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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/05/04 23:02:58 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@521b80af217a60bf4be3febd5a1e081eb4f76b93)
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 32e804202 deploying docs (apache/tvm@521b80af217a60bf4be3febd5a1e081eb4f76b93)
32e804202 is described below
commit 32e804202d7c11a5ba6c7ebde090d6891327ffc0
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed May 4 23:02:52 2022 +0000
deploying docs (apache/tvm@521b80af217a60bf4be3febd5a1e081eb4f76b93)
---
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.../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_models/deploy_model_on_rasp.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 6 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 4 +-
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.../deploy_models/deploy_ssd_gluoncv.rst.txt | 4 +-
.../deploy_models/sg_execution_times.rst.txt | 18 +-
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.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
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.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
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.../tune_conv2d_layer_cuda.rst.txt | 785 ++-
.../tune_network_cuda.rst.txt | 2 +-
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.../tune_sparse_x86.rst.txt | 131 +-
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.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 34 +-
.../tune_with_autotvm/tune_relay_cuda.rst.txt | 2 +-
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.../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 | 4 +-
.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 2 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 60 +-
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docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 158 +-
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 +-
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.../deploy_object_detection_pytorch.html | 68 +-
docs/how_to/deploy_models/deploy_prequantized.html | 8 +-
.../deploy_models/deploy_prequantized_tflite.html | 4 +-
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docs/how_to/deploy_models/sg_execution_times.html | 18 +-
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216 files changed, 5391 insertions(+), 5746 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 799794fc1..b352b6258 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.zip9e7d197c-2789-4b73-bf72-9c01563dd09c from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip7d90167a-60dc-48d1-8911-4b9dd0e1ebc0 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 70cdb4795..e879c7794 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 4b48ca12b..eba85fa87 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 27.295 seconds)
+ **Total running time of the script:** ( 1 minutes 9.805 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 20ee0c026..ed3f063f2 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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82%|########2 | 36.7M/44.7M [00:00<00:00, 195MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 200MB/s]
+
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40%|###9 | 17.8M/44.7M [00:00<00:00, 179MB/s]
95%|#########5| 42.6M/44.7M [00:00<00:00, 226MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 220MB/s]
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 e46c29122..f3bb6a994 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 3.442 seconds)
+ **Total running time of the script:** ( 1 minutes 6.815 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 18b9b6ffc..2aaa72443 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
=================
-**05:50.116** total execution time for **how_to_compile_models** files:
+**05:39.253** total execution time for **how_to_compile_models** files:
-- **01:27.295**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **01:03.442**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:56.561**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:41.867**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
-- **00:25.005**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:20.944**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:20.805**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:19.257**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:12.328**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.612**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:09.805**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:06.815**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:59.385**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:32.312**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
+- **00:25.918**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:23.989**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:23.978**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:19.845**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:14.557**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.650**: :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 bd63a0fd7..4109eb306 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)
- 15.5674 15.5431 15.6927 15.4704 0.0776
+ 17.0220 16.9537 17.5340 16.5876 0.3437
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
index fbed4031d..da854662a 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
@@ -252,7 +252,7 @@ to run this tutorial with a real device.
.. code-block:: none
- /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)
+ /workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
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 4ea26f48f..61073ea44 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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+
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s]
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81%|######## | 137M/170M [00:03<00:00, 48.4MB/s]
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93%|#########2| 158M/170M [00:03<00:00, 47.5MB/s]
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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').
@@ -173,7 +173,7 @@ Import the graph to Relay
.. code-block:: none
- /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)
+ /workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
@@ -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 1.507 seconds)
+ **Total running time of the script:** ( 3 minutes 26.010 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 be7ebad37..7a371590d 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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@@ -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)
- 90.3567 90.1864 99.1484 89.8744 0.9661
+ 90.6754 90.5349 91.6982 90.3244 0.3079
@@ -384,7 +384,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.791 seconds)
+ **Total running time of the script:** ( 1 minutes 13.411 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 818560366..25ec4fda4 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)
- 118.3741 118.4525 120.2847 116.1592 1.0702
+ 121.0929 120.8253 125.0510 119.9421 0.9224
@@ -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 59.468 seconds)
+ **Total running time of the script:** ( 1 minutes 55.896 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 18ccc66eb..6417f69a6 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -213,7 +213,7 @@ We create a Relay VM to build and execute the model.
.. code-block:: none
- /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)
+ /workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
@@ -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:** ( 2 minutes 5.876 seconds)
+ **Total running time of the script:** ( 1 minutes 21.284 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 e8fd3e9a4..82d713517 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -137,7 +137,7 @@ Convert and compile model for CPU.
data: None
input_sym_arg_type = in_param.infer_type()[0]
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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+
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@@ -202,7 +202,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 18.679 seconds)
+ **Total running time of the script:** ( 2 minutes 36.954 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 8f03c5a4b..436fb7017 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
=================
-**11:18.815** total execution time for **how_to_deploy_models** files:
+**11:27.811** total execution time for **how_to_deploy_models** files:
-- **03:01.507**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:18.679**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **02:05.876**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:59.468**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:03.791**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:27.496**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.795**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.202**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:26.010**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:36.954**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:55.896**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:21.284**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:13.411**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:31.015**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:23.025**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.215**: :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 3f5b1dd18..90e1c3780 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.zip5322494f-0b66-42e4-9a5c-9fd9bb331175 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipc97dc2a8-d241-4504-9f7e-2e4884bfe63f from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
@@ -525,7 +525,7 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
.. code-block:: none
- Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
+ Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
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 fc527ff7f..2f4096884 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
=================
-**00:37.776** total execution time for **how_to_extend_tvm** files:
+**00:40.869** total execution time for **how_to_extend_tvm** files:
-- **00:34.325**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.211**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.059**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.181**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:37.117**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.397**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.129**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.227**: :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 8acea0921..3d3850a55 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: 6204us [6204us] (45.81%; 45.81%)
- FoldScaleAxis: 7340us [3us] (54.19%; 54.19%)
- FoldConstant: 7337us [1522us] (54.17%; 99.96%)
- InferType: 5814us [5814us] (42.93%; 79.25%)
+ InferType: 6287us [6287us] (45.69%; 45.69%)
+ FoldScaleAxis: 7475us [3us] (54.31%; 54.31%)
+ FoldConstant: 7472us [1526us] (54.30%; 99.97%)
+ InferType: 5946us [5946us] (43.21%; 79.57%)
@@ -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: 5910us [5910us] (45.11%; 45.11%)
- FoldScaleAxis: 7190us [2us] (54.89%; 54.89%)
- FoldConstant: 7188us [1489us] (54.87%; 99.97%)
- InferType: 5699us [5699us] (43.50%; 79.28%)
+ InferType: 6068us [6068us] (44.90%; 44.90%)
+ FoldScaleAxis: 7447us [3us] (55.10%; 55.10%)
+ FoldConstant: 7444us [1552us] (55.08%; 99.97%)
+ InferType: 5892us [5892us] (43.60%; 79.16%)
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 5ff8b9377..c8cda71e5 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: 42.368728 ms
+ Convolution: 54.174938 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 d3e394825..3e8cabe25 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: 7.008247 ms
+ conv2d with tensor core: 6.466869 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 e7fb21582..aadf0c10a 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: 0.018506
- Baseline: 3.245997
+ Numpy running time: 0.020153
+ Baseline: 3.498409
@@ -210,7 +210,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.303008
+ Opt1: 0.325702
@@ -309,7 +309,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.336242
+ Opt2: 0.344468
@@ -401,7 +401,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.117632
+ Opt3: 0.133277
@@ -520,7 +520,7 @@ flattening.
.. code-block:: none
- Opt4: 0.110116
+ Opt4: 0.112306
@@ -638,7 +638,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.110598
+ Opt5: 0.113493
@@ -759,7 +759,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.144518
+ Opt6: 0.147983
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 16918002d..97d7822e6 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:34.387** total execution time for **how_to_optimize_operators** files:
+**00:36.467** total execution time for **how_to_optimize_operators** files:
-- **00:31.866**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.338**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.184**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:33.685**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.466**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.316**: :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 f48ea3f68..fde6afe03 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
=================
-**04:54.833** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:22.038**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:19.370**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:39.917**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:16.436**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.759**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.312**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**05:16.094** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:31.485**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:25.057**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:43.242**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:17.877**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:09.345**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:09.089**: :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 903815b76..9b8673ba9 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
@@ -222,292 +222,188 @@ 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" = 32;
- allocate(conv2d_nchw: Pointer(local float32), float32, [16]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [2016]), storage_scope = shared;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 16;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [1008]), storage_scope = shared;
allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope="local", align=64)[0] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[5] = 0f32
- conv2d_nchw_1[6] = 0f32
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[1] = 0f32
conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[3] = 0f32
conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[4] = 0f32
conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[5] = 0f32
conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[6] = 0f32
conv2d_nchw_1[13] = 0f32
- conv2d_nchw_1[14] = 0f32
- conv2d_nchw_1[15] = 0f32
- for (rc.outer.outer: int32, 0, 16) {
+ for (rc.outer.outer: int32, 0, 32) {
for (ry.outer.outer: int32, 0, 3) {
- let cse_var_4: int32 = (rc.outer.outer*1568)
+ let cse_var_4: int32 = (rc.outer.outer*784)
let cse_var_3: int32 = (ry.outer.outer*7)
- let cse_var_2: int32 = (rc.outer.outer*288)
+ let cse_var_2: int32 = (rc.outer.outer*144)
let cse_var_1: int32 = (ry.outer.outer*3)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [2016], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 49)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 49), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 98)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 98), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 98), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 98), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 147)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 147), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 147), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 147), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 196), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 245)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 245), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 245), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 245), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 294)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 294), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 294), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 294), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 343)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 343), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 343), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 343), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 392), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 392), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 441)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 335)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 490)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 490), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 490), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 490), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 539)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 539), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 539), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 539), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 588)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 588), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 588), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 588), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 637)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 637), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 637), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 637), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 686)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 686), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 686), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 686), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 735)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 735), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 735), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 735), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [1008], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((1 <= (floordiv(floormod(threadIdx.x_1, 63), 9) + ry.outer.outer)) && ((floordiv(floormod(threadIdx.x_1, 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 112), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 224), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 336), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 336), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 336), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 448), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 560), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 560), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 560), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 672)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 672), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 672), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 672), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
pad_temp.shared_1[(threadIdx.x_1 + 784)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 784), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 784), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 784), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 833)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 833), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 833), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 882)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 678)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 931)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 931), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 931), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 931), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 980)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 980), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 980), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 980), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1029)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1029), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1029), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1029), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1078)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1078), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1078), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1078), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1127)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1127), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1127), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1127), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1176)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1176), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1176), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1176), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1225)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1225), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1225), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1225), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1274)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 1274), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1274), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1323)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 1021)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1372)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1372), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1372), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1372), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1421)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1421), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1421), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1421), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1470)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1470), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1470), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1470), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1519)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1519), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1519), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1519), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1568)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1568), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1568), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1568), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1617)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1617), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1617), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1617), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1666)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1666), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1666), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1666), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1715)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 1715), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1715), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1764)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 1364)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1813)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1813), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1813), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1813), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1862)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1862), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1862), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1862), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1911)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1911), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1911), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1911), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1960)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1960), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1960), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1960), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- if @tir.likely((threadIdx.x_1 < 7), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1 + 2009)] = @tir.if_then_else((((floordiv(floormod((threadIdx.x_1 + 2009), 63), 9) + ry.outer.outer) < 8) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 2009), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- }
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*73728) + cse_var_2) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 49)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 49), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 49), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 98)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 98), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 98), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 147)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 147), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 17), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 196)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 196), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 196), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 245)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 245), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 245), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 294)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 294), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 2), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 343)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 343), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 343), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 392)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 392), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 392), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 441)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 441), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 19), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 490)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 490), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 490), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 539)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 539), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 539), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 588)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 588), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 637)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 637), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 637), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 686)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 686), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 686), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 735)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 735), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 21), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 784), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 833)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 833), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 833), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 882)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 882), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 6), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 931)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 931), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 931), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 980)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 980), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 980), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1029)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1029), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 23), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1078)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1078), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1078), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1127)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1127), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1127), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1176)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1176), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 8), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1225)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1225), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1225), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1274)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1274), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1274), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1323)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1323), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 25), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1372)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1372), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1372), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1421)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1421), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1421), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1470)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1470), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 10), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- if @tir.likely((threadIdx.x_2 < 17), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1519)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1519), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1519), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 896)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 896), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 896), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 896), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 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, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((((blockIdx.x*147456) + (floordiv(threadIdx.x_2, 48)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 14), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 35), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 560), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 56), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 96768)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1120), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 77), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1232), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 129024)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1456), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
}
- for (rc.outer.inner: int32, 0, 16) {
- let cse_var_5: int32 = (rc.outer.inner*6)
+ for (yy.outer.inner: int32, 0, 7) {
+ let cse_var_5: int32 = (yy.outer.inner + 7)
{
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[cse_var_5]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 96)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 192)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 288)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 384)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 480)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 576)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 672)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 768)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 864)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 960)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1056)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1152)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1248)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1344)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1440)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 3)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 99)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 195)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 291)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 387)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 483)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 579)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 675)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 771)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 867)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 963)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1059)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1155)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1251)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1347)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1443)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 97)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 193)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 289)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 385)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 481)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 577)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 673)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 769)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 865)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 961)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1057)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1153)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1249)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1345)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1441)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 4)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 100)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 196)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 292)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 388)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 484)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 580)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 676)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 772)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 868)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 964)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1060)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1156)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1252)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1348)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1444)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 2)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 98)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 194)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 290)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 386)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 482)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 578)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 674)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 770)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 866)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 962)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1058)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1154)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1250)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1346)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1442)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 5)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 101)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 197)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 293)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 389)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 485)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 581)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 677)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 773)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 869)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 965)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1061)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1157)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1253)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1349)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1445)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[((yy.outer.inner*9) + floormod(threadIdx.x, 7))]*kernel.shared_1[(floordiv(threadIdx.x, 7)*96)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((yy.outer.inner*9) + floormod(threadIdx.x, 7))]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 48)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 1)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 49)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 2)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 50)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 3)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 51)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 4)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 52)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 5)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 53)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 6)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 54)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 127)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 7)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 127)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 55)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 128)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 8)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 128)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 56)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 9)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 57)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 190)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 10)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 190)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 58)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 191)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 11)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 191)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 59)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 12)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 60)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 253)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 13)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 253)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 61)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 254)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 14)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 254)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 62)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 15)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 63)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 316)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 16)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 316)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 64)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 317)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 17)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 317)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 65)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 18)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 66)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 379)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 19)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 379)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 67)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 380)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 20)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 380)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 68)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 21)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 69)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 442)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 22)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 442)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 70)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 443)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 23)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 443)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 71)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 504)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 24)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 504)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 72)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 505)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 25)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 505)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 73)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 506)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 26)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 506)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 74)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 567)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 27)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 567)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 75)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 568)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 28)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 568)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 76)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 569)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 29)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 569)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 77)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 630)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 30)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 630)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 78)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 631)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 31)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 631)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 79)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 632)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 32)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 632)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 80)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 693)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 33)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 693)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 81)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 694)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 34)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 694)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 82)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 695)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 35)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 695)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 83)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 756)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 36)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 756)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 84)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 757)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 37)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 757)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 85)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 758)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 38)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 758)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 86)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 819)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 39)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 819)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 87)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 820)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 40)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 820)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 88)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 821)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 41)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 821)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 89)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 882)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 42)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 882)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 90)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 883)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 43)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 883)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 91)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 884)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 44)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 884)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 92)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 945)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 45)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 945)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 93)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 946)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 46)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 946)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 94)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 947)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 47)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 947)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 95)]))
}
}
}
}
}
- for (i1.inner: int32, 0, 16) {
- compute[(((blockIdx.x*784) + (i1.inner*49)) + threadIdx.x)] = max((conv2d_nchw_1[i1.inner] + bias[((blockIdx.x*16) + i1.inner)]), 0f32)
+ for (i1.inner: int32, 0, 2) {
+ for (i2.inner: int32, 0, 7) {
+ compute[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[((i1.inner*7) + i2.inner)] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+ }
}
}
}
@@ -560,7 +456,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.221 ms
+ Execution time of this operator: 0.323 ms
@@ -604,33 +500,33 @@ 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=16)
+ conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
- conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=1)
+ conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
- conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
- conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
+ conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=7)
+ 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_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=16)
+ conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=16)
+ conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
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=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)
+ conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=3)
+ conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2 [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
- compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=16)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=1)
+ compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
- compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
- compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+ compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=7)
+ compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
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)
@@ -653,14 +549,14 @@ 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=49)
+ 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)
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=49)
+ 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", 1024)
+ s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -678,210 +574,157 @@ 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__(49) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[16];
- __shared__ float pad_temp_shared[2016];
+ 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[14];
+ __shared__ float pad_temp_shared[1008];
__shared__ float kernel_shared[1536];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[1] = 0.000000e+00f;
conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[5] = 0.000000e+00f;
conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[13] = 0.000000e+00f;
- conv2d_nchw[14] = 0.000000e+00f;
- conv2d_nchw[15] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 16; ++rc_outer_outer) {
+ for (int rc_outer_outer = 0; rc_outer_outer < 32; ++rc_outer_outer) {
for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
__syncthreads();
- pad_temp_shared[((int)threadIdx.x)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 49)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 49) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 98)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 98) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 147)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 147) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 196)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 196) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 245)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 245) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 294)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 294) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 343)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 343) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 392)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 392) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 441)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 335)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 490)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 490) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 539)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 539) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 588)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 588) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 637)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 637) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 686)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 686) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 735)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 735) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 784) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 833)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 833) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 882)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 678)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 931)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 931) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 980)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 980) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1029)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1029) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1078)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1078) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1127)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1127) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1176)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1176) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1225)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1225) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1274)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1274) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1323)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1021)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1372)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1372) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1421)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1421) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1470)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1470) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1519)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1519) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1568)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1568) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1617)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1617) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1666)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1666) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1715)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1715) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1764)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1364)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1813)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1813) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1862)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1862) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1911)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1911) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1960)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1960) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- if (((int)threadIdx.x) < 7) {
- pad_temp_shared[(((int)threadIdx.x) + 2009)] = (((((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8) && (((int)threadIdx.x) < 6)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 2009) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) + 2)) - 8)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 73728) + (rc_outer_outer * 288)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 49)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 49) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 49) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 98)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 98) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 2) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 147)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 147) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 17) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 196)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 196) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 4) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 245)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 245) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 53) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 294)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 294) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 2) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 343)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 343) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 55) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 392)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 392) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 8) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 441)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 441) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 19) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 490)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 490) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 10) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 539)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 539) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 59) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 588)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 588) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 4) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 637)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 637) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 61) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 686)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 686) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 14) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 735)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 735) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 21) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 784) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 16) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 833)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 833) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 65) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 882)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 882) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 6) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 931)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 931) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 67) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 980)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 980) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 20) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1029)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1029) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 23) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1078)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1078) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 22) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1127)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1127) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 71) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1176)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1176) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 8) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1225)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1225) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 73) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1274)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1274) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 26) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1323)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1323) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 25) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1372)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1372) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 28) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1421)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1421) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 77) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1470)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1470) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 10) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- if (((int)threadIdx.x) < 17) {
- kernel_shared[(((int)threadIdx.x) + 1519)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1519) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 79) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ pad_temp_shared[((int)threadIdx.x)] = (((((1 <= (((((int)threadIdx.x) % 63) / 9) + ry_outer_outer)) && ((((((int)threadIdx.x) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 112) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 224) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 336)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 336) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 448)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 448) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 560)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 560) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 672)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 672) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 784) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 896)] = (((((1 <= ((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 896) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
+ kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 112) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 224) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 448) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 560) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 672)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 896) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 96768)];
+ kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1120) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1232) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 129024)];
+ if (((int)threadIdx.x) < 80) {
+ kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1456) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
}
__syncthreads();
- for (int rc_outer_inner = 0; rc_outer_inner < 16; ++rc_outer_inner) {
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[(rc_outer_inner * 6)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 96)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 192)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 288)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 384)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 480)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 576)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 672)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 768)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 864)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 960)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1056)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1152)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1248)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1344)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1440)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 3)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 99)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 195)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 291)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 387)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 483)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 579)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 675)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 771)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 867)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 963)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1059)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1155)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1251)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1347)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1443)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 97)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 193)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 289)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 385)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 481)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 577)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 673)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 769)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 865)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 961)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1057)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1153)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1249)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1345)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1441)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 4)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 100)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 196)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 292)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 388)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 484)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 580)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 676)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 772)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 868)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 964)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1060)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1156)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1252)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1348)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1444)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 2)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 98)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 194)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 290)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 386)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 482)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 578)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 674)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 770)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 866)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 962)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1058)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1154)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1250)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1346)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1442)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 5)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 101)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 197)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 293)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 389)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 485)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 581)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 677)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 773)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 869)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 965)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1061)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1157)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1253)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1349)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1445)]));
+ for (int yy_outer_inner = 0; yy_outer_inner < 7; ++yy_outer_inner) {
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[((yy_outer_inner * 9) + (((int)threadIdx.x) % 7))] * kernel_shared[((((int)threadIdx.x) / 7) * 96)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[((yy_outer_inner * 9) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 48)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 1)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 49)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 2)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 50)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 3)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 51)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 4)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 52)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 5)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 53)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 6)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 54)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 127)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 7)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 127)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 55)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 128)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 8)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 128)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 56)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 9)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 57)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 190)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 10)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 190)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 58)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 191)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 11)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 191)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 59)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 12)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 60)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 253)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 13)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 253)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 61)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 254)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 14)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 254)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 62)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 15)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 63)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 316)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 16)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 316)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 64)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 317)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 17)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 317)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 65)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 18)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 66)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 379)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 19)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 379)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 67)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 380)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 20)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 380)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 68)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 21)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 69)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 442)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 22)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 442)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 70)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 443)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 23)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 443)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 71)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 504)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 24)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 504)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 72)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 505)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 25)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 505)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 73)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 506)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 26)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 506)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 74)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 567)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 27)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 567)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 75)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 568)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 28)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 568)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 76)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 569)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 29)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 569)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 77)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 630)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 30)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 630)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 78)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 631)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 31)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 631)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 79)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 632)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 32)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 632)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 80)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 693)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 33)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 693)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 81)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 694)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 34)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 694)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 82)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 695)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 35)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 695)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 83)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 756)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 36)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 756)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 84)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 757)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 37)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 757)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 85)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 758)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 38)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 758)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 86)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 819)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 39)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 819)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 87)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 820)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 40)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 820)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 88)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 821)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 41)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 821)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 89)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 882)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 42)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 882)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 90)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 883)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 43)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 883)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 91)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 884)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 44)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 884)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 92)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 945)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 45)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 945)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 93)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 946)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 46)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 946)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 94)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 947)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 47)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 947)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 95)]));
}
}
}
- for (int i1_inner = 0; i1_inner < 16; ++i1_inner) {
- compute[(((((int)blockIdx.x) * 784) + (i1_inner * 49)) + ((int)threadIdx.x))] = max((conv2d_nchw[i1_inner] + bias[((((int)blockIdx.x) * 16) + i1_inner)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
+ for (int i2_inner = 0; i2_inner < 7; ++i2_inner) {
+ compute[(((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[((i1_inner * 7) + i2_inner)] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
+ }
}
}
@@ -940,7 +783,7 @@ 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 22.038 seconds)
+ **Total running time of the script:** ( 2 minutes 31.485 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 aae888a25..bc80bdc41 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)
- 10.0719 10.0476 10.1251 10.0430 0.0376
+ 9.8670 9.8753 9.8780 9.8477 0.0137
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 5e916642b..2bdb09c92 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)
- 753.7759 755.2016 755.7372 750.3888 2.4050
+ 806.8567 808.5949 811.0921 800.8830 4.3453
@@ -658,7 +658,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.370 seconds)
+ **Total running time of the script:** ( 1 minutes 25.057 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 c9ebaeadb..acb4a31a5 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
@@ -362,80 +362,75 @@ 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 = {compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [4096]), storage_scope = global {
+ preflattened_buffer_map = {placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_5: placeholder_18: Buffer(placeholder_10, float32, [128, 256], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
for (i.outer.inner: int32, 0, 2) {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- let cse_var_1: int32 = (((i.outer.inner*2048) + (i.inner.init*32)) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [4096], [])[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.inner.init: int32, 0, 64) {
+ let cse_var_1: int32 = ((i.outer.inner*1024) + (i.inner.init*16))
+ {
+ compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 64) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner)
- let cse_var_19: int32 = ((i.outer.inner*16384) + (i.inner*256))
- let cse_var_18: int32 = (((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16))
- let cse_var_17: int32 = (cse_var_18 + 1)
- let cse_var_16: int32 = (cse_var_18 + 11)
- let cse_var_15: int32 = (cse_var_18 + 12)
- let cse_var_14: int32 = (cse_var_18 + 13)
- let cse_var_13: int32 = (cse_var_18 + 14)
- let cse_var_12: int32 = (cse_var_18 + 15)
- let cse_var_11: int32 = (cse_var_18 + 2)
- let cse_var_10: int32 = (cse_var_18 + 3)
- let cse_var_9: int32 = (cse_var_18 + 4)
- let cse_var_8: int32 = (cse_var_18 + 5)
- let cse_var_7: int32 = (cse_var_18 + 6)
- let cse_var_6: int32 = (cse_var_18 + 7)
- let cse_var_5: int32 = (cse_var_18 + 8)
- let cse_var_4: int32 = (cse_var_18 + 9)
- let cse_var_3: int32 = (cse_var_18 + 10)
- {
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- }
+ }
+ for (elem_idx: int32, 0, (placeholder_3[(i0.outer.i1.outer.fused + 1)] - placeholder_3[i0.outer.i1.outer.fused])) {
+ for (i.inner: int32, 0, 64) {
+ let cse_var_19: int32 = (elem_idx*16)
+ let cse_var_18: int32 = ((i.outer.inner*16384) + (i.inner*256))
+ let cse_var_17: int32 = ((i.outer.inner*1024) + (i.inner*16))
+ let cse_var_16: int32 = (cse_var_17 + 1)
+ let cse_var_15: int32 = (cse_var_17 + 11)
+ let cse_var_14: int32 = (cse_var_17 + 12)
+ let cse_var_13: int32 = (cse_var_17 + 13)
+ let cse_var_12: int32 = (cse_var_17 + 14)
+ let cse_var_11: int32 = (cse_var_17 + 15)
+ let cse_var_10: int32 = (cse_var_17 + 2)
+ let cse_var_9: int32 = (cse_var_17 + 3)
+ let cse_var_8: int32 = (cse_var_17 + 4)
+ let cse_var_7: int32 = (cse_var_17 + 5)
+ let cse_var_6: int32 = (cse_var_17 + 6)
+ let cse_var_5: int32 = (cse_var_17 + 7)
+ let cse_var_4: int32 = (cse_var_17 + 8)
+ let cse_var_3: int32 = (cse_var_17 + 9)
+ let cse_var_2: int32 = (cse_var_17 + 10)
+ {
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 1)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 2)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 3)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 4)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 5)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 6)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 7)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 8)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 9)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 10)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 11)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 12)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 13)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 14)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 15)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 128) {
- for (i1.inner: int32, 0, 32) {
- let cse_var_22: int32 = (((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)
- compute[cse_var_22] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_22]), 0f32)
- }
+ let cse_var_20: int32 = ((i0.inner*512) + (i0.outer.i1.outer.fused*16))
+ compute[ramp(cse_var_20, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_20, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -489,7 +484,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.843 ms
+ Execution time of this operator: 1.836 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 97586ba86..a20cd5af9 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
=================
-**00:43.212** total execution time for **how_to_tune_with_autotvm** files:
+**00:45.368** total execution time for **how_to_tune_with_autotvm** files:
-- **00:42.348**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.221**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.219**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.215**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.209**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:44.411**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.253**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.235**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.235**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.234**: :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 027d1ef49..1ba96c340 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: 42.24/42.24 result: MeasureResult(costs=(0.005480043052631579,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5878784656524658, timestamp=1651674735.2251027) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
- No: 7 GFLOPS: 0.00/42.24 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 95.22/95.22 result: MeasureResult(costs=(0.0024311252708333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6658709049224854, timestamp=1651701422.3290932) [('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: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/95.22 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: 0x00007f07f3bebfa2
+ 12: 0x00007efce0994fa2
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: 141.52/141.52 result: MeasureResult(costs=(0.0016358666935483868,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1214752197265625, timestamp=1651674761.2399213) [('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: 145.02/145.02 result: MeasureResult(costs=(0.00159629946,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4468486309051514, timestamp=1651701448.3761618) [('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: 0.002050
+ Time cost of this operator: 0.001988
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_relay_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_relay_cuda.rst.txt
index 935221dd2..9c9c4678e 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_relay_cuda.rst.txt
@@ -164,7 +164,7 @@ Before tuning, we apply some configurations.
.. code-block:: none
- /workspace/python/tvm/target/target.py:288: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
+ /workspace/python/tvm/target/target.py:347: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
warnings.warn("Try specifying cuda arch by adding 'arch=sm_xx' to your target.")
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 d144f95c1..e81b8a028 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 316.3 98.758 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.958 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.908 0.284 (1, 1, 10, 10, 3) 1 1
- Total_time - 320.278 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 311.6 98.745 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.058 0.969 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.902 0.286 (1, 1, 10, 10, 3) 1 1
+ Total_time - 315.559 - - - -
@@ -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 90.45 97.16 (1, 6, 10, 10, 1) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 1.867 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.906 0.974 (1, 1, 10, 10, 3) 1 1
- Total_time - 93.094 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 206.7 98.597 (1, 1, 10, 10, 6) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.01 0.959 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.931 0.444 (1, 3, 10, 10, 1) 1 1
+ Total_time - 209.641 - - - -
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 9a47c6f4b..ac99dfaae 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
=================
-**00:42.736** total execution time for **how_to_work_with_microtvm** files:
+**00:47.764** total execution time for **how_to_work_with_microtvm** files:
-- **00:38.753**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.373**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.232**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
-- **00:00.192**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
-- **00:00.185**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
+- **00:43.313**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.779**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.238**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
+- **00:00.217**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+- **00:00.217**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.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 cdbc1747a..b6903f10d 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
=================
-**00:05.617** total execution time for **how_to_work_with_relay** files:
+**00:09.615** total execution time for **how_to_work_with_relay** files:
-- **00:04.034**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.366**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.217**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:07.481**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.898**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.236**: :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 5acfa6145..3fa1802a5 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
=================
-**00:05.148** total execution time for **how_to_work_with_schedules** files:
+**00:06.065** total execution time for **how_to_work_with_schedules** files:
-- **00:01.963**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:00.835**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.693**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.682**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.291**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.242**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.223**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
-- **00:00.218**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:02.204**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.203**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.778**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.777**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.338**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.265**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.259**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.241**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 475bf5cf5..9d9da17c4 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/tmpft4wlm_f/input0.cc'\nsource_filename = \"/tmp/tmpft4wlm_f/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/tmp8uh2wupo/input0.cc'\nsource_filename = \"/tmp/tmp8uh2wupo/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 8617b1d78..511dcbce8 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
=================
-**00:20.037** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:22.958** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:19.847**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.190**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:22.733**: :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 37916f9d0..5e8e923a4 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -261,11 +261,11 @@ The compilation steps are:
.. code-block:: none
- /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)
+ /workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
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 21.10s!
