688) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2800)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2800) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 2912)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 2912) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 3024)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 96768)];
+ kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3136) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3248)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3248) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3360)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3360) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3472)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3472) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 3584)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3584) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3696)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3696) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3808)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3808) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 3920) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 4032)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 129024)];
+ kernel_shared[(((int)threadIdx.x) + 4144)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4144) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4256)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4256) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4368)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4368) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4480)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4480) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 4592)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4592) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4704) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4816)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4816) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 4928)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 4928) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 5040)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 161280)];
+ kernel_shared[(((int)threadIdx.x) + 5152)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5152) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5264)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5264) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5376)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5376) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5488) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 5600)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5600) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5712)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5712) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5824)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5824) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 5936)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 5936) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 6048)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 193536)];
+ kernel_shared[(((int)threadIdx.x) + 6160)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6160) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6272)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6272) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6384)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6384) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6496)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6496) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 6608)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6608) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6720)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6720) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6832)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6832) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 6944)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 6944) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 7056)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 225792)];
+ kernel_shared[(((int)threadIdx.x) + 7168)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7168) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7280)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7280) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7392)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7392) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7504)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7504) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 7616)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7616) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7728)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7728) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7840)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7840) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 7952)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 7952) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 8064)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 258048)];
+ kernel_shared[(((int)threadIdx.x) + 8176)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8176) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 112) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8288)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8288) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 80) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8400)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8400) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 48) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8512)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8512) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 16))];
+ kernel_shared[(((int)threadIdx.x) + 8624)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8624) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 128) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8736)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8736) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 96) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8848)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8848) / 144) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) + 64) % 144))];
+ kernel_shared[(((int)threadIdx.x) + 8960)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 8960) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 32))];
+ kernel_shared[(((int)threadIdx.x) + 9072)] = kernel[((((((int)blockIdx.x) * 294912) + (rc_outer_outer * 144)) + ((int)threadIdx.x)) + 290304)];
+ if (((int)threadIdx.x) < 32) {
+ kernel_shared[(((int)threadIdx.x) + 9184)] = kernel[((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) + 9184) / 144) * 4608)) + (rc_outer_outer * 144)) + (((int)threadIdx.x) + 112))];
}
__syncthreads();
- for (int rc_outer_inner = 0; rc_outer_inner < 2; ++rc_outer_inner) {
- for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
- for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
- for (int rc_inner = 0; rc_inner < 2; ++rc_inner) {
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((((rc_outer_inner * 54) + (rc_inner * 27)) + (ry_outer_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((((rc_outer_inner * 54) + (rc_inner * 27)) + (ry_outer_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
- }
- }
+ for (int rc_outer_inner = 0; rc_outer_inner < 4; ++rc_outer_inner) {
+ for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 144)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 3)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 147)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 6)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 150)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 9)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 153)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 12)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 156)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 15)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 159)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 18)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 162)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 21)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 165)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 24)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 168)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 27)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 171)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 30)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 174)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 33)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 177)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 288)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 432)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 291)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 9)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 10)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 11)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 12)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 13)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 15)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 435)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 294)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 18)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 19)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 20)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 22)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 23)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 24)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 438)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 297)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 81)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 82)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 83)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 84)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 85)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 86)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 87)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 441)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 300)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 90)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 91)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 92)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 93)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 94)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 95)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 96)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 444)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 303)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 99)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 100)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 101)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 102)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 103)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 104)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 105)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 447)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 306)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 162)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 163)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 164)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 165)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 166)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 167)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 168)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 450)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 309)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 171)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 172)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 173)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 174)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 175)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 176)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 177)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 453)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 312)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 180)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 181)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 182)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 183)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 184)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 185)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 186)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 456)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 315)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 243)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 244)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 245)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 246)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 247)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 248)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 249)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 459)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 318)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 252)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 253)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 254)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 255)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 256)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 257)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 258)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 462)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 321)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 261)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 262)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 263)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 264)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 265)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 266)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_outer_inner * 324) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 267)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 576) + (rc_outer_inner * 36)) + rx_outer_inner) + 465)]));
}
}
}
- compute[(((((((int)blockIdx.x) / 7) * 6272) + ((((int)threadIdx.x) / 7) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) / 7))]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + ((((int)threadIdx.x) / 7) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7)) + 3136)] = max((conv2d_nchw[1] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) / 7)) + 64)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 4; ++i1_inner) {
+ compute[((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[(i1_inner + 4)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[(i1_inner + 8)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[(i1_inner + 12)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[(i1_inner + 16)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[(i1_inner + 20)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[(i1_inner + 24)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
+ }
}
</pre></div>
</div>
@@ -707,7 +1718,7 @@ 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 19.638 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 22.927 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 8079ea996..ac4e66467 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -876,7 +876,7 @@ 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)
- 9.6652 9.6713 9.7034 9.6209 0.0340
+ 9.9857 9.9803 10.0105 9.9663 0.0185
