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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/06/07 09:35:54 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@70884e957aa5c8de9c02c25a14d30563d7300cb9)
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 ce9d19c4d deploying docs (apache/tvm@70884e957aa5c8de9c02c25a14d30563d7300cb9)
ce9d19c4d is described below
commit ce9d19c4d1987a699a5aa6869323f96ad80fefc6
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Tue Jun 7 09:35:48 2022 +0000
deploying docs (apache/tvm@70884e957aa5c8de9c02c25a14d30563d7300cb9)
---
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 22 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 4 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/deploy_ssd_gluoncv.rst.txt | 4 +-
.../deploy_models/sg_execution_times.rst.txt | 18 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 10 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 16 +-
.../tune_conv2d_layer_cuda.rst.txt | 712 +--
.../tune_network_cuda.rst.txt | 2 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 42 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 12 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 34 +-
.../work_with_microtvm/micro_autotune.rst.txt | 16 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 12 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 16 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../how_to/work_with_schedules/tensorize.rst.txt | 2 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
.../frontend/deploy_classification.rst.txt | 2 +-
.../tutorials/frontend/deploy_detection.rst.txt | 2 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 13 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 54 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 26 +-
.../tutorial/tensor_expr_get_started.rst.txt | 46 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 109 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 7 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 22 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 18 +-
docs/how_to/deploy_models/deploy_prequantized.html | 6 +-
.../deploy_models/deploy_prequantized_tflite.html | 4 +-
docs/how_to/deploy_models/deploy_quantized.html | 2 +-
docs/how_to/deploy_models/deploy_ssd_gluoncv.html | 37 +-
docs/how_to/deploy_models/sg_execution_times.html | 18 +-
.../extend_tvm/bring_your_own_datatypes.html | 2 +-
docs/how_to/extend_tvm/sg_execution_times.html | 10 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 712 +--
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.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 34 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 16 +-
docs/how_to/work_with_microtvm/micro_train.html | 12 +-
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docs/tutorial/sg_execution_times.html | 26 +-
docs/tutorial/tensor_expr_get_started.html | 46 +-
200 files changed, 5283 insertions(+), 6472 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 c35fbe363..fa0eebf7f 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.zip67d7d724-c04c-4721-915c-d096871322ec from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip6d599b92-18c1-40d6-a64d-b9431e592e8c 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 2073194af..8c15264b3 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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03, 8.69MB/s]
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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 0e35de507..d6f4b7682 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -210,7 +210,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.874 seconds)
+ **Total running time of the script:** ( 1 minutes 7.165 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 3ea9b5b5e..b4737df54 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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28%|##8 | 12.7M/44.7M [00:00<00:00, 131MB/s]
81%|########1 | 36.4M/44.7M [00:00<00:00, 199MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 199MB/s]
+
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26%|##5 | 11.6M/44.7M [00:00<00:00, 58.5MB/s]
74%|#######4 | 33.2M/44.7M [00:00<00:00, 134MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 132MB/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 c1b658235..c149320e6 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -381,7 +381,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 1.631 seconds)
+ **Total running time of the script:** ( 1 minutes 4.652 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 2131c1a57..d188c761b 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
=================
-**05:24.515** total execution time for **how_to_compile_models** files:
+**05:30.301** total execution time for **how_to_compile_models** files:
-- **01:05.874**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **01:01.631**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:56.444**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:37.554**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
-- **00:23.872**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:22.405**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:21.090**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:19.078**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:13.722**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.846**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:07.165**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:04.652**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:59.961**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:31.997**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
+- **00:24.519**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:23.147**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:22.287**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:19.985**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:13.820**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.767**: :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 a14fe9181..aa9f599fd 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -402,7 +402,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.3281 15.3607 15.5444 15.1472 0.1172
+ 16.3197 16.2442 16.9251 16.1694 0.2110
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 66f7ee862..3d61a0aaa 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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42%|####2 | 71.4M/170M [00:00<00:00, 257MB/s]
57%|#####7 | 97.4M/170M [00:00<00:00, 263MB/s]
73%|#######2 | 124M/170M [00:00<00:00, 268MB/s]
89%|########8 | 150M/170M [00:00<00:00, 272MB/s]
100%|##########| 170M/170M [00:00<00:00, 264MB/s]