+ resnet18_v1 inference graph built in 25.10s!
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 be4d728c2..73899c79e 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -299,9 +299,9 @@ The compilation steps are:
.. code-block:: none
- /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)
+ /workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 14.64s!
+ yolov3-tiny inference graph built in 17.13s!
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 181ce9724..ee2e9c27d 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
=================
-**01:30.341** total execution time for **topic_vta_tutorials_frontend** files:
+**01:34.808** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:48.843**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:41.498**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:49.394**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:45.415**: :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 8efd96926..b2f5e54f7 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
=================
-**00:03.397** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.921** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:02.895**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.502**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.321**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.600**: :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 a46aa934f..9048326f3 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.936** total execution time for **topic_vta_tutorials** files:
+**00:01.082** total execution time for **topic_vta_tutorials** files:
-- **00:00.480**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.456**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.551**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.530**: :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 2c9ce78fb..44b6e2f2b 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -306,7 +306,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.648 ms
+ Execution time of this operator: 94.118 ms
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index d0ea8cc97..b60b76362 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': 489.13385313002436, 'median': 488.91485415006173, 'std': 1.0093729626319448}
+ {'mean': 502.3503507300029, 'median': 502.1625347000054, 'std': 1.0631175008182827}
@@ -482,31 +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: 9.08/ 23.87 GFLOPS | Progress: (4/10) | 5.28 s
[Task 1/25] Current/Best: 11.71/ 23.87 GFLOPS | Progress: (8/10) | 9.54 s
[Task 1/25] Current/Best: 10.30/ 23.87 GFLOPS | Progress: (10/10) | 10.95 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 19.36/ 19.36 GFLOPS | Progress: (4/10) | 2.66 s
[Task 2/25] Current/Best: 3.53/ 21.86 GFLOPS | Progress: (8/10) | 4.57 s
[Task 2/25] Current/Best: 4.74/ 21.86 GFLOPS | Progress: (10/10) | 5.14 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 17.20/ 17.20 GFLOPS | Progress: (4/10) | 2.99 s
[Task 3/25] Current/Best: 12.48/ 17.20 GFLOPS | Progress: (8/10) | 5.91 s
[Task 3/25] Current/Best: 22.61/ 22.61 GFLOPS | Progress: (10/10) | 6.64 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 9.69/ 16.54 GFLOPS | Progress: (4/10) | 3.50 s
[Task 4/25] Current/Best: 11.53/ 16.54 GFLOPS | Progress: (8/10) | 6.20 s
[Task 4/25] Current/Best: 19.32/ 19.32 GFLOPS | Progress: (10/10) | 6.96 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 13.72/ 14.85 GFLOPS | Progress: (4/10) | 2.86 s
[Task 5/25] Current/Best: 13.73/ 22.90 GFLOPS | Progress: (8/10) | 5.90 s
[Task 5/25] Current/Best: 5.85/ 22.90 GFLOPS | Progress: (10/10) | 6.70 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 14.62/ 14.62 GFLOPS | Progress: (4/10) | 3.17 s
[Task 6/25] Current/Best: 13.56/ 14.62 GFLOPS | Progress: (8/10) | 6.35 s
[Task 6/25] Current/Best: 15.01/ 15.01 GFLOPS | Progress: (10/10) | 7.28 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 6.51/ 19.54 GFLOPS | Progress: (4/10) | 3.22 s
[Task 7/25] Current/Best: 13.98/ 19.54 GFLOPS | Progress: (8/10) | 5.58 s
[Task 7/25] Current/Best: 10.48/ 19.54 GFLOPS | Progress: (10/10) | 7.20 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 9.40/ 10.37 GFLOPS | Progress: (4/10) | 10.33 s
[Task 8/25] Current/Best: 14.56/ 19.27 GFLOPS | Progress: (8/10) | 12.70 s
[Task 8/25] Current/Best: 6.58/ 19.27 GFLOPS | Progress: (10/10) | 21.81 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 16.02/ 16.02 GFLOPS | Progress: (4/10) | 3.64 s
[Task 9/25] Current/Best: 11.79/ 22.84 GFLOPS | Progress: (8/10) | 4.91 s
[Task 9/25] Current/Best: 19.14/ 22.84 GFLOPS | Progress: (10/10) | 5.51 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 11.99/ 15.32 GFLOPS | Progress: (4/10) | 2.71 s
[Task 10/25] Current/Best: 6.35/ 15.59 GFLOPS | Progress: (8/10) | 4.35 s
[Task 10/25] Current/Best: 14.43/ 15.59 GFLOPS | Progress: (10/10) | 5.60 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 20.88/ 21.05 GFLOPS | Progress: (4/10) | 3.97 s
[Task 11/25] Current/Best: 12.19/ 21.05 GFLOPS | Progress: (8/10) | 5.91 s
[Task 11/25] Current/Best: 11.82/ 21.05 GFLOPS | Progress: (10/10) | 6.98 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 10.58/ 15.23 GFLOPS | Progress: (4/10) | 7.43 s
[Task 12/25] Current/Best: 16.39/ 16.39 GFLOPS | Progress: (8/10) | 10.42 s
[Task 12/25] Current/Best: 15.94/ 22.45 GFLOPS | Progress: (10/10) | 11.13 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 11.41/ 21.37 GFLOPS | Progress: (4/10) | 4.48 s
[Task 13/25] Current/Best: 11.26/ 21.37 GFLOPS | Progress: (8/10) | 8.88 s
[Task 13/25] Current/Best: 10.88/ 21.37 GFLOPS | Progress: (10/10) | 11.00 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 10.56/ 14.19 GFLOPS | Progress: (4/10) | 2.68 s
[Task 14/25] Current/Best: 13.96/ 19.14 GFLOPS | Progress: (8/10) | 4.76 s
[Task 14/25] Current/Best: 13.27/ 19.14 GFLOPS | Progress: (10/10) | 5.68 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 9.28/ 19.15 GFLOPS | Progress: (4/10) | 8.04 s
[Task 15/25] Current/Best: 17.87/ 19.15 GFLOPS | Progress: (8/10) | 9.32 s
[Task 15/25] Current/Best: 9.86/ 19.15 GFLOPS | Progress: (10/10) | 11.65 s
[Task 16/25] 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: 5.75/ 14.28 GFLOPS | Progress: (4/10) | 6.22 s
[Task 1/25] Current/Best: 24.09/ 24.09 GFLOPS | Progress: (8/10) | 7.84 s
[Task 1/25] Current/Best: 16.79/ 24.09 GFLOPS | Progress: (10/10) | 8.61 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 14.40/ 17.41 GFLOPS | Progress: (4/10) | 2.30 s
[Task 2/25] Current/Best: 11.32/ 22.04 GFLOPS | Progress: (8/10) | 4.08 s
[Task 2/25] Current/Best: 14.82/ 22.04 GFLOPS | Progress: (10/10) | 5.04 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 6.21/ 23.35 GFLOPS | Progress: (4/10) | 3.10 s
[Task 3/25] Current/Best: 19.24/ 24.01 GFLOPS | Progress: (8/10) | 5.39 s
[Task 3/25] Current/Best: 10.64/ 24.01 GFLOPS | Progress: (10/10) | 6.33 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 16.12/ 16.12 GFLOPS | Progress: (4/10) | 7.07 s
[Task 4/25] Current/Best: 11.94/ 16.12 GFLOPS | Progress: (8/10) | 9.17 s
[Task 4/25] Current/Best: 9.83/ 16.12 GFLOPS | Progress: (10/10) | 11.82 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 17.47/ 17.68 GFLOPS | Progress: (4/10) | 2.88 s
[Task 5/25] Current/Best: 4.58/ 18.16 GFLOPS | Progress: (8/10) | 4.37 s
[Task 5/25] Current/Best: 8.13/ 18.16 GFLOPS | Progress: (10/10) | 5.33 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 13.19/ 17.77 GFLOPS | Progress: (4/10) | 3.36 s
[Task 6/25] Current/Best: 11.77/ 19.71 GFLOPS | Progress: (8/10) | 7.11 s
[Task 6/25] Current/Best: 13.92/ 19.71 GFLOPS | Progress: (10/10) | 7.93 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 13.86/ 20.38 GFLOPS | Progress: (4/10) | 3.16 s
[Task 7/25] Current/Best: 18.19/ 20.38 GFLOPS | Progress: (8/10) | 5.14 s
[Task 7/25] Current/Best: 15.03/ 20.38 GFLOPS | Progress: (10/10) | 6.13 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 7.85/ 13.30 GFLOPS | Progress: (4/10) | 4.68 s
[Task 8/25] Current/Best: 9.81/ 13.30 GFLOPS | Progress: (8/10) | 13.07 s
[Task 8/25] Current/Best: 8.96/ 13.30 GFLOPS | Progress: (10/10) | 16.39 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 4.79/ 22.93 GFLOPS | Progress: (4/10) | 2.88 s
[Task 9/25] Current/Best: 11.44/ 22.93 GFLOPS | Progress: (8/10) | 4.60 s
[Task 9/25] Current/Best: 6.38/ 22.93 GFLOPS | Progress: (10/10) | 5.33 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 10.58/ 15.68 GFLOPS | Progress: (4/10) | 2.50 s
[Task 10/25] Current/Best: 6.53/ 19.11 GFLOPS | Progress: (8/10) | 5.07 s
[Task 10/25] Current/Best: 9.98/ 19.11 GFLOPS | Progress: (10/10) | 6.18 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 20.13/ 20.13 GFLOPS | Progress: (4/10) | 2.79 s
[Task 11/25] Current/Best: 23.10/ 23.10 GFLOPS | Progress: (8/10) | 6.22 s
[Task 11/25] Current/Best: 9.55/ 23.10 GFLOPS | Progress: (10/10) | 7.35 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 10.82/ 16.41 GFLOPS | Progress: (4/10) | 5.70 s
[Task 12/25] Current/Best: 15.68/ 16.41 GFLOPS | Progress: (8/10) | 8.42 s
[Task 12/25] Current/Best: 16.79/ 16.79 GFLOPS | Progress: (10/10) | 9.60 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 13.80/ 20.68 GFLOPS | Progress: (4/10) | 4.96 s
[Task 13/25] Current/Best: 18.52/ 20.68 GFLOPS | Progress: (8/10) | 7.85 s
[Task 13/25] Current/Best: 3.10/ 20.68 GFLOPS | Progress: (10/10) | 9.69 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 18.73/ 20.43 GFLOPS | Progress: (4/10) | 3.01 s
[Task 14/25] Current/Best: 8.61/ 20.43 GFLOPS | Progress: (8/10) | 9.32 s
[Task 14/25] Current/Best: 17.48/ 20.43 GFLOPS | Progress: (10/10) | 10.31 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 9.29/ 17.62 GFLOPS | Progress: (4/10) | 6.05 s
[Task 15/25] Current/Best: 9.62/ 17.92 GFLOPS | Progress: (8/10) | 9.06 s Done.
+
[Task 15/25] Current/Best: 12.24/ 17.92 GFLOPS | Progress: (10/10) | 10.41 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 15.91/ 17.20 GFLOPS | Progress: (4/10) | 3.68 s
[Task 16/25] Current/Best: 13.68/ 20.66 GFLOPS | Progress: (8/10) | 5.40 s
[Task 16/25] Current/Best: 9.57/ 20.66 GFLOPS | Progress: (10/10) | 7.89 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 6.14/ 14.58 GFLOPS | Progress: (4/10) | 3.81 s
[Task 17/25] Current/Best: 16.11/ 17.47 GFLOPS | Progress: (8/10) | 5.80 s
[Task 17/25] Current/Best: 20.92/ 20.92 GFLOPS | Progress: (10/10) | 6.65 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 10.05/ 16.34 GFLOPS | Progress: (4/10) | 3.27 s
[Task 18/25] Current/Best: 11.72/ 16.34 GFLOPS | Progress: (8/10) | 6.88 s
[Task 18/25] Current/Best: 14.92/ 16.34 GFLOPS | Progress: (10/10) | 7.89 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 16.42/ 16.42 GFLOPS | Progress: (4/10) | 4.51 s
[Task 19/25] Current/Best: 10.61/ 16.42 GFLOPS | Progress: (8/10) | 6.72 s
[Task 19/25] Current/Best: 16.80/ 16.80 GFLOPS | Progress: (10/10) | 7.69 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 20.67/ 20.67 GFLOPS | Progress: (4/10) | 3.92 s
[Task 20/25] Current/Best: 12.15/ 20.67 GFLOPS | Progress: (8/10) | 6.09 s
[Task 20/25] Current/Best: 10.09/ 20.67 GFLOPS | Progress: (10/10) | 7.00 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 7.42/ 20.39 GFLOPS | Progress: (4/10) | 2.64 s
[Task 21/25] Current/Best: 22.58/ 22.58 GFLOPS | Progress: (8/10) | 4.37 s
[Task 21/25] Current/Best: 15.71/ 22.58 GFLOPS | Progress: (10/10) | 5.55 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 5.97/ 17.32 GFLOPS | Progress: (4/10) | 4.89 s
[Task 22/25] Current/Best: 11.08/ 18.32 GFLOPS | Progress: (8/10) | 7.69 s
[Task 22/25] Current/Best: 16.99/ 18.32 GFLOPS | Progress: (10/10) | 8.32 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 9.33/ 14.22 GFLOPS | Progress: (4/10) | 5.03 s
[Task 23/25] Current/Best: 9.20/ 16.88 GFLOPS | Progress: (8/10) | 7.97 s
[Task 23/25] Current/Best: 18.42/ 18.42 GFLOPS | Progress: (10/10) | 9.39 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 3.37/ 3.37 GFLOPS | Progress: (4/10) | 16.54 s
[Task 24/25] Current/Best: 5.91/ 8.71 GFLOPS | Progress: (8/10) | 44.35 s
[Task 24/25] Current/Best: 2.84/ 8.71 GFLOPS | Progress: (10/10) | 51.37 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
Done.
-
[Task 16/25] Current/Best: 14.20/ 16.74 GFLOPS | Progress: (4/10) | 2.83 s
[Task 16/25] Current/Best: 15.62/ 16.74 GFLOPS | Progress: (8/10) | 4.93 s
[Task 16/25] Current/Best: 14.46/ 16.74 GFLOPS | Progress: (10/10) | 6.17 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 10.55/ 16.85 GFLOPS | Progress: (4/10) | 3.46 s
[Task 17/25] Current/Best: 6.24/ 16.85 GFLOPS | Progress: (8/10) | 5.53 s
[Task 17/25] Current/Best: 9.42/ 18.70 GFLOPS | Progress: (10/10) | 6.60 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 15.20/ 15.65 GFLOPS | Progress: (4/10) | 5.45 s
[Task 18/25] Current/Best: 17.37/ 17.37 GFLOPS | Progress: (8/10) | 8.34 s
[Task 18/25] Current/Best: 17.42/ 17.42 GFLOPS | Progress: (10/10) | 9.11 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 9.86/ 22.80 GFLOPS | Progress: (4/10) | 4.82 s
[Task 19/25] Current/Best: 10.11/ 22.80 GFLOPS | Progress: (8/10) | 7.98 s
[Task 19/25] Current/Best: 3.09/ 22.80 GFLOPS | Progress: (10/10) | 9.58 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 17.63/ 21.64 GFLOPS | Progress: (4/10) | 2.77 s
[Task 20/25] Current/Best: 13.71/ 21.64 GFLOPS | Progress: (8/10) | 4.66 s
[Task 20/25] Current/Best: 10.30/ 21.64 GFLOPS | Progress: (10/10) | 6.17 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 17.88/ 17.88 GFLOPS | Progress: (4/10) | 2.92 s
[Task 21/25] Current/Best: 19.58/ 19.58 GFLOPS | Progress: (8/10) | 4.13 s
[Task 21/25] Current/Best: 12.74/ 19.58 GFLOPS | Progress: (10/10) | 5.51 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 16.12/ 17.48 GFLOPS | Progress: (4/10) | 4.38 s
[Task 22/25] Current/Best: 13.67/ 17.67 GFLOPS | Progress: (8/10) | 7.38 s
[Task 22/25] Current/Best: 15.01/ 17.67 GFLOPS | Progress: (10/10) | 8.04 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 18.80/ 22.75 GFLOPS | Progress: (4/10) | 3.52 s
[Task 23/25] Current/Best: 20.78/ 22.75 GFLOPS | Progress: (8/10) | 6.84 s
[Task 23/25] Current/Best: 12.20/ 22.75 GFLOPS | Progress: (10/10) | 8.57 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 5.57/ 8.06 GFLOPS | Progress: (4/10) | 11.89 s
[Task 24/25] Current/Best: 2.97/ 8.06 GFLOPS | Progress: (8/10) | 68.27 s
[Task 24/25] Current/Best: 3.80/ 8.06 GFLOPS | Progress: (10/10) | 69.35 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
-
[Task 25/25] Current/Best: 5.92/ 9.76 GFLOPS | Progress: (4/10) | 13.54 s
[Task 25/25] Current/Best: 3.55/ 9.76 GFLOPS | Progress: (8/10) | 15.01 s
[Task 25/25] Current/Best: 8.30/ 9.76 GFLOPS | Progress: (10/10) | 299.04 s
+
[Task 25/25] Current/Best: 1.53/ 5.66 GFLOPS | Progress: (4/10) | 31.92 s
[Task 25/25] Current/Best: 2.85/ 5.73 GFLOPS | Progress: (8/10) | 49.78 s
[Task 25/25] Current/Best: 1.53/ 5.73 GFLOPS | Progress: (10/10) | 50.45 s
The output from this tuning process will look something like this:
@@ -602,8 +602,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621102
- class='n02123159 tiger cat' with probability=0.356380
+ class='n02123045 tabby, tabby cat' with probability=0.621104
+ class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -656,8 +656,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 455.29671436999706, 'median': 455.13899144998504, 'std': 1.5743355271314416}
- unoptimized: {'mean': 489.13385313002436, 'median': 488.91485415006173, 'std': 1.0093729626319448}
+ optimized: {'mean': 443.23648075000165, 'median': 443.2520545999978, 'std': 1.2965896223192286}
+ unoptimized: {'mean': 502.3503507300029, 'median': 502.1625347000054, 'std': 1.0631175008182827}
@@ -677,7 +677,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 12 minutes 29.970 seconds)
+ **Total running time of the script:** ( 8 minutes 5.560 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 1962dc123..f8889132e 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
- 1.235e-07 secs/op
+ 1.282e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 7e9dcb253..1d3dbf755 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -185,7 +185,7 @@ we can schedule the following series of operations ending with :code:`topi.sum`
.. code-block:: none
- /workspace/python/tvm/target/target.py:288: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
+ /workspace/python/tvm/target/target.py:347: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
warnings.warn("Try specifying cuda arch by adding 'arch=sm_xx' to your target.")
@main = primfn(a_1: handle, b_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
@@ -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, 0x28221e40)), stage(b, placeholder(b, 0xa3bc300)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
+ [stage(a, placeholder(a, 0xe3ecd80)), stage(b, placeholder(b, 0xc5ccb80)), 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/relay_quick_start.rst.txt b/docs/_sources/tutorial/relay_quick_start.rst.txt
index bc4dcdbd2..0096e846b 100644
--- a/docs/_sources/tutorial/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorial/relay_quick_start.rst.txt
@@ -226,7 +226,7 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
- /workspace/python/tvm/target/target.py:288: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
+ /workspace/python/tvm/target/target.py:347: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
warnings.warn("Try specifying cuda arch by adding 'arch=sm_xx' to your target.")
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 83c83bc29..22fb840ba 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
=================
-**15:15.558** total execution time for **tutorial** files:
+**10:49.422** total execution time for **tutorial** files:
-- **12:29.970**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **00:58.683**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:56.533**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:26.142**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:22.663**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:00.699**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.542**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.216**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.029**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.027**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
-- **00:00.027**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
-- **00:00.027**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
+- **08:05.560**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:03.169**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:50.024**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **00:27.317**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:20.768**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:01.374**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.754**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.232**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.056**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
+- **00:00.056**: :ref:`sphx_glr_tutorial_install.py` (``install.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_tvmc_python.py` (``tvmc_python.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index c7786bd43..76ca75c80 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
@@ -335,7 +335,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000007
+ parallel: 0.000006
@@ -438,10 +438,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.19355000203359e-06 1.0
- naive 5.8551999999999994e-06 0.7146108827732515
- parallel 7.012400000000001e-06 0.8558439258025601
- vector 2.46237e-05 3.005254132078104
+ numpy 7.1260000004258476e-06 1.0
+ naive 5.8158e-06 0.816138085833911
+ parallel 6.0976e-06 0.8556834128032008
+ vector 2.45775e-05 3.4489896152864516
@@ -830,7 +830,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017732
+ Numpy running time: 0.019207
@@ -886,7 +886,7 @@ optimizations.
.. code-block:: none
- none: 3.264217
+ none: 3.512506
@@ -985,7 +985,7 @@ schedule.
.. code-block:: none
- blocking: 0.296393
+ blocking: 0.331071
@@ -1077,7 +1077,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.328019
+ vectorization: 0.347813
@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: 0.115767
+ loop permutation: 0.136599
@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: 0.108397
+ array packing: 0.111624
@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: 0.110292
+ block caching: 0.111918
@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: 0.144191
+ parallelization: 0.147626
@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 3.2642167188 1.0
- blocking 0.2963927547 0.09080057491065135
- vectorization 0.3280194928 0.10048949596722467
- loop permutation 0.11576726200000001 0.03546555635636799
- array packing 0.10839654920000001 0.03320752221373617
- block caching 0.11029192940000002 0.033788176123473145
- parallelization 0.1441911041 0.04417326315055696
+ none 3.5125063933 1.0
+ blocking 0.33107129129999996 0.09425500034149645
+ vectorization 0.3478128892 0.09902128288319777
+ loop permutation 0.1365990129 0.038889327905725246
+ array packing 0.1116241107 0.03177904840626614
+ block caching 0.11191796059999999 0.03186270658851472
+ parallelization 0.14762583219999997 0.04202863017746866
@@ -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 3.169 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index 756e35f43..6aa644c1c 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-3d0fc36ae4967a5ee24a75cff311828447003235
+521b80af217a60bf4be3febd5a1e081eb4f76b93
diff --git a/docs/genindex.html b/docs/genindex.html
index 9ad6e585d..c9e1549e8 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -729,6 +729,12 @@
<li><a href="reference/api/python/relay/dataflow_pattern.html#tvm.relay.dataflow_pattern.CallPattern">CallPattern (class in tvm.relay.dataflow_pattern)</a>
</li>
<li><a href="reference/api/python/runtime.html#tvm.runtime.Report.calls">calls (tvm.runtime.Report attribute)</a>
+</li>
+ <li><a href="reference/api/python/target.html#tvm.target.Target.canonicalize_multi_targets">canonicalize_multi_targets() (tvm.target.Target static method)</a>
+</li>
+ <li><a href="reference/api/python/target.html#tvm.target.Target.canonicalize_target">canonicalize_target() (tvm.target.Target static method)</a>
+</li>
+ <li><a href="reference/api/python/target.html#tvm.target.Target.canonicalize_target_and_host">canonicalize_target_and_host() (tvm.target.Target static method)</a>
</li>
<li><a href="reference/api/python/relay/transform.html#tvm.relay.transform.CanonicalizeCast">CanonicalizeCast() (in module tvm.relay.transform)</a>
</li>
@@ -903,11 +909,11 @@
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_ncw">conv1d_ncw() (in module tvm.topi.nn)</a>
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_nwc">conv1d_nwc() (in module tvm.topi.nn)</a>
-</li>
- <li><a href="reference/api/python/relay/nn.html#tvm.relay.nn.conv1d_transpose">conv1d_transpose() (in module tvm.relay.nn)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="reference/api/python/relay/nn.html#tvm.relay.nn.conv1d_transpose">conv1d_transpose() (in module tvm.relay.nn)</a>
+</li>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_transpose_ncw">conv1d_transpose_ncw() (in module tvm.topi.nn)</a>
</li>
<li><a href="reference/api/python/relay/nn.html#tvm.relay.nn.conv2d">conv2d() (in module tvm.relay.nn)</a>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index ff9a700d2..74165a84a 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.zip9e7d197c-2789-4b73-bf72-9c01563dd09c 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.zip7d90167a-60dc-48d1-8911-4b9dd0e1ebc0 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 a9f7a70ee..eae2d8f7c 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -406,129 +406,41 @@ 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 fa35d8032..646773a28 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 27.295 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 9.805 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 2c5cda0e7..e988ef815 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 f1be82f8b..21d8b6924 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 3.442 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 6.815 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 d7589b51c..506d1bb36 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>05:50.116</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:39.253</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:27.295</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>01:03.442</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>00:56.561</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>00:41.867</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>00:25.005</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>00:20.944</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>00:20.805</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
-<li><p><strong>00:19.257</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>00:12.328</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>00:02.612</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>01:09.805</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>01:06.815</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>00:59.385</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>00:32.312</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>00:25.918</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>00:23.989</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>00:23.978</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>00:19.845</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>00:14.557</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>00:02.650</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 0289b314c..b1a6fc56a 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)
- 15.5674 15.5431 15.6927 15.4704 0.0776
+ 17.0220 16.9537 17.5340 16.5876 0.3437
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_rasp.html b/docs/how_to/deploy_models/deploy_model_on_rasp.html
index 47f555b3e..a039c43e3 100644
--- a/docs/how_to/deploy_models/deploy_model_on_rasp.html
+++ b/docs/how_to/deploy_models/deploy_model_on_rasp.html
@@ -497,7 +497,7 @@ to run this tutorial with a real device.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
</pre></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 318661610..7a47d2a35 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,36 +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').