</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 3a50a90de..7d2194081 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -895,7 +895,7 @@ 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)
- 755.8455 755.8360 760.4283 751.2723 3.7379
+ 767.0790 765.1935 774.2340 761.8095 5.2446
</pre></div>
</div>
</div>
@@ -917,7 +917,7 @@ 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 20.116 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.573 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 694c45bcd..dd6629655 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 +600,28 @@ 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_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_7: placeholder_19: Buffer(placeholder_12, int32, [4916], [])} {
+ 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_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
allocate(compute_4: Pointer(global float32), float32, [256]), storage_scope = global {
- for (i.outer.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 8) {
- let cse_var_1: int32 = ((i.outer.inner*128) + (i.inner.init*16))
- {
- compute_5: Buffer(compute_4, float32, [256], [])[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 (i.outer.inner: int32, 0, 4) {
+ for (i.inner.init: int32, 0, 4) {
+ for (j.init: int32, 0, 16) {
+ compute_5: Buffer(compute_4, float32, [256], [])[(((i.outer.inner*64) + (i.inner.init*16)) + j.init)] = 0f32
}
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 8) {
- let cse_var_21: int32 = floormod(i0.outer.i1.outer.fused, 32)
- let cse_var_20: int32 = (elem_idx*16)
- let cse_var_19: int32 = ((i.outer.inner*128) + (i.inner*16))
- let cse_var_18: int32 = (cse_var_19 + 10)
- let cse_var_17: int32 = (cse_var_19 + 11)
- let cse_var_16: int32 = (cse_var_19 + 12)
- let cse_var_15: int32 = (cse_var_19 + 13)
- let cse_var_14: int32 = (cse_var_19 + 14)
- let cse_var_13: int32 = (cse_var_19 + 15)
- let cse_var_12: int32 = (cse_var_19 + 2)
- let cse_var_11: int32 = (cse_var_19 + 3)
- let cse_var_10: int32 = (cse_var_19 + 4)
- let cse_var_9: int32 = (cse_var_19 + 5)
- let cse_var_8: int32 = (cse_var_19 + 6)
- let cse_var_7: int32 = (cse_var_19 + 7)
- let cse_var_6: int32 = (cse_var_19 + 8)
- let cse_var_5: int32 = (cse_var_19 + 9)
- let cse_var_4: int32 = (cse_var_19 + 1)
- let cse_var_3: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*4096) + (i.outer.inner*2048)) + (i.inner*256))
- {
- compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_21]*16) + cse_var_20)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 1)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 2)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 3)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 4)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 5)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 6)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 7)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 8)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 9)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 10)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 11)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 12)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 13)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 14)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 15)]*max(placeholder[(cse_var_3 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_1: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
+ for (i.inner: int32, 0, 4) {
+ for (j: int32, 0, 16) {
+ let cse_var_3: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ let cse_var_2: int32 = (((i.outer.inner*64) + (i.inner*16)) + j)
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*4096) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 16) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
- compute[ramp(cse_var_22, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_22, 1, 16)]), broadcast(0f32, 16))
+ let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
+ compute[ramp(cse_var_4, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_4, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -708,7 +660,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
</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>Execution time of this operator: 1.814 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.477 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 cca2a281e..d6ce588bd 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>00:44.451</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:44.715</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:43.538</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>00:00.234</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>00:00.228</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>00:00.226</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>00:00.225</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>00:43.793</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>00:00.233</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>00:00.231</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>00:00.229</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>00:00.229</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 b84daa164..409812e5d 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: 66.20/66.20 result: MeasureResult(costs=(0.0034970898333333334,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5783522129058838, timestamp=1651604888.775878) [('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: 0.00/66.20 result: Traceback (most recent call last):
+No: 6 GFLOPS: 109.84/109.84 result: MeasureResult(costs=(0.0021076316874999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6486032009124756, timestamp=1651611068.0073724) [('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: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/109.84 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: 0.00/66.20 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/109.84 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: 0.00/66.20 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/109.84 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: 0x00007fa8985eafa2
+ 12: 0x00007f47684b0fa2
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: 144.46/144.46 result: MeasureResult(costs=(0.00160254381,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4126038551330566, timestamp=1651604915.2084603) [('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: 143.05/143.05 result: MeasureResult(costs=(0.0016183370322580643,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.144789695739746, timestamp=1651611093.6134984) [('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,
@@ -2706,7 +2706,7 @@ and measure running time.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best config:
[('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
-Time cost of this operator: 0.002042
+Time cost of this operator: 0.001996
</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 c74bd6ceb..2c750cdd9 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -553,10 +553,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## 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 313.0 98.738 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.968 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.931 0.294 (1, 1, 10, 10, 3) 1 1
-Total_time - 317.0 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 316.7 98.757 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.064 0.955 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.288 (1, 1, 10, 10, 3) 1 1
+Total_time - 320.687 - - - -
</pre></div>
</div>
</div>
@@ -608,10 +608,10 @@ 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 138.4 98.1 (1, 6, 10, 10, 1) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.756 1.245 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.924 0.655 (1, 1, 10, 10, 3) 1 1
-Total_time - 141.08 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 219.1 98.691 (1, 1, 10, 10, 6) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.976 0.89 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.93 0.419 (1, 3, 10, 10, 1) 1 1
+Total_time - 222.006 - - - -
</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 056409d2d..fe27e27db 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>00:44.019</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:45.447</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:39.990</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>00:03.429</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</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_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>00:00.199</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>00:41.237</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>00:03.582</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>00:00.219</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</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_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>00:00.204</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 46825d35d..8b500b8a2 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>00:08.886</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:08.679</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:06.998</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>00:01.665</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>00:00.224</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>00:06.803</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>00:01.663</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>00:00.213</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 47cb8fa07..c5d683acc 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>00:05.757</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.677</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.105</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>00:01.146</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>00:00.740</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>00:00.725</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>00:00.313</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>00:00.251</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>00:00.247</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>00:00.230</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>00:02.050</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>00:01.164</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>00:00.716</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>00:00.712</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>00:00.308</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>00:00.255</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>00:00.240</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>00:00.232</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>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 2252b3fe1..5a12e8655 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/tmprmbn71_z/input0.cc'\nsource_filename = \"/tmp/tmprmbn71_z/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/tmpiwdso78j/input0.cc'\nsource_filename = \"/tmp/tmpiwdso78j/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 ecf98e7df..fba68e00d 100644
--- a/docs/reference/api/python/auto_scheduler.html
+++ b/docs/reference/api/python/auto_scheduler.html
@@ -1713,7 +1713,7 @@ 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">
@@ -1750,7 +1750,7 @@ 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 c90051135..9587c8e7b 100644
--- a/docs/reference/api/typedoc/classes/bytestreamreader.html
+++ b/docs/reference/api/typedoc/classes/bytestreamreader.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/0fb155c3c/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -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/0fb155c3c/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
</ul>
</aside>
</section>
@@ -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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
</ul>
</aside>
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@@ -168,7 +168,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -202,7 +202,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
</ul>
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<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 aa89f75f7..a0d401d40 100644
--- a/docs/reference/api/typedoc/classes/cachedcallstack.html
+++ b/docs/reference/api/typedoc/classes/cachedcallstack.html
@@ -144,7 +144,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L223">memory.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L223">memory.ts:223</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -172,7 +172,7 @@
<div class="tsd-signature tsd-kind-icon">temp<wbr>Args<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><a href="../interfaces/disposable.html" class="tsd-signature-type">Disposable</a><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/0fb155c3c/web/src/memory.ts#L208">memory.ts:208</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L208">memory.ts:208</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -194,7 +194,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L312">memory.ts:312</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L312">memory.ts:312</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -226,7 +226,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L284">memory.ts:284</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L388">memory.ts:388</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L388">memory.ts:388</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -300,7 +300,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L376">memory.ts:376</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L376">memory.ts:376</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -340,7 +340,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L267">memory.ts:267</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L267">memory.ts:267</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -373,7 +373,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L243">memory.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/memory.ts#L321">memory.ts:321</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L321">memory.ts:321</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -422,7 +422,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L252">memory.ts:252</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L359">memory.ts:359</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/memory.ts#L342">memory.ts:342</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L342">memory.ts:342</a></li>
</ul>
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<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/0fb155c3c/web/src/memory.ts#L350">memory.ts:350</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/memory.ts#L326">memory.ts:326</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L326">memory.ts:326</a></li>
</ul>
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<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/0fb155c3c/web/src/memory.ts#L363">memory.ts:363</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L363">memory.ts:363</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -574,7 +574,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L346">memory.ts:346</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L346">memory.ts:346</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -600,7 +600,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L334">memory.ts:334</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L334">memory.ts:334</a></li>
</ul>
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<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 c647762d0..eeb0f0691 100644
--- a/docs/reference/api/typedoc/classes/dldatatype.html
+++ b/docs/reference/api/typedoc/classes/dldatatype.html
@@ -119,7 +119,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L262">runtime.ts:262</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">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/0fb155c3c/web/src/runtime.ts#L260">runtime.ts:260</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L260">runtime.ts:260</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L258">runtime.ts:258</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L258">runtime.ts:258</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L262">runtime.ts:262</a></li>