+
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3%|3 | 5.33M/170M [00:00<00:03, 55.9MB/s]
6%|6 | 10.7M/170M [00:00<00:03, 54.8MB/s]
21%|##1 | 36.4M/170M [00:00<00:00, 152MB/s]
37%|###7 | 63.0M/170M [00:00<00:00, 202MB/s]
52%|#####2 | 89.1M/170M [00:00<00:00, 228MB/s]
68%|######7 | 115M/170M [00:00<00:00, 242MB/s]
82%|########2 | 140M/170M [00:00<00:00, 249MB/s]
98%|#########7| 166M/170M [00:00<00:00, 256MB/s]
100%|##########| 170M/170M [00:00<00:00, 217MB/s]
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -262,7 +262,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 55.302 seconds)
+ **Total running time of the script:** ( 3 minutes 1.783 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 29d5cc456..5feb686c3 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
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 162MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 163MB/s]
@@ -353,7 +353,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 88.9107 88.8655 89.4770 88.5857 0.1836
+ 90.3297 90.2424 91.1498 90.1341 0.2079
@@ -393,7 +393,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.883 seconds)
+ **Total running time of the script:** ( 1 minutes 7.638 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 033b780ec..9c7a7a3a1 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -360,7 +360,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 118.3542 117.7878 141.4023 116.8825 2.5086
+ 121.9273 121.9259 122.8546 121.2343 0.3097
@@ -394,7 +394,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 57.635 seconds)
+ **Total running time of the script:** ( 1 minutes 59.432 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 66c031cf0..2eebc09e1 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -223,7 +223,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.581 seconds)
+ **Total running time of the script:** ( 1 minutes 12.219 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 494417c5a..0f6470d65 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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11%|# | 13952/132723 [00:00<00:01, 71318.85KB/s]
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70%|######9 | 92733/132723 [00:01<00:00, 78795.10KB/s]
76%|#######5 | 100613/132723 [00:01<00:00, 76515.08KB/s]
82%|########1 | 108409/132723 [00:01<00:00, 76939.89KB/s]
87%|########7 | 116114/132723 [00:01<00:00, 67244.82KB/s]
94%|########
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+
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@@ -211,7 +211,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 14.928 seconds)
+ **Total running time of the script:** ( 2 minutes 23.827 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 4ec3246d7..f5d7b7f78 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**10:15.614** total execution time for **how_to_deploy_models** files:
+**10:37.779** total execution time for **how_to_deploy_models** files:
-- **02:55.302**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:14.928**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:57.635**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:12.581**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:04.883**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:28.414**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.674**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.195**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:01.783**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:23.827**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:59.432**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:12.219**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:07.638**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:29.918**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:22.755**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.207**: :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 52c58137e..11c416e39 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -425,7 +425,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipc7c5c4bd-3b6e-48f6-98a8-bca21dacb4b4 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipa6f0d07b-9158-4cb5-baa5-bef7f9e22e30 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index e8c4d7f67..bcd0689e4 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
=================
-**00:39.585** total execution time for **how_to_extend_tvm** files:
+**00:40.986** total execution time for **how_to_extend_tvm** files:
-- **00:35.913**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.360**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.104**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.208**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:37.152**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.477**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.140**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.217**: :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 b831292e4..11fc80c05 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: 6595us [6595us] (45.61%; 45.61%)
- FoldScaleAxis: 7866us [6us] (54.39%; 54.39%)
- FoldConstant: 7860us [1599us] (54.35%; 99.93%)
- InferType: 6261us [6261us] (43.30%; 79.66%)
+ InferType: 6878us [6878us] (45.75%; 45.75%)
+ FoldScaleAxis: 8154us [6us] (54.25%; 54.25%)
+ FoldConstant: 8148us [1620us] (54.21%; 99.93%)
+ InferType: 6528us [6528us] (43.43%; 80.12%)
@@ -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: 6198us [6198us] (44.48%; 44.48%)
- FoldScaleAxis: 7735us [5us] (55.52%; 55.52%)
- FoldConstant: 7730us [1582us] (55.48%; 99.94%)
- InferType: 6149us [6149us] (44.13%; 79.54%)
+ InferType: 6589us [6589us] (44.80%; 44.80%)
+ FoldScaleAxis: 8118us [5us] (55.20%; 55.20%)
+ FoldConstant: 8112us [1658us] (55.16%; 99.94%)
+ InferType: 6454us [6454us] (43.89%; 79.56%)
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 59162d87a..f59c8d2e8 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: 54.131989 ms
+ Convolution: 51.575460 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 f1d68e1c5..6cec98369 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: 6.614921 ms
+ conv2d with tensor core: 10.396211 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 96680100a..10fd89416 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: 0.018521
- Baseline: 3.399815
+ Numpy running time: 0.019390
+ Baseline: 3.448056
@@ -210,7 +210,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.290889
+ Opt1: 0.317239
@@ -309,7 +309,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.331642
+ Opt2: 0.345401
@@ -401,7 +401,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.114744
+ Opt3: 0.120997
@@ -520,7 +520,7 @@ flattening.