@@ -483,7 +487,7 @@ be unstable.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
</pre></div>
</div>
@@ -531,7 +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 1.507 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 26.010 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index 25919142a..8cd426a42 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,9 +450,7 @@ training. Other models require a full post training calibration.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
</div>
</div>
@@ -541,7 +539,7 @@ 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)
- 90.3567 90.1864 99.1484 89.8744 0.9661
+ 90.6754 90.5349 91.6982 90.3244 0.3079
</pre></div>
</div>
<div class="admonition note">
@@ -580,7 +578,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.791 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 13.411 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index e095ba427..244a91f9a 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)
- 118.3741 118.4525 120.2847 116.1592 1.0702
+ 121.0929 120.8253 125.0510 119.9421 0.9224
</pre></div>
</div>
<div class="admonition note">
@@ -568,7 +568,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 59.468 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 55.896 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index cb22789b4..8e11a6092 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -476,11 +476,11 @@ for calibration. But the accuracy might be impacted.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:431: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 5.876 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.284 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index a87ec6e6c..3373bb2db 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,24 +415,22 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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</div>
<p>Create TVM runtime and do inference
@@ -472,7 +470,7 @@ 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 18.679 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 36.954 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 7bbfd61be..540776838 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>11:18.815</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:27.811</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>03:01.507</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>02:18.679</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>02:05.876</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>01:59.468</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>01:03.791</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>00:27.496</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>00:21.795</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
-<li><p><strong>00:00.202</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
+<li><p><strong>03:26.010</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>02:36.954</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>01:55.896</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>01:21.284</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>01:13.411</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>00:31.015</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>00:23.025</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>00:00.215</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 5a3922ab7..e8d892610 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.zip5322494f-0b66-42e4-9a5c-9fd9bb331175 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.zipc97dc2a8-d241-4504-9f7e-2e4884bfe63f 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>
@@ -650,7 +650,7 @@ 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>Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
</pre></div>
</div>
<p>When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index d2d5ecf44..d99c55d42 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>00:37.776</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:40.869</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:34.325</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
-<li><p><strong>00:02.211</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
-<li><p><strong>00:01.059</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>00:00.181</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>00:37.117</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>00:02.397</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>00:01.129</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>00:00.227</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 4a15cff11..239690ff9 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: 6204us [6204us] (45.81%; 45.81%)
-FoldScaleAxis: 7340us [3us] (54.19%; 54.19%)
- FoldConstant: 7337us [1522us] (54.17%; 99.96%)
- InferType: 5814us [5814us] (42.93%; 79.25%)
+InferType: 6287us [6287us] (45.69%; 45.69%)
+FoldScaleAxis: 7475us [3us] (54.31%; 54.31%)
+ FoldConstant: 7472us [1526us] (54.30%; 99.97%)
+ InferType: 5946us [5946us] (43.21%; 79.57%)
</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: 5910us [5910us] (45.11%; 45.11%)
-FoldScaleAxis: 7190us [2us] (54.89%; 54.89%)
- FoldConstant: 7188us [1489us] (54.87%; 99.97%)
- InferType: 5699us [5699us] (43.50%; 79.28%)
+InferType: 6068us [6068us] (44.90%; 44.90%)
+FoldScaleAxis: 7447us [3us] (55.10%; 55.10%)
+ FoldConstant: 7444us [1552us] (55.08%; 99.97%)
+ InferType: 5892us [5892us] (43.60%; 79.16%)
</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 0d1a7df17..ee6448b2f 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: 42.368728 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.174938 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 584b025f6..ec0c12ff7 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: 7.008247 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 6.466869 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 df681d7c3..9865bccd3 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: 0.018506
-Baseline: 3.245997
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.020153
+Baseline: 3.498409
</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: 0.303008
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.325702
</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: 0.336242
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.344468
</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: 0.117632
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.133277
</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: 0.110116
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.112306
</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: 0.110598
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.113493
</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: 0.144518
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.147983
</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 d5f45fb2c..aea5f7656 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.387</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:36.467</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:31.866</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>00:01.338</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>00:01.184</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>00:33.685</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>00:01.466</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>00:01.316</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 03295cb1f..41744a936 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>04:54.833</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>05:16.094</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:22.038</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>01:19.370</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
-<li><p><strong>00:39.917</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>00:16.436</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>00:08.759</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>00:08.312</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>02:31.485</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>01:25.057</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>00:43.242</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
+<li><p><strong>00:17.877</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>00:09.345</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>00:09.089</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 da8112fcb..ac6369a98 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
@@ -470,292 +470,188 @@ 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" = 32;
- allocate(conv2d_nchw: Pointer(local float32), float32, [16]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [2016]), storage_scope = shared;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 16;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [1008]), storage_scope = shared;
allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope="local", align=64)[0] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[5] = 0f32
- conv2d_nchw_1[6] = 0f32
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[1] = 0f32
conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[3] = 0f32
conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[4] = 0f32
conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[5] = 0f32
conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[6] = 0f32
conv2d_nchw_1[13] = 0f32
- conv2d_nchw_1[14] = 0f32
- conv2d_nchw_1[15] = 0f32
- for (rc.outer.outer: int32, 0, 16) {
+ for (rc.outer.outer: int32, 0, 32) {
for (ry.outer.outer: int32, 0, 3) {
- let cse_var_4: int32 = (rc.outer.outer*1568)
+ let cse_var_4: int32 = (rc.outer.outer*784)
let cse_var_3: int32 = (ry.outer.outer*7)
- let cse_var_2: int32 = (rc.outer.outer*288)
+ let cse_var_2: int32 = (rc.outer.outer*144)
let cse_var_1: int32 = (ry.outer.outer*3)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [2016], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 49)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 49), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 98)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 98), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 98), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 98), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 147)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 147), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 147), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 147), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 196), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 245)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 245), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 245), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 245), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 294)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 294), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 294), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 294), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 343)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 343), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 343), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 343), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 392), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 392), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 441)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 335)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 490)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 490), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 490), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 490), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 539)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 539), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 539), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 539), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 588)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 588), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 588), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 588), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 637)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 637), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 637), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 637), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 686)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 686), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 686), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 686), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 735)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 735), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 735), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 735), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [1008], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((1 <= (floordiv(floormod(threadIdx.x_1, 63), 9) + ry.outer.outer)) && ((floordiv(floormod(threadIdx.x_1, 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 112), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 224), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 336), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 336), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 336), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 448), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 560), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 560), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 560), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 672)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 672), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 672), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 672), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
pad_temp.shared_1[(threadIdx.x_1 + 784)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 784), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 784), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 784), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 833)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 833), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 833), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 882)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 678)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 931)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 931), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 931), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 931), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 980)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 980), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 980), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 980), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1029)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1029), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1029), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1029), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1078)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1078), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1078), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1078), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1127)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1127), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1127), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1127), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1176)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1176), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1176), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1176), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1225)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1225), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1225), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1225), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1274)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 1274), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1274), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1323)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 1021)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1372)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1372), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1372), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1372), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1421)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1421), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1421), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1421), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1470)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1470), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1470), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1470), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1519)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1519), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1519), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1519), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1568)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1568), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1568), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1568), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1617)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1617), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1617), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1617), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1666)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1666), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1666), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1666), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1715)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 1715), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1715), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1764)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 1364)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1813)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1813), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1813), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1813), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1862)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1862), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1862), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1862), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1911)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1911), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1911), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1911), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- pad_temp.shared_1[(threadIdx.x_1 + 1960)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1960), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1960), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 1960), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- if @tir.likely((threadIdx.x_1 < 7), dtype=bool) {
- pad_temp.shared_1[(threadIdx.x_1 + 2009)] = @tir.if_then_else((((floordiv(floormod((threadIdx.x_1 + 2009), 63), 9) + ry.outer.outer) < 8) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 2009), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- }
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*73728) + cse_var_2) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 49)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 49), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 49), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 98)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 98), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 98), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 147)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 147), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 17), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 196)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 196), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 196), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 245)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 245), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 245), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 294)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 294), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 2), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 343)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 343), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 343), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 392)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 392), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 392), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 441)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 441), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 19), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 490)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 490), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 490), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 539)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 539), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 539), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 588)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 588), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 637)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 637), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 637), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 686)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 686), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 686), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 735)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 735), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 21), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 784), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 833)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 833), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 833), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 882)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 882), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 6), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 931)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 931), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 931), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 980)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 980), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 980), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1029)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1029), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 23), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1078)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1078), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1078), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1127)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1127), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1127), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1176)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1176), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 8), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1225)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1225), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1225), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1274)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1274), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1274), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1323)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1323), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 25), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1372)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1372), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1372), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1421)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1421), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1421), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- kernel.shared_1[(threadIdx.x_2 + 1470)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1470), 96)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 10), 32)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 49;
- if @tir.likely((threadIdx.x_2 < 17), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 1519)] = kernel[((((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1519), 96)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1519), 96), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 896)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 896), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 896), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 896), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 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, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((((blockIdx.x*147456) + (floordiv(threadIdx.x_2, 48)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 14), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 560)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 35), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 560), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 672)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 784)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 56), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1008)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 96768)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1120)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1120), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1232)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 77), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1232), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((((blockIdx.x*147456) + (floordiv(floordiv(threadIdx.x_2, 16), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 48), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 129024)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_2 < 80), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 1456)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1456), 48), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
}
- for (rc.outer.inner: int32, 0, 16) {
- let cse_var_5: int32 = (rc.outer.inner*6)
+ for (yy.outer.inner: int32, 0, 7) {
+ let cse_var_5: int32 = (yy.outer.inner + 7)
{
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[cse_var_5]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 96)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 192)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 288)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 384)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 480)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 576)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 672)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 768)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 864)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 960)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1056)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1152)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1248)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1344)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[(((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(cse_var_5 + 1440)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 3)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 99)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 195)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 291)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 387)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 483)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 579)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 675)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 771)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 867)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 963)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1059)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1155)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1251)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1347)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(cse_var_5 + 1443)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 97)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 193)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 289)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 385)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 481)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 577)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 673)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 769)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 865)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 961)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1057)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1153)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1249)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1345)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[(cse_var_5 + 1441)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 4)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 100)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 196)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 292)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 388)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 484)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 580)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 676)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 772)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 868)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 964)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1060)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1156)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1252)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1348)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[(cse_var_5 + 1444)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 2)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 98)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 194)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 290)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 386)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 482)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 578)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 674)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 770)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 866)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 962)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1058)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1154)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1250)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1346)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[(cse_var_5 + 1442)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 5)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 101)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 197)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 293)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 389)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 485)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 581)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 677)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 773)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 869)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 965)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1061)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1157)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1253)]))
- conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1349)]))
- conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.outer.inner*126) + (floordiv(threadIdx.x, 7)*9)) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[(cse_var_5 + 1445)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[((yy.outer.inner*9) + floormod(threadIdx.x, 7))]*kernel.shared_1[(floordiv(threadIdx.x, 7)*96)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((yy.outer.inner*9) + floormod(threadIdx.x, 7))]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 48)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 1)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 1)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 49)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 2)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 2)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 50)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 3)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 51)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 4)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 64)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 52)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 5)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 65)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 53)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 6)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 54)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 127)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 7)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 127)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 55)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 128)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 8)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 128)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 56)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 9)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 57)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 190)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 10)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 190)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 58)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 191)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 11)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 191)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 59)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 12)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 60)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 253)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 13)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 253)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 61)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 254)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 14)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 254)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 62)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 15)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 63)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 316)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 16)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 316)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 64)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 317)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 17)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 317)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 65)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 18)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 66)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 379)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 19)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 379)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 67)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 380)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 20)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 380)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 68)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 21)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 69)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 442)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 22)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 442)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 70)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 443)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 23)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 443)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 71)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 504)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 24)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 504)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 72)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 505)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 25)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 505)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 73)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 506)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 26)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 506)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 74)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 567)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 27)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 567)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 75)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 568)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 28)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 568)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 76)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 569)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 29)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 569)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 77)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 630)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 30)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 630)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 78)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 631)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 31)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 631)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 79)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 632)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 32)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 632)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 80)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 693)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 33)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 693)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 81)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 694)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 34)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 694)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 82)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 695)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 35)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 695)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 83)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 756)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 36)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 756)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 84)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 757)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 37)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 757)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 85)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 758)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 38)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 758)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 86)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 819)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 39)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 819)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 87)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 820)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 40)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 820)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 88)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 821)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 41)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 821)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 89)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 882)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 42)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 882)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 90)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 883)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 43)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 883)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 91)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 884)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 44)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 884)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 92)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 945)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 45)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 945)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 93)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 946)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 46)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 946)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 94)]))
+ conv2d_nchw_1[yy.outer.inner] = (conv2d_nchw_1[yy.outer.inner] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 947)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 47)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((yy.outer.inner*9) + floormod(threadIdx.x, 7)) + 947)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + 95)]))
}
}
}
}
}
- for (i1.inner: int32, 0, 16) {
- compute[(((blockIdx.x*784) + (i1.inner*49)) + threadIdx.x)] = max((conv2d_nchw_1[i1.inner] + bias[((blockIdx.x*16) + i1.inner)]), 0f32)
+ for (i1.inner: int32, 0, 2) {
+ for (i2.inner: int32, 0, 7) {
+ compute[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[((i1.inner*7) + i2.inner)] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+ }
}
}
}
@@ -793,7 +689,7 @@ 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: 0.221 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.323 ms
</pre></div>
</div>
</div>
@@ -823,33 +719,33 @@ 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=16)
+conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
-conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=1)
+conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
-conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
-conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
+conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=7)
+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_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=16)
+conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=16)
+conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
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=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)
+conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=3)
+conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2d_nc [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=16)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=1)
+compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
-compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
-compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=7)
+compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
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)
@@ -872,14 +768,14 @@ 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=49)
+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)
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=49)
+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", 1024)
+s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -897,210 +793,157 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(49) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[16];
- __shared__ float pad_temp_shared[2016];
+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[14];
+ __shared__ float pad_temp_shared[1008];
__shared__ float kernel_shared[1536];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[1] = 0.000000e+00f;
conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[5] = 0.000000e+00f;
conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[13] = 0.000000e+00f;
- conv2d_nchw[14] = 0.000000e+00f;
- conv2d_nchw[15] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 16; ++rc_outer_outer) {
+ for (int rc_outer_outer = 0; rc_outer_outer < 32; ++rc_outer_outer) {
for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
__syncthreads();
- pad_temp_shared[((int)threadIdx.x)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 49)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 49) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 98)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 98) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 147)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 147) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 196)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 196) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 245)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 245) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 294)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 294) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 343)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 343) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 392)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 392) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 441)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 335)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 490)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 490) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 539)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 539) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 588)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 588) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 637)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 637) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 686)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 686) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 735)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 735) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 784) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 833)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 833) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 882)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 678)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 931)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 931) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 980)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 980) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1029)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1029) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1078)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1078) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1127)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1127) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1176)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1176) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1225)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1225) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1274)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1274) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1323)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1021)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1372)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1372) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1421)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1421) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1470)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1470) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1519)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1519) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1568)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1568) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1617)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1617) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1666)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1666) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1715)] = ((((((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1715) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1764)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1364)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1813)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1813) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1862)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1862) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1911)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1911) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1960)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 1960) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- if (((int)threadIdx.x) < 7) {
- pad_temp_shared[(((int)threadIdx.x) + 2009)] = (((((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8) && (((int)threadIdx.x) < 6)) ? data[(((((rc_outer_outer * 1568) + (((((int)threadIdx.x) + 2009) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) + 2)) - 8)] : 0.000000e+00f);
- }
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 73728) + (rc_outer_outer * 288)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 49)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 49) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 49) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 98)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 98) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 2) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 147)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 147) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 17) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 196)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 196) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 4) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 245)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 245) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 53) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 294)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 294) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 2) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 343)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 343) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 55) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 392)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 392) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 8) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 441)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 441) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 19) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 490)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 490) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 10) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 539)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 539) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 59) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 588)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 588) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 4) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 637)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 637) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 61) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 686)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 686) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 14) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 735)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 735) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 21) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 784) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 16) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 833)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 833) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 65) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 882)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 882) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 6) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 931)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 931) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 67) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 980)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 980) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 20) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1029)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1029) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 23) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1078)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1078) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 22) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1127)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1127) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 71) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1176)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1176) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 8) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1225)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1225) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 73) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1274)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1274) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 26) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1323)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1323) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) / 3) + 25) & 31) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1372)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1372) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 28) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1421)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1421) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 77) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1470)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1470) / 96) * 4608)) + (rc_outer_outer * 288)) + (((((int)threadIdx.x) / 3) + 10) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- if (((int)threadIdx.x) < 17) {
- kernel_shared[(((int)threadIdx.x) + 1519)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 1519) / 96) * 4608)) + (rc_outer_outer * 288)) + ((((((int)threadIdx.x) + 79) % 96) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ pad_temp_shared[((int)threadIdx.x)] = (((((1 <= (((((int)threadIdx.x) % 63) / 9) + ry_outer_outer)) && ((((((int)threadIdx.x) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 112) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = (((((1 <= ((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 224) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 336)] = (((((1 <= ((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 21) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 336) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 448)] = (((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 448) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 560)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 560) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 672)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 672) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 784)] = (((((1 <= ((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 784) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 896)] = (((((1 <= ((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 14) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 896) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
+ kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 112) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 224) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 448) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 560)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 560) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 672)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
+ kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 896)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 896) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1008)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 96768)];
+ kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1120) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1232)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1232) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 32) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+ kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[(((((((((int)blockIdx.x) * 147456) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 129024)];
+ if (((int)threadIdx.x) < 80) {
+ kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1456) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((((int)threadIdx.x) + 16) % 48) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
}
__syncthreads();
- for (int rc_outer_inner = 0; rc_outer_inner < 16; ++rc_outer_inner) {
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[(rc_outer_inner * 6)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 96)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 192)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 288)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 384)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 480)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 576)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 672)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 768)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 864)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 960)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1056)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1152)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1248)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1344)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[(((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7))] * kernel_shared[((rc_outer_inner * 6) + 1440)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 3)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 99)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 195)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 291)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 387)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 483)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 579)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 675)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 771)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 867)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 963)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1059)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1155)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1251)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1347)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((rc_outer_inner * 6) + 1443)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 97)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 193)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 289)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 385)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 481)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 577)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 673)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 769)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 865)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 961)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1057)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1153)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1249)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1345)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[((rc_outer_inner * 6) + 1441)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 4)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 100)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 196)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 292)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 388)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 484)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 580)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 676)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 772)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 868)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 964)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1060)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1156)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1252)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1348)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[((rc_outer_inner * 6) + 1444)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 2)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 98)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 194)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 290)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 386)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 482)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 578)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 674)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 770)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 866)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 962)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1058)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1154)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1250)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1346)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[((rc_outer_inner * 6) + 1442)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 5)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 101)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 197)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 293)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 389)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 485)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 581)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 677)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 773)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 869)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 965)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1061)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1157)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1253)]));
- conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1349)]));
- conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_outer_inner * 126) + ((((int)threadIdx.x) / 7) * 9)) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[((rc_outer_inner * 6) + 1445)]));
+ for (int yy_outer_inner = 0; yy_outer_inner < 7; ++yy_outer_inner) {
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[((yy_outer_inner * 9) + (((int)threadIdx.x) % 7))] * kernel_shared[((((int)threadIdx.x) / 7) * 96)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[((yy_outer_inner * 9) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 48)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 1)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 49)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 2)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 50)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 3)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 51)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 4)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 64)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 52)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 5)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 65)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 53)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 6)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 54)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 127)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 7)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 127)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 55)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 128)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 8)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 128)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 56)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 9)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 57)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 190)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 10)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 190)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 58)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 191)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 11)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 191)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 59)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 12)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 60)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 253)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 13)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 253)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 61)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 254)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 14)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 254)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 62)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 15)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 63)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 316)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 16)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 316)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 64)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 317)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 17)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 317)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 65)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 18)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 66)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 379)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 19)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 379)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 67)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 380)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 20)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 380)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 68)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 21)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 69)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 442)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 22)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 442)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 70)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 443)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 23)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 443)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 71)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 504)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 24)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 504)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 72)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 505)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 25)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 505)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 73)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 506)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 26)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 506)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 74)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 567)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 27)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 567)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 75)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 568)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 28)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 568)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 76)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 569)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 29)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 569)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 77)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 630)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 30)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 630)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 78)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 631)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 31)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 631)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 79)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 632)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 32)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 632)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 80)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 693)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 33)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 693)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 81)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 694)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 34)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 694)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 82)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 695)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 35)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 695)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 83)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 756)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 36)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 756)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 84)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 757)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 37)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 757)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 85)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 758)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 38)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 758)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 86)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 819)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 39)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 819)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 87)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 820)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 40)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 820)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 88)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 821)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 41)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 821)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 89)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 882)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 42)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 882)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 90)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 883)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 43)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 883)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 91)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 884)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 44)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 884)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 92)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 945)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 45)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 945)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 93)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 946)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 46)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 946)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 94)]));
+ conv2d_nchw[yy_outer_inner] = (conv2d_nchw[yy_outer_inner] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 947)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 47)]));
+ conv2d_nchw[(yy_outer_inner + 7)] = (conv2d_nchw[(yy_outer_inner + 7)] + (pad_temp_shared[(((yy_outer_inner * 9) + (((int)threadIdx.x) % 7)) + 947)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + 95)]));
}
}
}
- for (int i1_inner = 0; i1_inner < 16; ++i1_inner) {
- compute[(((((int)blockIdx.x) * 784) + (i1_inner * 49)) + ((int)threadIdx.x))] = max((conv2d_nchw[i1_inner] + bias[((((int)blockIdx.x) * 16) + i1_inner)]), 0.000000e+00f);
+ for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
+ for (int i2_inner = 0; i2_inner < 7; ++i2_inner) {
+ compute[(((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[((i1_inner * 7) + i2_inner)] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
+ }
}
}
</pre></div>
@@ -1138,7 +981,7 @@ 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 22.038 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 31.485 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 628a94888..55994ef92 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)
- 10.0719 10.0476 10.1251 10.0430 0.0376
+ 9.8670 9.8753 9.8780 9.8477 0.0137
</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 0c91859a2..725a7509e 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)
- 753.7759 755.2016 755.7372 750.3888 2.4050
+ 806.8567 808.5949 811.0921 800.8830 4.3453
</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 19.370 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.057 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 4250ccdfe..14983a85e 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,80 +600,75 @@ 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 = {compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [4096]), storage_scope = global {
+ preflattened_buffer_map = {placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_5: placeholder_18: Buffer(placeholder_10, float32, [128, 256], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
for (i.outer.inner: int32, 0, 2) {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- let cse_var_1: int32 = (((i.outer.inner*2048) + (i.inner.init*32)) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [4096], [])[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.inner.init: int32, 0, 64) {
+ let cse_var_1: int32 = ((i.outer.inner*1024) + (i.inner.init*16))
+ {
+ compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 64) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner)
- let cse_var_19: int32 = ((i.outer.inner*16384) + (i.inner*256))
- let cse_var_18: int32 = (((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16))
- let cse_var_17: int32 = (cse_var_18 + 1)
- let cse_var_16: int32 = (cse_var_18 + 11)
- let cse_var_15: int32 = (cse_var_18 + 12)
- let cse_var_14: int32 = (cse_var_18 + 13)
- let cse_var_13: int32 = (cse_var_18 + 14)
- let cse_var_12: int32 = (cse_var_18 + 15)
- let cse_var_11: int32 = (cse_var_18 + 2)
- let cse_var_10: int32 = (cse_var_18 + 3)
- let cse_var_9: int32 = (cse_var_18 + 4)
- let cse_var_8: int32 = (cse_var_18 + 5)
- let cse_var_7: int32 = (cse_var_18 + 6)
- let cse_var_6: int32 = (cse_var_18 + 7)
- let cse_var_5: int32 = (cse_var_18 + 8)
- let cse_var_4: int32 = (cse_var_18 + 9)
- let cse_var_3: int32 = (cse_var_18 + 10)
- {
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
- }
+ }
+ for (elem_idx: int32, 0, (placeholder_3[(i0.outer.i1.outer.fused + 1)] - placeholder_3[i0.outer.i1.outer.fused])) {
+ for (i.inner: int32, 0, 64) {
+ let cse_var_19: int32 = (elem_idx*16)
+ let cse_var_18: int32 = ((i.outer.inner*16384) + (i.inner*256))
+ let cse_var_17: int32 = ((i.outer.inner*1024) + (i.inner*16))
+ let cse_var_16: int32 = (cse_var_17 + 1)
+ let cse_var_15: int32 = (cse_var_17 + 11)
+ let cse_var_14: int32 = (cse_var_17 + 12)
+ let cse_var_13: int32 = (cse_var_17 + 13)
+ let cse_var_12: int32 = (cse_var_17 + 14)
+ let cse_var_11: int32 = (cse_var_17 + 15)
+ let cse_var_10: int32 = (cse_var_17 + 2)
+ let cse_var_9: int32 = (cse_var_17 + 3)
+ let cse_var_8: int32 = (cse_var_17 + 4)
+ let cse_var_7: int32 = (cse_var_17 + 5)
+ let cse_var_6: int32 = (cse_var_17 + 6)
+ let cse_var_5: int32 = (cse_var_17 + 7)
+ let cse_var_4: int32 = (cse_var_17 + 8)
+ let cse_var_3: int32 = (cse_var_17 + 9)
+ let cse_var_2: int32 = (cse_var_17 + 10)
+ {
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 1)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 2)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 3)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 4)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 5)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 6)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 7)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 8)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 9)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 10)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 11)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 12)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 13)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 14)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + cse_var_19) + 15)]*max(placeholder[(cse_var_18 + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 128) {
- for (i1.inner: int32, 0, 32) {
- let cse_var_22: int32 = (((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)
- compute[cse_var_22] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_22]), 0f32)
- }
+ let cse_var_20: int32 = ((i0.inner*512) + (i0.outer.i1.outer.fused*16))
+ compute[ramp(cse_var_20, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_20, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -712,7 +707,7 @@ 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: 1.843 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.836 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 6edde6c29..05ad7ad45 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>00:43.212</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:45.368</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:42.348</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>00:00.221</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>00:00.219</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>00:00.215</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>00:00.209</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>00:44.411</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>00:00.253</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>00:00.235</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>00:00.235</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>00:00.234</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 4454441f2..397066f88 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: 42.24/42.24 result: MeasureResult(costs=(0.005480043052631579,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5878784656524658, timestamp=1651674735.2251027) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-No: 7 GFLOPS: 0.00/42.24 result: Traceback (most recent call last):
+No: 6 GFLOPS: 95.22/95.22 result: MeasureResult(costs=(0.0024311252708333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6658709049224854, timestamp=1651701422.3290932) [('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: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/95.22 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: 0.00/42.24 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/95.22 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: 0.00/42.24 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/95.22 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: 0x00007f07f3bebfa2
+ 12: 0x00007efce0994fa2
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: 141.52/141.52 result: MeasureResult(costs=(0.0016358666935483868,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1214752197265625, timestamp=1651674761.2399213) [('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: 145.02/145.02 result: MeasureResult(costs=(0.00159629946,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4468486309051514, timestamp=1651701448.3761618) [('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: 0.002050
+Time cost of this operator: 0.001988
</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/tune_with_autotvm/tune_relay_cuda.html b/docs/how_to/tune_with_autotvm/tune_relay_cuda.html
index 5ca115e49..14cb258c3 100644
--- a/docs/how_to/tune_with_autotvm/tune_relay_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_relay_cuda.html
@@ -452,7 +452,7 @@ We can also load models from MXNet, ONNX and TensorFlow.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/target/target.py:288: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/target/target.py:347: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
warnings.warn("Try specifying cuda arch by adding 'arch=sm_xx' to your target.")
</pre></div>
</div>
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 36fafd9e8..09c2c6be6 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 316.3 98.758 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.958 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.908 0.284 (1, 1, 10, 10, 3) 1 1
-Total_time - 320.278 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 311.6 98.745 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.058 0.969 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.902 0.286 (1, 1, 10, 10, 3) 1 1
+Total_time - 315.559 - - - -
</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 90.45 97.16 (1, 6, 10, 10, 1) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 1.867 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.906 0.974 (1, 1, 10, 10, 3) 1 1
-Total_time - 93.094 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 206.7 98.597 (1, 1, 10, 10, 6) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.01 0.959 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.931 0.444 (1, 3, 10, 10, 1) 1 1
+Total_time - 209.641 - - - -
</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 8ce9d8f3a..516491c7a 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>00:42.736</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:47.764</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:38.753</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>00:03.373</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>00:00.232</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>00:00.192</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>00:00.185</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>00:43.313</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>00:03.779</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>00:00.238</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>00:00.217</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>00:00.217</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>
</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 90019f50d..a57a6a053 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>00:05.617</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:09.615</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:04.034</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>00:01.366</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>00:00.217</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>00:07.481</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>00:01.898</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>00:00.236</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 08b3faf26..eaccb1ba5 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>00:05.148</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:06.065</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:01.963</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>00:00.835</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>00:00.693</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>00:00.682</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>00:00.291</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>00:00.242</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>00:00.223</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>00:00.218</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>00:02.204</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>00:01.203</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>00:00.778</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>00:00.777</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>00:00.338</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>00:00.265</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>00:00.259</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>00:00.241</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 89f1fb475..8bfe3f83f 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/tmpft4wlm_f/input0.cc'\nsource_filename = \"/tmp/tmpft4wlm_f/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/tmp8uh2wupo/input0.cc'\nsource_filename = \"/tmp/tmp8uh2wupo/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/objects.inv b/docs/objects.inv
index 6b67cf246..f3493d6e2 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/reference/api/doxygen/annotated.html b/docs/reference/api/doxygen/annotated.html
index 6285a2d9c..0293d3cf7 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -922,7 +922,7 @@ $(function() {
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<tr id="row_1_33_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">Bool</a></td><td class="desc">Boolean constant </td></tr>
<tr id="row_1_34_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
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+<tr id="row_1_35_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Cen [...]
<tr id="row_1_36_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompileError.html" target="_self">CompileError</a></td><td class="desc">Custom Error class to be thrown during compilation </td></tr>
<tr id="row_1_37_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
<tr id="row_1_38_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ConstructorNode.html" target="_self">ConstructorNode</a></td><td class="desc">ADT constructor. Constructors compare by pointer equality </td></tr>
diff --git a/docs/reference/api/doxygen/classes.html b/docs/reference/api/doxygen/classes.html
index e5680d65f..de77a9a8a 100644
--- a/docs/reference/api/doxygen/classes.html
+++ b/docs/reference/api/doxygen/classes.html
@@ -67,8 +67,8 @@ $(function() {
<tr><td rowspan="2" valign="bottom"><a name="letter_a"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  a  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html">Conv3DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">IterAdapter</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html">PrefetchNode</a> (<a class="el" [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html">ConvGemmWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html">MapNode::iterator</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Preload [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html">AccessAnalyzer</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1run [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html">AccessAnalyzerNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html">AccessAnalyzer</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1run [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html">AccessAnalyzerNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool1DAttrs.html">AdaptivePool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1support_1_1Span_1_1iterator__base.html">Sp [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html">I [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1auto__scheduler_1_1AttachMapNode_ [...]
@@ -133,20 +133,20 @@ $(function() {
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<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrNopEntry.html">AttrNopEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFunc.html">EnvFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MatchNode.html">MatchNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNormalVisitor.html">AttrNormalVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFuncNode.html">EnvFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MatmulAttrs.html">MatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html">MatrixSetDiagAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html">MatrixSetDiagAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistry.html">AttrRegistry</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1ErrorBuilder.html">ErrorBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MaxNode.html">MaxNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ErrorReporter.html">ErrorReporter</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html">MaxPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>) &# [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>) &# [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html">Executable</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> (<a class="el" href="namespace [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Executor.html">Executor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html">MeasureCallback</a> (<a class [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html">ExecutorNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html">MeasureCallbackNode< [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExecutorRegEntry.html">ExecutorRegEntry</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallbackNode.html">MeasureCallbackNode</a> (<a class="el" [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html">MeasureCandidate</a> (<a class="el" [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html">Mea [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_1auto__schedule [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html"> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_1auto__schedule [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html"> [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href=" [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfo.html">MemoryInfo</a> (<a class="el" href="namespacetvm.html">tv [...]
@@ -199,9 +199,9 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html" [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html">FuseStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_o"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><d [...]
</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html">ScatterNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeCallNode.html">TypeCallNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Schedule.html">Schedule</a> (<a class="el" hr [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeConstraintNode.html">TypeConstraintNode</a> (<a class="el" href="namespa [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeConstraintNode.html">TypeConstraintNode</a> (<a class="el" href="namespacet [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html">BufferRegion</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html">ScheduleNode</a> (<a class="el" href="namespacetvm_1_1tir.html" [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html">BufferRegionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespace [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1r [...]
diff --git a/docs/reference/api/doxygen/classtvm_1_1CompilationConfig-members.html b/docs/reference/api/doxygen/classtvm_1_1CompilationConfig-members.html
index 7f0339072..85624ddb6 100644
--- a/docs/reference/api/doxygen/classtvm_1_1CompilationConfig-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1CompilationConfig-members.html
@@ -71,7 +71,7 @@ $(function() {
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a3e9b0901b6e01257b060a45e159cc37e">_type_is_nullable</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a2d76fa1fb628ff276a284e61123589c5">as</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfig.html#a90f982ae2b0992cdd9241e481c3f4c42">CompilationConfig</a>(const transform::PassContext &pass_ctx, TargetMap legacy_target_map_arg, Target optional_host_target_arg)</td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfig.html">tvm::CompilationConfig</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfig.html#a94ca0dbfadfbd8959fb81a2ec415e4c6">CompilationConfig</a>(const transform::PassContext &pass_ctx, const Array< Target > &raw_targets)</td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfig.html">tvm::CompilationConfig</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aa5c355fbb7d2f7402ee360dba8a52cdd">ContainerType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#ac261cdb80487fb29ac42b28678f8cbef">data_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a17d8d5ad92691f9e18e3e0ae8ef69e4f">defined</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1CompilationConfig.html b/docs/reference/api/doxygen/classtvm_1_1CompilationConfig.html
index d4c0149e5..7df558bd4 100644
--- a/docs/reference/api/doxygen/classtvm_1_1CompilationConfig.html
+++ b/docs/reference/api/doxygen/classtvm_1_1CompilationConfig.html
@@ -89,9 +89,9 @@ Collaboration diagram for tvm::CompilationConfig:</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
-<tr class="memitem:a90f982ae2b0992cdd9241e481c3f4c42"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfig.html#a90f982ae2b0992cdd9241e481c3f4c42">CompilationConfig</a> (const <a class="el" href="classtvm_1_1transform_1_1PassContext.html">transform::PassContext</a> &pass_ctx, <a class="el" href="namespacetvm.html#ab8a1f05408d27b16d67fe0c558fd6d9f">TargetMap</a> legacy_target_map_arg, < [...]