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<div class="tsd-comment tsd-typography">
@@ -199,7 +199,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L279">runtime.ts:279</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L279">runtime.ts:279</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -216,7 +216,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L270">runtime.ts:270</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L270">runtime.ts:270</a></li>
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<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 798498d6e..adb5cabde 100644
--- a/docs/reference/api/typedoc/classes/dldevice.html
+++ b/docs/reference/api/typedoc/classes/dldevice.html
@@ -118,7 +118,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L202">runtime.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L202">runtime.ts:202</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L200">runtime.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L200">runtime.ts:200</a></li>
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<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/0fb155c3c/web/src/runtime.ts#L198">runtime.ts:198</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L198">runtime.ts:198</a></li>
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<div class="tsd-comment tsd-typography">
@@ -183,7 +183,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L223">runtime.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L223">runtime.ts:223</a></li>
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<div class="tsd-comment tsd-typography">
@@ -205,7 +205,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L230">runtime.ts:230</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L230">runtime.ts:230</a></li>
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<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 bcfd7d4df..dc1daf4e9 100644
--- a/docs/reference/api/typedoc/classes/environment.html
+++ b/docs/reference/api/typedoc/classes/environment.html
@@ -125,7 +125,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/environment.ts#L86">environment.ts:86</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L86">environment.ts:86</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
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<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/0fb155c3c/web/src/environment.ts#L70">environment.ts:70</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/environment.ts#L69">environment.ts:69</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L69">environment.ts:69</a></li>
</ul>
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<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/0fb155c3c/web/src/environment.ts#L78">environment.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L78">environment.ts:78</a></li>
</ul>
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<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/0fb155c3c/web/src/environment.ts#L84">environment.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L84">environment.ts:84</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -250,7 +250,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/environment.ts#L105">environment.ts:105</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L105">environment.ts:105</a></li>
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<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 29eb15319..d535d2cf5 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/0fb155c3c/web/src/runtime.ts#L49">runtime.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L49">runtime.ts:49</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L46">runtime.ts:46</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L46">runtime.ts:46</a></li>
</ul>
</aside>
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@@ -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/0fb155c3c/web/src/runtime.ts#L45">runtime.ts:45</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L45">runtime.ts:45</a></li>
</ul>
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@@ -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/0fb155c3c/web/src/runtime.ts#L44">runtime.ts:44</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L44">runtime.ts:44</a></li>
</ul>
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@@ -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/0fb155c3c/web/src/runtime.ts#L47">runtime.ts:47</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L47">runtime.ts:47</a></li>
</ul>
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@@ -203,7 +203,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L76">runtime.ts:76</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L76">runtime.ts:76</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L66">runtime.ts:66</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L66">runtime.ts:66</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L84">runtime.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L84">runtime.ts:84</a></li>
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<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/0fb155c3c/web/src/runtime.ts#L95">runtime.ts:95</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L95">runtime.ts:95</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L72">runtime.ts:72</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L72">runtime.ts:72</a></li>
</ul>
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<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 5c7844b0b..5cd555d26 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/0fb155c3c/web/src/runtime.ts#L583">runtime.ts:583</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L583">runtime.ts:583</a></li>
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<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/0fb155c3c/web/src/runtime.ts#L579">runtime.ts:579</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L579">runtime.ts:579</a></li>
</ul>
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@@ -179,7 +179,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L654">runtime.ts:654</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L654">runtime.ts:654</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L597">runtime.ts:597</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L597">runtime.ts:597</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L631">runtime.ts:631</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L631">runtime.ts:631</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L644">runtime.ts:644</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L644">runtime.ts:644</a></li>
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<div class="tsd-comment tsd-typography">
@@ -310,7 +310,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L621">runtime.ts:621</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L621">runtime.ts:621</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L609">runtime.ts:609</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L609">runtime.ts:609</a></li>
</ul>
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<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 0ee83b3eb..cef996117 100644
--- a/docs/reference/api/typedoc/classes/instance.html
+++ b/docs/reference/api/typedoc/classes/instance.html
@@ -139,7 +139,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L692">runtime.ts:692</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L692">runtime.ts:692</a></li>
</ul>
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<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/0fb155c3c/web/src/runtime.ts#L684">runtime.ts:684</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L684">runtime.ts:684</a></li>
</ul>
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@@ -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/0fb155c3c/web/src/runtime.ts#L683">runtime.ts:683</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L683">runtime.ts:683</a></li>
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@@ -229,7 +229,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L932">runtime.ts:932</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L932">runtime.ts:932</a></li>
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<div class="tsd-comment tsd-typography">
@@ -260,7 +260,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L994">runtime.ts:994</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L994">runtime.ts:994</a></li>
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<div class="tsd-comment tsd-typography">
@@ -303,7 +303,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L924">runtime.ts:924</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L924">runtime.ts:924</a></li>
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<div class="tsd-comment tsd-typography">
@@ -341,7 +341,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L732">runtime.ts:732</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L732">runtime.ts:732</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -358,7 +358,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L952">runtime.ts:952</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L952">runtime.ts:952</a></li>
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<div class="tsd-comment tsd-typography">
@@ -402,7 +402,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L816">runtime.ts:816</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L816">runtime.ts:816</a></li>
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<div class="tsd-comment tsd-typography">
@@ -434,7 +434,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
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<div class="tsd-comment tsd-typography">
@@ -465,7 +465,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L846">runtime.ts:846</a></li>
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@@ -497,7 +497,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L750">runtime.ts:750</a></li>
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@@ -520,7 +520,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
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<div class="tsd-comment tsd-typography">
@@ -568,7 +568,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L789">runtime.ts:789</a></li>
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@@ -608,7 +608,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L914">runtime.ts:914</a></li>
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@@ -646,7 +646,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
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@@ -722,7 +722,7 @@
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L868">runtime.ts:868</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L940">runtime.ts:940</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L940">runtime.ts:940</a></li>
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index b07fccddb..58de07ca4 100644
--- a/docs/reference/api/typedoc/classes/memory.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/0fb155c3c/web/src/memory.ts#L40">memory.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L40">memory.ts:40</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -152,7 +152,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L32">memory.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L32">memory.ts:32</a></li>
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@@ -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>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L33">memory.ts:33</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L33">memory.ts:33</a></li>
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@@ -179,7 +179,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L154">memory.ts:154</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L154">memory.ts:154</a></li>
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@@ -210,7 +210,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L90">memory.ts:90</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L90">memory.ts:90</a></li>
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@@ -233,7 +233,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L97">memory.ts:97</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L97">memory.ts:97</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -256,7 +256,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L74">memory.ts:74</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L74">memory.ts:74</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -279,7 +279,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L81">memory.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L81">memory.ts:81</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -302,7 +302,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L104">memory.ts:104</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L104">memory.ts:104</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -325,7 +325,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L132">memory.ts:132</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L132">memory.ts:132</a></li>
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<div class="tsd-comment tsd-typography">
@@ -362,7 +362,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L145">memory.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L145">memory.ts:145</a></li>
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@@ -393,7 +393,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L60">memory.ts:60</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L60">memory.ts:60</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -416,7 +416,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L67">memory.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L67">memory.ts:67</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -439,7 +439,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L53">memory.ts:53</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L53">memory.ts:53</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -462,7 +462,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L114">memory.ts:114</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/memory.ts#L114">memory.ts:114</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -485,7 +485,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/memory.ts#L124">memory.ts:124</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/memory.ts#L175">memory.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 5200433fe..67cdfb8aa 100644
--- a/docs/reference/api/typedoc/classes/module.html
+++ b/docs/reference/api/typedoc/classes/module.html
@@ -124,7 +124,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L504">runtime.ts:504</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L504">runtime.ts:504</a></li>
</ul>
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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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L502">runtime.ts:502</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L502">runtime.ts:502</a></li>
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@@ -187,7 +187,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L516">runtime.ts:516</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L530">runtime.ts:530</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L561">runtime.ts:561</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L561">runtime.ts:561</a></li>
</ul>
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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 2c24775ee..edf738276 100644
--- a/docs/reference/api/typedoc/classes/ndarray.html
+++ b/docs/reference/api/typedoc/classes/ndarray.html
@@ -130,7 +130,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L304">runtime.ts:304</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L304">runtime.ts:304</a></li>
</ul>