.. code-block:: none
- Opt4: 0.109034
+ Opt4: 0.110805
@@ -638,7 +638,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.109441
+ Opt5: 0.111641
@@ -759,7 +759,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.142529
+ Opt6: 0.145231
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 2b5c3e2e4..d470fdd4f 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
=================
-**00:34.606** total execution time for **how_to_optimize_operators** files:
+**00:35.774** total execution time for **how_to_optimize_operators** files:
-- **00:31.993**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.390**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.223**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:32.978**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.532**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.264**: :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 67885dbfc..a29952ac5 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
=================
-**05:16.718** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:32.299**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:19.117**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:42.185**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:26.270**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.595**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.252**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**05:11.887** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:32.057**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:21.484**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:43.568**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:16.939**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:08.956**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.884**: :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 016405f6b..af07f288c 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
@@ -222,359 +222,78 @@ 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" = 16;
- allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [4032]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [6144]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
- conv2d_nchw_1[7] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 56;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [7]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [84]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [768]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope="local", align=4)[0] = 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
- for (rc.outer.outer: int32, 0, 8) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_4: int32 = (rc.outer.outer*3136)
- let cse_var_3: int32 = (ry.outer.outer*7)
- let cse_var_2: int32 = (rc.outer.outer*576)
- let cse_var_1: int32 = (ry.outer.outer*3)
+ for (rc.outer.outer: int32, 0, 128) {
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_1: int32 = (rc.outer.outer*36)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [4032], [], 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" = 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_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1008)] = @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)) + 776)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1120)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1120), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1120), 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 + 1120), 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 + 1232)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1232), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1232), 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 + 1232), 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 + 1344)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1344), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1344), 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 + 1344), 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 + 1456)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1456), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1456), 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 + 1456), 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 + 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1680)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1680), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1680), 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 + 1680), 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 + 1792)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1792), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1792), 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 + 1792), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1904)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1904), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1904), 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 + 1904), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2016)] = @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)) + 1560)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2128)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2128), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2128), 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 + 2128), 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 + 2240)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2240), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2240), 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 + 2240), 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 + 2352)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2352), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2352), 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 + 2352), 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 + 2464)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2464), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2464), 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 + 2464), 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 + 2576)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2576), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2576), 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 + 2576), 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 + 2688)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2688), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2688), 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 + 2688), 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 + 2800)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2800), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2800), 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 + 2800), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2912)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2912), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2912), 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 + 2912), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3024)] = @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)) + 2344)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3136)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3136), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3136), 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 + 3136), 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 + 3248)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3248), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3248), 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 + 3248), 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 + 3360)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3360), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3360), 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 + 3360), 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 + 3472)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3472), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3472), 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 + 3472), 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 + 3584)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3584), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3584), 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 + 3584), 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 + 3696)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3696), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3696), 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 + 3696), 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 + 3808)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3808), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3808), 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 + 3808), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3920)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3920), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3920), 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 + 3920), 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, [6144], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*147456) + 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" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 192), 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) + 21), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 448)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 560), 192), 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) + 42), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 784)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 192), 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) + 63), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 1120)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1120), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1232), 192), 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) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 1456)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1456), 192), 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 + 1568)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 98), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1568), 192), 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 + 1680)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 105), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 1792)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 112), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1792), 192), 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 + 1904)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 119), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1904), 192), 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 + 2016)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 126), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 2128)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 133), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2128), 192), 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 + 2240)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 140), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2240), 192), 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 + 2352)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 147), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 2464)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 154), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2464), 192), 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 + 2576)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 161), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2576), 192), 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 + 2688)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 2800)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 175), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2800), 192), 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 + 2912)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 182), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2912), 192), 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 + 3024)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 189), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 3136)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 196), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3136), 192), 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 + 3248)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 203), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3248), 192), 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 + 3360)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 210), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 3472)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 217), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3472), 192), 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 + 3584)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 224), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3584), 192), 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 + 3696)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 231), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 3808)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 238), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3808), 192), 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 + 3920)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 245), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3920), 192), 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 + 4032)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 4144)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 259), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4144), 192), 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 + 4256)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 266), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4256), 192), 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 + 4368)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 273), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 4480)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 280), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4480), 192), 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 + 4592)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 287), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4592), 192), 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 + 4704)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 294), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 4816)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 301), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4816), 192), 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 + 4928)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 308), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4928), 192), 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 + 5040)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 315), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 5152)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 322), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5152), 192), 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 + 5264)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 329), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5264), 192), 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 + 5376)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 129024)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 343), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5488), 192), 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 + 5600)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 350), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5600), 192), 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 + 5712)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 357), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 5824)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 364), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5824), 192), 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 + 5936)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 371), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5936), 192), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 96), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 6048)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 378), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
+ if @tir.likely((threadIdx.x_1 < 42), dtype=bool) {
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [84], [], scope="shared")[(threadIdx.x_1*2)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1*2), 21), 7) + floormod(blockIdx.x, 7))) && ((floordiv(floormod((threadIdx.x_1*2), 21), 7) + floormod(blockIdx.x, 7)) < 8)) && (1 <= (rx.outer.outer + floormod((threadIdx.x_1*2), 7)))) && ((rx.outer.outer + floormod((threadIdx.x_1*2), 7)) < 8)), data[((((((rc.outer.outer*196) + (floordiv((threadIdx.x_1*2), 21)*49) [...]
+ }
+ if @tir.likely((threadIdx.x_1 < 42), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*2) + 1)] = @tir.if_then_else(((((1 <= (floordiv(floormod(((threadIdx.x_1*2) + 1), 21), 7) + floormod(blockIdx.x, 7))) && ((floordiv(floormod(((threadIdx.x_1*2) + 1), 21), 7) + floormod(blockIdx.x, 7)) < 8)) && (1 <= (rx.outer.outer + floormod(((threadIdx.x_1*2) + 1), 7)))) && ((rx.outer.outer + floormod(((threadIdx.x_1*2) + 1), 7)) < 8)), data[((((((rc.outer.outer*196) + (floordiv(((threadIdx.x_1*2) + 1), 21)*49)) + (floormod(blockIdx.x [...]