-<tr class="memdesc:a90f982ae2b0992cdd9241e481c3f4c42"><td class="mdescLeft"> </td><td class="mdescRight">Constructs the compilation config given the available <code>Targets</code> in the <code>legacy_target_map_arg</code> and an optional <code>optional_host_target_arg</code>. May use 'relay.fallback_device_type' and the availability of the LLVM compilation module to decide on appropriate default devices. <a href="#a90f982ae2b0992cdd9241e481c3f4c42">More...</a><br /></td></tr>
-<tr class="separator:a90f982ae2b0992cdd9241e481c3f4c42"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a94ca0dbfadfbd8959fb81a2ec415e4c6"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfig.html#a94ca0dbfadfbd8959fb81a2ec415e4c6">CompilationConfig</a> (const <a class="el" href="classtvm_1_1transform_1_1PassContext.html">transform::PassContext</a> &pass_ctx, const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1Targ [...]
+<tr class="memdesc:a94ca0dbfadfbd8959fb81a2ec415e4c6"><td class="mdescLeft"> </td><td class="mdescRight">Constructs the compilation config given the settings in <code>pass_ctx</code> and supplied <code>raw_targets</code>. See <code>CompilationConfigNode::Init</code> for details. <a href="#a94ca0dbfadfbd8959fb81a2ec415e4c6">More...</a><br /></td></tr>
+<tr class="separator:a94ca0dbfadfbd8959fb81a2ec415e4c6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8672f1df5f2d5127d72ac625432a7bfc"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfig.html#a8672f1df5f2d5127d72ac625432a7bfc">TVM_DEFINE_OBJECT_REF_METHODS</a> (<a class="el" href="classtvm_1_1CompilationConfig.html">CompilationConfig</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a class="el" href="classtvm_1_1CompilationConfigNode.htm [...]
<tr class="separator:a8672f1df5f2d5127d72ac625432a7bfc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_methods_classtvm_1_1runtime_1_1ObjectRef"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classtvm_1_1runtime_1_1ObjectRef')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td></tr>
@@ -161,8 +161,8 @@ Additional Inherited Members</h2></td></tr>
<div class="textblock"><p>Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code>. </p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a> </dd></dl>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a90f982ae2b0992cdd9241e481c3f4c42"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a90f982ae2b0992cdd9241e481c3f4c42">◆ </a></span>CompilationConfig()</h2>
+<a id="a94ca0dbfadfbd8959fb81a2ec415e4c6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a94ca0dbfadfbd8959fb81a2ec415e4c6">◆ </a></span>CompilationConfig()</h2>
<div class="memitem">
<div class="memproto">
@@ -176,14 +176,8 @@ Additional Inherited Members</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacetvm.html#ab8a1f05408d27b16d67fe0c558fd6d9f">TargetMap</a> </td>
- <td class="paramname"><em>legacy_target_map_arg</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1Target.html">Target</a> </td>
- <td class="paramname"><em>optional_host_target_arg</em> </td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1Target.html">Target</a> > & </td>
+ <td class="paramname"><em>raw_targets</em> </td>
</tr>
<tr>
<td></td>
@@ -193,7 +187,7 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
-<p>Constructs the compilation config given the available <code>Targets</code> in the <code>legacy_target_map_arg</code> and an optional <code>optional_host_target_arg</code>. May use 'relay.fallback_device_type' and the availability of the LLVM compilation module to decide on appropriate default devices. </p>
+<p>Constructs the compilation config given the settings in <code>pass_ctx</code> and supplied <code>raw_targets</code>. See <code>CompilationConfigNode::Init</code> for details. </p>
</div>
</div>
diff --git a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode-members.html b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode-members.html
index 81d069b81..1b88c2ff8 100644
--- a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode-members.html
@@ -84,14 +84,14 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#abe4569cf32c57b710be99b50e7118876">default_primitive_virtual_device</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af4407d2b59132e803ff791482dbe0145">deleter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a9e84841ca982bff376a978ade0132631">FDeleter</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">GetOrAllocRuntimeTypeIndex</a>(const std::string &key, uint32_t static_tindex, uint32_t parent_tindex, uint32_t type_child_slots, bool type_child_slots_can_overflow)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">GetTypeKey</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a5693cbadcc1168b96db7b1cc5c200b86">GetTypeKeyHash</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aad59e791b8292600a3d86ae182a85269">host_target</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a2b5e8ebdb467811b76acae96e2cd3d3a">host_virtual_device</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ac9e5eed7719e322117bde996a171e33a">IncRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aea9de8943ac60347359e749067f6bae8">legacy_target_map</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a93c439b9682879b81a4888673b786429">FindPrimitiveTargetOrFail</a>(DLDeviceType device_type) const</td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">GetOrAllocRuntimeTypeIndex</a>(const std::string &key, uint32_t static_tindex, uint32_t parent_tindex, uint32_t type_child_slots, bool type_child_slots_can_overflow)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">static</span [...]
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">GetTypeKey</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a5693cbadcc1168b96db7b1cc5c200b86">GetTypeKeyHash</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aad59e791b8292600a3d86ae182a85269">host_target</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a2b5e8ebdb467811b76acae96e2cd3d3a">host_virtual_device</a></td><td class="entry"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ac9e5eed7719e322117bde996a171e33a">IncRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">Object</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ab7968feb6ad38ecaffc320e13819d826">Object</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#aa1612f69ea5b4225d4cda759cd517323">Object</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode.html b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode.html
index 9de7340e2..38f9b2c8f 100644
--- a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode.html
@@ -73,7 +73,7 @@ $(function() {
</div><!--header-->
<div class="contents">
-<p>Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module. Centralizes any setup and validation logic needed to transition from configuration options conveyed implicitly (eg in <code>PassContexts</code>) or explicitly (eg a a list of <code>Targets</code>) to the configuration.
+<p>Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Centralizes the validation and canonicalization logic needed to transition from targets supplied by the Python APIs to a single internal representation. Also holds a cache of canonical <code>VirtualDevices</code> so that structural equal virtual devices have pointer equal canonical virtual devices.
<a href="classtvm_1_1CompilationConfigNode.html#details">More...</a></p>
<p><code>#include <<a class="el" href="compilation__config_8h_source.html">compilation_config.h</a>></code></p>
@@ -86,7 +86,7 @@ Inheritance diagram for tvm::CompilationConfigNode:</div>
<div class="dynheader">
Collaboration diagram for tvm::CompilationConfigNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1CompilationConfigNode__coll__graph.svg" width="1176" height="1360"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1CompilationConfigNode__coll__graph.svg" width="999" height="1375"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
@@ -94,6 +94,9 @@ Collaboration diagram for tvm::CompilationConfigNode:</div>
Public Member Functions</h2></td></tr>
<tr class="memitem:a013ec90098f6cc435024dc75dd4e76ea"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a013ec90098f6cc435024dc75dd4e76ea">VisitAttrs</a> (<a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> *v)</td></tr>
<tr class="separator:a013ec90098f6cc435024dc75dd4e76ea"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a93c439b9682879b81a4888673b786429"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1Target.html">Target</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a93c439b9682879b81a4888673b786429">FindPrimitiveTargetOrFail</a> (DLDeviceType device_type) const</td></tr>
+<tr class="memdesc:a93c439b9682879b81a4888673b786429"><td class="mdescLeft"> </td><td class="mdescRight">Return the unique <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to use for <code>device_type</code>. Fail if no such target exists. <a href="#a93c439b9682879b81a4888673b786429">More...</a><br /></td></tr>
+<tr class="separator:a93c439b9682879b81a4888673b786429"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a91de372f958bbe17e7c170f6abccc2a4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1VirtualDevice.html">VirtualDevice</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a91de372f958bbe17e7c170f6abccc2a4">CanonicalVirtualDevice</a> (const <a class="el" href="classtvm_1_1VirtualDevice.html">VirtualDevice</a> &virtual_device) const</td></tr>
<tr class="memdesc:a91de372f958bbe17e7c170f6abccc2a4"><td class="mdescLeft"> </td><td class="mdescRight">Returns a <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> agreeing with <code>virtual_device</code> on all its constrained fields, however: <a href="#a91de372f958bbe17e7c170f6abccc2a4">More...</a><br /></td></tr>
<tr class="separator:a91de372f958bbe17e7c170f6abccc2a4"><td class="memSeparator" colspan="2"> </td></tr>
@@ -122,17 +125,14 @@ Public Member Functions</h2></td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
-<tr class="memitem:aea9de8943ac60347359e749067f6bae8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm.html#ab8a1f05408d27b16d67fe0c558fd6d9f">TargetMap</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aea9de8943ac60347359e749067f6bae8">legacy_target_map</a></td></tr>
-<tr class="memdesc:aea9de8943ac60347359e749067f6bae8"><td class="mdescLeft"> </td><td class="mdescRight">The legacy targets map, mapping device type to the corresponding <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to use when compiling primitive functions. Does not include an entry for the host target, however each <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. [...]
-<tr class="separator:aea9de8943ac60347359e749067f6bae8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aad59e791b8292600a3d86ae182a85269"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1Target.html">Target</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aad59e791b8292600a3d86ae182a85269">host_target</a></td></tr>
-<tr class="memdesc:aad59e791b8292600a3d86ae182a85269"><td class="mdescLeft"> </td><td class="mdescRight">The host target. Used for 'scalar' data and code (such as shapes and shape functions) and residual Relay expressions and data (such as conditionals and ADTs). <a href="#aad59e791b8292600a3d86ae182a85269">More...</a><br /></td></tr>
+<tr class="memdesc:aad59e791b8292600a3d86ae182a85269"><td class="mdescLeft"> </td><td class="mdescRight">The host target. Used for 'scalar' data and code (such as shapes and shape functions) and residual Relay expressions and data (such as conditionals and ADTs). Each <code>primitive_target</code> below will have this exact target object as its 'host'. <a href="#aad59e791b8292600a3d86ae182a85269">More...</a><br /></td></tr>
<tr class="separator:aad59e791b8292600a3d86ae182a85269"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aaf237580f1684eaf97e1852c6b69ecbd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1Target.html">Target</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#aaf237580f1684eaf97e1852c6b69ecbd">primitive_targets</a></td></tr>
-<tr class="memdesc:aaf237580f1684eaf97e1852c6b69ecbd"><td class="mdescLeft"> </td><td class="mdescRight">Vector of all available <code>Targets</code> for compiling primitive operators. May contain a <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> for the same device type as for the <code>host_target</code>, however the <code>host_target</code> should be used for all host computations and data. Each <code><a class=" [...]
+<tr class="memdesc:aaf237580f1684eaf97e1852c6b69ecbd"><td class="mdescLeft"> </td><td class="mdescRight">Vector of all available <code>Targets</code> for partitioning or compiling primitive tensor operators (kernels). May contain a <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> for the same device type as for the <code>host_target</code>, however the <code>host_target</code> should be used for all host computation [...]
<tr class="separator:aaf237580f1684eaf97e1852c6b69ecbd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abe4569cf32c57b710be99b50e7118876"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1VirtualDevice.html">VirtualDevice</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#abe4569cf32c57b710be99b50e7118876">default_primitive_virtual_device</a> = <a class="el" href="classtvm_1_1VirtualDevice.html#a8238a76dcecef304fa173ceaf706c7b1">VirtualDevice::FullyUnconstrained</a>()</td></tr>
-<tr class="memdesc:abe4569cf32c57b710be99b50e7118876"><td class="mdescLeft"> </td><td class="mdescRight"><code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> for primitive operators which are not otherwise constrained to a particular device. <a href="#abe4569cf32c57b710be99b50e7118876">More...</a><br /></td></tr>
+<tr class="memdesc:abe4569cf32c57b710be99b50e7118876"><td class="mdescLeft"> </td><td class="mdescRight"><code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> for primitive operators which are not otherwise constrained to a particular device. Used by the PlanDevices pass to determine a virtual device for every sub-expression. <a href="#abe4569cf32c57b710be99b50e7118876">More...</a><br /></td></tr>
<tr class="separator:abe4569cf32c57b710be99b50e7118876"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2b5e8ebdb467811b76acae96e2cd3d3a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1VirtualDevice.html">VirtualDevice</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html#a2b5e8ebdb467811b76acae96e2cd3d3a">host_virtual_device</a> = <a class="el" href="classtvm_1_1VirtualDevice.html#a8238a76dcecef304fa173ceaf706c7b1">VirtualDevice::FullyUnconstrained</a>()</td></tr>
<tr class="memdesc:a2b5e8ebdb467811b76acae96e2cd3d3a"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a> for the host. <a href="#a2b5e8ebdb467811b76acae96e2cd3d3a">More...</a><br /></td></tr>
@@ -213,8 +213,9 @@ Additional Inherited Members</h2></td></tr>
<tr class="separator:af4407d2b59132e803ff791482dbe0145 inherit pro_attribs_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><p>Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module. Centralizes any setup and validation logic needed to transition from configuration options conveyed implicitly (eg in <code>PassContexts</code>) or explicitly (eg a a list of <code>Targets</code>) to the configuration. </p>
-<p>CAUTION: This is subject to change as we rework compilation options in general. See <a href="https://github.com/apache/tvm-rfcs/blob/main/rfcs/0028-command-line-registry-composition.md">https://github.com/apache/tvm-rfcs/blob/main/rfcs/0028-command-line-registry-composition.md</a>. So far this class is only focussed on carrying just the configuration needed by PlanDevices, and removing target-munging code duplication and inconsistencies between the three major build flows for the VM ( [...]
+<div class="textblock"><p>Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Centralizes the validation and canonicalization logic needed to transition from targets supplied by the Python APIs to a single internal representation. Also holds a cache of canonical <code>VirtualDevices</code> so that structural equal virtual devices have pointer equal canonical vi [...]
+<p>The construction of <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> is idempotent, in that given the same <code>PassContext</code> <code>ctx</code> and an arbitrary <code>Array<Target></code> <code>raw_targets:</code> </p>
+<div class="fragment"><div class="line"><a class="code" href="classtvm_1_1CompilationConfigNode.html#a770bdaa15e07052d9ee3434c7131ff27">CompilationConfig</a>(ctxt, raw_targets)</div><div class="line"> is structurally <a class="code" href="namespacetvm.html#a1c4f14382b85bcfa57d9a3460db2354a">equal</a> to</div><div class="line"><a class="code" href="classtvm_1_1CompilationConfigNode.html#a770bdaa15e07052d9ee3434c7131ff27">CompilationConfig</a>(ctxt, <a class="code" href="classtvm_1_1Comp [...]
</div><h2 class="groupheader">Member Function Documentation</h2>
<a id="a91de372f958bbe17e7c170f6abccc2a4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a91de372f958bbe17e7c170f6abccc2a4">◆ </a></span>CanonicalVirtualDevice()</h2>
@@ -234,10 +235,31 @@ Additional Inherited Members</h2></td></tr>
<p>Returns a <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> agreeing with <code>virtual_device</code> on all its constrained fields, however: </p>
<ul>
-<li>If the target is null then it is filled in from the known available primitive targets by matching on device type. Fails if no such target is known.</li>
+<li>If the target is null then it is filled in using <code>FindPrimitiveTargetOrFail</code> to match the device type.</li>
<li>The returned object is unique for the field values w.r.t. all other <code>VirtualDevices</code> returned by this method.</li>
</ul>
-<p>We call the result the 'canonical' <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code>. Two canonical <code>VirtualDevices</code> are structurally equal if and only if they are pointer equal. </p>
+<p>We call the result the 'canonical' <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code>. Two canonical <code>VirtualDevices</code> are structurally equal if and only if they are pointer equal. In this way we can build maps from virtual devices using just pointer equality. </p>
+
+</div>
+</div>
+<a id="a93c439b9682879b81a4888673b786429"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a93c439b9682879b81a4888673b786429">◆ </a></span>FindPrimitiveTargetOrFail()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1Target.html">Target</a> tvm::CompilationConfigNode::FindPrimitiveTargetOrFail </td>
+ <td>(</td>
+ <td class="paramtype">DLDeviceType </td>
+ <td class="paramname"><em>device_type</em></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Return the unique <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to use for <code>device_type</code>. Fail if no such target exists. </p>
+<p>This will be the first primitive target with matching device type. </p>
</div>
</div>
@@ -317,7 +339,7 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
-<p><code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> for primitive operators which are not otherwise constrained to a particular device. </p>
+<p><code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> for primitive operators which are not otherwise constrained to a particular device. Used by the PlanDevices pass to determine a virtual device for every sub-expression. </p>
</div>
</div>
@@ -333,7 +355,7 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
-<p>The host target. Used for 'scalar' data and code (such as shapes and shape functions) and residual Relay expressions and data (such as conditionals and ADTs). </p>
+<p>The host target. Used for 'scalar' data and code (such as shapes and shape functions) and residual Relay expressions and data (such as conditionals and ADTs). Each <code>primitive_target</code> below will have this exact target object as its 'host'. </p>
<p>Note that it is possible for a <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> used for primitive operations to be structurally equal to the host <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> (up to the <code>host</code> field.) However the <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a [...]
</div>
@@ -352,25 +374,6 @@ Additional Inherited Members</h2></td></tr>
<p><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a> for the host. </p>
-</div>
-</div>
-<a id="aea9de8943ac60347359e749067f6bae8"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aea9de8943ac60347359e749067f6bae8">◆ </a></span>legacy_target_map</h2>
-
-<div class="memitem">
-<div class="memproto">
- <table class="memname">
- <tr>
- <td class="memname"><a class="el" href="namespacetvm.html#ab8a1f05408d27b16d67fe0c558fd6d9f">TargetMap</a> tvm::CompilationConfigNode::legacy_target_map</td>
- </tr>
- </table>
-</div><div class="memdoc">
-
-<p>The legacy targets map, mapping device type to the corresponding <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to use when compiling primitive functions. Does not include an entry for the host target, however each <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> in this map will have it's <code>host</code> field set to the <code>host_target</code>. </p>
-<p>Currently we require at most one <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> per <code>DLDeviceType</code>, though we want to get rid of that limitation.</p>
-<p>CAUTION: Since keys are <code>Integers</code> they are compared by object equality not integer value.</p>
-<p>TODO(mbs): Remove once codegen updated for new target conventions. </p>
-
</div>
</div>
<a id="ac29775462c7f70a3711221871fbd3453"></a>
@@ -386,8 +389,8 @@ Additional Inherited Members</h2></td></tr>
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<p>If defined then compile and/or run in 'homogenous execution mode'. In this mode all primitives are compiled for this target only. </p>
-<p>This is to support legacy passes which have not been adapted to hetrogeneous execution and rely on an implicit global <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to be in scope.</p>
-<p>TODO(mbs): Remove once all passes are 'hetrogeneous aware'. </p>
+<p>This is to support legacy passes which have not been adapted to heterogeneous execution and rely on an implicit global <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> to be in scope.</p>
+<p>TODO(mbs): Remove once all passes are 'heterogeneous aware'. </p>
</div>
</div>
@@ -403,8 +406,14 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
-<p>Vector of all available <code>Targets</code> for compiling primitive operators. May contain a <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> for the same device type as for the <code>host_target</code>, however the <code>host_target</code> should be used for all host computations and data. Each <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> will h [...]
-
+<p>Vector of all available <code>Targets</code> for partitioning or compiling primitive tensor operators (kernels). May contain a <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> for the same device type as for the <code>host_target</code>, however the <code>host_target</code> should be used for all host computations and data. Each <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to Targe [...]
+<p>It is possible to have multiple primitive targets for the same device type. However given primitive targets left and right where:</p><ul>
+<li>left appears before right in the array</li>
+<li>left->kind->device_type == right->kind->device_type then:</li>
+<li>right.IsExternalCodegenFor(left) must be true In this way the FindPrimitiveTargetOrFail method will find the 'most general' target for the requested device type.</li>
+</ul>
+<p>In the homogeneous case primitive_targets will have just one entry, which will be pointer equal to optional_homogeneous_target.</p>
+<p>In the homogenous case where the 'host' is the same device as used for compiling kernels it is <em>not</em> the case that optional_homogenous_target == host_target. This is because all primitive always have their host field set to the host_target. Ie, it is valid to have: </p><div class="fragment"><div class="line"><a class="code" href="classtvm_1_1CompilationConfigNode.html#aad59e791b8292600a3d86ae182a85269">host_target</a>=Target(<span class="stringliteral">"llvm"</span>)< [...]
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-<text text-anchor="start" x="8" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="8" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
<text text-anchor="start" x="8" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="8" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="8" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="8" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="8" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
-<text text-anchor="start" x="8" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
-<text text-anchor="start" x="8" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
-<text text-anchor="start" x="8" y="-145.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="8" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="8" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="8" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="8" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="8" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node3->Node2 -->
<g id="edge1" class="edge">
<title>Node3->Node2</title>
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-<polygon fill="none" stroke="#191970" points="187.1894,-207.3891 183.1428,-217.1808 192.4123,-212.0498 187.1894,-207.3891"/>
+<path fill="none" stroke="#191970" d="M189.767,-223.8711C212.7134,-198.2622 237.7154,-172.0028 262.5,-149 289.2736,-124.1512 321.5009,-99.3452 348.6735,-79.6891"/>
+<polygon fill="none" stroke="#191970" points="187.0705,-221.6362 183.034,-231.432 192.2982,-226.2915 187.0705,-221.6362"/>
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<!-- Node3->Node3 -->
<g id="edge2" class="edge">
<title>Node3->Node3</title>
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-<text text-anchor="middle" x="227" y="-329.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
+<path fill="none" stroke="#404040" d="M183.3625,-376.9248C194.0482,-370.6637 201,-359.3555 201,-343 201,-332.0112 197.8618,-323.3007 192.5615,-316.8687"/>
+<polygon fill="none" stroke="#404040" points="192.5184,-316.8322 185.3548,-316.0056 183.3625,-309.0752 190.5261,-309.9017 192.5184,-316.8322"/>
+<text text-anchor="middle" x="227" y="-340.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
</g>
<!-- Node4 -->
<g id="node3" class="node">
<title>Node4</title>
<g id="a_node3"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::Target \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 24 more...\l}">
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-<text text-anchor="start" x="279" y="-409" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="327.5" y="-398" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::Target ></text>
-<polyline fill="none" stroke="#000000" points="271,-391 384,-391 "/>
-<text text-anchor="middle" x="327.5" y="-379" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="271,-372 384,-372 "/>
+<polygon fill="#ffffff" stroke="#000000" points="271,-254 271,-432 384,-432 384,-254 271,-254"/>
+<text text-anchor="start" x="279" y="-420" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="middle" x="327.5" y="-409" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::Target ></text>
+<polyline fill="none" stroke="#000000" points="271,-402 384,-402 "/>
+<text text-anchor="middle" x="327.5" y="-390" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="271,-383 384,-383 "/>
+<text text-anchor="start" x="279" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="279" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="279" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="279" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
@@ -93,198 +95,162 @@
<text text-anchor="start" x="279" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="279" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="279" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="279" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="279" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
<text text-anchor="start" x="279" y="-272" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="279" y="-261" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="279" y="-250" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
+<text text-anchor="start" x="279" y="-261" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
</a>
</g>
</g>
<!-- Node4->Node2 -->
<g id="edge3" class="edge">
<title>Node4->Node2</title>
-<path fill="none" stroke="#404040" d="M319.957,-242.9929C320.1394,-193.0587 328.1694,-131.9599 359.5,-87 362.8282,-82.224 366.7292,-77.8498 371.0256,-73.8482"/>
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-<text text-anchor="middle" x="407.5" y="-101" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +primitive_targets</text>
+<path fill="none" stroke="#404040" d="M326.2304,-253.891C328.5041,-205.9392 336.1952,-146.7959 357.5,-98 358.6571,-95.3498 359.9757,-92.7279 361.4197,-90.1489"/>
+<polygon fill="none" stroke="#404040" points="361.5404,-89.9586 361.377,-82.7493 367.9689,-79.8257 368.1322,-87.035 361.5404,-89.9586"/>
+<text text-anchor="middle" x="405.5" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +primitive_targets</text>
</g>
<!-- Node5 -->
<g id="node4" class="node">
<title>Node5</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
-<polygon fill="#ffffff" stroke="#000000" points="471.5,-563.5 471.5,-785.5 605.5,-785.5 605.5,-563.5 471.5,-563.5"/>
-<text text-anchor="middle" x="538.5" y="-773.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="471.5,-766.5 605.5,-766.5 "/>
-<text text-anchor="start" x="479.5" y="-754.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="471.5,-747.5 605.5,-747.5 "/>
-<text text-anchor="start" x="479.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="479.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="479.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="479.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="479.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="479.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="479.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="479.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="479.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="479.5" y="-636.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="479.5" y="-625.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="479.5" y="-614.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
-<text text-anchor="start" x="479.5" y="-603.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
-<text text-anchor="start" x="479.5" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="479.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="479.5" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
+<polygon fill="#ffffff" stroke="#000000" points="418.5,-574.5 418.5,-796.5 552.5,-796.5 552.5,-574.5 418.5,-574.5"/>
+<text text-anchor="middle" x="485.5" y="-784.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="418.5,-777.5 552.5,-777.5 "/>
+<text text-anchor="start" x="426.5" y="-765.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="418.5,-758.5 552.5,-758.5 "/>
+<text text-anchor="start" x="426.5" y="-746.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="426.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="426.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="426.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="426.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="426.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="426.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="426.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="426.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="426.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="426.5" y="-636.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="426.5" y="-625.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="426.5" y="-614.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="426.5" y="-603.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="426.5" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="426.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
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<!-- Node5->Node4 -->
<g id="edge4" class="edge">
<title>Node5->Node4</title>
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-<polygon fill="none" stroke="#191970" points="461.7791,-613.5968 471.4159,-617.9993 466.6279,-608.5481 461.7791,-613.5968"/>
+<path fill="none" stroke="#191970" d="M413.3289,-575.0489C406.2184,-562.4377 399.4463,-549.5887 393.5,-537 377.7733,-503.706 363.9751,-465.6228 353.1165,-432.0651"/>
+<polygon fill="none" stroke="#191970" points="410.2952,-576.7945 418.2996,-583.7356 416.3708,-573.3178 410.2952,-576.7945"/>
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<!-- Node7 -->
<g id="node6" class="node">
<title>Node7</title>
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-<text text-anchor="start" x="410" y="-409" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Map<</text>
-<text text-anchor="middle" x="459.5" y="-398" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> Integer, Target ></text>
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-<text text-anchor="middle" x="459.5" y="-379" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="402,-372 517,-372 "/>
-<text text-anchor="start" x="410" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-327" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
-<text text-anchor="start" x="410" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="410" y="-272" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="410" y="-261" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="410" y="-250" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 12 more...</text>
+<g id="a_node6"><a xlink:href="classtvm_1_1VirtualDevice.html" target="_top" xlink:title="Managed reference class to VirtualDeviceNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="402.5,-237.5 402.5,-448.5 568.5,-448.5 568.5,-237.5 402.5,-237.5"/>
+<text text-anchor="middle" x="485.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::VirtualDevice</text>
+<polyline fill="none" stroke="#000000" points="402.5,-429.5 568.5,-429.5 "/>
+<text text-anchor="middle" x="485.5" y="-417.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="402.5,-410.5 568.5,-410.5 "/>
+<text text-anchor="start" x="410.5" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VirtualDevice()</text>
+<text text-anchor="start" x="410.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_NOTNULLABLE</text>
+<text text-anchor="start" x="410.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_OBJECT_REF_METHODS()</text>
+<text text-anchor="start" x="410.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ FullyUnconstrained()</text>
+<text text-anchor="start" x="410.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForDeviceType()</text>
+<text text-anchor="start" x="410.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForDeviceType()</text>
+<text text-anchor="start" x="410.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForDeviceType()</text>
+<text text-anchor="start" x="410.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForDevice()</text>
+<text text-anchor="start" x="410.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForDeviceAndTarget()</text>
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+<text text-anchor="start" x="410.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ForMemoryScope()</text>
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<title>Node8</title>
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<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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+<text text-anchor="start" x="423.5" y="-961.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
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+<text text-anchor="start" x="423.5" y="-928.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
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<title>Node7->Node2</title>
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<title>Node8->Node2</title>
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+<text text-anchor="middle" x="661.5" y="-101" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+host_target</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode__inherit__graph.svg