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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/0fb155c3c/web/src/runtime.ts#L297">runtime.ts:297</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L297">runtime.ts:297</a></li>
</ul>
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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/0fb155c3c/web/src/runtime.ts#L293">runtime.ts:293</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L293">runtime.ts:293</a></li>
</ul>
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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/0fb155c3c/web/src/runtime.ts#L289">runtime.ts:289</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L289">runtime.ts:289</a></li>
</ul>
</aside>
<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/0fb155c3c/web/src/runtime.ts#L291">runtime.ts:291</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L291">runtime.ts:291</a></li>
</ul>
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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/0fb155c3c/web/src/runtime.ts#L295">runtime.ts:295</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L295">runtime.ts:295</a></li>
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<div class="tsd-comment tsd-typography">
@@ -240,7 +240,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L370">runtime.ts:370</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L414">runtime.ts:414</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L414">runtime.ts:414</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -305,7 +305,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L355">runtime.ts:355</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L474">runtime.ts:474</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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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- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L443">runtime.ts:443</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 ab6753b54..d1abbc633 100644
--- a/docs/reference/api/typedoc/classes/packedfunccell.html
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@@ -122,7 +122,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L158">runtime.ts:158</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L158">runtime.ts:158</a></li>
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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/0fb155c3c/web/src/runtime.ts#L157">runtime.ts:157</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L157">runtime.ts:157</a></li>
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@@ -164,7 +164,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L165">runtime.ts:165</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L165">runtime.ts:165</a></li>
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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 0157c8014..9d9a1eb6a 100644
--- a/docs/reference/api/typedoc/classes/rpcserver.html
+++ b/docs/reference/api/typedoc/classes/rpcserver.html
@@ -115,7 +115,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -176,7 +176,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
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<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/0fb155c3c/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
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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/0fb155c3c/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
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</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/0fb155c3c/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/classes/scalar.html b/docs/reference/api/typedoc/classes/scalar.html
index 98d21546c..39eebcb07 100644
--- a/docs/reference/api/typedoc/classes/scalar.html
+++ b/docs/reference/api/typedoc/classes/scalar.html
@@ -112,7 +112,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
</aside>
<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/0fb155c3c/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
</aside>
<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/0fb155c3c/web/src/runtime.ts#L143">runtime.ts:143</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L143">runtime.ts:143</a></li>
</ul>
</aside>
<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 cc3ee910e..8be2ed05b 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/0fb155c3c/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
</ul>
</aside>
<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/0fb155c3c/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
</ul>
</aside>
</section>
@@ -172,7 +172,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -209,7 +209,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
</ul>
</aside>
<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 393d73e98..95a9996a5 100644
--- a/docs/reference/api/typedoc/enums/argtypecode.html
+++ b/docs/reference/api/typedoc/enums/argtypecode.html
@@ -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/0fb155c3c/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
</ul>
</aside>
</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/0fb155c3c/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 58edaabb6..c994b7b71 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/0fb155c3c/web/src/runtime.ts#L676">runtime.ts:676</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L675">runtime.ts:675</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 055837b50..c338e6048 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/0fb155c3c/web/src/runtime.ts#L242">runtime.ts:242</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L240">runtime.ts:240</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L243">runtime.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L241">runtime.ts:241</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 c745680c5..4a0e5f807 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/0fb155c3c/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 af3e1ef83..a3ef3ef24 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/0fb155c3c/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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 1674987a7..901965c9b 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/0fb155c3c/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/runtime.ts#L36">runtime.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/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/0fb155c3c/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1239,7 +1239,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/support.ts#L25">support.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/support.ts#L25">support.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1271,7 +1271,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/support.ts#L39">support.ts:39</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/support.ts#L39">support.ts:39</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1300,7 +1300,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/support.ts#L52">support.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/support.ts#L52">support.ts:52</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1337,7 +1337,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/compact.ts#L38">compact.ts:38</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/compact.ts#L38">compact.ts:38</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1368,7 +1368,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1390,7 +1390,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/environment.ts#L32">environment.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/environment.ts#L32">environment.ts:32</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1421,7 +1421,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/compact.ts#L24">compact.ts:24</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/compact.ts#L24">compact.ts:24</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1443,7 +1443,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L1356">runtime.ts:1356</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L1356">runtime.ts:1356</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1508,7 +1508,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/support.ts#L62">support.ts:62</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/support.ts#L62">support.ts:62</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1530,7 +1530,7 @@
<div class="tsd-signature tsd-kind-icon">DLData<wbr>Type<wbr>Code<wbr>ToStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L246">runtime.ts:246</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L246">runtime.ts:246</a></li>
</ul>
</aside>
<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1539,7 +1539,7 @@
<div class="tsd-signature tsd-kind-icon">0<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "int"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L247">runtime.ts:247</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L247">runtime.ts:247</a></li>
</ul>
</aside>
</section>
@@ -1549,7 +1549,7 @@
<div class="tsd-signature tsd-kind-icon">1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "uint"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L248">runtime.ts:248</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L248">runtime.ts:248</a></li>
</ul>
</aside>
</section>
@@ -1559,7 +1559,7 @@
<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "float"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L249">runtime.ts:249</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L249">runtime.ts:249</a></li>
</ul>
</aside>
</section>
@@ -1569,7 +1569,7 @@
<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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L250">runtime.ts:250</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L250">runtime.ts:250</a></li>
</ul>
</aside>
</section>
@@ -1580,7 +1580,7 @@
<div class="tsd-signature tsd-kind-icon">Device<wbr>Enum<wbr>ToStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L175">runtime.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L175">runtime.ts:175</a></li>
</ul>
</aside>
<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1589,7 +1589,7 @@
<div class="tsd-signature tsd-kind-icon">1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "cpu"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L176">runtime.ts:176</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L176">runtime.ts:176</a></li>
</ul>
</aside>
</section>
@@ -1599,7 +1599,7 @@
<div class="tsd-signature tsd-kind-icon">15<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "webgpu"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L180">runtime.ts:180</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L180">runtime.ts:180</a></li>
</ul>
</aside>
</section>
@@ -1609,7 +1609,7 @@
<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "cuda"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L177">runtime.ts:177</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L177">runtime.ts:177</a></li>
</ul>
</aside>
</section>
@@ -1619,7 +1619,7 @@
<div class="tsd-signature tsd-kind-icon">4<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "opencl"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L178">runtime.ts:178</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L178">runtime.ts:178</a></li>
</ul>
</aside>
</section>
@@ -1629,7 +1629,7 @@
<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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L179">runtime.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L179">runtime.ts:179</a></li>
</ul>
</aside>
</section>
@@ -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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L183">runtime.ts:183</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L183">runtime.ts:183</a></li>
</ul>
</aside>
<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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/runtime.ts#L186">runtime.ts:186</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L186">runtime.ts:186</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L184">runtime.ts:184</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L184">runtime.ts:184</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L185">runtime.ts:185</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L185">runtime.ts:185</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L189">runtime.ts:189</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L189">runtime.ts:189</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L187">runtime.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L187">runtime.ts:187</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L188">runtime.ts:188</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L188">runtime.ts:188</a></li>
</ul>
</aside>
</section>
@@ -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/0fb155c3c/web/src/runtime.ts#L190">runtime.ts:190</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/runtime.ts#L190">runtime.ts:190</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/interfaces/disposable.html b/docs/reference/api/typedoc/interfaces/disposable.html
index 188069efe..f4aabbdb5 100644
--- a/docs/reference/api/typedoc/interfaces/disposable.html
+++ b/docs/reference/api/typedoc/interfaces/disposable.html
@@ -113,7 +113,7 @@
<div class="tsd-signature tsd-kind-icon">dispose<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">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/types.ts#L52">types.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/types.ts#L52">types.ts:52</a></li>
</ul>
</aside>
<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 2e84f538c..4a46d6b84 100644
--- a/docs/reference/api/typedoc/interfaces/functioninfo.html
+++ b/docs/reference/api/typedoc/interfaces/functioninfo.html
@@ -95,7 +95,7 @@
<div class="tsd-signature tsd-kind-icon">arg_<wbr>types<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
</ul>
</aside>
</section>
@@ -105,7 +105,7 @@
<div class="tsd-signature tsd-kind-icon">launch_<wbr>param_<wbr>tags<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
</ul>
</aside>
</section>
@@ -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>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/interfaces/libraryprovider.html b/docs/reference/api/typedoc/interfaces/libraryprovider.html
index addb90d6c..f14af43d7 100644
--- a/docs/reference/api/typedoc/interfaces/libraryprovider.html
+++ b/docs/reference/api/typedoc/interfaces/libraryprovider.html
@@ -112,7 +112,7 @@
<div class="tsd-signature tsd-kind-icon">imports<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">any</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/0fb155c3c/web/src/types.ts#L34">types.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/types.ts#L34">types.ts:34</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -127,7 +127,7 @@
<div class="tsd-signature tsd-kind-icon">start<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>inst<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Instance</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/0fb155c3c/web/src/types.ts#L39">types.ts:39</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/633fb5461/web/src/types.ts#L39">types.ts:39</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/searchindex.js b/docs/searchindex.js
index cf87ca894..f242ca29d 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
-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
+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 72e3a58df..67230030c 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>00:20.347</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
+<p><strong>00:21.374</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:20.138</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>00:00.209</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>00:21.149</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>00:00.225</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 24c55aedd..ad00e6219 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_classification.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_classification.html
@@ -539,7 +539,7 @@ 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 21.75s!