+ }
}
- for (rx.outer.inner: int32, 0, 3) {
- for (xx.outer.inner: int32, 0, 7) {
- let cse_var_5: int32 = (xx.outer.inner + 7)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1: Buffer(kernel.shared, float32, [768], [], scope="shared")[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 16), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 4), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 32), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 8), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 73728)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 64), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 16), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 80), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 20), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 384)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 147456)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 112), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 28), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 512)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 128), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 32), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 576)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 221184)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 160), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 40), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 704)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 176), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 44), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ for (rc.inner: int32, 0, 4) {
+ for (ry.inner: int32, 0, 3) {
+ let cse_var_2: int32 = ((rc.inner*21) + (ry.inner*7))
{
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[(((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*384) + rx.outer.inner)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 192)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 3)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 195)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 6)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 198)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 9)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 201)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 12)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 204)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 15)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 207)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 18)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 210)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 21)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 213)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 24)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 216)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 27)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 219)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 30)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 222)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 33)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 225)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 36)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 228)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 39)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 231)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 42)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 234)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 45)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 237)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 48)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 240)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 51)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 243)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 54)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 246)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 57)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 249)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 60)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 252)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 63)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 255)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 66)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 258)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 69)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 261)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 72)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 264)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 75)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 267)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 78)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 270)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 81)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 273)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 84)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 276)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 87)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 279)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 90)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 282)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 93)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 285)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2016)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 96)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2016)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 288)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2079)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 99)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2079)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 291)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2142)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 102)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2142)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 294)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2205)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 105)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2205)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 297)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2268)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 108)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2268)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 300)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2331)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 111)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2331)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 303)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2394)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 114)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2394)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 306)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2457)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 117)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2457)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 309)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2520)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 120)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2520)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 312)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2583)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 123)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2583)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 315)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2646)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 126)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2646)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 318)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2709)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 129)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2709)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 321)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2772)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 132)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2772)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 324)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2835)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 135)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2835)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 327)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2898)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 138)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2898)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 330)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2961)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 141)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2961)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 333)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3024)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 144)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3024)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 336)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3087)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 147)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3087)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 339)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3150)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 150)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3150)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 342)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3213)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 153)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3213)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 345)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3276)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 156)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3276)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 348)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3339)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 159)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3339)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 351)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3402)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 162)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3402)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 354)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3465)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 165)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3465)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 357)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3528)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 168)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3528)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 360)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3591)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 171)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3591)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 363)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3654)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 174)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3654)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 366)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3717)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 177)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3717)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 369)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3780)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 180)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3780)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 372)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3843)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 183)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3843)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 375)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3906)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 186)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3906)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 378)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3969)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 189)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3969)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 381)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[cse_var_2]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(cse_var_2 + 1)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(cse_var_2 + 2)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(cse_var_2 + 3)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[(cse_var_2 + 4)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[(cse_var_2 + 5)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[(cse_var_2 + 6)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
}
}
}
}
}
}
- for (i1.inner: int32, 0, 2) {
- for (i3.inner: int32, 0, 7) {
- compute[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[((i1.inner*7) + i3.inner)] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
- }
- }
+ compute[(((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[1] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[2] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[3] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[4] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[5] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[6] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
}
}
@@ -626,7 +345,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.373 ms
+ Execution time of this operator: 0.383 ms
@@ -670,37 +389,37 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_i, factor=1)
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
- conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
+ conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=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_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=64)
conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
- conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
+ conv2d_nchw_yy_o_o_o_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=7)
+ conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
- conv2d_nchw_xx_o_o_o_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=64)
+ conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+ conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=4)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
- conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
+ conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=3)
conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
- conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=3)
+ conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2 [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
- compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
+ compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
- compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+ compute_i2_o_o_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=7)
+ compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
- compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+ compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -719,14 +438,14 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=2)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
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", 512)
+ s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 16)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -744,260 +463,59 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[14];
- __shared__ float pad_temp_shared[4032];
- __shared__ float kernel_shared[6144];
+ extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[7];
+ __shared__ float pad_temp_shared[84];
+ __shared__ float kernel_shared[768];
conv2d_nchw[0] = 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;
- for (int rc_outer_outer = 0; rc_outer_outer < 8; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
+ for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
__syncthreads();
- 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 * 3136) + ((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((int)threadIdx.x) + 896) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1008)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 776)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1120)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1120) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1232)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1232) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1344)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1344) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1456)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1456) / 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 * 3136) + (((((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) + 1680)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1680) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1792)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1792) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1904)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1904) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2016)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1560)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2128)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2128) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2240)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2240) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2352)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2352) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2464)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2464) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2576)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2576) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2688)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2688) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2800)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2800) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2912)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2912) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3024)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 2344)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3136)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3136) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3248)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3248) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3360)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3360) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3472)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3472) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3584)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3584) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3696)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3696) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3808)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3808) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3920)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3920) / 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) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 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) + 336) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 448) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 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) + 672) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 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) + 1008) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1120) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1456) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1568) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1680)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1680) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1792) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1904)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1904) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2016)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2016) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2128)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2128) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2240) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2352) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2464)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2464) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2576)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2576) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
- kernel_shared[(((int)threadIdx.x) + 2800)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2800) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2912)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2912) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3024)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3024) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3136) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3248)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3248) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3360)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3360) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3472)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3472) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3584)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3584) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3696)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3696) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3808)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3808) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3920) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4032)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 96768)];
- kernel_shared[(((int)threadIdx.x) + 4144)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4144) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4256)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4256) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4368)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4368) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4480)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4480) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4592)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4592) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4704) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4816)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4816) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4928)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4928) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5040)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5040) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5152)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5152) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5264)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5264) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5376)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 129024)];
- kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5488) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5600)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5600) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5712)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5712) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5824)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5824) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5936)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5936) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- if (((int)threadIdx.x) < 96) {
- kernel_shared[(((int)threadIdx.x) + 6048)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 6048) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 32) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+ if (((int)threadIdx.x) < 42) {
+ pad_temp_shared[(((int)threadIdx.x) * 2)] = (((((1 <= ((((((int)threadIdx.x) * 2) % 21) / 7) + (((int)blockIdx.x) % 7))) && (((((((int)threadIdx.x) * 2) % 21) / 7) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (rx_outer_outer + ((((int)threadIdx.x) * 2) % 7)))) && ((rx_outer_outer + ((((int)threadIdx.x) * 2) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) * 2) / 21) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + rx_outer_outer) + ((((int)threadIdx.x) * 2) % 21)) [...]