index a4740fc75..69c242f3d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1CompilationConfigNode__inherit__graph.svg
@@ -15,17 +15,17 @@
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-<text text-anchor="start" x="20.5" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ host_virtual_device</text>
-<text text-anchor="start" x="20.5" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ optional_homogeneous</text>
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-<text text-anchor="start" x="20.5" y="-37.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="12.5,-30.5 170.5,-30.5 "/>
-<text text-anchor="start" x="20.5" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="20.5" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ host_target</text>
+<text text-anchor="start" x="20.5" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ primitive_targets</text>
+<text text-anchor="start" x="20.5" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ default_primitive_virtual</text>
+<text text-anchor="start" x="20.5" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_device</text>
+<text text-anchor="start" x="20.5" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ host_virtual_device</text>
+<text text-anchor="start" x="20.5" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ optional_homogeneous</text>
+<text text-anchor="start" x="20.5" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_target</text>
+<text text-anchor="start" x="20.5" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="12.5,-41.5 170.5,-41.5 "/>
+<text text-anchor="start" x="20.5" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="20.5" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ FindPrimitiveTargetOrFail()</text>
<text text-anchor="start" x="20.5" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ CanonicalVirtualDevice()</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1Target-members.html b/docs/reference/api/doxygen/classtvm_1_1Target-members.html
index 54bf31654..97b8e876e 100644
--- a/docs/reference/api/doxygen/classtvm_1_1Target-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1Target-members.html
@@ -80,6 +80,8 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aadbc0886ffa80162ff31eefd0431ba09">get</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#ae423057ecf93c18714d17f53cd1d318f">get_mutable</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aed593996e4076632450de8fde776707c">GetDataPtr</a>(const ObjectRef &ref)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1Target.html#a4e4aca0d3516a0998b55d043e12cd832">IsExternalCodegen</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1Target.html">tvm::Target</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1Target.html#acc8e9cae4385eb160ca79b1ddf8a7510">IsExternalCodegenFor</a>(const Target &that) const</td><td class="entry"><a class="el" href="classtvm_1_1Target.html">tvm::Target</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aa07c1f6d66a438ea950637d13ed09471">ObjectRef</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a6a7dd7404edf1c26f8dbd9bd92d03a02">ObjectRef</a>(ObjectPtr< Object > data)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aa1bd13a7185cb4b2b6bdde49416e8aa4">operator!=</a>(const ObjectRef &other) const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1Target.html b/docs/reference/api/doxygen/classtvm_1_1Target.html
index 87ffd2ac8..8ffa29e63 100644
--- a/docs/reference/api/doxygen/classtvm_1_1Target.html
+++ b/docs/reference/api/doxygen/classtvm_1_1Target.html
@@ -79,13 +79,13 @@ $(function() {
<div class="dynheader">
Inheritance diagram for tvm::Target:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1Target__inherit__graph.svg" width="216" height="551"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1Target__inherit__graph.svg" width="216" height="580"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<div class="dynheader">
Collaboration diagram for tvm::Target:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1Target__coll__graph.svg" width="216" height="839"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1Target__coll__graph.svg" width="216" height="868"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
@@ -105,6 +105,12 @@ Public Member Functions</h2></td></tr>
<tr class="separator:a1abb29217d8e394f0b88e28178c70bb0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac176a69f4e4649c73d5ffdb5a0d31312"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1Target.html#ac176a69f4e4649c73d5ffdb5a0d31312">TVM_DEFINE_OBJECT_REF_METHODS</a> (<a class="el" href="classtvm_1_1Target.html">Target</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a class="el" href="classtvm_1_1TargetNode.html">TargetNode</a>)</td></tr>
<tr class="separator:ac176a69f4e4649c73d5ffdb5a0d31312"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4e4aca0d3516a0998b55d043e12cd832"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1Target.html#a4e4aca0d3516a0998b55d043e12cd832">IsExternalCodegen</a> () const</td></tr>
+<tr class="memdesc:a4e4aca0d3516a0998b55d043e12cd832"><td class="mdescLeft"> </td><td class="mdescRight">Returns true if <code>this</code> target represents an external codegen. If so, <code>this->kind->name</code> can be used as the "Compiler" attribute on partitioned functions, and can be used to retrieve a partitioning pattern table using <code>get_pattern_table</code>. <a href="#a4e4aca0d3516a0998b55d043e12cd832">More...</a><br /></td></tr>
+<tr class="separator:a4e4aca0d3516a0998b55d043e12cd832"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acc8e9cae4385eb160ca79b1ddf8a7510"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1Target.html#acc8e9cae4385eb160ca79b1ddf8a7510">IsExternalCodegenFor</a> (const <a class="el" href="classtvm_1_1Target.html">Target</a> &that) const</td></tr>
+<tr class="memdesc:acc8e9cae4385eb160ca79b1ddf8a7510"><td class="mdescLeft"> </td><td class="mdescRight">Returns true if <code>this</code> target represents an external codegen which is compatible with <code>that</code> target. In particular: <a href="#acc8e9cae4385eb160ca79b1ddf8a7510">More...</a><br /></td></tr>
+<tr class="separator:acc8e9cae4385eb160ca79b1ddf8a7510"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_methods_classtvm_1_1runtime_1_1ObjectRef"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classtvm_1_1runtime_1_1ObjectRef')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td></tr>
<tr class="memitem:aa07c1f6d66a438ea950637d13ed09471 inherit pub_methods_classtvm_1_1runtime_1_1ObjectRef"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aa07c1f6d66a438ea950637d13ed09471">ObjectRef</a> ()=default</td></tr>
<tr class="memdesc:aa07c1f6d66a438ea950637d13ed09471 inherit pub_methods_classtvm_1_1runtime_1_1ObjectRef"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="classtvm_1_1runtime_1_1ObjectRef.html#aa07c1f6d66a438ea950637d13ed09471">More...</a><br /></td></tr>
@@ -364,6 +370,52 @@ Additional Inherited Members</h2></td></tr>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The target that is the current context. The target may not be defined if allow_not_defined is true. </dd></dl>
+</div>
+</div>
+<a id="a4e4aca0d3516a0998b55d043e12cd832"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a4e4aca0d3516a0998b55d043e12cd832">◆ </a></span>IsExternalCodegen()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">bool tvm::Target::IsExternalCodegen </td>
+ <td>(</td>
+ <td class="paramname"></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Returns true if <code>this</code> target represents an external codegen. If so, <code>this->kind->name</code> can be used as the "Compiler" attribute on partitioned functions, and can be used to retrieve a partitioning pattern table using <code>get_pattern_table</code>. </p>
+
+</div>
+</div>
+<a id="acc8e9cae4385eb160ca79b1ddf8a7510"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#acc8e9cae4385eb160ca79b1ddf8a7510">◆ </a></span>IsExternalCodegenFor()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">bool tvm::Target::IsExternalCodegenFor </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Target.html">Target</a> & </td>
+ <td class="paramname"><em>that</em></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Returns true if <code>this</code> target represents an external codegen which is compatible with <code>that</code> target. In particular: </p>
+<ul>
+<li><code>this</code> has a true <a class="el" href="namespacetvm_1_1attr.html#a17f834882ba3cd00890329433e8e81dd" title="A TargetKind attribute of type Bool. If true, then the target kind name also corresponds to an extern...">tvm::attr::kIsExternalCodegen</a> attribute</li>
+<li><code>that</code> does not have a true <a class="el" href="namespacetvm_1_1attr.html#a17f834882ba3cd00890329433e8e81dd" title="A TargetKind attribute of type Bool. If true, then the target kind name also corresponds to an extern...">tvm::attr::kIsExternalCodegen</a> attribute</li>
+<li><code>this</code> and <code>that</code> have the same kind->device_type</li>
+</ul>
+<p>After partitioning, the external codegen compilation path may use <code>that</code> to guide it's compilation to a <code><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM. ">runtime::Module</a></code>. Given <code>this</code>, an appropriate <code>that</code> can be found using <code>CompilationConfig::FindPrimitiveTargetOrFail</code>(this->kind->device_type).</p>
+<p>The <code>CollagePartition</code> pass uses this method to guide it's search over candidate partitions using external codegen. </p>
+
</div>
</div>
<a id="ac176a69f4e4649c73d5ffdb5a0d31312"></a>
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index 4cecfe84a..b5c5effa5 100644
--- a/docs/reference/api/doxygen/classtvm_1_1Target__coll__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1Target__coll__graph.svg
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+<text text-anchor="start" x="8" y="-106.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
+<text text-anchor="start" x="8" y="-95.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
<text text-anchor="start" x="8" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
<text text-anchor="start" x="8" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
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-<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
-<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
-<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsExternalCodegen()</text>
+<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsExternalCodegenFor()</text>
<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Current()</text>
<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ WithHost()</text>
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-<text text-anchor="start" x="18" y="-344.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="18" y="-333.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
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-<text text-anchor="start" x="18" y="-179.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
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+<text text-anchor="middle" x="77" y="-404.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="10,-397.5 144,-397.5 "/>
+<text text-anchor="start" x="18" y="-385.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="10,-378.5 144,-378.5 "/>
+<text text-anchor="start" x="18" y="-366.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="18" y="-355.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="18" y="-344.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="18" y="-333.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="18" y="-322.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="18" y="-311.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="18" y="-300.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="18" y="-289.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="18" y="-278.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="18" y="-267.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="18" y="-256.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="18" y="-245.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="18" y="-234.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="18" y="-223.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="18" y="-212.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="18" y="-201.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
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</g>
<!-- Node3->Node2 -->
<g id="edge1" class="edge">
<title>Node3->Node2</title>
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-<polygon fill="none" stroke="#191970" points="73.5001,-162.3363 77,-172.3363 80.5001,-162.3364 73.5001,-162.3363"/>
+<path fill="none" stroke="#191970" d="M77,-184.2158C77,-174.9116 77,-165.6775 77,-156.7651"/>
+<polygon fill="none" stroke="#191970" points="73.5001,-184.3275 77,-194.3275 80.5001,-184.3275 73.5001,-184.3275"/>
</g>
<!-- Node4 -->
<g id="node3" class="node">
<title>Node4</title>
<g id="a_node3"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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-<text text-anchor="start" x="15" y="-608.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
-<text text-anchor="middle" x="77" y="-597.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Object ></text>
-<polyline fill="none" stroke="#000000" points="7,-590.5 147,-590.5 "/>
-<text text-anchor="middle" x="77" y="-578.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="7,-571.5 147,-571.5 "/>
+<polygon fill="#ffffff" stroke="#000000" points="7,-464.5 7,-642.5 147,-642.5 147,-464.5 7,-464.5"/>
+<text text-anchor="start" x="15" y="-630.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
+<text text-anchor="middle" x="77" y="-619.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Object ></text>
+<polyline fill="none" stroke="#000000" points="7,-612.5 147,-612.5 "/>
+<text text-anchor="middle" x="77" y="-600.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="7,-593.5 147,-593.5 "/>
+<text text-anchor="start" x="15" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="15" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="15" y="-559.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="15" y="-548.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="15" y="-537.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="15" y="-526.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="15" y="-515.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="15" y="-504.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="15" y="-493.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
-<text text-anchor="start" x="15" y="-482.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
-<text text-anchor="start" x="15" y="-471.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="15" y="-460.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="15" y="-449.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
+<text text-anchor="start" x="15" y="-515.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
+<text text-anchor="start" x="15" y="-504.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
+<text text-anchor="start" x="15" y="-493.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="15" y="-482.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="15" y="-471.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
</a>
</g>
</g>
<!-- Node4->Node3 -->
<g id="edge2" class="edge">
<title>Node4->Node3</title>
-<path fill="none" stroke="#404040" d="M77,-442.3167C77,-430.8765 77,-419.0062 77,-407.1402"/>
-<polygon fill="none" stroke="#404040" points="77.0001,-406.7944 73,-400.7944 77,-394.7944 81,-400.7943 77.0001,-406.7944"/>
-<text text-anchor="middle" x="96.5" y="-416" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
+<path fill="none" stroke="#404040" d="M77,-464.3167C77,-452.8765 77,-441.0062 77,-429.1402"/>
+<polygon fill="none" stroke="#404040" points="77.0001,-428.7944 73,-422.7944 77,-416.7944 81,-422.7943 77.0001,-428.7944"/>
+<text text-anchor="middle" x="96.5" y="-438" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
</g>
</g>
</svg>
diff --git a/docs/reference/api/doxygen/classtvm_1_1Target__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1Target__inherit__graph.svg
index be5043aa1..b84f145c5 100644
--- a/docs/reference/api/doxygen/classtvm_1_1Target__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1Target__inherit__graph.svg
@@ -4,25 +4,27 @@
<!-- Generated by graphviz version 2.40.1 (20161225.0304)
-->
<!-- Title: tvm::Target Pages: 1 -->
-<svg width="162pt" height="413pt"
- viewBox="0.00 0.00 162.00 413.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 409)">
+<svg width="162pt" height="435pt"
+ viewBox="0.00 0.00 162.00 435.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 431)">
<title>tvm::Target</title>
-<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-409 158,-409 158,4 -4,4"/>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-431 158,-431 158,4 -4,4"/>
<!-- Node0 -->
<g id="node1" class="node">
<title>Node0</title>
-<polygon fill="#bfbfbf" stroke="#000000" points="0,-.5 0,-134.5 154,-134.5 154,-.5 0,-.5"/>
-<text text-anchor="middle" x="77" y="-122.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::Target</text>
-<polyline fill="none" stroke="#000000" points="0,-115.5 154,-115.5 "/>
-<text text-anchor="middle" x="77" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="0,-96.5 154,-96.5 "/>
+<polygon fill="#bfbfbf" stroke="#000000" points="0,-.5 0,-156.5 154,-156.5 154,-.5 0,-.5"/>
+<text text-anchor="middle" x="77" y="-144.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::Target</text>
+<polyline fill="none" stroke="#000000" points="0,-137.5 154,-137.5 "/>
+<text text-anchor="middle" x="77" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="0,-118.5 154,-118.5 "/>
+<text text-anchor="start" x="8" y="-106.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
+<text text-anchor="start" x="8" y="-95.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
<text text-anchor="start" x="8" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
<text text-anchor="start" x="8" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
-<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
-<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Target()</text>
-<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
-<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsExternalCodegen()</text>
+<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsExternalCodegenFor()</text>
<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Current()</text>
<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ WithHost()</text>
</g>
@@ -30,36 +32,36 @@
<g id="node2" class="node">
<title>Node1</title>
<g id="a_node2"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
-<polygon fill="#ffffff" stroke="#000000" points="10,-171.5 10,-404.5 144,-404.5 144,-171.5 10,-171.5"/>
-<text text-anchor="middle" x="77" y="-392.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="10,-385.5 144,-385.5 "/>
-<text text-anchor="start" x="18" y="-373.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<text text-anchor="start" x="18" y="-362.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># data_</text>
-<polyline fill="none" stroke="#000000" points="10,-355.5 144,-355.5 "/>
-<text text-anchor="start" x="18" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="18" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="18" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="18" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="18" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="18" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="18" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="18" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="18" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="18" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="18" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="18" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
-<text text-anchor="start" x="18" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
-<text text-anchor="start" x="18" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="18" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="18" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
+<polygon fill="#ffffff" stroke="#000000" points="10,-193.5 10,-426.5 144,-426.5 144,-193.5 10,-193.5"/>
+<text text-anchor="middle" x="77" y="-414.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="10,-407.5 144,-407.5 "/>
+<text text-anchor="start" x="18" y="-395.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<text text-anchor="start" x="18" y="-384.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># data_</text>
+<polyline fill="none" stroke="#000000" points="10,-377.5 144,-377.5 "/>
+<text text-anchor="start" x="18" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="18" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="18" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="18" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="18" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="18" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="18" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="18" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="18" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="18" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="18" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="18" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="18" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="18" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="18" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="18" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
</g>
</g>
<!-- Node1->Node0 -->
<g id="edge1" class="edge">
<title>Node1->Node0</title>
-<path fill="none" stroke="#191970" d="M77,-161.0779C77,-152.0753 77,-143.2246 77,-134.7733"/>
-<polygon fill="none" stroke="#191970" points="73.5001,-161.2933 77,-171.2933 80.5001,-161.2934 73.5001,-161.2933"/>
+<path fill="none" stroke="#191970" d="M77,-183.108C77,-174.1638 77,-165.3093 77,-156.7606"/>
+<polygon fill="none" stroke="#191970" points="73.5001,-183.2326 77,-193.2327 80.5001,-183.2327 73.5001,-183.2326"/>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1VirtualDeviceNode.html b/docs/reference/api/doxygen/classtvm_1_1VirtualDeviceNode.html
index 6809a0d9c..6a7e1375d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1VirtualDeviceNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1VirtualDeviceNode.html
@@ -282,7 +282,7 @@ Additional Inherited Members</h2></td></tr>
<p>Furthermore, not all memory scopes are accessible to all devices, and it is possible for a memory scope to only be accessible to a device when code is compiled with particular <code><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a></code> options.</p>
<p><code>VirtualDevices</code> themselves have no system-level understanding. Currently the <code>PlanDevices</code> pass will simply insert "device_copy" operators wherever <code>VirtualDevices</code> are not exactly pointwise equal. We may revisit this in the future as the work on memory pools matures.</p>
<h2>Joining and Defaulting </h2>
-<p>It is possible to 'join' two <code>VirtualDevices</code> to yield the most constrained <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> which agrees with both join arguments. Eg: </p><div class="fragment"><div class="line">Join((kDLCPU, -1, <span class="stringliteral">"llvm"</span>, <span class="stringliteral">""</span>), (<a class="code" href="namespacetvm.html#ab3c85920678b8b [...]
+<p>It is possible to 'join' two <code>VirtualDevices</code> to yield the most constrained <code><a class="el" href="classtvm_1_1VirtualDevice.html" title="Managed reference class to VirtualDeviceNode. ">VirtualDevice</a></code> which agrees with both join arguments. Eg: </p><div class="fragment"><div class="line">Join((kDLCPU, -1, <span class="stringliteral">"llvm"</span>, <span class="stringliteral">""</span>), (<a class="code" href="namespacetvm.html#ab3c85920678b8b [...]
</div><h2 class="groupheader">Member Function Documentation</h2>
<a id="a5e3f67045652bc27b937acf1ddc677a7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a5e3f67045652bc27b937acf1ddc677a7">◆ </a></span>device_type()</h2>
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<p><code>#include <<a class="el" href="object_8h_source.html">object.h</a>></code></p>
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+<p>Inherited by <a class="el" href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array< Range ></a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array< Region ></a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array< tvm::arith::IterSplitExpr ></a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array< tvm::AttrFieldInfo ></a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">tvm::ru [...]
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Collaboration diagram for tvm::runtime::ObjectRef:</div>
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</div><!--header-->
<div class="contents">
-<p>A helper class to collect all the targets in canonical form necessary for compilation. CAUTION: Preliminary, currently only used to support device planning, very likely to change.
+<p>A helper class to collect all the targets in canonical form necessary for compilation.
<a href="#details">More...</a></p>
<div class="textblock"><code>#include <<a class="el" href="virtual__device_8h_source.html">tvm/target/virtual_device.h</a>></code><br />
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Centralizes the validation and canonicalization logic needed to transition from targets supplied by the Python APIs to a single internal representation. Also holds a cache of canonical <code>VirtualDevices</code> so that structural eq [...]
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1CompilationConfig.html">tvm::CompilationConfig</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code>. <a href="classtvm_1_1CompilationConfig.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><p>A helper class to collect all the targets in canonical form necessary for compilation. CAUTION: Preliminary, currently only used to support device planning, very likely to change. </p>
+<div class="textblock"><p>A helper class to collect all the targets in canonical form necessary for compilation. </p>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/compilation__config_8h_source.html b/docs/reference/api/doxygen/compilation__config_8h_source.html
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+<a href="compilation__config_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * [...]
+<div class="ttc" id="classtvm_1_1CompilationConfig_html"><div class="ttname"><a href="classtvm_1_1CompilationConfig.html">tvm::CompilationConfig</a></div><div class="ttdoc">Managed reference class to CompilationConfig. </div><div class="ttdef"><b>Definition:</b> compilation_config.h:170</div></div>
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<div class="ttc" id="virtual__device_8h_html"><div class="ttname"><a href="virtual__device_8h.html">virtual_device.h</a></div><div class="ttdoc">A compile time representation for where data is to be stored at runtime, and how to compile code to c...</div></div>
<div class="ttc" id="namespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript. </div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="classtvm_1_1VirtualDevice_html_a8238a76dcecef304fa173ceaf706c7b1"><div class="ttname"><a href="classtvm_1_1VirtualDevice.html#a8238a76dcecef304fa173ceaf706c7b1">tvm::VirtualDevice::FullyUnconstrained</a></div><div class="ttdeci">static VirtualDevice FullyUnconstrained()</div><div class="ttdoc">Returns the unique fully unconstrained VirtualDevice. </div></div>
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+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_aa2eef530c37a784a2f8de16ff4e26893"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#aa2eef530c37a784a2f8de16ff4e26893">tvm::CompilationConfigNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> compilation_config.h:137</div></div>
<div class="ttc" id="classtvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
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+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_ac29775462c7f70a3711221871fbd3453"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#ac29775462c7f70a3711221871fbd3453">tvm::CompilationConfigNode::optional_homogeneous_target</a></div><div class="ttdeci">Target optional_homogeneous_target</div><div class="ttdoc">If defined then compile and/or run in &#39;homogenous execution mode&#39;. In this mode all primitives are co...</div><div class="ttdef"><b>De [...]
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<div class="ttc" id="classtvm_1_1transform_1_1PassContext_html"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContext.html">tvm::transform::PassContext</a></div><div class="ttdoc">PassContext that is used to configure the pass behavior. </div><div class="ttdef"><b>Definition:</b> transform.h:154</div></div>
<div class="ttc" id="object_8h_html_ac6e7295a4999e2c8e4a2c990beca887a"><div class="ttname"><a href="object_8h.html#ac6e7295a4999e2c8e4a2c990beca887a">TVM_DEFINE_OBJECT_REF_METHODS</a></div><div class="ttdeci">#define TVM_DEFINE_OBJECT_REF_METHODS(TypeName, ParentType, ObjectName)</div><div class="ttdef"><b>Definition:</b> object.h:713</div></div>
<div class="ttc" id="classtvm_1_1Target_html"><div class="ttname"><a href="classtvm_1_1Target.html">tvm::Target</a></div><div class="ttdoc">Managed reference class to TargetNode. </div><div class="ttdef"><b>Definition:</b> target.h:141</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
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<div class="ttc" id="object_8h_html_a3aea9b3f65aeb9150c0fa7800e5573c6"><div class="ttname"><a href="object_8h.html#a3aea9b3f65aeb9150c0fa7800e5573c6">TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">#define TVM_DECLARE_FINAL_OBJECT_INFO(TypeName, ParentType)</div><div class="ttdoc">helper macro to declare type information in a final class. </div><div class="ttdef"><b>Definition:</b> object.h:671</div></div>
<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_a013ec90098f6cc435024dc75dd4e76ea"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#a013ec90098f6cc435024dc75dd4e76ea">tvm::CompilationConfigNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div></div>
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-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html">tvm::runtime::Map</a></div><div class="ttdoc">Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than two places. </div><div class="ttdef"><b>Definition:</b> map.h:1268</div></div>
+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_a2b5e8ebdb467811b76acae96e2cd3d3a"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#a2b5e8ebdb467811b76acae96e2cd3d3a">tvm::CompilationConfigNode::host_virtual_device</a></div><div class="ttdeci">VirtualDevice host_virtual_device</div><div class="ttdoc">VirtualDevice for the host. </div><div class="ttdef"><b>Definition:</b> compilation_config.h:101</div></div>
+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_a93c439b9682879b81a4888673b786429"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#a93c439b9682879b81a4888673b786429">tvm::CompilationConfigNode::FindPrimitiveTargetOrFail</a></div><div class="ttdeci">Target FindPrimitiveTargetOrFail(DLDeviceType device_type) const</div><div class="ttdoc">Return the unique Target to use for device_type. Fail if no such target exists. </div></div>
<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_a91de372f958bbe17e7c170f6abccc2a4"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#a91de372f958bbe17e7c170f6abccc2a4">tvm::CompilationConfigNode::CanonicalVirtualDevice</a></div><div class="ttdeci">VirtualDevice CanonicalVirtualDevice(const VirtualDevice &virtual_device) const</div><div class="ttdoc">Returns a VirtualDevice agreeing with virtual_device on all its constrained fields, however: </div></div>
-<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_aaf237580f1684eaf97e1852c6b69ecbd"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#aaf237580f1684eaf97e1852c6b69ecbd">tvm::CompilationConfigNode::primitive_targets</a></div><div class="ttdeci">Array< Target > primitive_targets</div><div class="ttdoc">Vector of all available Targets for compiling primitive operators. May contain a Target for the same ...</div><div class="ttdef"><b>Definition:</b> compila [...]
-<div class="ttc" id="classtvm_1_1CompilationConfigNode_html"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></div><div class="ttdoc">Gathers the Targets and distinguished VirtualDevices in canonical form needed to compile a Relay modu...</div><div class="ttdef"><b>Definition:</b> compilation_config.h:49</div></div>
+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html_aaf237580f1684eaf97e1852c6b69ecbd"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html#aaf237580f1684eaf97e1852c6b69ecbd">tvm::CompilationConfigNode::primitive_targets</a></div><div class="ttdeci">Array< Target > primitive_targets</div><div class="ttdoc">Vector of all available Targets for partitioning or compiling primitive tensor operators (kernels)...</div><div class="ttdef"><b>Definition:</b> compilatio [...]
+<div class="ttc" id="classtvm_1_1CompilationConfigNode_html"><div class="ttname"><a href="classtvm_1_1CompilationConfigNode.html">tvm::CompilationConfigNode</a></div><div class="ttdoc">Gathers the Targets and distinguished VirtualDevices in canonical form needed to compile a Relay modu...</div><div class="ttdef"><b>Definition:</b> compilation_config.h:52</div></div>
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@@ -79,7 +79,7 @@ Files</h2></td></tr>
<tr class="memdesc:codegen_8h"><td class="mdescLeft"> </td><td class="mdescRight">Translates IRModule to runtime::Module. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:compilation__config_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="compilation__config_8h.html">compilation_config.h</a> <a href="compilation__config_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:compilation__config_8h"><td class="mdescLeft"> </td><td class="mdescRight">A helper class to collect all the targets in canonical form necessary for compilation. CAUTION: Preliminary, currently only used to support device planning, very likely to change. <br /></td></tr>
+<tr class="memdesc:compilation__config_8h"><td class="mdescLeft"> </td><td class="mdescRight">A helper class to collect all the targets in canonical form necessary for compilation. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:generic__func_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="generic__func_8h.html">generic_func.h</a> <a href="generic__func_8h_source.html">[code]</a></td></tr>
<tr class="memdesc:generic__func_8h"><td class="mdescLeft"> </td><td class="mdescRight">Generic function that can be specialzied on a per target basis. <br /></td></tr>
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diff --git a/docs/reference/api/doxygen/files.html b/docs/reference/api/doxygen/files.html
index 12352d5f7..9066e6207 100644
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@@ -236,7 +236,7 @@ $(function() {
<tr id="row_0_0_9_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="with_8h_source.html"><span class="icondoc"></span></a><a class="el" href="with_8h.html" target="_self">with.h</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
<tr id="row_0_0_10_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_0_0_10_" class="arrow" onclick="toggleFolder('0_0_10_')">►</span><span id="img_0_0_10_" class="iconfclosed" onclick="toggleFolder('0_0_10_')"> </span><a class="el" href="dir_5da96592f3a7c442b838b075c58254c2.html" target="_self">target</a></td><td class="desc"></td></tr>
<tr id="row_0_0_10_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="codegen_8h_source.html"><span class="icondoc"></span></a><a class="el" href="codegen_8h.html" target="_self">codegen.h</a></td><td class="desc">Translates IRModule to runtime::Module </td></tr>
-<tr id="row_0_0_10_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="compilation__config_8h_source.html"><span class="icondoc"></span></a><a class="el" href="compilation__config_8h.html" target="_self">compilation_config.h</a></td><td class="desc">A helper class to collect all the targets in canonical form necessary for compilation. CAUTION: Preliminary, currently only used to support device planning, very likely to change </ [...]