+resnet18_v1 inference graph built in 22.54s!
</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 713881923..432e1ee2f 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -557,7 +557,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
<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/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
-yolov3-tiny inference graph built in 15.24s!
+yolov3-tiny inference graph built in 15.26s!
</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 76d97d666..9b0735a9a 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>01:29.123</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:29.992</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:47.257</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>00:41.866</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>00:47.290</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>00:42.702</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 b49b9f783..a4c882bc9 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>00:03.526</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.630</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.987</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>00:00.539</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>00:03.057</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>00:00.573</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 76648af53..1d6ca28f3 100644
--- a/docs/topic/vta/tutorials/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -302,7 +302,7 @@
<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>00:00.997</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:00.509</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>00:00.508</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>00:00.488</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 e39ff112b..7535ecb44 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>.T
</pre></div>
</div>
</div>
@@ -545,7 +545,7 @@ operator fusion.</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>Execution time of this operator: 91.859 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 93.925 ms
</pre></div>
</div>
</div>
@@ -621,6 +621,7 @@ 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 5.133 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 6b6cde25e..c32406e56 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -513,7 +513,7 @@ 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': 485.61423607999586, 'median': 485.6955056000061, 'std': 0.5002350431385256}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 495.9099344000015, 'median': 494.7740689999989, 'std': 3.001615037945105}
</pre></div>
</div>
</div>
@@ -667,129 +667,129 @@ 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: (0/10) | 0.00 s
-[Task 1/25] Current/Best: 23.79/ 23.79 GFLOPS | Progress: (4/10) | 5.12 s
-[Task 1/25] Current/Best: 13.17/ 23.81 GFLOPS | Progress: (8/10) | 7.22 s
-[Task 1/25] Current/Best: 5.80/ 23.81 GFLOPS | Progress: (10/10) | 9.60 s Done.
+[Task 1/25] Current/Best: 14.01/ 14.77 GFLOPS | Progress: (4/10) | 5.87 s
+[Task 1/25] Current/Best: 5.41/ 16.53 GFLOPS | Progress: (8/10) | 9.73 s
+[Task 1/25] Current/Best: 1.93/ 16.53 GFLOPS | Progress: (10/10) | 11.83 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 2/25] Current/Best: 9.24/ 22.42 GFLOPS | Progress: (4/10) | 2.28 s
-[Task 2/25] Current/Best: 8.61/ 22.42 GFLOPS | Progress: (8/10) | 3.33 s
-[Task 2/25] Current/Best: 6.22/ 22.42 GFLOPS | Progress: (10/10) | 4.12 s Done.
+[Task 2/25] Current/Best: 6.82/ 19.62 GFLOPS | Progress: (4/10) | 2.18 s
+[Task 2/25] Current/Best: 12.71/ 19.62 GFLOPS | Progress: (8/10) | 3.93 s
+[Task 2/25] Current/Best: 12.82/ 19.62 GFLOPS | Progress: (10/10) | 4.65 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 3/25] Current/Best: 9.26/ 18.05 GFLOPS | Progress: (4/10) | 2.96 s
-[Task 3/25] Current/Best: 24.29/ 24.29 GFLOPS | Progress: (8/10) | 4.43 s
-[Task 3/25] Current/Best: 11.96/ 24.29 GFLOPS | Progress: (10/10) | 5.44 s Done.
+[Task 3/25] Current/Best: 23.29/ 23.29 GFLOPS | Progress: (4/10) | 2.73 s
+[Task 3/25] Current/Best: 14.45/ 24.37 GFLOPS | Progress: (8/10) | 4.69 s
+[Task 3/25] Current/Best: 8.39/ 24.37 GFLOPS | Progress: (10/10) | 5.98 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 4/25] Current/Best: 5.05/ 21.54 GFLOPS | Progress: (4/10) | 5.66 s
-[Task 4/25] Current/Best: 5.95/ 21.54 GFLOPS | Progress: (8/10) | 8.62 s
-[Task 4/25] Current/Best: 16.69/ 21.54 GFLOPS | Progress: (10/10) | 9.32 s Done.
+[Task 4/25] Current/Best: 4.15/ 18.09 GFLOPS | Progress: (4/10) | 2.45 s
+[Task 4/25] Current/Best: 17.96/ 22.71 GFLOPS | Progress: (8/10) | 3.86 s
+[Task 4/25] Current/Best: 14.60/ 22.71 GFLOPS | Progress: (10/10) | 4.65 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 5/25] Current/Best: 16.64/ 16.64 GFLOPS | Progress: (4/10) | 3.50 s
-[Task 5/25] Current/Best: 16.58/ 16.64 GFLOPS | Progress: (8/10) | 5.29 s
-[Task 5/25] Current/Best: 19.44/ 19.44 GFLOPS | Progress: (10/10) | 5.99 s Done.
+[Task 5/25] Current/Best: 14.94/ 21.31 GFLOPS | Progress: (4/10) | 2.72 s
+[Task 5/25] Current/Best: 8.15/ 21.31 GFLOPS | Progress: (8/10) | 4.45 s
+[Task 5/25] Current/Best: 17.51/ 21.31 GFLOPS | Progress: (10/10) | 5.08 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 6/25] Current/Best: 21.95/ 21.95 GFLOPS | Progress: (4/10) | 2.86 s
-[Task 6/25] Current/Best: 3.25/ 21.95 GFLOPS | Progress: (8/10) | 5.94 s
-[Task 6/25] Current/Best: 15.89/ 21.95 GFLOPS | Progress: (10/10) | 7.02 s Done.