}
+ if (((int)threadIdx.x) < 42) {
+ pad_temp_shared[((((int)threadIdx.x) * 2) + 1)] = (((((1 <= (((((((int)threadIdx.x) * 2) + 1) % 21) / 7) + (((int)blockIdx.x) % 7))) && ((((((((int)threadIdx.x) * 2) + 1) % 21) / 7) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (rx_outer_outer + (((((int)threadIdx.x) * 2) + 1) % 7)))) && ((rx_outer_outer + (((((int)threadIdx.x) * 2) + 1) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + ((((((int)threadIdx.x) * 2) + 1) / 21) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + rx_outer_outer [...]
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 64) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 128) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 192)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 73728)];
+ kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 256) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 320) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 384)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 147456)];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 512)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 512) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 576)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 221184)];
+ kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 640) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 704)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 704) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
__syncthreads();
- for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
- for (int xx_outer_inner = 0; xx_outer_inner < 7; ++xx_outer_inner) {
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner)] * kernel_shared[(((((int)threadIdx.x) / 7) * 384) + rx_outer_inner)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 192)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 3)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 195)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 6)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 198)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 9)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 201)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 12)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 204)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 15)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 207)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 18)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 210)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 21)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 213)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 504)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 24)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 504)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 216)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 567)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 27)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 567)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 219)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 630)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 30)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 630)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 222)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 693)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 33)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 693)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 225)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 756)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 36)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 756)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 228)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 819)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 39)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 819)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 231)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 882)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 42)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 882)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 234)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 945)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 45)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 945)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 237)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 48)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 240)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 51)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 243)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 54)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 246)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 57)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 249)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 60)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 252)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 63)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 255)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 66)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 258)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 69)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 261)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 72)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 264)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 75)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 267)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 78)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 270)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 81)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 273)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 84)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 276)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 87)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 279)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 90)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 282)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 93)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 285)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2016)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 96)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2016)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 288)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2079)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 99)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2079)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 291)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2142)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 102)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2142)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 294)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2205)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 105)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2205)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 297)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2268)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 108)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2268)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 300)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2331)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 111)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2331)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 303)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2394)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 114)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2394)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 306)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2457)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 117)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2457)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 309)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2520)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 120)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2520)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 312)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2583)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 123)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2583)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 315)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2646)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 126)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2646)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 318)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2709)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 129)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2709)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 321)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2772)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 132)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2772)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 324)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2835)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 135)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2835)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 327)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2898)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 138)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2898)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 330)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2961)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 141)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2961)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 333)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3024)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 144)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3024)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 336)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3087)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 147)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3087)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 339)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3150)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 150)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3150)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 342)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3213)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 153)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3213)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 345)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3276)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 156)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3276)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 348)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3339)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 159)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3339)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 351)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3402)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 162)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3402)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 354)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3465)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 165)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3465)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 357)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3528)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 168)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3528)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 360)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3591)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 171)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3591)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 363)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3654)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 174)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3654)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 366)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3717)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 177)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3717)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 369)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3780)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 180)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3780)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 372)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3843)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 183)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3843)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 375)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3906)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 186)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3906)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 378)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3969)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 189)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3969)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 381)]));