+<tr id="row_0_0_10_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="compilation__config_8h_source.html"><span class="icondoc"></span></a><a class="el" href="compilation__config_8h.html" target="_self">compilation_config.h</a></td><td class="desc">A helper class to collect all the targets in canonical form necessary for compilation </td></tr>
<tr id="row_0_0_10_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="generic__func_8h_source.html"><span class="icondoc"></span></a><a class="el" href="generic__func_8h.html" target="_self">generic_func.h</a></td><td class="desc">Generic function that can be specialzied on a per target basis </td></tr>
<tr id="row_0_0_10_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="tag_8h_source.html"><span class="icondoc"></span></a><a class="el" href="tag_8h.html" target="_self">tag.h</a></td><td class="desc">Target tag registry </td></tr>
<tr id="row_0_0_10_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a href="target_8h_source.html"><span class="icondoc"></span></a><a class="el" href="target_8h.html" target="_self">target.h</a></td><td class="desc">Compilation target object </td></tr>
diff --git a/docs/reference/api/doxygen/functions_c.html b/docs/reference/api/doxygen/functions_c.html
index 541310f55..1bd1db18a 100644
--- a/docs/reference/api/doxygen/functions_c.html
+++ b/docs/reference/api/doxygen/functions_c.html
@@ -295,7 +295,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1String.html#ad3af61a0b16c6d8a4e6b1c2f6e8f4a44">tvm::runtime::String</a>
</li>
<li>CompilationConfig()
-: <a class="el" href="classtvm_1_1CompilationConfig.html#a90f982ae2b0992cdd9241e481c3f4c42">tvm::CompilationConfig</a>
+: <a class="el" href="classtvm_1_1CompilationConfig.html#a94ca0dbfadfbd8959fb81a2ec415e4c6">tvm::CompilationConfig</a>
, <a class="el" href="classtvm_1_1CompilationConfigNode.html#a770bdaa15e07052d9ee3434c7131ff27">tvm::CompilationConfigNode</a>
</li>
<li>CompileError()
diff --git a/docs/reference/api/doxygen/functions_f.html b/docs/reference/api/doxygen/functions_f.html
index d6022ae9c..c62a7cedb 100644
--- a/docs/reference/api/doxygen/functions_f.html
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@@ -303,6 +303,9 @@ $(function() {
<li>FindBestRange()
: <a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#aa3483fc05bb117933c2aca5bc81e07c8">tvm::arith::IntGroupBounds</a>
</li>
+<li>FindPrimitiveTargetOrFail()
+: <a class="el" href="classtvm_1_1CompilationConfigNode.html#a93c439b9682879b81a4888673b786429">tvm::CompilationConfigNode</a>
+</li>
<li>FinishMessage()
: <a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Session.html#ae51f50c0eb8743fc4c4f3e58400a9ab1">tvm::runtime::micro_rpc::Session</a>
</li>
@@ -362,7 +365,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1State.html#a3cd6d26db25a42427d6fb51b3717effc">tvm::auto_scheduler::State</a>
</li>
<li>FollowFusedSplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html#a2bfadae4b32c30ec9e9ba52b0c88d9aa">tvm::auto_scheduler::FollowFusedSplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html#a14a76fac3e871cbb16e1c63c543a445f">tvm::auto_scheduler::FollowFusedSplitStep</a>
</li>
<li>FollowSplitStep()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html#a60e0e081cf7e367d844163e7c2dcd768">tvm::auto_scheduler::FollowSplitStep</a>
@@ -458,7 +461,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html#aa071662c3084d7ad3322351cb44c3dbf">tvm::runtime::PackedFuncValueConverter< PrimExpr ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Bool_01_4.html#a8afe0a2b788c12dcebec930db368f8cc">tvm::runtime::PackedFuncValueConverter< tvm::Bool ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html#a863930925e7354b87d35fde2ac310124">tvm::runtime::PackedFuncValueConverter< tvm::Integer ></a>
-, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html#a1d57124f26aeed2583d3ae24226d89c7">tvm::runtime::PackedFuncValueConverter<::tvm::runtime::String ></a>
+, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html#a9ac48d52f86dc3718590acc119e88741">tvm::runtime::PackedFuncValueConverter<::tvm::runtime::String ></a>
</li>
<li>from
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a50da0d506e9a2bffeaf75c702cf2cc33">tvm::runtime::vm::Instruction</a>
@@ -597,7 +600,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Fuse.html#a10b77eec10eb7dbc536b0c8d65163f9c">tvm::te::Fuse</a>
</li>
<li>fuse()
-: <a class="el" href="classtvm_1_1te_1_1Stage.html#a5658065d9cbbee620bbd107d30c4ae72">tvm::te::Stage</a>
+: <a class="el" href="classtvm_1_1te_1_1Stage.html#a07b721494aa3c0c79e8a8654c433708f">tvm::te::Stage</a>
</li>
<li>Fuse()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a4e60246388be76fe6a1f46c4255da3ff">tvm::tir::ScheduleNode</a>
@@ -609,7 +612,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html#a19c1a7b47f59a4f004a2dd9f354835eb">tvm::auto_scheduler::FuseStepNode</a>
</li>
<li>FuseStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html#a77c478295e170275d7d5da7345a03546">tvm::auto_scheduler::FuseStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html#a345e30fc54e9782faa9b8744c9ed5d14">tvm::auto_scheduler::FuseStep</a>
</li>
<li>FVisitAttrs
: <a class="el" href="classtvm_1_1ReflectionVTable.html#a486eb682af89ac025c0db1f8f6045b95">tvm::ReflectionVTable</a>
diff --git a/docs/reference/api/doxygen/functions_func_c.html b/docs/reference/api/doxygen/functions_func_c.html
index 07c6b69db..e365fa1f8 100644
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@@ -195,7 +195,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1String.html#ad3af61a0b16c6d8a4e6b1c2f6e8f4a44">tvm::runtime::String</a>
</li>
<li>CompilationConfig()
-: <a class="el" href="classtvm_1_1CompilationConfig.html#a90f982ae2b0992cdd9241e481c3f4c42">tvm::CompilationConfig</a>
+: <a class="el" href="classtvm_1_1CompilationConfig.html#a94ca0dbfadfbd8959fb81a2ec415e4c6">tvm::CompilationConfig</a>
</li>
<li>CompileError()
: <a class="el" href="classtvm_1_1CompileError.html#ac603e8927fd05acb728fe44ecbe9b6a5">tvm::CompileError</a>
diff --git a/docs/reference/api/doxygen/functions_func_f.html b/docs/reference/api/doxygen/functions_func_f.html
index c7b76c8b3..a283d7a89 100644
--- a/docs/reference/api/doxygen/functions_func_f.html
+++ b/docs/reference/api/doxygen/functions_func_f.html
@@ -91,6 +91,9 @@ $(function() {
<li>FindBestRange()
: <a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#aa3483fc05bb117933c2aca5bc81e07c8">tvm::arith::IntGroupBounds</a>
</li>
+<li>FindPrimitiveTargetOrFail()
+: <a class="el" href="classtvm_1_1CompilationConfigNode.html#a93c439b9682879b81a4888673b786429">tvm::CompilationConfigNode</a>
+</li>
<li>FinishMessage()
: <a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Session.html#ae51f50c0eb8743fc4c4f3e58400a9ab1">tvm::runtime::micro_rpc::Session</a>
</li>
@@ -188,7 +191,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html#aa071662c3084d7ad3322351cb44c3dbf">tvm::runtime::PackedFuncValueConverter< PrimExpr ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Bool_01_4.html#a8afe0a2b788c12dcebec930db368f8cc">tvm::runtime::PackedFuncValueConverter< tvm::Bool ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html#a863930925e7354b87d35fde2ac310124">tvm::runtime::PackedFuncValueConverter< tvm::Integer ></a>
-, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html#a1d57124f26aeed2583d3ae24226d89c7">tvm::runtime::PackedFuncValueConverter<::tvm::runtime::String ></a>
+, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html#a9ac48d52f86dc3718590acc119e88741">tvm::runtime::PackedFuncValueConverter<::tvm::runtime::String ></a>
</li>
<li>FromDLPack()
: <a class="el" href="classtvm_1_1runtime_1_1NDArray.html#abec485628a0ca451b668c42fd8fa691a">tvm::runtime::NDArray</a>
@@ -263,13 +266,13 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Fuse.html#a10b77eec10eb7dbc536b0c8d65163f9c">tvm::te::Fuse</a>
</li>
<li>fuse()
-: <a class="el" href="classtvm_1_1te_1_1Stage.html#a5658065d9cbbee620bbd107d30c4ae72">tvm::te::Stage</a>
+: <a class="el" href="classtvm_1_1te_1_1Stage.html#a07b721494aa3c0c79e8a8654c433708f">tvm::te::Stage</a>
</li>
<li>Fuse()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a4e60246388be76fe6a1f46c4255da3ff">tvm::tir::ScheduleNode</a>
</li>
<li>FuseStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html#a345e30fc54e9782faa9b8744c9ed5d14">tvm::auto_scheduler::FuseStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html#a77c478295e170275d7d5da7345a03546">tvm::auto_scheduler::FuseStep</a>
</li>
</ul>
</div><!-- contents -->
diff --git a/docs/reference/api/doxygen/functions_func_i.html b/docs/reference/api/doxygen/functions_func_i.html
index bc4577e97..c7bbda8f7 100644
--- a/docs/reference/api/doxygen/functions_func_i.html
+++ b/docs/reference/api/doxygen/functions_func_i.html
@@ -217,7 +217,7 @@ $(function() {
</li>
<li>Invoke()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#acb19406a24fa95bf39a29d15ad6be256">tvm::runtime::vm::Instruction</a>
-, <a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html#a1094291352e07e4c827a88b1167b89ad">tvm::runtime::vm::VirtualMachine</a>
+, <a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html#aa5f4724e2e702ef9d5c34e85dec53b02">tvm::runtime::vm::VirtualMachine</a>
</li>
<li>InvokeClosure()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#aff2701f8ac1a6232115e73fbb3fe522a">tvm::runtime::vm::Instruction</a>
@@ -293,6 +293,12 @@ $(function() {
<li>IsEverything()
: <a class="el" href="classtvm_1_1arith_1_1IntSet.html#a7754207d93bf25efdd733829ddfa8a7e">tvm::arith::IntSet</a>
</li>
+<li>IsExternalCodegen()
+: <a class="el" href="classtvm_1_1Target.html#a4e4aca0d3516a0998b55d043e12cd832">tvm::Target</a>
+</li>
+<li>IsExternalCodegenFor()
+: <a class="el" href="classtvm_1_1Target.html#acc8e9cae4385eb160ca79b1ddf8a7510">tvm::Target</a>
+</li>
<li>IsFullyConstrained()
: <a class="el" href="classtvm_1_1VirtualDeviceNode.html#ae3c848e08b56105cdb046907ccbfc59c">tvm::VirtualDeviceNode</a>
</li>
@@ -349,7 +355,7 @@ $(function() {
</li>
<li>iterator()
: <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ad605c9f9aaed23e669c2a3c595d08ba4">tvm::runtime::MapNode::iterator</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a75e3f2657cdb7cc613bf922429983165">tvm::runtime::MapNode::iterator</a>
</li>
<li>iterator_base()
: <a class="el" href="classtvm_1_1support_1_1Span_1_1iterator__base.html#a8280a97672c014b1d2005158896ed43a">tvm::support::Span< T, W >::iterator_base< W1 ></a>
@@ -358,7 +364,7 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1IterMark.html#a7b46a2bc2460f43e529a6fc65a0a618d">tvm::arith::IterMark</a>
</li>
<li>IterSplitExpr()
-: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#a754a9d8338aa2d2b5fac9e10c95c9128">tvm::arith::IterSplitExpr</a>
+: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#a59bd2fa8d07f4ad2c4ac09c8f7004cb8">tvm::arith::IterSplitExpr</a>
</li>
<li>IterSumExpr()
: <a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html#a1b9f8013f3978bafe4da3a6cad65fb36">tvm::arith::IterSumExpr</a>
@@ -367,10 +373,10 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1IterVar.html#a1c0d6998203092c953b7da00f16c5c31">tvm::tir::IterVar</a>
</li>
<li>IterVarAttr()
-: <a class="el" href="classtvm_1_1te_1_1IterVarAttr.html#a5549479b7e3ce243d89b219b0dd7ef71">tvm::te::IterVarAttr</a>
+: <a class="el" href="classtvm_1_1te_1_1IterVarAttr.html#aa20680587a1c880b659063cd37ba4763">tvm::te::IterVarAttr</a>
</li>
<li>IterVarRelation()
-: <a class="el" href="classtvm_1_1te_1_1IterVarRelation.html#a3e611ee0870d9a542b8deb79575dbf66">tvm::te::IterVarRelation</a>
+: <a class="el" href="classtvm_1_1te_1_1IterVarRelation.html#a4b50caede957f1cb50587ce15a87109f">tvm::te::IterVarRelation</a>
</li>
</ul>
</div><!-- contents -->
diff --git a/docs/reference/api/doxygen/functions_i.html b/docs/reference/api/doxygen/functions_i.html
index 5f27a4e24..44c0ff96e 100644
--- a/docs/reference/api/doxygen/functions_i.html
+++ b/docs/reference/api/doxygen/functions_i.html
@@ -309,7 +309,7 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html#a1c5ea6cf05289065ee8967cbfb897181">tvm::arith::IntConstraintsTransform</a>
</li>
<li>Integer()
-: <a class="el" href="classtvm_1_1Integer.html#a4bdb4edd6acf99ecfca13bf34da04fae">tvm::Integer</a>
+: <a class="el" href="classtvm_1_1Integer.html#a2d3969d98441b5b2ee5d8a986a56c410">tvm::Integer</a>
</li>
<li>Internal
: <a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html#a8bde6eb35df6b3a9f53810e0bc79fdfd">tvm::te::SpecializedCondition</a>
@@ -463,6 +463,12 @@ $(function() {
<li>IsEverything()
: <a class="el" href="classtvm_1_1arith_1_1IntSet.html#a7754207d93bf25efdd733829ddfa8a7e">tvm::arith::IntSet</a>
</li>
+<li>IsExternalCodegen()
+: <a class="el" href="classtvm_1_1Target.html#a4e4aca0d3516a0998b55d043e12cd832">tvm::Target</a>
+</li>
+<li>IsExternalCodegenFor()
+: <a class="el" href="classtvm_1_1Target.html#acc8e9cae4385eb160ca79b1ddf8a7510">tvm::Target</a>
+</li>
<li>IsFullyConstrained()
: <a class="el" href="classtvm_1_1VirtualDeviceNode.html#ae3c848e08b56105cdb046907ccbfc59c">tvm::VirtualDeviceNode</a>
</li>
@@ -551,7 +557,7 @@ $(function() {
<li>iterator
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a98e5ad633b8195d954c98067213ae29f">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ad605c9f9aaed23e669c2a3c595d08ba4">tvm::runtime::MapNode::iterator</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a75e3f2657cdb7cc613bf922429983165">tvm::runtime::MapNode::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html#a79703afd52032715cc2d9d4c6830147f">tvm::runtime::metadata::ArrayAccessor< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html#a3216d91ef7fb0771463e76c9a0afcd1c">tvm::runtime::metadata::ArrayAccessor< const char *, ::tvm::runtime::String ></a>
, <a class="el" href="classtvm_1_1support_1_1Span.html#af6fbb6a54a3ecf8673146acae0c8228b">tvm::support::Span< T, W ></a>
@@ -572,7 +578,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html#a65304957db6f84d8d7c90ad553453bb9">tvm::auto_scheduler::StageNode</a>
</li>
<li>IterSplitExpr()
-: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#a59bd2fa8d07f4ad2c4ac09c8f7004cb8">tvm::arith::IterSplitExpr</a>
+: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#a754a9d8338aa2d2b5fac9e10c95c9128">tvm::arith::IterSplitExpr</a>
</li>
<li>IterSumExpr()
: <a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html#a1b9f8013f3978bafe4da3a6cad65fb36">tvm::arith::IterSumExpr</a>
@@ -581,7 +587,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1IterVar.html#a1c0d6998203092c953b7da00f16c5c31">tvm::tir::IterVar</a>
</li>
<li>IterVarAttr()
-: <a class="el" href="classtvm_1_1te_1_1IterVarAttr.html#aa20680587a1c880b659063cd37ba4763">tvm::te::IterVarAttr</a>
+: <a class="el" href="classtvm_1_1te_1_1IterVarAttr.html#a5549479b7e3ce243d89b219b0dd7ef71">tvm::te::IterVarAttr</a>
</li>
<li>IterVarRelation()
: <a class="el" href="classtvm_1_1te_1_1IterVarRelation.html#a4b50caede957f1cb50587ce15a87109f">tvm::te::IterVarRelation</a>
diff --git a/docs/reference/api/doxygen/functions_l.html b/docs/reference/api/doxygen/functions_l.html
index 3cf0e1b17..2092d3479 100644
--- a/docs/reference/api/doxygen/functions_l.html
+++ b/docs/reference/api/doxygen/functions_l.html
@@ -114,9 +114,6 @@ $(function() {
<li>left
: <a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html#a02ffb32a51710af5bf595069c2dd7a33">tvm::relay::AltPatternNode</a>
</li>
-<li>legacy_target_map
-: <a class="el" href="classtvm_1_1CompilationConfigNode.html#aea9de8943ac60347359e749067f6bae8">tvm::CompilationConfigNode</a>
-</li>
<li>length()
: <a class="el" href="classtvm_1_1runtime_1_1String.html#a812045bfefb0f80f850d1b39f0e40489">tvm::runtime::String</a>
</li>
@@ -248,7 +245,7 @@ $(function() {
: <a class="el" href="classtvm_1_1IRModuleNode.html#ae078ad8def39579701d144578c787bcf">tvm::IRModuleNode</a>
</li>
<li>LookupTypeDef()
-: <a class="el" href="classtvm_1_1IRModuleNode.html#a23f3769fe60b3b06c9d163650ea7caaf">tvm::IRModuleNode</a>
+: <a class="el" href="classtvm_1_1IRModuleNode.html#ae095c1fd87642bd417224668c5b4d910">tvm::IRModuleNode</a>
</li>
<li>loop_var
: <a class="el" href="classtvm_1_1tir_1_1ForNode.html#a7dbf66bdcf8ed397321517f0915a0946">tvm::tir::ForNode</a>
diff --git a/docs/reference/api/doxygen/functions_m.html b/docs/reference/api/doxygen/functions_m.html
index 9e89a8991..f56ff9eb7 100644
--- a/docs/reference/api/doxygen/functions_m.html
+++ b/docs/reference/api/doxygen/functions_m.html
@@ -296,7 +296,7 @@ $(function() {
: <a class="el" href="classtvm_1_1DiagnosticContextNode.html#adea7e38a6e47cbab7fb5639f208aa536">tvm::DiagnosticContextNode</a>
</li>
<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
, <a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html#a21f639900c480510650969df9c74d17d">tvm::runtime::ModuleNode</a>
</li>
<li>ModuleInternal
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index 83cf3fa59..67fe6e2f6 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -1046,7 +1046,7 @@ $(function() {
, <a class="el" href="classtvm_1_1tir_1_1BufferNode.html#ac18ddd10b79a30ae57d3a8283686259d">tvm::tir::BufferNode</a>
</li>
<li>String()
-: <a class="el" href="classtvm_1_1runtime_1_1String.html#acf549b3c43142639879e0fc31ea5cd77">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#a02fca36e3ff55cc1e83635b02a11fca3">tvm::runtime::String</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj::FromStd</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index be6324dfc..9ece852b2 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -78,7 +78,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a46879dbe84105fb621a6167f8d73b223">tvm::runtime::vm::Instruction</a>
</li>
<li>Target()
-: <a class="el" href="classtvm_1_1Target.html#a77f3d7cc97d8cfd7172af58b4e784d89">tvm::Target</a>
+: <a class="el" href="classtvm_1_1Target.html#ab825b350cf478bf948d807b6fdf636a0">tvm::Target</a>
</li>
<li>target
: <a class="el" href="classtvm_1_1VirtualDeviceNode.html#a8b2d427d9e21886ccaeaae5e9cc55aaf">tvm::VirtualDeviceNode</a>
@@ -1258,10 +1258,10 @@ $(function() {
: <a class="el" href="classtvm_1_1TypeData.html#a0a98fd1095812379d2bd1337db1511c1">tvm::TypeData</a>
</li>
<li>TypedEnvFunc()
-: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a0d72a6fa7263821c14bcd37837998ed9">tvm::TypedEnvFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a41a6b9014d0feeb628ca7edfd0d26f0b">tvm::TypedEnvFunc< R(Args...)></a>
</li>
<li>TypedPackedFunc()
-: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a8941c80982a1b2a289440f3c79bb0ac8">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#afd8ee9dd9648c19b468bb4b0b00e8e4e">tvm::runtime::TypedPackedFunc< R(Args...)></a>
</li>
<li>TypeIndex2Key()
: <a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object</a>
diff --git a/docs/reference/api/doxygen/functions_vars_l.html b/docs/reference/api/doxygen/functions_vars_l.html
index 6f5cbeeae..0d49c39a3 100644
--- a/docs/reference/api/doxygen/functions_vars_l.html
+++ b/docs/reference/api/doxygen/functions_vars_l.html
@@ -107,9 +107,6 @@ $(function() {
<li>left
: <a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html#a02ffb32a51710af5bf595069c2dd7a33">tvm::relay::AltPatternNode</a>
</li>
-<li>legacy_target_map
-: <a class="el" href="classtvm_1_1CompilationConfigNode.html#aea9de8943ac60347359e749067f6bae8">tvm::CompilationConfigNode</a>
-</li>
<li>lengths
: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html#aac7336b4924fc50da9178c271f1cb2ed">tvm::auto_scheduler::SplitStepNode</a>
</li>
diff --git a/docs/reference/api/doxygen/hierarchy.html b/docs/reference/api/doxygen/hierarchy.html
index e73661552..23c8beec4 100644
--- a/docs/reference/api/doxygen/hierarchy.html
+++ b/docs/reference/api/doxygen/hierarchy.html
@@ -601,7 +601,7 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_98_31_1_13_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">tvm::relay::TupleGetItemNode</a></td><td class="desc"></td></tr>
<tr id="row_98_31_1_14_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">tvm::relay::TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
<tr id="row_98_31_1_15_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">tvm::relay::VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_98_32_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">tvm::CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module. Centralizes any setup and validation logic needed to transit [...]
+<tr id="row_98_32_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">tvm::CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Centralize [...]
<tr id="row_98_33_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContextNode.html" target="_self">tvm::DiagnosticContextNode</a></td><td class="desc"></td></tr>
<tr id="row_98_34_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticNode.html" target="_self">tvm::DiagnosticNode</a></td><td class="desc">A compiler diagnostic message </td></tr>
<tr id="row_98_35_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRendererNode.html" target="_self">tvm::DiagnosticRendererNode</a></td><td class="desc">Display diagnostics in a given display format </td></tr>
@@ -866,344 +866,343 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_107_50_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< tvm::Type ></a></td><td class="desc"></td></tr>
<tr id="row_107_51_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< tvm::TypeConstraint ></a></td><td class="desc"></td></tr>
<tr id="row_107_52_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< tvm::TypeVar ></a></td><td class="desc"></td></tr>
-<tr id="row_107_53_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< Integer, Target ></a></td><td class="desc"></td></tr>
-<tr id="row_107_54_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::GlobalTypeVar, tvm::TypeData ></a></td><td class="desc"></td></tr>
-<tr id="row_107_55_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::GlobalVar, tvm::BaseFunc ></a></td><td class="desc"></td></tr>
-<tr id="row_107_56_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::meta_schedule::Mutator, tvm::FloatImm ></a></td><td class="desc"></td></tr>
-<tr id="row_107_57_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::relay::Var, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
-<tr id="row_107_58_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::GlobalTypeVar ></a></td><td class="desc"></td></tr>
-<tr id="row_107_59_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::GlobalVar ></a></td><td class="desc"></td></tr>
-<tr id="row_107_60_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::runtime::Map< tvm::runtime::String, tvm::runtime::ObjectRef > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_61_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
-<tr id="row_107_62_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::SourceName, tvm::parser::Source ></a></td><td class="desc"></td></tr>
-<tr id="row_107_63_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::Target, tvm::Integer ></a></td><td class="desc"></td></tr>
-<tr id="row_107_64_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::Target, tvm::runtime::String ></a></td><td class="desc"></td></tr>
-<tr id="row_107_65_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::te::Operation, tvm::te::Stage ></a></td><td class="desc"></td></tr>
-<tr id="row_107_66_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Instruction, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
-<tr id="row_107_67_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::IterVar, tvm::te::IterVarAttr ></a></td><td class="desc"></td></tr>
-<tr id="row_107_68_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::usmp::BufferInfo, tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
-<tr id="row_107_69_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::PrimExpr ></a></td><td class="desc"></td></tr>
-<tr id="row_107_70_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::Range ></a></td><td class="desc"></td></tr>
-<tr id="row_107_71_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::tir::Buffer ></a></td><td class="desc"></td></tr>
-<tr id="row_107_72_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::DiagnosticContext ></a></td><td class="desc"></td></tr>
-<tr id="row_107_73_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::FloatImm ></a></td><td class="desc"></td></tr>
-<tr id="row_107_74_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Integer ></a></td><td class="desc"></td></tr>
-<tr id="row_107_75_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::IRModule ></a></td><td class="desc"></td></tr>
-<tr id="row_107_76_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::CostModel ></a></td><td class="desc"></td></tr>
-<tr id="row_107_77_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::SearchStrategy ></a></td><td class="desc"></td></tr>
-<tr id="row_107_78_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::SpaceGenerator ></a></td><td class="desc"></td></tr>
-<tr id="row_107_79_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::PrimExpr ></a></td><td class="desc"></td></tr>
-<tr id="row_107_80_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::auto_scheduler::MeasureCallback > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::FloatImm > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::Integer > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_83_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::BuilderResult > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::MeasureCandidate > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::RunnerFuture > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Map< tvm::runtime::String, tvm::runtime::NDArray > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::NDArray ></a></td><td class="desc"></td></tr>
-<tr id="row_107_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
-<tr id="row_107_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::String ></a></td><td class="desc"></td></tr>
-<tr id="row_107_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Target ></a></td><td class="desc"></td></tr>
-<tr id="row_107_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::IterVar ></a></td><td class="desc"></td></tr>
-<tr id="row_107_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
-<tr id="row_107_93_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_93_" class="arrow" onclick="toggleFolder('107_93_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AffineType.html" target="_self">tvm::AffineType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1AffineTypeNode.html" title="AffineType representation. ">Affi [...]
-<tr id="row_107_93_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">tvm::TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
-<tr id="row_107_93_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">tvm::TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
-<tr id="row_107_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html" target="_self">tvm::arith::ConstIntBound</a></td><td class="desc">Reference class to <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html" title="Constant integer up and lower bound(inclusive). Useful for value bound analysis. ">Con [...]
-<tr id="row_107_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html" target="_self">tvm::arith::IntConstraints</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html" title="Represent integer constrains including (integer) variables, their ranges and the [...]
-<tr id="row_107_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html" target="_self">tvm::arith::IntConstraintsTransform</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" title="We can have different set of variables to represent th [...]
-<tr id="row_107_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html" target="_self">tvm::arith::IntGroupBounds</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusiv [...]
-<tr id="row_107_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">tvm::arith::IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> [...]
-<tr id="row_107_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
-<tr id="row_107_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">tvm::arith::ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a [...]
-<tr id="row_107_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrFieldInfo.html" target="_self">tvm::AttrFieldInfo</a></td><td class="desc"><a class="el" href="classtvm_1_1AttrFieldInfo.html" title="AttrFieldInfo. ">AttrFieldInfo</a> </td></tr>
-<tr id="row_107_102_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_102_" class="arrow" onclick="toggleFolder('107_102_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Attrs.html" target="_self">tvm::Attrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseAttrsNode.html" title="Base class of all attribute class. ">Base [...]
-<tr id="row_107_102_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">tvm::DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode< [...]
-<tr id="row_107_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html" target="_self">tvm::auto_scheduler::AccessAnalyzer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html" title="Static analyzer for a ComputeDAG. ">AccessAnalyzerN [...]
-<tr id="row_107_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html" target="_self">tvm::auto_scheduler::AttachMap</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html" title="stores the compute_at relation between stages This stores a bi-dir [...]
-<tr id="row_107_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html" target="_self">tvm::auto_scheduler::BuildResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html" title="Store the result of a build. ">BuildResultNode</a> </td></tr>
-<tr id="row_107_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html" target="_self">tvm::auto_scheduler::ComputeDAG</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html" title="The auto-scheduler's computational graph and related progra [...]
-<tr id="row_107_107_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_107_" class="arrow" onclick="toggleFolder('107_107_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html" target="_self">tvm::auto_scheduler::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CostMo [...]
-<tr id="row_107_107_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">tvm::auto_scheduler::PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python [...]
-<tr id="row_107_107_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">tvm::auto_scheduler::RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. [...]
-<tr id="row_107_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html" target="_self">tvm::auto_scheduler::HardwareParams</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html" title="The parameters of target hardware used to guide the [...]
-<tr id="row_107_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.html" target="_self">tvm::auto_scheduler::Iterator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html" title="An iterator of a for-loop Similar to tvm::IterVar in include/tvm/tir/ [...]
-<tr id="row_107_110_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_110_" class="arrow" onclick="toggleFolder('107_110_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html" target="_self">tvm::auto_scheduler::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__schedul [...]
-<tr id="row_107_110_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html" target="_self">tvm::auto_scheduler::PythonBasedMeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" title="A wrapper for [...]
-<tr id="row_107_110_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">tvm::auto_scheduler::RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measureme [...]
-<tr id="row_107_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html" target="_self">tvm::auto_scheduler::MeasureInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html" title="Store the input of a measurement. ">MeasureInputNode</a> [...]
-<tr id="row_107_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html" target="_self">tvm::auto_scheduler::MeasureResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html" title="Store the results of a measurement. ">MeasureResultNod [...]
-<tr id="row_107_113_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_113_" class="arrow" onclick="toggleFolder('107_113_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html" target="_self">tvm::auto_scheduler::ProgramBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
-<tr id="row_107_113_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html" target="_self">tvm::auto_scheduler::LocalBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html" title="LocalBuilder use local CPU cores to build programs in p [...]
-<tr id="row_107_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurer.html" target="_self">tvm::auto_scheduler::ProgramMeasurer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html" title="Measurer that measures the time costs of tvm pro [...]
-<tr id="row_107_115_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_115_" class="arrow" onclick="toggleFolder('107_115_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" target="_self">tvm::auto_scheduler::ProgramRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1 [...]
-<tr id="row_107_115_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html" target="_self">tvm::auto_scheduler::LocalRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html" title="LocalRunner that uses local CPU/GPU to measure the time co [...]
-<tr id="row_107_115_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">tvm::auto_scheduler::RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of progra [...]
-<tr id="row_107_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">tvm::auto_scheduler::RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderN [...]
-<tr id="row_107_117_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_117_" class="arrow" onclick="toggleFolder('107_117_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">tvm::auto_scheduler::SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
-<tr id="row_107_117_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStates.html" target="_self">tvm::auto_scheduler::PreloadMeasuredStates</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStatesNode.html" title="Preload measured states from [...]
-<tr id="row_107_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">tvm::auto_scheduler::SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> [...]
-<tr id="row_107_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">tvm::auto_scheduler::SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a speci [...]
-<tr id="row_107_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">tvm::auto_scheduler::Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/ [...]
-<tr id="row_107_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">tvm::auto_scheduler::State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a [...]
-<tr id="row_107_122_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_122_" class="arrow" onclick="toggleFolder('107_122_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">tvm::auto_scheduler::Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" t [...]
-<tr id="row_107_122_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html" target="_self">tvm::auto_scheduler::AnnotationStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html" title="Annotation step that corresponds to vectorize, pa [...]
-<tr id="row_107_122_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html" target="_self">tvm::auto_scheduler::CacheReadStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html" title="Cache read step that corresponds to te::Schedule::ca [...]
-<tr id="row_107_122_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html" target="_self">tvm::auto_scheduler::CacheWriteStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html" title="Cache write step that corresponds to te::Schedule [...]
-<tr id="row_107_122_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html" target="_self">tvm::auto_scheduler::ComputeAtStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html" title="Compute at step that corresponds to te::Stage::compu [...]
-<tr id="row_107_122_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html" target="_self">tvm::auto_scheduler::ComputeInlineStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html" title="Compute inline step that corresponds to [...]
-<tr id="row_107_122_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html" target="_self">tvm::auto_scheduler::ComputeRootStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html" title="Compute root step that corresponds to te::Stag [...]
-<tr id="row_107_122_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html" target="_self">tvm::auto_scheduler::FollowFusedSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html" title="Similar to FollowSplitStep, but [...]
-<tr id="row_107_122_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html" target="_self">tvm::auto_scheduler::FollowSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html" title="Similar to SplitStepNode, but uses split facto [...]
-<tr id="row_107_122_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html" target="_self">tvm::auto_scheduler::FuseStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html" title="Fuse step that corresponds to te::Stage::fuse. ">FuseStepNode</a> < [...]
-<tr id="row_107_122_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html" target="_self">tvm::auto_scheduler::PragmaStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStepNode.html" title="Pragma step that corresponds to te::Stage::pragma. ">PragmaSt [...]
-<tr id="row_107_122_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">tvm::auto_scheduler::ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">Re [...]
-<tr id="row_107_122_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">tvm::auto_scheduler::RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::r [...]
-<tr id="row_107_122_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">tvm::auto_scheduler::SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional [...]
-<tr id="row_107_122_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">tvm::auto_scheduler::StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te: [...]
-<tr id="row_107_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">tvm::auto_scheduler::TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a [...]