+[Task 6/25] Current/Best: 3.25/ 10.13 GFLOPS | Progress: (4/10) | 3.91 s
+[Task 6/25] Current/Best: 20.88/ 20.88 GFLOPS | Progress: (8/10) | 6.90 s
+[Task 6/25] Current/Best: 10.76/ 20.88 GFLOPS | Progress: (10/10) | 8.59 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 7/25] Current/Best: 14.12/ 23.29 GFLOPS | Progress: (4/10) | 3.05 s
-[Task 7/25] Current/Best: 19.55/ 23.29 GFLOPS | Progress: (8/10) | 5.79 s
-[Task 7/25] Current/Best: 18.51/ 23.29 GFLOPS | Progress: (10/10) | 6.95 s Done.
+[Task 7/25] Current/Best: 6.08/ 22.59 GFLOPS | Progress: (4/10) | 2.88 s
+[Task 7/25] Current/Best: 14.56/ 22.59 GFLOPS | Progress: (8/10) | 5.03 s
+[Task 7/25] Current/Best: 18.15/ 22.59 GFLOPS | Progress: (10/10) | 5.85 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 8/25] Current/Best: 15.27/ 15.27 GFLOPS | Progress: (4/10) | 3.89 s
-[Task 8/25] Current/Best: 11.12/ 16.18 GFLOPS | Progress: (8/10) | 6.15 s
-[Task 8/25] Current/Best: 4.17/ 16.18 GFLOPS | Progress: (10/10) | 7.43 s Done.
-
+[Task 8/25] Current/Best: 9.29/ 15.44 GFLOPS | Progress: (4/10) | 2.79 s
+[Task 8/25] Current/Best: 17.89/ 17.89 GFLOPS | Progress: (8/10) | 5.74 s
+[Task 8/25] Current/Best: 16.73/ 17.89 GFLOPS | Progress: (10/10) | 26.87 s
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 9/25] Current/Best: 9.91/ 15.94 GFLOPS | Progress: (4/10) | 8.08 s
-[Task 9/25] Current/Best: 4.82/ 17.36 GFLOPS | Progress: (8/10) | 9.67 s
-[Task 9/25] Current/Best: 7.85/ 17.36 GFLOPS | Progress: (10/10) | 14.78 s Done.
+[Task 9/25] Current/Best: 20.59/ 20.59 GFLOPS | Progress: (4/10) | 3.31 s
+[Task 9/25] Current/Best: 10.44/ 20.59 GFLOPS | Progress: (8/10) | 5.00 s
+[Task 9/25] Current/Best: 23.02/ 23.02 GFLOPS | Progress: (10/10) | 5.72 s Done.
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 10/25] Current/Best: 14.88/ 22.61 GFLOPS | Progress: (4/10) | 2.63 s
-[Task 10/25] Current/Best: 10.15/ 22.61 GFLOPS | Progress: (8/10) | 4.59 s
-[Task 10/25] Current/Best: 10.66/ 22.61 GFLOPS | Progress: (10/10) | 6.29 s Done.
+[Task 10/25] Current/Best: 6.40/ 14.51 GFLOPS | Progress: (4/10) | 2.55 s
+[Task 10/25] Current/Best: 21.95/ 21.95 GFLOPS | Progress: (8/10) | 4.02 s
+[Task 10/25] Current/Best: 3.18/ 21.95 GFLOPS | Progress: (10/10) | 5.25 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 11/25] Current/Best: 16.01/ 21.00 GFLOPS | Progress: (4/10) | 3.02 s
-[Task 11/25] Current/Best: 17.59/ 21.00 GFLOPS | Progress: (8/10) | 4.79 s
-[Task 11/25] Current/Best: 19.71/ 21.00 GFLOPS | Progress: (10/10) | 5.63 s Done.
+[Task 11/25] Current/Best: 11.03/ 20.00 GFLOPS | Progress: (4/10) | 3.27 s
+[Task 11/25] Current/Best: 22.52/ 22.52 GFLOPS | Progress: (8/10) | 4.96 s
+[Task 11/25] Current/Best: 10.11/ 22.52 GFLOPS | Progress: (10/10) | 7.30 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 12/25] Current/Best: 9.42/ 18.73 GFLOPS | Progress: (4/10) | 9.51 s
-[Task 12/25] Current/Best: 5.75/ 22.09 GFLOPS | Progress: (8/10) | 14.41 s
-[Task 12/25] Current/Best: 19.08/ 22.09 GFLOPS | Progress: (10/10) | 15.12 s Done.
+[Task 12/25] Current/Best: 18.60/ 18.60 GFLOPS | Progress: (4/10) | 3.81 s
+[Task 12/25] Current/Best: 10.19/ 18.60 GFLOPS | Progress: (8/10) | 7.90 s
+[Task 12/25] Current/Best: 22.10/ 22.10 GFLOPS | Progress: (10/10) | 8.69 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 13/25] Current/Best: 13.11/ 13.11 GFLOPS | Progress: (4/10) | 3.89 s
-[Task 13/25] Current/Best: 6.59/ 18.34 GFLOPS | Progress: (8/10) | 6.88 s
-[Task 13/25] Current/Best: 6.15/ 21.58 GFLOPS | Progress: (10/10) | 8.24 s Done.
+[Task 13/25] Current/Best: 11.21/ 18.41 GFLOPS | Progress: (4/10) | 4.42 s
+[Task 13/25] Current/Best: 12.12/ 19.75 GFLOPS | Progress: (8/10) | 6.60 s
+[Task 13/25] Current/Best: 12.19/ 19.75 GFLOPS | Progress: (10/10) | 9.30 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 14/25] Current/Best: 10.75/ 10.75 GFLOPS | Progress: (4/10) | 7.11 s
-[Task 14/25] Current/Best: 13.97/ 17.11 GFLOPS | Progress: (8/10) | 8.88 s
-[Task 14/25] Current/Best: 9.99/ 17.11 GFLOPS | Progress: (10/10) | 9.84 s
-[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
-
-[Task 15/25] Current/Best: 12.06/ 22.14 GFLOPS | Progress: (4/10) | 3.65 s
-[Task 15/25] Current/Best: 21.31/ 22.14 GFLOPS | Progress: (8/10) | 5.32 s
-[Task 15/25] Current/Best: 17.66/ 22.14 GFLOPS | Progress: (10/10) | 5.91 s Done.
+[Task 14/25] Current/Best: 13.06/ 19.06 GFLOPS | Progress: (4/10) | 2.79 s
+[Task 14/25] Current/Best: 3.13/ 19.06 GFLOPS | Progress: (8/10) | 6.23 s
+[Task 14/25] Current/Best: 10.45/ 19.06 GFLOPS | Progress: (10/10) | 9.38 s
+[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
+[Task 15/25] Current/Best: 12.96/ 13.51 GFLOPS | Progress: (4/10) | 4.18 s
+[Task 15/25] Current/Best: 10.34/ 16.11 GFLOPS | Progress: (8/10) | 8.63 s
+[Task 15/25] Current/Best: 16.22/ 16.22 GFLOPS | Progress: (10/10) | 9.28 s Done.
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 16/25] Current/Best: 17.85/ 17.85 GFLOPS | Progress: (4/10) | 3.30 s
-[Task 16/25] Current/Best: 6.57/ 17.85 GFLOPS | Progress: (8/10) | 4.73 s
-[Task 16/25] Current/Best: 6.92/ 17.85 GFLOPS | Progress: (10/10) | 5.39 s Done.