+ for (int rc_inner = 0; rc_inner < 4; ++rc_inner) {
+ for (int ry_inner = 0; ry_inner < 3; ++ry_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rc_inner * 21) + (ry_inner * 7))] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 3)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 4)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 5)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 6)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
}
}
}
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- for (int i3_inner = 0; i3_inner < 7; ++i3_inner) {
- compute[(((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
- }
- }
+ compute[((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[1] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[2] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[3] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[4] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[5] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[6] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
}
@@ -1055,7 +573,7 @@ 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 32.299 seconds)
+ **Total running time of the script:** ( 2 minutes 32.057 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 3d8a54ef9..6cc0d8590 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -616,7 +616,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.9812 9.9720 10.0057 9.9658 0.0175
+ 10.0095 10.0044 10.0492 9.9750 0.0305
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 9cfea0687..593e4f854 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -635,7 +635,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 748.7067 748.2991 750.5136 747.3074 1.3403
+ 769.7185 769.6068 770.5845 768.9642 0.6662
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.117 seconds)
+ **Total running time of the script:** ( 1 minutes 21.484 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 806b8d170..3f815893f 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
@@ -362,30 +362,32 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_7: placeholder_16: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (i.outer.inner: int32, 0, 4) {
- for (i.inner.init: int32, 0, 8) {
- for (j.init: int32, 0, 16) {
- compute_5: Buffer(compute_4, float32, [512], [])[(((i.outer.inner*128) + (i.inner.init*16)) + j.init)] = 0f32
+ preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, 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, 32) {
+ for (j.init: int32, 0, 16) {
+ compute_5: Buffer(compute_4, float32, [2048], [])[((((i.outer.inner*1024) + (i.inner.init*32)) + (nb_j.inner*16)) + j.init)] = 0f32
+ }
}
- }
- for (elem_idx: int32, 0, let cse_var_1: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 64), 2) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- for (i.inner: int32, 0, 8) {
- for (j: int32, 0, 16) {
- let cse_var_3: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 64), 2)
- let cse_var_2: int32 = (((i.outer.inner*128) + (i.inner*16)) + j)
- compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 64)*8192) + (i.outer.inner*2048)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_1: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
+ for (i.inner: int32, 0, 32) {
+ for (j: int32, 0, 16) {
+ let cse_var_3: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_2: int32 = ((((i.outer.inner*1024) + (i.inner*32)) + (nb_j.inner*16)) + j)
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*8192)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_5: int32 = floormod(i0.outer.i1.outer.fused, 64)
- let cse_var_6: int32 = (cse_var_5*8)
- let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 64)*16384) + (i0.inner*512)) + cse_var_6)
- compute[ramp(cse_var_4, 1, 8)] = max((compute_5[ramp((((i0.inner*16) + cse_var_6) - (floordiv(cse_var_5, 2)*16)), 1, 8)] + placeholder_4[ramp(cse_var_4, 1, 8)]), broadcast(0f32, 8))
+ for (i0.inner: int32, 0, 64) {
+ for (i1.inner: int32, 0, 32) {
+ let cse_var_4: int32 = ((((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)) + i1.inner)
+ compute[cse_var_4] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_4]), 0f32)
+ }
}
}
}
@@ -439,7 +441,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 3.013 ms
+ Execution time of this operator: 1.656 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 b13167e6c..ca42a1ac5 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:44.633** total execution time for **how_to_tune_with_autotvm** files:
+**00:45.406** total execution time for **how_to_tune_with_autotvm** files:
-- **00:43.803**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.213**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.201**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.200**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:44.480**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.243**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.229**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.227**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.226**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.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 3b376e737..e2dbaf1f0 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: 92.34/92.34 result: MeasureResult(costs=(0.0025069530416666668,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.607266902923584, timestamp=1654534569.8616302) [('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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 93.25/93.25 result: MeasureResult(costs=(0.002482552083333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6437036991119385, timestamp=1654592794.6007924) [('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: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/93.25 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: 0x00007f12b294cfa2
+ 12: 0x00007fc885257fa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2384,7 +2384,7 @@ for this template
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
- No: 20 GFLOPS: 144.88/144.88 result: MeasureResult(costs=(0.00159785495,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4223277568817139, timestamp=1654534596.2005908) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+ No: 20 GFLOPS: 143.47/143.47 result: MeasureResult(costs=(0.0016135417399999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3977861404418945, timestamp=1654592821.1712134) [('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: 0.001968
+ Time cost of this operator: 0.002017
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 f9a921271..7d22275b0 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -294,10 +294,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.6 98.74 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.026 0.968 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.913 0.292 (1, 1, 10, 10, 3) 1 1
- Total_time - 312.539 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.3 98.745 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.073 0.965 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.921 0.289 (1, 1, 10, 10, 3) 1 1
+ Total_time - 318.294 - - - -
@@ -359,10 +359,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 78.65 96.621 (1, 6, 10, 10, 1) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.825 2.242 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.926 1.137 (1, 1, 10, 10, 3) 1 1
- Total_time - 81.401 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 190.4 98.416 (1, 1, 10, 10, 6) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.1 1.085 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.964 0.499 (1, 3, 10, 10, 1) 1 1
+ Total_time - 193.464 - - - -
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
index 03c7893f1..dca87607e 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -297,8 +297,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmp1lta35dh/images/target contains 8144 images
- /tmp/tmp1lta35dh/images/random contains 5000 images
+ /tmp/tmpy4bdfu_a/images/target contains 8144 images
+ /tmp/tmpy4bdfu_a/images/random contains 5000 images
@@ -459,11 +459,11 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 53s - loss: 0.2230 - accuracy: 0.9233 - val_loss: 0.1561 - val_accuracy: 0.9566
+ 328/328 - 54s - loss: 0.2123 - accuracy: 0.9228 - val_loss: 0.1276 - val_accuracy: 0.9603
Epoch 2/3
- 328/328 - 51s - loss: 0.1036 - accuracy: 0.9618 - val_loss: 0.1126 - val_accuracy: 0.9637
+ 328/328 - 52s - loss: 0.1009 - accuracy: 0.9640 - val_loss: 0.1362 - val_accuracy: 0.9603
Epoch 3/3
- 328/328 - 51s - loss: 0.0630 - accuracy: 0.9757 - val_loss: 0.1042 - val_accuracy: 0.9664
+ 328/328 - 52s - loss: 0.0695 - accuracy: 0.9728 - val_loss: 0.1461 - val_accuracy: 0.9573
@@ -825,7 +825,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 29.896 seconds)
+ **Total running time of the script:** ( 4 minutes 36.088 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_train.py:
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 c264ece0d..78eee722c 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
@@ -5,11 +5,11 @@
Computation times
=================
-**05:15.036** total execution time for **how_to_work_with_microtvm** files:
-
-- **04:29.896**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)
-- **00:40.961**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.572**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.206**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
-- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
-- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+**05:23.790** total execution time for **how_to_work_with_microtvm** files:
+
+- **04:36.088**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)
+- **00:43.346**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.732**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.210**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
+- **00:00.208**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:00.206**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.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 f77b2873d..a0eef18b0 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
=================
-**00:11.872** total execution time for **how_to_work_with_relay** files:
+**00:12.465** total execution time for **how_to_work_with_relay** files:
-- **00:09.955**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.702**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.214**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:10.112**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:02.125**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.228**: :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 1ca887c3f..63f2cc066 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
=================
-**00:05.641** total execution time for **how_to_work_with_schedules** files:
+**00:05.963** total execution time for **how_to_work_with_schedules** files:
-- **00:02.083**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.173**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.722**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.697**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.310**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.230**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.215**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.211**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:02.189**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.207**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.766**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.748**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.319**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.252**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.248**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.234**: :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 b62310c5c..d7f356eac 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/tmpx0612196/input0.cc'\nsource_filename = \"/tmp/tmpx0612196/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/tmpvvh2m_cg/input0.cc'\nsource_filename = \"/tmp/tmpvvh2m_cg/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 f6d84681a..255a856d1 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
=================
-**00:20.191** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:21.532** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:20.004**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.187**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:21.313**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.218**: :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 19268952b..b14be8b49 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -267,7 +267,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
- resnet18_v1 inference graph built in 21.60s!