-<tr id="row_107_124_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_124_" class="arrow" onclick="toggleFolder('107_124_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseExpr.html" target="_self">tvm::BaseExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseExprNode.html" title="Base type of all the expressions. "> [...]
-<tr id="row_107_124_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_124_0_" class="arrow" onclick="toggleFolder('107_124_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">tvm::PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions [...]
-<tr id="row_107_124_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_124_0_0_" class="arrow" onclick="toggleFolder('107_124_0_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html" target="_self">tvm::arith::IterMapExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.htm [...]
-<tr id="row_107_124_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html" target="_self">tvm::arith::IterSplitExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html" title="Split of an iterator. ">IterSplitExprNode</a> </td></tr>
-<tr id="row_107_124_0_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html" target="_self">tvm::arith::IterSumExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html" title="Fuse multiple iterators by summing them with scaling. ">IterSumExprNode</a> </td></tr>
-<tr id="row_107_124_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">tvm::FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
-<tr id="row_107_124_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_124_0_2_" class="arrow" onclick="toggleFolder('107_124_0_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">tvm::IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literal [...]
-<tr id="row_107_124_0_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">tvm::Bool</a></td><td class="desc">Boolean constant </td></tr>
-<tr id="row_107_124_0_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">tvm::Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
-<tr id="row_107_124_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">tvm::tir::Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b ">AddNode</a> </td></tr>
-<tr id="row_107_124_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">tvm::tir::And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b ">AndNode</a> </td></tr>
-<tr id="row_107_124_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">tvm::tir::Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape. ">AnyNode</a> </td></tr>
-<tr id="row_107_124_0_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">tvm::tir::Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value. ">BroadcastNode</a> </td></tr>
-<tr id="row_107_124_0_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">tvm::tir::BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer. ">BufferLoadNode</a> </td></tr>
-<tr id="row_107_124_0_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">tvm::tir::Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node. ">CallNode</a> </td></tr>
-<tr id="row_107_124_0_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">tvm::tir::Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another. ">CastNode</a> </td></tr>
-<tr id="row_107_124_0_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">tvm::tir::Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics. ">DivNode</a> </td></tr>
-<tr id="row_107_124_0_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">tvm::tir::EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b ">EQNode</a> </td></tr>
-<tr id="row_107_124_0_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">tvm::tir::FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b) ">FloorDivNode</a> </td></tr>
-<tr id="row_107_124_0_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">tvm::tir::FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv. ">FloorModNode</a> </td></tr>
-<tr id="row_107_124_0_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">tvm::tir::GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b ">GENode</a> </td></tr>
-<tr id="row_107_124_0_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">tvm::tir::GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b ">GTNode</a> </td></tr>
-<tr id="row_107_124_0_16_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">tvm::tir::LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b ">LENode</a> </td></tr>
-<tr id="row_107_124_0_17_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">tvm::tir::Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body. ">LetNode</a> </td></tr>
-<tr id="row_107_124_0_18_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Load.html" target="_self">tvm::tir::Load</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoadNode.html" title="Load the value from buffer_var. ">LoadNode</a> </td></tr>
-<tr id="row_107_124_0_19_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">tvm::tir::LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b ">LTNode</a> </td></tr>
-<tr id="row_107_124_0_20_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">tvm::tir::Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b) ">MaxNode</a> </td></tr>
-<tr id="row_107_124_0_21_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">tvm::tir::Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b) ">MinNode</a> </td></tr>
-<tr id="row_107_124_0_22_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">tvm::tir::Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics. ">ModNode</a> </td></tr>
-<tr id="row_107_124_0_23_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">tvm::tir::Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b ">MulNode</a> </td></tr>
-<tr id="row_107_124_0_24_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">tvm::tir::NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b ">NENode</a> </td></tr>
-<tr id="row_107_124_0_25_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">tvm::tir::Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a ">NotNode</a> </td></tr>
-<tr id="row_107_124_0_26_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">tvm::tir::Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b ">OrNode</a> </td></tr>
-<tr id="row_107_124_0_27_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">tvm::tir::ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer. ">ProducerLoadNode</a> </td></tr>
-<tr id="row_107_124_0_28_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">tvm::tir::Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to const [...]
-<tr id="row_107_124_0_29_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">tvm::tir::Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator. ">ReduceNode</a> </td></tr>
-<tr id="row_107_124_0_30_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">tvm::tir::Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value. ">SelectNode</a> </td></tr>
-<tr id="row_107_124_0_31_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">tvm::tir::Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...">ShuffleNode [...]
-<tr id="row_107_124_0_32_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">tvm::tir::StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts. ">StringImmNode</a> </td></tr>
-<tr id="row_107_124_0_33_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">tvm::tir::Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b ">SubNode</a> </td></tr>
-<tr id="row_107_124_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_124_0_34_" class="arrow" onclick="toggleFolder('107_124_0_34_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">tvm::tir::Var</a></td><td class="desc">Named variable in TIR </td></tr>
-<tr id="row_107_124_0_34_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">tvm::tir::SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
-<tr id="row_107_124_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_124_1_" class="arrow" onclick="toggleFolder('107_124_1_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">tvm::RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primit [...]
-<tr id="row_107_124_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_124_1_0_" class="arrow" onclick="toggleFolder('107_124_1_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseFunc.html" target="_self">tvm::BaseFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseFuncNode.html" title="Base node of all functio [...]
-<tr id="row_107_124_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">tvm::relay::Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
-<tr id="row_107_124_1_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">tvm::tir::PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
-<tr id="row_107_124_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">tvm::Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
-<tr id="row_107_124_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">tvm::GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
-<tr id="row_107_124_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">tvm::Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
-<tr id="row_107_124_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">tvm::relay::Call</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">tvm::relay::Constant</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">tvm::relay::If</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">tvm::relay::Let</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">tvm::relay::Match</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">tvm::relay::RefCreate</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">tvm::relay::RefRead</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">tvm::relay::RefWrite</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">tvm::relay::TempExpr</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">tvm::relay::Tuple</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">tvm::relay::TupleGetItem</a></td><td class="desc"></td></tr>
-<tr id="row_107_124_1_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">tvm::relay::Var</a></td><td class="desc"></td></tr>
-<tr id="row_107_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">tvm::CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
-<tr id="row_107_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">tvm::Diagnostic</a></td><td class="desc"></td></tr>
-<tr id="row_107_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</a></td><td class="desc"></td></tr>
-<tr id="row_107_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></td></tr>
-<tr id="row_107_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">tvm::EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
-<tr id="row_107_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">tvm::GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_107_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html" target="_self">tvm::instrument::PassInstrument</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It [...]
-<tr id="row_107_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">tvm::IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
-<tr id="row_107_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
-<tr id="row_107_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBest.html" target="_self">tvm::meta_schedule::ApplyHistoryBest</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" title="An integration context that allows application o [...]
-<tr id="row_107_135_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_135_" class="arrow" onclick="toggleFolder('107_135_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">tvm::meta_schedule::ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.h [...]
-<tr id="row_107_135_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">tvm::meta_schedule::TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
-<tr id="row_107_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">tvm::meta_schedule::Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
-<tr id="row_107_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">tvm::meta_schedule::BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the targ [...]
-<tr id="row_107_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">tvm::meta_schedule::BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or [...]
-<tr id="row_107_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html" target="_self">tvm::meta_schedule::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html" title="Cost model. ">CostModelNode</a> </td></tr>
-<tr id="row_107_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">tvm::meta_schedule::Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
-<tr id="row_107_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_self">tvm::meta_schedule::ExtractedTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" title="A tuning task extracted from the high-level IR. ">Extract [...]
-<tr id="row_107_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_self">tvm::meta_schedule::FeatureExtractor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html" title="Extractor for features from measure candidates f [...]
-<tr id="row_107_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_self">tvm::meta_schedule::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html" title="Rules to apply after measure results is available. [...]
-<tr id="row_107_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">tvm::meta_schedule::MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. [...]
-<tr id="row_107_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_self">tvm::meta_schedule::Mutator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MutatorNode.html" title="Mutator is designed to mutate the trace to explore the design space. ">Muta [...]
-<tr id="row_107_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_self">tvm::meta_schedule::Postproc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1PostprocNode.html" title="Rules to apply a postprocessor to a schedule. ">PostprocNode</a> </td></tr>
-<tr id="row_107_147_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">tvm::meta_schedule::Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
-<tr id="row_107_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">tvm::meta_schedule::RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">Runne [...]
-<tr id="row_107_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">tvm::meta_schedule::RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device [...]
-<tr id="row_107_150_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">tvm::meta_schedule::RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of Measure [...]
-<tr id="row_107_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_self">tvm::meta_schedule::ScheduleRule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRuleNode.html" title="Rules to modify a block in a schedule. ">ScheduleRuleNode</a [...]
-<tr id="row_107_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">tvm::meta_schedule::SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. [...]
-<tr id="row_107_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">tvm::meta_schedule::SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">Spac [...]
-<tr id="row_107_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">tvm::meta_schedule::TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedule [...]
-<tr id="row_107_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">tvm::meta_schedule::TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
-<tr id="row_107_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">tvm::meta_schedule::TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
-<tr id="row_107_157_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">tvm::meta_schedule::Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> [...]
-<tr id="row_107_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1Source.html" target="_self">tvm::parser::Source</a></td><td class="desc"></td></tr>
-<tr id="row_107_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</a></td><td class="desc"></td></tr>
-<tr id="row_107_160_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PoolInfo.html" target="_self">tvm::PoolInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_161_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">tvm::Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
-<tr id="row_107_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></td></tr>
-<tr id="row_107_163_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_164_" class="arrow" onclick="toggleFolder('107_164_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">tvm::relay::DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
-<tr id="row_107_164_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AltPattern.html" target="_self">tvm::relay::AltPattern</a></td><td class="desc">A pattern which matches either of two patterns </td></tr>
-<tr id="row_107_164_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html" target="_self">tvm::relay::AttrPattern</a></td><td class="desc">A pattern which matches attributes in another pattern </td></tr>
-<tr id="row_107_164_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">tvm::relay::CallPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">tvm::relay::ConstantPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">tvm::relay::DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_164_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">tvm::relay::DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
-<tr id="row_107_164_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">tvm::relay::ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
-<tr id="row_107_164_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html" target="_self">tvm::relay::FunctionPattern</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
-<tr id="row_107_164_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfPattern.html" target="_self">tvm::relay::IfPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetPattern.html" target="_self">tvm::relay::LetPattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> binding that binds a local var </td></tr>
-<tr id="row_107_164_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">tvm::relay::ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_164_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">tvm::relay::TupleGetItemPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">tvm::relay::TuplePattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">tvm::relay::TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_164_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">tvm::relay::VarPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_164_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">tvm::relay::WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
-<tr id="row_107_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
-<tr id="row_107_166_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Executor.html" target="_self">tvm::relay::Executor</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html" title="Executor information. ">ExecutorNode</a> </td></tr>
-<tr id="row_107_167_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></td></tr>
-<tr id="row_107_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
-<tr id="row_107_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
-<tr id="row_107_170_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
-<tr id="row_107_171_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_171_" class="arrow" onclick="toggleFolder('107_171_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">tvm::relay::Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT mat [...]
-<tr id="row_107_171_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">tvm::relay::PatternConstructor</a></td><td class="desc"></td></tr>
-<tr id="row_107_171_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">tvm::relay::PatternTuple</a></td><td class="desc"></td></tr>
-<tr id="row_107_171_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">tvm::relay::PatternVar</a></td><td class="desc"></td></tr>
-<tr id="row_107_171_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">tvm::relay::PatternWildcard</a></td><td class="desc"></td></tr>
-<tr id="row_107_172_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">tvm::relay::RecClosure</a></td><td class="desc"></td></tr>
-<tr id="row_107_173_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></td></tr>
-<tr id="row_107_174_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Runtime.html" target="_self">tvm::relay::Runtime</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html" title="Runtime information. ">RuntimeNode</a> </td></tr>
-<tr id="row_107_175_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
-<tr id="row_107_176_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< T, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs. ">Array</a>, container representing a [...]
-<tr id="row_107_177_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_177_" class="arrow" onclick="toggleFolder('107_177_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_107_177_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
-<tr id="row_107_177_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">tvm::runtime::vm::VMClosure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_107_178_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< K, V, typename, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which m [...]
-<tr id="row_107_179_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_179_" class="arrow" onclick="toggleFolder('107_179_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html" target="_self">tvm::runtime::metadata::MetadataBase</a></td><td class="desc">Reference class for the common <a class="el" href="classtvm_1_1ru [...]
-<tr id="row_107_179_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" target="_self">tvm::runtime::metadata::MetadataArray</a></td><td class="desc">Reference class for <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" title="Reference class for MetadataArray. ">MetadataA [...]
-<tr id="row_107_180_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">tvm::runtime::Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM. ">Module</a> container of TVM </td></tr>
-<tr id="row_107_181_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">tvm::runtime::NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by reference counted blocks. ">NDArray</a>. The array is backed by referenc [...]
-<tr id="row_107_182_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T. ">Optional</a> container that to represen [...]
-<tr id="row_107_183_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">tvm::runtime::PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
-<tr id="row_107_184_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </td></tr>
-<tr id="row_107_185_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">tvm::runtime::profiling::MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling [...]
-<tr id="row_107_186_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></td></tr>
-<tr id="row_107_187_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
-<tr id="row_107_188_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </td></tr>
-<tr id="row_107_189_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">tvm::runtime::Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device. ">Timer</a> for a specific device </td></tr>
-<tr id="row_107_190_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Storage.html" target="_self">tvm::runtime::vm::Storage</a></td><td class="desc">Reference to storage </td></tr>
-<tr id="row_107_191_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </td></tr>
-<tr id="row_107_192_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></td></tr>
-<tr id="row_107_193_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">tvm::Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
-<tr id="row_107_194_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">tvm::TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
-<tr id="row_107_195_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">tvm::TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
-<tr id="row_107_196_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">tvm::te::IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
-<tr id="row_107_197_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_197_" class="arrow" onclick="toggleFolder('107_197_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">tvm::te::IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be <a class="el" href="classtvm_1_1te_1_1Split.h [...]
-<tr id="row_107_197_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Fuse.html" target="_self">tvm::te::Fuse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1FuseNode.html" title="Fuse two domains into one domain. ">FuseNode</a> </td></tr>
-<tr id="row_107_197_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Rebase.html" target="_self">tvm::te::Rebase</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1RebaseNode.html" title="Rebase the iteration to make min to be 0. This is useful to normalize the Schedule to make every leaf...">Rebas [...]
-<tr id="row_107_197_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Singleton.html" target="_self">tvm::te::Singleton</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SingletonNode.html" title="Singleton iterator [0, 1) ">SingletonNode</a> </td></tr>
-<tr id="row_107_197_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Split.html" target="_self">tvm::te::Split</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SplitNode.html" title="Split the parent domain into product of outer and iter. ">SplitNode</a> </td></tr>
-<tr id="row_107_197_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Transform.html" target="_self">tvm::te::Transform</a></td><td class="desc"></td></tr>
-<tr id="row_107_198_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_198_" class="arrow" onclick="toggleFolder('107_198_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">tvm::te::Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operati [...]
-<tr id="row_107_198_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOp.html" target="_self">tvm::te::ComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" title="A Compute op that compute a tensor on certain domain. ">ComputeOpNode</a> </td></tr>
-<tr id="row_107_198_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOp.html" target="_self">tvm::te::ExternOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" title="External computation that cannot be splitted. ">ExternOpNode</a> </td></tr>
-<tr id="row_107_198_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOp.html" target="_self">tvm::te::HybridOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" title="A computation operator that generated by hybrid script. ">HybridOpNode</a> </td></tr>
-<tr id="row_107_198_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html" target="_self">tvm::te::PlaceholderOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" title="A placeholder op represents an input placeholder. ">PlaceholderOpNode</a> </td></tr>
-<tr id="row_107_198_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOp.html" target="_self">tvm::te::ScanOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" title="Symbolic scan. ">ScanOpNode</a> </td></tr>
-<tr id="row_107_198_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html" target="_self">tvm::te::TensorComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" title="A TenorCompute op that compute a tensor with an tensor intrinsic. ">TensorComputeOpN [...]
-<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Schedule.html" target="_self">tvm::te::Schedule</a></td><td class="desc">Global schedule container For operations and all the operations they depend on. The schedule per <a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. " [...]
-<tr id="row_107_200_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </td></tr>
-<tr id="row_107_201_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Stage.html" target="_self">tvm::te::Stage</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a>, contains scheduling for a stage of computation </td></tr>
-<tr id="row_107_202_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">tvm::te::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" title="Node to represent a Tensor intrinsic operator. ">TensorIntrinNode</a> </td></tr>
-<tr id="row_107_203_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">tvm::te::TensorIntrinCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> </td></tr>
-<tr id="row_107_204_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">tvm::tir::BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a>, < [...]
-<tr id="row_107_205_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRV.html" target="_self">tvm::tir::BlockRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html" title="A random variable that evaluates to a TensorIR block. ">BlockRVNode</a> </td></tr>
-<tr id="row_107_206_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">tvm::tir::BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structu [...]
-<tr id="row_107_207_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">tvm::tir::Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structur [...]
-<tr id="row_107_208_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html" target="_self">tvm::tir::BufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html" title="Representing the region of multi-dimensional buffer access. ">BufferRegionNode</a> </td></tr>
-<tr id="row_107_209_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">tvm::tir::CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element..." [...]
-<tr id="row_107_210_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_210_" class="arrow" onclick="toggleFolder('107_210_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">tvm::tir::DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base [...]
-<tr id="row_107_210_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor.html" target="_self">tvm::te::Tensor</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> structure representing a possible input [...]
-<tr id="row_107_211_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">tvm::tir::Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A, B, kRAW) means [...]
-<tr id="row_107_212_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></td></tr>
-<tr id="row_107_213_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Instruction.html" target="_self">tvm::tir::Instruction</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionNode.html" title="Schedule instructions each corresponds to a schedule primitive. ">InstructionNode</a> </td></tr>
-<tr id="row_107_214_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html" target="_self">tvm::tir::InstructionKind</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" title="Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of i [...]
-<tr id="row_107_215_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">tvm::tir::IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
-<tr id="row_107_216_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">tvm::tir::Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">Layo [...]
-<tr id="row_107_217_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoopRV.html" target="_self">tvm::tir::LoopRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoopRVNode.html" title="A random variable that evaluates to a TensorIR for loop. ">LoopRVNode</a> </td></tr>
-<tr id="row_107_218_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MatchBufferRegion.html" target="_self">tvm::tir::MatchBufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MatchBufferRegionNode.html" title="Match introduces a constraint that the source buffer region can be remapped t [...]
-<tr id="row_107_219_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Schedule.html" target="_self">tvm::tir::Schedule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html" title="The user-facing schedule class. ">ScheduleNode</a> </td></tr>
-<tr id="row_107_220_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ScheduleState.html" target="_self">tvm::tir::ScheduleState</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html" title="The state of scheduling, which exposes a Replace method as the primary interface for all [...]
-<tr id="row_107_221_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_221_" class="arrow" onclick="toggleFolder('107_221_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">tvm::tir::Stmt</a></td><td class="desc">Container of all statements </td></tr>
-<tr id="row_107_221_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Allocate.html" target="_self">tvm::tir::Allocate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" title="Allocate a buffer that can be used in body. ">AllocateNode</a> </td></tr>
-<tr id="row_107_221_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateConst.html" target="_self">tvm::tir::AllocateConst</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html" title="Allocate a buffer that can be used in body. ">AllocateConstNode</a> </td></tr>
-<tr id="row_107_221_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html" target="_self">tvm::tir::AssertStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" title="Assert condition, if an error occurs, return the error message. ">AssertStmtNode</a> </td></tr>
-<tr id="row_107_221_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html" target="_self">tvm::tir::AttrStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" title="Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary inform. [...]
-<tr id="row_107_221_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Block.html" target="_self">tvm::tir::Block</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockNode.html" title="A block is a basic schedule unit in TIR. ">BlockNode</a> </td></tr>
-<tr id="row_107_221_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html" target="_self">tvm::tir::BlockRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html" title="A block realization node represents execution of the block at the binding values. ...">Blo [...]
-<tr id="row_107_221_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">tvm::tir::BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only nee [...]
-<tr id="row_107_221_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">tvm::tir::BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
-<tr id="row_107_221_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Evaluate.html" target="_self">tvm::tir::Evaluate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" title="Evaluates an expression. This is mostly used for putting a Call node into Stmt. ">EvaluateNode</a> </td></tr>
-<tr id="row_107_221_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1For.html" target="_self">tvm::tir::For</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ForNode.html" title="A for loop, with poissible type annotations. ">ForNode</a> </td></tr>
-<tr id="row_107_221_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" target="_self">tvm::tir::IfThenElse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" title="IfThenElse statment. ">IfThenElseNode</a> </td></tr>
-<tr id="row_107_221_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmt.html" target="_self">tvm::tir::LetStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" title="Let binding, bind var to value, then run body. ">LetStmtNode</a> </td></tr>
-<tr id="row_107_221_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">tvm::tir::Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for a buffer. ">PrefetchNode</a> </td></tr>
-<tr id="row_107_221_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealize.html" target="_self">tvm::tir::ProducerRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" title="Annotate the bounds where the data produced by the producer need to be written a [...]
-<tr id="row_107_221_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html" target="_self">tvm::tir::ProducerStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" title="Store value into mult-dimensional array that will be read by the consumer of the produ [...]
-<tr id="row_107_221_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">tvm::tir::SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
-<tr id="row_107_221_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Store.html" target="_self">tvm::tir::Store</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StoreNode.html" title="Store value to the buffer. ">StoreNode</a> </td></tr>
-<tr id="row_107_221_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1While.html" target="_self">tvm::tir::While</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1WhileNode.html" title="A While loop. ">WhileNode</a> </td></tr>
-<tr id="row_107_222_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">tvm::tir::StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref". ">Stmt [...]
-<tr id="row_107_223_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">tvm::tir::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization. ">TensorIntrinNode</a> </td></tr>
-<tr id="row_107_224_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Trace.html" target="_self">tvm::tir::Trace</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TraceNode.html" title="An execution trace of a scheduling program. ">TraceNode</a> </td></tr>
-<tr id="row_107_225_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_226_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_227_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</a></td><td class="desc"></td></tr>
-<tr id="row_107_228_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
-<tr id="row_107_229_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_229_" class="arrow" onclick="toggleFolder('107_229_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Pass.html" target="_self">tvm::transform::Pass</a></td><td class="desc"></td></tr>
-<tr id="row_107_229_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Sequential.html" target="_self">tvm::transform::Sequential</a></td><td class="desc"></td></tr>
-<tr id="row_107_230_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassContext.html" target="_self">tvm::transform::PassContext</a></td><td class="desc"><a class="el" href="classtvm_1_1transform_1_1PassContext.html" title="PassContext that is used to configure the pass behavior. ">PassContext</a> that is used to configure t [...]
-<tr id="row_107_231_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassInfo.html" target="_self">tvm::transform::PassInfo</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html" title="Meta data that will be used to help optimization and analysis. ">PassInfoNode</ [...]
-<tr id="row_107_232_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_232_" class="arrow" onclick="toggleFolder('107_232_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">tvm::Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</ [...]
-<tr id="row_107_232_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">tvm::BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType. ...">BaseTensorTypeNo [...]
-<tr id="row_107_232_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">tvm::FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
-<tr id="row_107_232_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">tvm::GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
-<tr id="row_107_232_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">tvm::IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNod [...]
-<tr id="row_107_232_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">tvm::PointerType</a></td><td class="desc"></td></tr>
-<tr id="row_107_232_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">tvm::PrimType</a></td><td class="desc"></td></tr>
-<tr id="row_107_232_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">tvm::RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
-<tr id="row_107_232_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">tvm::TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> < [...]
-<tr id="row_107_232_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">tvm::TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
-<tr id="row_107_232_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">tvm::TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
-<tr id="row_107_232_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_232_10_" class="arrow" onclick="toggleFolder('107_232_10_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">tvm::TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential [...]
-<tr id="row_107_232_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">tvm::TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
-<tr id="row_107_232_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">tvm::TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
-<tr id="row_107_232_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">tvm::TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
-<tr id="row_107_233_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNo [...]
-<tr id="row_107_234_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">tvm::TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
-<tr id="row_107_235_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDevice.html" target="_self">tvm::VirtualDevice</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1VirtualDeviceNode.html" title="Describes at compile time the constraints on where data is to be stored at runtime down to the ( [...]
-<tr id="row_107_236_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1WorkspaceMemoryPools.html" target="_self">tvm::WorkspaceMemoryPools</a></td><td class="desc"></td></tr>
+<tr id="row_107_53_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::GlobalTypeVar, tvm::TypeData ></a></td><td class="desc"></td></tr>
+<tr id="row_107_54_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::GlobalVar, tvm::BaseFunc ></a></td><td class="desc"></td></tr>
+<tr id="row_107_55_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::meta_schedule::Mutator, tvm::FloatImm ></a></td><td class="desc"></td></tr>
+<tr id="row_107_56_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::relay::Var, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
+<tr id="row_107_57_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::GlobalTypeVar ></a></td><td class="desc"></td></tr>
+<tr id="row_107_58_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::GlobalVar ></a></td><td class="desc"></td></tr>
+<tr id="row_107_59_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::runtime::Map< tvm::runtime::String, tvm::runtime::ObjectRef > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_60_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::runtime::String, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
+<tr id="row_107_61_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::SourceName, tvm::parser::Source ></a></td><td class="desc"></td></tr>
+<tr id="row_107_62_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::Target, tvm::Integer ></a></td><td class="desc"></td></tr>
+<tr id="row_107_63_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::Target, tvm::runtime::String ></a></td><td class="desc"></td></tr>
+<tr id="row_107_64_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::te::Operation, tvm::te::Stage ></a></td><td class="desc"></td></tr>
+<tr id="row_107_65_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Instruction, tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
+<tr id="row_107_66_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::IterVar, tvm::te::IterVarAttr ></a></td><td class="desc"></td></tr>
+<tr id="row_107_67_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::usmp::BufferInfo, tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
+<tr id="row_107_68_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::PrimExpr ></a></td><td class="desc"></td></tr>
+<tr id="row_107_69_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::Range ></a></td><td class="desc"></td></tr>
+<tr id="row_107_70_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< tvm::tir::Var, tvm::tir::Buffer ></a></td><td class="desc"></td></tr>
+<tr id="row_107_71_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::DiagnosticContext ></a></td><td class="desc"></td></tr>
+<tr id="row_107_72_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::FloatImm ></a></td><td class="desc"></td></tr>
+<tr id="row_107_73_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Integer ></a></td><td class="desc"></td></tr>
+<tr id="row_107_74_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::IRModule ></a></td><td class="desc"></td></tr>
+<tr id="row_107_75_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::CostModel ></a></td><td class="desc"></td></tr>
+<tr id="row_107_76_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::SearchStrategy ></a></td><td class="desc"></td></tr>
+<tr id="row_107_77_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::meta_schedule::SpaceGenerator ></a></td><td class="desc"></td></tr>
+<tr id="row_107_78_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::PrimExpr ></a></td><td class="desc"></td></tr>
+<tr id="row_107_79_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::auto_scheduler::MeasureCallback > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_80_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::FloatImm > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::Integer > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::BuilderResult > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_83_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::MeasureCandidate > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::RunnerFuture > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Map< tvm::runtime::String, tvm::runtime::NDArray > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::NDArray ></a></td><td class="desc"></td></tr>
+<tr id="row_107_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
+<tr id="row_107_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::String ></a></td><td class="desc"></td></tr>
+<tr id="row_107_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Target ></a></td><td class="desc"></td></tr>
+<tr id="row_107_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::IterVar ></a></td><td class="desc"></td></tr>
+<tr id="row_107_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
+<tr id="row_107_92_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_92_" class="arrow" onclick="toggleFolder('107_92_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AffineType.html" target="_self">tvm::AffineType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1AffineTypeNode.html" title="AffineType representation. ">Affi [...]
+<tr id="row_107_92_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">tvm::TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
+<tr id="row_107_92_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">tvm::TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
+<tr id="row_107_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html" target="_self">tvm::arith::ConstIntBound</a></td><td class="desc">Reference class to <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html" title="Constant integer up and lower bound(inclusive). Useful for value bound analysis. ">Con [...]
+<tr id="row_107_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html" target="_self">tvm::arith::IntConstraints</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html" title="Represent integer constrains including (integer) variables, their ranges and the [...]
+<tr id="row_107_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html" target="_self">tvm::arith::IntConstraintsTransform</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" title="We can have different set of variables to represent th [...]
+<tr id="row_107_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html" target="_self">tvm::arith::IntGroupBounds</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusiv [...]
+<tr id="row_107_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">tvm::arith::IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> [...]
+<tr id="row_107_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
+<tr id="row_107_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">tvm::arith::ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a> [...]
+<tr id="row_107_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrFieldInfo.html" target="_self">tvm::AttrFieldInfo</a></td><td class="desc"><a class="el" href="classtvm_1_1AttrFieldInfo.html" title="AttrFieldInfo. ">AttrFieldInfo</a> </td></tr>
+<tr id="row_107_101_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_101_" class="arrow" onclick="toggleFolder('107_101_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Attrs.html" target="_self">tvm::Attrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseAttrsNode.html" title="Base class of all attribute class. ">Base [...]
+<tr id="row_107_101_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">tvm::DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode< [...]
+<tr id="row_107_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html" target="_self">tvm::auto_scheduler::AccessAnalyzer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html" title="Static analyzer for a ComputeDAG. ">AccessAnalyzerN [...]
+<tr id="row_107_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html" target="_self">tvm::auto_scheduler::AttachMap</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html" title="stores the compute_at relation between stages This stores a bi-dir [...]
+<tr id="row_107_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html" target="_self">tvm::auto_scheduler::BuildResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html" title="Store the result of a build. ">BuildResultNode</a> </td></tr>
+<tr id="row_107_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html" target="_self">tvm::auto_scheduler::ComputeDAG</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html" title="The auto-scheduler's computational graph and related progra [...]
+<tr id="row_107_106_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_106_" class="arrow" onclick="toggleFolder('107_106_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html" target="_self">tvm::auto_scheduler::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CostMo [...]
+<tr id="row_107_106_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">tvm::auto_scheduler::PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python [...]
+<tr id="row_107_106_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">tvm::auto_scheduler::RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. [...]