+[Task 16/25] Current/Best: 17.73/ 17.73 GFLOPS | Progress: (4/10) | 2.53 s
+[Task 16/25] Current/Best: 12.55/ 18.17 GFLOPS | Progress: (8/10) | 5.81 s
+[Task 16/25] Current/Best: 20.76/ 20.76 GFLOPS | Progress: (10/10) | 6.36 s Done.
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 17/25] Current/Best: 12.16/ 22.24 GFLOPS | Progress: (4/10) | 3.14 s
-[Task 17/25] Current/Best: 10.09/ 22.24 GFLOPS | Progress: (8/10) | 5.29 s
-[Task 17/25] Current/Best: 11.54/ 22.24 GFLOPS | Progress: (10/10) | 6.54 s Done.
+[Task 17/25] Current/Best: 19.77/ 19.77 GFLOPS | Progress: (4/10) | 3.73 s Done.
+ Done.
+
+[Task 17/25] Current/Best: 9.00/ 19.77 GFLOPS | Progress: (8/10) | 5.49 s
+[Task 17/25] Current/Best: 7.47/ 19.77 GFLOPS | Progress: (10/10) | 7.17 s Done.
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 18/25] Current/Best: 13.82/ 17.13 GFLOPS | Progress: (4/10) | 3.28 s
-[Task 18/25] Current/Best: 10.32/ 21.82 GFLOPS | Progress: (8/10) | 7.37 s
-[Task 18/25] Current/Best: 9.78/ 21.82 GFLOPS | Progress: (10/10) | 10.99 s Done.
+[Task 18/25] Current/Best: 21.64/ 21.64 GFLOPS | Progress: (4/10) | 2.67 s
+[Task 18/25] Current/Best: 9.42/ 21.64 GFLOPS | Progress: (8/10) | 4.97 s
+[Task 18/25] Current/Best: 11.04/ 21.64 GFLOPS | Progress: (10/10) | 6.81 s Done.
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 19/25] Current/Best: 10.56/ 22.50 GFLOPS | Progress: (4/10) | 3.73 s
-[Task 19/25] Current/Best: 12.20/ 22.50 GFLOPS | Progress: (8/10) | 6.71 s
-[Task 19/25] Current/Best: 5.37/ 22.50 GFLOPS | Progress: (10/10) | 7.93 s Done.
+[Task 19/25] Current/Best: 14.09/ 14.09 GFLOPS | Progress: (4/10) | 4.34 s
+[Task 19/25] Current/Best: 16.68/ 20.82 GFLOPS | Progress: (8/10) | 6.21 s
+[Task 19/25] Current/Best: 15.84/ 20.82 GFLOPS | Progress: (10/10) | 8.30 s Done.
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 20/25] Current/Best: 9.91/ 15.24 GFLOPS | Progress: (4/10) | 2.78 s
-[Task 20/25] Current/Best: 9.22/ 17.51 GFLOPS | Progress: (8/10) | 6.48 s
-[Task 20/25] Current/Best: 21.52/ 21.52 GFLOPS | Progress: (10/10) | 7.27 s
+[Task 20/25] Current/Best: 19.14/ 19.14 GFLOPS | Progress: (4/10) | 4.84 s
+[Task 20/25] Current/Best: 9.63/ 19.14 GFLOPS | Progress: (8/10) | 6.68 s
+[Task 20/25] Current/Best: 9.03/ 19.14 GFLOPS | Progress: (10/10) | 9.39 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 21/25] Current/Best: 21.97/ 21.97 GFLOPS | Progress: (4/10) | 2.40 s
-[Task 21/25] Current/Best: 6.54/ 21.97 GFLOPS | Progress: (8/10) | 4.23 s
-[Task 21/25] Current/Best: 9.35/ 21.97 GFLOPS | Progress: (10/10) | 4.86 s Done.
-
+[Task 21/25] Current/Best: 16.76/ 20.61 GFLOPS | Progress: (4/10) | 2.88 s
+[Task 21/25] Current/Best: 4.93/ 20.61 GFLOPS | Progress: (8/10) | 4.50 s
+[Task 21/25] Current/Best: 17.35/ 20.71 GFLOPS | Progress: (10/10) | 5.12 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 22/25] Current/Best: 23.32/ 23.32 GFLOPS | Progress: (4/10) | 2.31 s
-[Task 22/25] Current/Best: 8.08/ 23.32 GFLOPS | Progress: (8/10) | 4.89 s
-[Task 22/25] Current/Best: 11.45/ 23.32 GFLOPS | Progress: (10/10) | 6.24 s Done.
+[Task 22/25] Current/Best: 19.99/ 20.54 GFLOPS | Progress: (4/10) | 2.73 s
+[Task 22/25] Current/Best: 1.56/ 21.76 GFLOPS | Progress: (8/10) | 4.59 s
+[Task 22/25] Current/Best: 19.29/ 21.76 GFLOPS | Progress: (10/10) | 5.20 s Done.
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 23/25] Current/Best: 6.57/ 12.00 GFLOPS | Progress: (4/10) | 4.54 s
-[Task 23/25] Current/Best: 10.81/ 12.00 GFLOPS | Progress: (8/10) | 8.53 s
-[Task 23/25] Current/Best: 20.63/ 20.63 GFLOPS | Progress: (10/10) | 10.52 s Done.
+[Task 23/25] Current/Best: 18.71/ 19.39 GFLOPS | Progress: (4/10) | 3.11 s
+[Task 23/25] Current/Best: 8.94/ 19.98 GFLOPS | Progress: (8/10) | 8.42 s
+[Task 23/25] Current/Best: 23.39/ 23.39 GFLOPS | Progress: (10/10) | 9.97 s Done.
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 24/25] Current/Best: 3.99/ 9.70 GFLOPS | Progress: (4/10) | 15.95 s
-[Task 24/25] Current/Best: 3.65/ 9.70 GFLOPS | Progress: (8/10) | 21.80 s
-[Task 24/25] Current/Best: 3.77/ 10.59 GFLOPS | Progress: (10/10) | 24.75 s
+[Task 24/25] Current/Best: 8.22/ 9.53 GFLOPS | Progress: (4/10) | 25.16 s
+[Task 24/25] Current/Best: 8.43/ 10.19 GFLOPS | Progress: (8/10) | 36.91 s
+[Task 24/25] Current/Best: 1.60/ 10.19 GFLOPS | Progress: (10/10) | 47.28 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
Done.
+ Done.
-[Task 25/25] Current/Best: 1.51/ 8.96 GFLOPS | Progress: (4/10) | 6.22 s
-[Task 25/25] Current/Best: 8.31/ 8.96 GFLOPS | Progress: (8/10) | 11.77 s
-[Task 25/25] Current/Best: 8.83/ 9.81 GFLOPS | Progress: (10/10) | 15.94 s Done.