+ resnet18_v1 inference graph built in 22.94s!
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 25b1040f8..efb6f3e7d 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -303,7 +303,7 @@ The compilation steps are:
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py:389: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 14.91s!
+ yolov3-tiny inference graph built in 16.05s!
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 b4ea7b22e..0fa62961c 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
=================
-**01:28.529** total execution time for **topic_vta_tutorials_frontend** files:
+**01:31.860** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:46.641**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:41.888**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:48.505**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:43.355**: :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 2fdd28111..cb349e279 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
=================
-**00:03.532** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.613** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:02.969**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.564**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.998**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.615**: :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 dc0c159b1..07f4a3ad4 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:01.056** total execution time for **topic_vta_tutorials** files:
+**00:01.170** total execution time for **topic_vta_tutorials** files:
-- **00:00.539**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.517**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.598**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.572**: :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 1ff305227..e9a472def 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -184,8 +184,8 @@ trials, we can load the best schedule from the log file and apply it.
.. code-block:: none
-
-
+ *E
+ *E
@@ -306,7 +306,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 91.693 ms
+ Execution time of this operator: 93.803 ms
@@ -402,7 +402,7 @@ resume the status and do more 5 trials.
Resume search:
/usr/local/lib/python3.7/dist-packages/xgboost/training.py:17: UserWarning: Old style callback is deprecated. See: https://xgboost.readthedocs.io/en/latest/python/callbacks.html
warnings.warn(f'Old style callback is deprecated. See: {link}', UserWarning)
-
+ .T
@@ -415,6 +415,11 @@ Expression (TE) language that demonstrates how TVM can optimize computational
operations.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 17.200 seconds)
+
+
.. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 72053ca3c..0b5127deb 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -280,7 +280,7 @@ standard deviation.
.. code-block:: none
- {'mean': 485.34359255000254, 'median': 485.3246965500034, 'std': 1.1821005350493337}
+ {'mean': 495.57246422999015, 'median': 495.29582899999696, 'std': 0.6631744310948293}
@@ -494,31 +494,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.74/ 17.74 GFLOPS | Progress: (4/20) | 5.91 s
[Task 1/25] Current/Best: 6.26/ 17.74 GFLOPS | Progress: (8/20) | 8.81 s
[Task 1/25] Current/Best: 11.30/ 23.14 GFLOPS | Progress: (12/20) | 11.22 s
[Task 1/25] Current/Best: 17.12/ 23.21 GFLOPS | Progress: (16/20) | 12.87 s
[Task 1/25] Current/Best: 11.80/ 24.24 GFLOPS | Progress: (20/20) | 14.57 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.51/ 13.37 GFLOPS | Progress: (4/20) | 3.83 s
[Task 2/25] Current/Best: 14.35/ 17.01 GFLOPS | Progress: (8/20) | 5.11 s
[Task 2/25] Current/Best: 21.47/ 21.47 GFLOPS | Progress: (12/20) | 6.43 s
[Task 2/25] Current/Best: 13.04/ 21.47 GFLOPS | Progress: (16/20) | 7.70 s
[Task 2/25] Current/Best: 20.13/ 21.47 GFLOPS | Progress: (20/20) | 9.28 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.65/ 10.68 GFLOPS | Progress: (4/20) | 5.71 s
[Task 3/25] Current/Best: 15.84/ 17.19 GFLOPS | Progress: (8/20) | 7.61 s
[Task 3/25] Current/Best: 15.15/ 17.19 GFLOPS | Progress: (12/20) | 9.29 s
[Task 3/25] Current/Best: 7.33/ 24.00 GFLOPS | Progress: (16/20) | 11.20 s