+<tr id="row_107_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html" target="_self">tvm::auto_scheduler::HardwareParams</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html" title="The parameters of target hardware used to guide the [...]
+<tr id="row_107_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.html" target="_self">tvm::auto_scheduler::Iterator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html" title="An iterator of a for-loop Similar to tvm::IterVar in include/tvm/tir/ [...]
+<tr id="row_107_109_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_109_" class="arrow" onclick="toggleFolder('107_109_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html" target="_self">tvm::auto_scheduler::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__schedul [...]
+<tr id="row_107_109_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html" target="_self">tvm::auto_scheduler::PythonBasedMeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" title="A wrapper for [...]
+<tr id="row_107_109_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">tvm::auto_scheduler::RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measureme [...]
+<tr id="row_107_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html" target="_self">tvm::auto_scheduler::MeasureInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html" title="Store the input of a measurement. ">MeasureInputNode</a> [...]
+<tr id="row_107_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html" target="_self">tvm::auto_scheduler::MeasureResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html" title="Store the results of a measurement. ">MeasureResultNod [...]
+<tr id="row_107_112_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_112_" class="arrow" onclick="toggleFolder('107_112_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html" target="_self">tvm::auto_scheduler::ProgramBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
+<tr id="row_107_112_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html" target="_self">tvm::auto_scheduler::LocalBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html" title="LocalBuilder use local CPU cores to build programs in p [...]
+<tr id="row_107_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurer.html" target="_self">tvm::auto_scheduler::ProgramMeasurer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html" title="Measurer that measures the time costs of tvm pro [...]
+<tr id="row_107_114_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_114_" class="arrow" onclick="toggleFolder('107_114_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" target="_self">tvm::auto_scheduler::ProgramRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1 [...]
+<tr id="row_107_114_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html" target="_self">tvm::auto_scheduler::LocalRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html" title="LocalRunner that uses local CPU/GPU to measure the time co [...]
+<tr id="row_107_114_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">tvm::auto_scheduler::RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of progra [...]
+<tr id="row_107_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">tvm::auto_scheduler::RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderN [...]
+<tr id="row_107_116_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_116_" class="arrow" onclick="toggleFolder('107_116_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">tvm::auto_scheduler::SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
+<tr id="row_107_116_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStates.html" target="_self">tvm::auto_scheduler::PreloadMeasuredStates</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStatesNode.html" title="Preload measured states from [...]
+<tr id="row_107_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">tvm::auto_scheduler::SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> [...]
+<tr id="row_107_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">tvm::auto_scheduler::SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a speci [...]
+<tr id="row_107_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">tvm::auto_scheduler::Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/ [...]
+<tr id="row_107_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">tvm::auto_scheduler::State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a [...]
+<tr id="row_107_121_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_121_" class="arrow" onclick="toggleFolder('107_121_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">tvm::auto_scheduler::Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" t [...]
+<tr id="row_107_121_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html" target="_self">tvm::auto_scheduler::AnnotationStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html" title="Annotation step that corresponds to vectorize, pa [...]
+<tr id="row_107_121_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html" target="_self">tvm::auto_scheduler::CacheReadStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html" title="Cache read step that corresponds to te::Schedule::ca [...]
+<tr id="row_107_121_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html" target="_self">tvm::auto_scheduler::CacheWriteStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html" title="Cache write step that corresponds to te::Schedule [...]
+<tr id="row_107_121_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html" target="_self">tvm::auto_scheduler::ComputeAtStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html" title="Compute at step that corresponds to te::Stage::compu [...]
+<tr id="row_107_121_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html" target="_self">tvm::auto_scheduler::ComputeInlineStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html" title="Compute inline step that corresponds to [...]
+<tr id="row_107_121_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html" target="_self">tvm::auto_scheduler::ComputeRootStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html" title="Compute root step that corresponds to te::Stag [...]
+<tr id="row_107_121_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html" target="_self">tvm::auto_scheduler::FollowFusedSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html" title="Similar to FollowSplitStep, but [...]
+<tr id="row_107_121_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html" target="_self">tvm::auto_scheduler::FollowSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html" title="Similar to SplitStepNode, but uses split facto [...]
+<tr id="row_107_121_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html" target="_self">tvm::auto_scheduler::FuseStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html" title="Fuse step that corresponds to te::Stage::fuse. ">FuseStepNode</a> < [...]
+<tr id="row_107_121_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html" target="_self">tvm::auto_scheduler::PragmaStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStepNode.html" title="Pragma step that corresponds to te::Stage::pragma. ">PragmaSt [...]
+<tr id="row_107_121_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">tvm::auto_scheduler::ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">Re [...]
+<tr id="row_107_121_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">tvm::auto_scheduler::RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::r [...]
+<tr id="row_107_121_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">tvm::auto_scheduler::SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional [...]
+<tr id="row_107_121_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">tvm::auto_scheduler::StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te: [...]
+<tr id="row_107_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">tvm::auto_scheduler::TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a [...]
+<tr id="row_107_123_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_123_" class="arrow" onclick="toggleFolder('107_123_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseExpr.html" target="_self">tvm::BaseExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseExprNode.html" title="Base type of all the expressions. "> [...]
+<tr id="row_107_123_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_123_0_" class="arrow" onclick="toggleFolder('107_123_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">tvm::PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions [...]
+<tr id="row_107_123_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_123_0_0_" class="arrow" onclick="toggleFolder('107_123_0_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html" target="_self">tvm::arith::IterMapExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.htm [...]
+<tr id="row_107_123_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html" target="_self">tvm::arith::IterSplitExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html" title="Split of an iterator. ">IterSplitExprNode</a> </td></tr>
+<tr id="row_107_123_0_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html" target="_self">tvm::arith::IterSumExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html" title="Fuse multiple iterators by summing them with scaling. ">IterSumExprNode</a> </td></tr>
+<tr id="row_107_123_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">tvm::FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
+<tr id="row_107_123_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_123_0_2_" class="arrow" onclick="toggleFolder('107_123_0_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">tvm::IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literal [...]
+<tr id="row_107_123_0_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">tvm::Bool</a></td><td class="desc">Boolean constant </td></tr>
+<tr id="row_107_123_0_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">tvm::Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
+<tr id="row_107_123_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">tvm::tir::Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b ">AddNode</a> </td></tr>
+<tr id="row_107_123_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">tvm::tir::And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b ">AndNode</a> </td></tr>
+<tr id="row_107_123_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">tvm::tir::Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape. ">AnyNode</a> </td></tr>
+<tr id="row_107_123_0_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">tvm::tir::Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value. ">BroadcastNode</a> </td></tr>
+<tr id="row_107_123_0_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">tvm::tir::BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer. ">BufferLoadNode</a> </td></tr>
+<tr id="row_107_123_0_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">tvm::tir::Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node. ">CallNode</a> </td></tr>
+<tr id="row_107_123_0_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">tvm::tir::Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another. ">CastNode</a> </td></tr>
+<tr id="row_107_123_0_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">tvm::tir::Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics. ">DivNode</a> </td></tr>
+<tr id="row_107_123_0_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">tvm::tir::EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b ">EQNode</a> </td></tr>
+<tr id="row_107_123_0_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">tvm::tir::FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b) ">FloorDivNode</a> </td></tr>
+<tr id="row_107_123_0_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">tvm::tir::FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv. ">FloorModNode</a> </td></tr>
+<tr id="row_107_123_0_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">tvm::tir::GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b ">GENode</a> </td></tr>
+<tr id="row_107_123_0_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">tvm::tir::GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b ">GTNode</a> </td></tr>
+<tr id="row_107_123_0_16_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">tvm::tir::LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b ">LENode</a> </td></tr>
+<tr id="row_107_123_0_17_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">tvm::tir::Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body. ">LetNode</a> </td></tr>
+<tr id="row_107_123_0_18_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Load.html" target="_self">tvm::tir::Load</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoadNode.html" title="Load the value from buffer_var. ">LoadNode</a> </td></tr>
+<tr id="row_107_123_0_19_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">tvm::tir::LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b ">LTNode</a> </td></tr>
+<tr id="row_107_123_0_20_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">tvm::tir::Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b) ">MaxNode</a> </td></tr>
+<tr id="row_107_123_0_21_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">tvm::tir::Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b) ">MinNode</a> </td></tr>
+<tr id="row_107_123_0_22_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">tvm::tir::Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics. ">ModNode</a> </td></tr>
+<tr id="row_107_123_0_23_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">tvm::tir::Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b ">MulNode</a> </td></tr>
+<tr id="row_107_123_0_24_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">tvm::tir::NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b ">NENode</a> </td></tr>
+<tr id="row_107_123_0_25_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">tvm::tir::Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a ">NotNode</a> </td></tr>
+<tr id="row_107_123_0_26_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">tvm::tir::Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b ">OrNode</a> </td></tr>
+<tr id="row_107_123_0_27_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">tvm::tir::ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer. ">ProducerLoadNode</a> </td></tr>
+<tr id="row_107_123_0_28_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">tvm::tir::Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to const [...]
+<tr id="row_107_123_0_29_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">tvm::tir::Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator. ">ReduceNode</a> </td></tr>
+<tr id="row_107_123_0_30_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">tvm::tir::Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value. ">SelectNode</a> </td></tr>
+<tr id="row_107_123_0_31_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">tvm::tir::Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...">ShuffleNode [...]
+<tr id="row_107_123_0_32_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">tvm::tir::StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts. ">StringImmNode</a> </td></tr>
+<tr id="row_107_123_0_33_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">tvm::tir::Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b ">SubNode</a> </td></tr>
+<tr id="row_107_123_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_123_0_34_" class="arrow" onclick="toggleFolder('107_123_0_34_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">tvm::tir::Var</a></td><td class="desc">Named variable in TIR </td></tr>
+<tr id="row_107_123_0_34_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">tvm::tir::SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
+<tr id="row_107_123_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_123_1_" class="arrow" onclick="toggleFolder('107_123_1_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">tvm::RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primit [...]
+<tr id="row_107_123_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_123_1_0_" class="arrow" onclick="toggleFolder('107_123_1_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseFunc.html" target="_self">tvm::BaseFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseFuncNode.html" title="Base node of all functio [...]
+<tr id="row_107_123_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">tvm::relay::Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_107_123_1_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">tvm::tir::PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
+<tr id="row_107_123_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">tvm::Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
+<tr id="row_107_123_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">tvm::GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
+<tr id="row_107_123_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">tvm::Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
+<tr id="row_107_123_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">tvm::relay::Call</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">tvm::relay::Constant</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">tvm::relay::If</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">tvm::relay::Let</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">tvm::relay::Match</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">tvm::relay::RefCreate</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">tvm::relay::RefRead</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">tvm::relay::RefWrite</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">tvm::relay::TempExpr</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">tvm::relay::Tuple</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">tvm::relay::TupleGetItem</a></td><td class="desc"></td></tr>
+<tr id="row_107_123_1_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">tvm::relay::Var</a></td><td class="desc"></td></tr>
+<tr id="row_107_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">tvm::CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
+<tr id="row_107_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">tvm::Diagnostic</a></td><td class="desc"></td></tr>
+<tr id="row_107_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></td></tr>
+<tr id="row_107_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">tvm::EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
+<tr id="row_107_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">tvm::GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_107_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html" target="_self">tvm::instrument::PassInstrument</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It [...]
+<tr id="row_107_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">tvm::IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
+<tr id="row_107_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
+<tr id="row_107_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBest.html" target="_self">tvm::meta_schedule::ApplyHistoryBest</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" title="An integration context that allows application o [...]
+<tr id="row_107_134_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_134_" class="arrow" onclick="toggleFolder('107_134_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">tvm::meta_schedule::ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.h [...]
+<tr id="row_107_134_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">tvm::meta_schedule::TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
+<tr id="row_107_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">tvm::meta_schedule::Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
+<tr id="row_107_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">tvm::meta_schedule::BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the targ [...]
+<tr id="row_107_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">tvm::meta_schedule::BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or [...]
+<tr id="row_107_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html" target="_self">tvm::meta_schedule::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html" title="Cost model. ">CostModelNode</a> </td></tr>
+<tr id="row_107_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">tvm::meta_schedule::Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
+<tr id="row_107_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_self">tvm::meta_schedule::ExtractedTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" title="A tuning task extracted from the high-level IR. ">Extract [...]
+<tr id="row_107_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_self">tvm::meta_schedule::FeatureExtractor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html" title="Extractor for features from measure candidates f [...]
+<tr id="row_107_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_self">tvm::meta_schedule::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html" title="Rules to apply after measure results is available. [...]
+<tr id="row_107_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">tvm::meta_schedule::MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. [...]
+<tr id="row_107_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_self">tvm::meta_schedule::Mutator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MutatorNode.html" title="Mutator is designed to mutate the trace to explore the design space. ">Muta [...]
+<tr id="row_107_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_self">tvm::meta_schedule::Postproc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1PostprocNode.html" title="Rules to apply a postprocessor to a schedule. ">PostprocNode</a> </td></tr>
+<tr id="row_107_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">tvm::meta_schedule::Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
+<tr id="row_107_147_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">tvm::meta_schedule::RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">Runne [...]
+<tr id="row_107_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">tvm::meta_schedule::RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device [...]
+<tr id="row_107_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">tvm::meta_schedule::RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of Measure [...]
+<tr id="row_107_150_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_self">tvm::meta_schedule::ScheduleRule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRuleNode.html" title="Rules to modify a block in a schedule. ">ScheduleRuleNode</a [...]
+<tr id="row_107_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">tvm::meta_schedule::SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. [...]
+<tr id="row_107_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">tvm::meta_schedule::SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">Spac [...]
+<tr id="row_107_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">tvm::meta_schedule::TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedule [...]
+<tr id="row_107_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">tvm::meta_schedule::TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
+<tr id="row_107_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">tvm::meta_schedule::TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
+<tr id="row_107_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">tvm::meta_schedule::Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> [...]
+<tr id="row_107_157_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1Source.html" target="_self">tvm::parser::Source</a></td><td class="desc"></td></tr>
+<tr id="row_107_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</a></td><td class="desc"></td></tr>
+<tr id="row_107_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PoolInfo.html" target="_self">tvm::PoolInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_160_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">tvm::Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
+<tr id="row_107_161_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></td></tr>
+<tr id="row_107_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_163_" class="arrow" onclick="toggleFolder('107_163_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">tvm::relay::DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
+<tr id="row_107_163_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AltPattern.html" target="_self">tvm::relay::AltPattern</a></td><td class="desc">A pattern which matches either of two patterns </td></tr>
+<tr id="row_107_163_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html" target="_self">tvm::relay::AttrPattern</a></td><td class="desc">A pattern which matches attributes in another pattern </td></tr>
+<tr id="row_107_163_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">tvm::relay::CallPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">tvm::relay::ConstantPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">tvm::relay::DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_163_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">tvm::relay::DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
+<tr id="row_107_163_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">tvm::relay::ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
+<tr id="row_107_163_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html" target="_self">tvm::relay::FunctionPattern</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_107_163_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfPattern.html" target="_self">tvm::relay::IfPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetPattern.html" target="_self">tvm::relay::LetPattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> binding that binds a local var </td></tr>
+<tr id="row_107_163_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">tvm::relay::ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_163_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">tvm::relay::TupleGetItemPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">tvm::relay::TuplePattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">tvm::relay::TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_163_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">tvm::relay::VarPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">tvm::relay::WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
+<tr id="row_107_164_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
+<tr id="row_107_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Executor.html" target="_self">tvm::relay::Executor</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html" title="Executor information. ">ExecutorNode</a> </td></tr>
+<tr id="row_107_166_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
+<tr id="row_107_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
+<tr id="row_107_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_107_170_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_170_" class="arrow" onclick="toggleFolder('107_170_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">tvm::relay::Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT mat [...]
+<tr id="row_107_170_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">tvm::relay::PatternConstructor</a></td><td class="desc"></td></tr>
+<tr id="row_107_170_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">tvm::relay::PatternTuple</a></td><td class="desc"></td></tr>
+<tr id="row_107_170_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">tvm::relay::PatternVar</a></td><td class="desc"></td></tr>
+<tr id="row_107_170_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">tvm::relay::PatternWildcard</a></td><td class="desc"></td></tr>
+<tr id="row_107_171_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">tvm::relay::RecClosure</a></td><td class="desc"></td></tr>
+<tr id="row_107_172_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></td></tr>
+<tr id="row_107_173_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Runtime.html" target="_self">tvm::relay::Runtime</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html" title="Runtime information. ">RuntimeNode</a> </td></tr>
+<tr id="row_107_174_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
+<tr id="row_107_175_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< T, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs. ">Array</a>, container representing a [...]
+<tr id="row_107_176_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_176_" class="arrow" onclick="toggleFolder('107_176_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_107_176_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_107_176_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">tvm::runtime::vm::VMClosure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_107_177_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< K, V, typename, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which m [...]
+<tr id="row_107_178_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_178_" class="arrow" onclick="toggleFolder('107_178_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html" target="_self">tvm::runtime::metadata::MetadataBase</a></td><td class="desc">Reference class for the common <a class="el" href="classtvm_1_1ru [...]
+<tr id="row_107_178_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" target="_self">tvm::runtime::metadata::MetadataArray</a></td><td class="desc">Reference class for <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" title="Reference class for MetadataArray. ">MetadataA [...]
+<tr id="row_107_179_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">tvm::runtime::Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM. ">Module</a> container of TVM </td></tr>
+<tr id="row_107_180_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">tvm::runtime::NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by reference counted blocks. ">NDArray</a>. The array is backed by referenc [...]
+<tr id="row_107_181_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T. ">Optional</a> container that to represen [...]
+<tr id="row_107_182_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">tvm::runtime::PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
+<tr id="row_107_183_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </td></tr>
+<tr id="row_107_184_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">tvm::runtime::profiling::MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling [...]
+<tr id="row_107_185_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></td></tr>
+<tr id="row_107_186_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
+<tr id="row_107_187_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </td></tr>
+<tr id="row_107_188_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">tvm::runtime::Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device. ">Timer</a> for a specific device </td></tr>
+<tr id="row_107_189_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Storage.html" target="_self">tvm::runtime::vm::Storage</a></td><td class="desc">Reference to storage </td></tr>
+<tr id="row_107_190_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </td></tr>
+<tr id="row_107_191_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></td></tr>
+<tr id="row_107_192_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">tvm::Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
+<tr id="row_107_193_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">tvm::TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
+<tr id="row_107_194_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">tvm::TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
+<tr id="row_107_195_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">tvm::te::IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
+<tr id="row_107_196_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_196_" class="arrow" onclick="toggleFolder('107_196_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">tvm::te::IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be <a class="el" href="classtvm_1_1te_1_1Split.h [...]
+<tr id="row_107_196_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Fuse.html" target="_self">tvm::te::Fuse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1FuseNode.html" title="Fuse two domains into one domain. ">FuseNode</a> </td></tr>
+<tr id="row_107_196_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Rebase.html" target="_self">tvm::te::Rebase</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1RebaseNode.html" title="Rebase the iteration to make min to be 0. This is useful to normalize the Schedule to make every leaf...">Rebas [...]
+<tr id="row_107_196_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Singleton.html" target="_self">tvm::te::Singleton</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SingletonNode.html" title="Singleton iterator [0, 1) ">SingletonNode</a> </td></tr>
+<tr id="row_107_196_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Split.html" target="_self">tvm::te::Split</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SplitNode.html" title="Split the parent domain into product of outer and iter. ">SplitNode</a> </td></tr>
+<tr id="row_107_196_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Transform.html" target="_self">tvm::te::Transform</a></td><td class="desc"></td></tr>
+<tr id="row_107_197_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_197_" class="arrow" onclick="toggleFolder('107_197_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">tvm::te::Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operati [...]
+<tr id="row_107_197_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOp.html" target="_self">tvm::te::ComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" title="A Compute op that compute a tensor on certain domain. ">ComputeOpNode</a> </td></tr>
+<tr id="row_107_197_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOp.html" target="_self">tvm::te::ExternOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" title="External computation that cannot be splitted. ">ExternOpNode</a> </td></tr>
+<tr id="row_107_197_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOp.html" target="_self">tvm::te::HybridOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" title="A computation operator that generated by hybrid script. ">HybridOpNode</a> </td></tr>
+<tr id="row_107_197_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html" target="_self">tvm::te::PlaceholderOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" title="A placeholder op represents an input placeholder. ">PlaceholderOpNode</a> </td></tr>
+<tr id="row_107_197_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOp.html" target="_self">tvm::te::ScanOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" title="Symbolic scan. ">ScanOpNode</a> </td></tr>
+<tr id="row_107_197_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html" target="_self">tvm::te::TensorComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" title="A TenorCompute op that compute a tensor with an tensor intrinsic. ">TensorComputeOpN [...]
+<tr id="row_107_198_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Schedule.html" target="_self">tvm::te::Schedule</a></td><td class="desc">Global schedule container For operations and all the operations they depend on. The schedule per <a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. " [...]
+<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </td></tr>
+<tr id="row_107_200_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Stage.html" target="_self">tvm::te::Stage</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a>, contains scheduling for a stage of computation </td></tr>
+<tr id="row_107_201_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">tvm::te::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" title="Node to represent a Tensor intrinsic operator. ">TensorIntrinNode</a> </td></tr>
+<tr id="row_107_202_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">tvm::te::TensorIntrinCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> </td></tr>
+<tr id="row_107_203_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">tvm::tir::BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a>, < [...]
+<tr id="row_107_204_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRV.html" target="_self">tvm::tir::BlockRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html" title="A random variable that evaluates to a TensorIR block. ">BlockRVNode</a> </td></tr>
+<tr id="row_107_205_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">tvm::tir::BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structu [...]
+<tr id="row_107_206_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">tvm::tir::Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structur [...]
+<tr id="row_107_207_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html" target="_self">tvm::tir::BufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html" title="Representing the region of multi-dimensional buffer access. ">BufferRegionNode</a> </td></tr>
+<tr id="row_107_208_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">tvm::tir::CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element..." [...]
+<tr id="row_107_209_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_209_" class="arrow" onclick="toggleFolder('107_209_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">tvm::tir::DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base [...]
+<tr id="row_107_209_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor.html" target="_self">tvm::te::Tensor</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> structure representing a possible input [...]
+<tr id="row_107_210_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">tvm::tir::Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A, B, kRAW) means [...]
+<tr id="row_107_211_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></td></tr>
+<tr id="row_107_212_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Instruction.html" target="_self">tvm::tir::Instruction</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionNode.html" title="Schedule instructions each corresponds to a schedule primitive. ">InstructionNode</a> </td></tr>
+<tr id="row_107_213_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html" target="_self">tvm::tir::InstructionKind</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" title="Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of i [...]
+<tr id="row_107_214_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">tvm::tir::IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
+<tr id="row_107_215_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">tvm::tir::Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">Layo [...]
+<tr id="row_107_216_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoopRV.html" target="_self">tvm::tir::LoopRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoopRVNode.html" title="A random variable that evaluates to a TensorIR for loop. ">LoopRVNode</a> </td></tr>
+<tr id="row_107_217_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MatchBufferRegion.html" target="_self">tvm::tir::MatchBufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MatchBufferRegionNode.html" title="Match introduces a constraint that the source buffer region can be remapped t [...]
+<tr id="row_107_218_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Schedule.html" target="_self">tvm::tir::Schedule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html" title="The user-facing schedule class. ">ScheduleNode</a> </td></tr>
+<tr id="row_107_219_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ScheduleState.html" target="_self">tvm::tir::ScheduleState</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html" title="The state of scheduling, which exposes a Replace method as the primary interface for all [...]
+<tr id="row_107_220_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_220_" class="arrow" onclick="toggleFolder('107_220_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">tvm::tir::Stmt</a></td><td class="desc">Container of all statements </td></tr>
+<tr id="row_107_220_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Allocate.html" target="_self">tvm::tir::Allocate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" title="Allocate a buffer that can be used in body. ">AllocateNode</a> </td></tr>
+<tr id="row_107_220_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateConst.html" target="_self">tvm::tir::AllocateConst</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html" title="Allocate a buffer that can be used in body. ">AllocateConstNode</a> </td></tr>
+<tr id="row_107_220_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html" target="_self">tvm::tir::AssertStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" title="Assert condition, if an error occurs, return the error message. ">AssertStmtNode</a> </td></tr>
+<tr id="row_107_220_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html" target="_self">tvm::tir::AttrStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" title="Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary inform. [...]
+<tr id="row_107_220_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Block.html" target="_self">tvm::tir::Block</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockNode.html" title="A block is a basic schedule unit in TIR. ">BlockNode</a> </td></tr>
+<tr id="row_107_220_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html" target="_self">tvm::tir::BlockRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html" title="A block realization node represents execution of the block at the binding values. ...">Blo [...]
+<tr id="row_107_220_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">tvm::tir::BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only nee [...]
+<tr id="row_107_220_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">tvm::tir::BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
+<tr id="row_107_220_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Evaluate.html" target="_self">tvm::tir::Evaluate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" title="Evaluates an expression. This is mostly used for putting a Call node into Stmt. ">EvaluateNode</a> </td></tr>
+<tr id="row_107_220_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1For.html" target="_self">tvm::tir::For</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ForNode.html" title="A for loop, with poissible type annotations. ">ForNode</a> </td></tr>
+<tr id="row_107_220_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" target="_self">tvm::tir::IfThenElse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" title="IfThenElse statment. ">IfThenElseNode</a> </td></tr>
+<tr id="row_107_220_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmt.html" target="_self">tvm::tir::LetStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" title="Let binding, bind var to value, then run body. ">LetStmtNode</a> </td></tr>
+<tr id="row_107_220_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">tvm::tir::Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for a buffer. ">PrefetchNode</a> </td></tr>
+<tr id="row_107_220_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealize.html" target="_self">tvm::tir::ProducerRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" title="Annotate the bounds where the data produced by the producer need to be written a [...]
+<tr id="row_107_220_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html" target="_self">tvm::tir::ProducerStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" title="Store value into mult-dimensional array that will be read by the consumer of the produ [...]
+<tr id="row_107_220_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">tvm::tir::SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
+<tr id="row_107_220_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Store.html" target="_self">tvm::tir::Store</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StoreNode.html" title="Store value to the buffer. ">StoreNode</a> </td></tr>
+<tr id="row_107_220_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1While.html" target="_self">tvm::tir::While</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1WhileNode.html" title="A While loop. ">WhileNode</a> </td></tr>
+<tr id="row_107_221_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">tvm::tir::StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref". ">Stmt [...]
+<tr id="row_107_222_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">tvm::tir::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization. ">TensorIntrinNode</a> </td></tr>
+<tr id="row_107_223_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Trace.html" target="_self">tvm::tir::Trace</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TraceNode.html" title="An execution trace of a scheduling program. ">TraceNode</a> </td></tr>
+<tr id="row_107_224_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_225_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_226_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</a></td><td class="desc"></td></tr>
+<tr id="row_107_227_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
+<tr id="row_107_228_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_228_" class="arrow" onclick="toggleFolder('107_228_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Pass.html" target="_self">tvm::transform::Pass</a></td><td class="desc"></td></tr>
+<tr id="row_107_228_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Sequential.html" target="_self">tvm::transform::Sequential</a></td><td class="desc"></td></tr>
+<tr id="row_107_229_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassContext.html" target="_self">tvm::transform::PassContext</a></td><td class="desc"><a class="el" href="classtvm_1_1transform_1_1PassContext.html" title="PassContext that is used to configure the pass behavior. ">PassContext</a> that is used to configure t [...]
+<tr id="row_107_230_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassInfo.html" target="_self">tvm::transform::PassInfo</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html" title="Meta data that will be used to help optimization and analysis. ">PassInfoNode</ [...]
+<tr id="row_107_231_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_231_" class="arrow" onclick="toggleFolder('107_231_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">tvm::Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</ [...]
+<tr id="row_107_231_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">tvm::BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType. ...">BaseTensorTypeNo [...]
+<tr id="row_107_231_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">tvm::FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
+<tr id="row_107_231_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">tvm::GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
+<tr id="row_107_231_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">tvm::IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNod [...]
+<tr id="row_107_231_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">tvm::PointerType</a></td><td class="desc"></td></tr>
+<tr id="row_107_231_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">tvm::PrimType</a></td><td class="desc"></td></tr>
+<tr id="row_107_231_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">tvm::RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
+<tr id="row_107_231_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">tvm::TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> < [...]
+<tr id="row_107_231_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">tvm::TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
+<tr id="row_107_231_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">tvm::TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
+<tr id="row_107_231_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_231_10_" class="arrow" onclick="toggleFolder('107_231_10_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">tvm::TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential [...]
+<tr id="row_107_231_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">tvm::TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
+<tr id="row_107_231_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">tvm::TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
+<tr id="row_107_231_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">tvm::TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
+<tr id="row_107_232_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNo [...]
+<tr id="row_107_233_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">tvm::TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
+<tr id="row_107_234_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDevice.html" target="_self">tvm::VirtualDevice</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1VirtualDeviceNode.html" title="Describes at compile time the constraints on where data is to be stored at runtime down to the ( [...]
+<tr id="row_107_235_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1WorkspaceMemoryPools.html" target="_self">tvm::WorkspaceMemoryPools</a></td><td class="desc"></td></tr>
<tr id="row_108_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html" target="_self">tvm::runtime::ObjectTypeChecker< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> traits for runtime type check during FFI conversion </td></tr>
<tr id="row_109_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html" target="_self">tvm::runtime::ObjectTypeChecker< Array< T > ></a></td><td class="desc"></td></tr>
<tr id="row_110_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html" target="_self">tvm::runtime::ObjectTypeChecker< Map< K, V > ></a></td><td class="desc"></td></tr>
@@ -1223,8 +1222,8 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_122_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">tvm::relay::PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can use t [...]
<tr id="row_123_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Profiler.html" target="_self">tvm::runtime::profiling::Profiler</a></td><td class="desc"></td></tr>
<tr id="row_124_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">tvm::ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
-<tr id="row_125_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">tvm::runtime::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function. ">Registry</a> for global function </td></tr>
-<tr id="row_126_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">tvm::ReflectionVTable::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
+<tr id="row_125_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">tvm::ReflectionVTable::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
+<tr id="row_126_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">tvm::runtime::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function. ">Registry</a> for global function </td></tr>
<tr id="row_127_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">tvm::ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
<tr id="row_128_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html" target="_self">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
<tr id="row_129_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1RewriteSimplifier.html" target="_self">tvm::arith::RewriteSimplifier</a></td><td class="desc">Rewrite-rule based simplifier </td></tr>
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