+[Task 25/25] Current/Best: 9.89/ 9.89 GFLOPS | Progress: (4/10) | 5.82 s
+[Task 25/25] Current/Best: 2.94/ 9.89 GFLOPS | Progress: (8/10) | 41.60 s
+[Task 25/25] Current/Best: 1.56/ 9.89 GFLOPS | Progress: (10/10) | 57.76 s
</pre></div>
</div>
<p>The output from this tuning process will look something like this:</p>
@@ -851,8 +851,8 @@ model using optimized operators to speed up our computations.</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>class='n02123045 tabby, tabby cat' with probability=0.621104
-class='n02123159 tiger cat' with probability=0.356378
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class='n02123045 tabby, tabby cat' with probability=0.621102
+class='n02123159 tiger cat' with probability=0.356379
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -890,8 +890,8 @@ improvement in comparing the optimized model to the unoptimized model.</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>optimized: {'mean': 423.85818206000295, 'median': 423.7159835000057, 'std': 0.8548689843661389}
-unoptimized: {'mean': 485.61423607999586, 'median': 485.6955056000061, 'std': 0.5002350431385256}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 429.88311026000247, 'median': 429.5241749499951, 'std': 2.345377712720502}
+unoptimized: {'mean': 495.9099344000015, 'median': 494.7740689999989, 'std': 3.001615037945105}
</pre></div>
</div>
</div>
@@ -905,7 +905,7 @@ models.</p>
<p>Here we presented a simple example using ResNet-50 v2 locally. However, TVM
supports many more features including cross-compilation, remote execution and
profiling/benchmarking.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 46.086 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes 59.447 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-autotvm-relay-x86-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../_downloads/57a45d9bef1af358191e7d50043e652c/autotvm_relay_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">autotvm_relay_x86.py</span></code></a></p>
diff --git a/docs/tutorial/cross_compilation_and_rpc.html b/docs/tutorial/cross_compilation_and_rpc.html
index 174609929..123c1b774 100644
--- a/docs/tutorial/cross_compilation_and_rpc.html
+++ b/docs/tutorial/cross_compilation_and_rpc.html
@@ -496,7 +496,7 @@ device and returns the measured cost. Network overhead is excluded.</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>1.271e-07 secs/op
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.256e-07 secs/op
</pre></div>
</div>
</div>
diff --git a/docs/tutorial/intro_topi.html b/docs/tutorial/intro_topi.html
index bf412cb10..c7e905106 100644
--- a/docs/tutorial/intro_topi.html
+++ b/docs/tutorial/intro_topi.html
@@ -461,7 +461,7 @@ we can schedule the following series of operations ending with <code class="code
</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>[stage(a, placeholder(a, 0x223dd2b0)), stage(b, placeholder(b, 0x21173260)), 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=[ [...]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x22418200)), stage(b, placeholder(b, 0x57b0b50)), 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=[i [...]
</pre></div>
</div>
<p>We can test the correctness by comparing with <code class="code docutils literal notranslate"><span class="pre">numpy</span></code> result as follows</p>
diff --git a/docs/tutorial/sg_execution_times.html b/docs/tutorial/sg_execution_times.html
index 56c5510b8..7146133c7 100644
--- a/docs/tutorial/sg_execution_times.html
+++ b/docs/tutorial/sg_execution_times.html
@@ -300,20 +300,20 @@
<div class="section" id="computation-times">
<span id="sphx-glr-tutorial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:25.084</strong> total execution time for <strong>tutorial</strong> files:</p>
+<p><strong>10:48.210</strong> total execution time for <strong>tutorial</strong> files:</p>
<ul class="simple">
-<li><p><strong>06:46.086</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
-<li><p><strong>00:59.062</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
-<li><p><strong>00:52.052</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:26.227</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
-<li><p><strong>00:19.467</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:01.159</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
-<li><p><strong>00:00.695</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
-<li><p><strong>00:00.193</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
-<li><p><strong>00:00.040</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
-<li><p><strong>00:00.037</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
-<li><p><strong>00:00.035</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
-<li><p><strong>00:00.031</strong>: <a class="reference internal" href="introduction.html#sphx-glr-tutorial-introduction-py"><span class="std std-ref">Introduction</span></a> (<code class="docutils literal notranslate"><span class="pre">introduction.py</span></code>)</p></li>
+<li><p><strong>07:59.447</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
+<li><p><strong>01:05.133</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>01:01.623</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
+<li><p><strong>00:26.525</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
+<li><p><strong>00:13.161</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>00:01.195</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
+<li><p><strong>00:00.704</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
+<li><p><strong>00:00.198</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
+<li><p><strong>00:00.057</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
+<li><p><strong>00:00.056</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
+<li><p><strong>00:00.056</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
+<li><p><strong>00:00.056</strong>: <a class="reference internal" href="introduction.html#sphx-glr-tutorial-introduction-py"><span class="std std-ref">Introduction</span></a> (<code class="docutils literal notranslate"><span class="pre">introduction.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/tutorial/tensor_expr_get_started.html b/docs/tutorial/tensor_expr_get_started.html
index de46db9dc..f8994c527 100644
--- a/docs/tutorial/tensor_expr_get_started.html
+++ b/docs/tutorial/tensor_expr_get_started.html
@@ -507,7 +507,7 @@ helper function to run a profile of the TVM generated code.</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>Numpy running time: 0.000008
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.000007
naive: 0.000006
</pre></div>
</div>
@@ -633,10 +633,10 @@ factor to be the number of threads on your CPU.</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>Operator Timing Performance
- numpy 7.589809999899444e-06 1.0
- naive 5.9376e-06 0.7823120736986388
-parallel 6.2355e-06 0.8215620680995457
- vector 2.4689900000000002e-05 3.2530326846557576
+ numpy 7.073049998780334e-06 1.0
+ naive 5.8333000000000005e-06 0.8247220083282156
+parallel 6.0573000000000004e-06 0.8563915144166253
+ vector 2.4640200000000002e-05 3.4836739460697888
</pre></div>
</div>
<div class="admonition-code-specialization admonition">
@@ -954,7 +954,7 @@ matrix multiplication.</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>Numpy running time: 0.017953
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019796
</pre></div>
</div>
<p>Now we write a basic matrix multiplication using TVM TE and verify that it
@@ -996,7 +996,7 @@ optimizations.</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>none: 3.264705
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.442003
</pre></div>
</div>
<p>Let’s take a look at the intermediate representation of the operator and
@@ -1063,7 +1063,7 @@ schedule.</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>blocking: 0.313538
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.306660
</pre></div>
</div>
<p>By reordering the computation to take advantage of caching, you should see a
@@ -1124,7 +1124,7 @@ already cache friendly from our previous optimizations.</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>vectorization: 0.341040
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.335407
@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], []),
@@ -1180,7 +1180,7 @@ 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>loop permutation: 0.114859
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.118970
@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.</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>array packing: 0.108777
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.110969
@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], []),
@@ -1332,7 +1332,7 @@ to `C</cite> 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>block caching: 0.110779
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.112964
@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], []),
@@ -1400,7 +1400,7 @@ of thread-level parallelization.</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>parallelization: 0.143930
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.147856
@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], []),
@@ -1463,13 +1463,13 @@ working, we can compare the results.</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> Operator Timing Performance
- none 3.264705298 1.0
- blocking 0.31353826199999996 0.09603876410899247
- vectorization 0.34103952530000003 0.10446257599695911
-loop permutation 0.1148587813 0.03518197534410348
- array packing 0.1087768657 0.03331904590795319
- block caching 0.11077945 0.03393245021774704
- parallelization 0.1439299752 0.04408666696138648
+ none 3.4420034504 1.0
+ blocking 0.3066602424 0.08909353137468894
+ vectorization 0.3354069085 0.09744525632623113
+loop permutation 0.1189697054 0.03456408661826715
+ array packing 0.11096905990000001 0.03223967131325919
+ block caching 0.1129637478 0.032819184939187764
+ parallelization 0.1478555552 0.04295624839737377
</pre></div>
</div>
<p>Note that the outputs on the web page reflect the running times on a
@@ -1501,6 +1501,7 @@ is</p>
you can build generic templates of the matrix multiplication and other
operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.623 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-tensor-expr-get-started-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.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">tensor_expr_get_started.py</span></code></a></p>