[Task 3/25] Current/Best: 12.43/ 24.00 GFLOPS | Progress: (20/20) | 15.71 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.61/ 20.60 GFLOPS | Progress: (4/20) | 2.31 s
[Task 4/25] Current/Best: 6.97/ 20.60 GFLOPS | Progress: (8/20) | 6.92 s
[Task 4/25] Current/Best: 22.84/ 22.84 GFLOPS | Progress: (12/20) | 11.80 s
[Task 4/25] Current/Best: 17.73/ 22.84 GFLOPS | Progress: (16/20) | 14.20 s
[Task 4/25] Current/Best: 13.68/ 22.84 GFLOPS | Progress: (20/20) | 16.20 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.67/ 10.42 GFLOPS | Progress: (4/20) | 2.48 s
[Task 5/25] Current/Best: 11.34/ 12.95 GFLOPS | Progress: (8/20) | 4.54 s
[Task 5/25] Current/Best: 11.88/ 18.39 GFLOPS | Progress: (12/20) | 7.71 s
[Task 5/25] Current/Best: 11.85/ 22.94 GFLOPS | Progress: (16/20) | 9.10 s
[Task 5/25] Current/Best: 12.22/ 22.94 GFLOPS | Progress: (20/20) | 10.97 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.31/ 21.16 GFLOPS | Progress: (4/20) | 3.99 s
[Task 6/25] Current/Best: 19.28/ 21.16 GFLOPS | Progress: (8/20) | 5.73 s
[Task 6/25] Current/Best: 13.50/ 21.16 GFLOPS | Progress: (12/20) | 7.65 s
[Task 6/25] Current/Best: 20.41/ 21.16 GFLOPS | Progress: (16/20) | 9.87 s
[Task 6/25] Current/Best: 3.77/ 21.16 GFLOPS | Progress: (20/20) | 12.33 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.43/ 12.38 GFLOPS | Progress: (4/20) | 3.52 s
[Task 7/25] Current/Best: 20.53/ 21.27 GFLOPS | Progress: (8/20) | 5.02 s
[Task 7/25] Current/Best: 15.97/ 21.27 GFLOPS | Progress: (12/20) | 6.89 s
[Task 7/25] Current/Best: 12.45/ 21.27 GFLOPS | Progress: (16/20) | 8.91 s
[Task 7/25] Current/Best: 6.35/ 21.90 GFLOPS | Progress: (20/20) | 11.32 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 9.75/ 14.13 GFLOPS | Progress: (4/20) | 2.80 s
[Task 8/25] Current/Best: 9.30/ 14.13 GFLOPS | Progress: (8/20) | 7.86 s
[Task 8/25] Current/Best: 12.74/ 14.13 GFLOPS | Progress: (12/20) | 14.26 s
[Task 8/25] Current/Best: 19.11/ 19.11 GFLOPS | Progress: (16/20) | 16.34 s
[Task 8/25] Current/Best: 20.11/ 20.11 GFLOPS | Progress: (20/20) | 23.29 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.52/ 15.98 GFLOPS | Progress: (4/20) | 11.88 s
[Task 9/25] Current/Best: 23.76/ 23.76 GFLOPS | Progress: (8/20) | 13.54 s
[Task 9/25] Current/Best: 8.40/ 23.76 GFLOPS | Progress: (12/20) | 16.03 s
[Task 9/25] Current/Best: 18.07/ 23.76 GFLOPS | Progress: (16/20) | 18.81 s
[Task 9/25] Current/Best: 9.22/ 23.76 GFLOPS | Progress: (20/20) | 27.30 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.46/ 18.46 GFLOPS | Progress: (4/20) | 2.46 s
[Task 10/25] Current/Best: 15.74/ 18.46 GFLOPS | Progress: (8/20) | 4.07 s
[Task 10/25] Current/Best: 12.56/ 19.21 GFLOPS | Progress: (12/20) | 5.59 s
[Task 10/25] Current/Best: 19.19/ 20.58 GFLOPS | Progress: (16/20) | 6.68 s
[Task 10/25] Current/Best: 8.93/ 20.58 GFLOPS | Progress: (20/20
) | 8.21 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.59/ 18.47 GFLOPS | Progress: (4/20) | 3.17 s
[Task 11/25] Current/Best: 17.25/ 18.47 GFLOPS | Progress: (8/20) | 5.95 s
[Task 11/25] Current/Best: 18.47/ 18.47 GFLOPS | Progress: (12/20) | 7.95 s
[Task 11/25] Current/Best: 13.73/ 21.54 GFLOPS | Progress: (16/20) | 10.74 s
[Task 11/25] Current/Best: 19.73/ 21.88 GFLOPS | Progress: (20/20) | 12.82 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.96/ 18.31 GFLOPS | Progress: (4/20) | 5.55 s
[Task 12/25] Current/Best: 5.22/ 18.31 GFLOPS | Progress: (8/20) | 9.41 s
[Task 12/25] Current/Best: 19.18/ 19.18 GFLOPS | Progress: (12/20) | 11.38 s
[Task 12/25] Current/Best: 15.59/ 19.18 GFLOPS | Progress: (16/20) | 14.26 s
[Task 12/25] Current/Best: 15.39/ 19.18 GFLOPS | Progress: (20/20) | 16.19 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.46/ 17.58 GFLOPS | Progress: (4/20) | 3.64 s
[Task 13/25] Current/Best: 16.42/ 21.33 GFLOPS | Progress: (8/20) | 6.16 s
[Task 13/25] Current/Best: 19.97/ 22.01 GFLOPS | Progress: (12/20) | 9.18 s
[Task 13/25] Current/Best: 12.44/ 22.01 GFLOPS | Progress: (16/20) | 12.59 s
[Task 13/25] Current/Best: 18.86/ 22.01 GFLOPS | Progress: (20/20) | 14.89 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
-
[Task 14/25] Current/Best: 13.81/ 13.81 GFLOPS | Progress: (4/20) | 3.28 s
[Task 14/25] Current/Best: 6.22/ 13.81 GFLOPS | Progress: (8/20) | 5.50 s
[Task 14/25] Current/Best: 20.99/ 20.99 GFLOPS | Progress: (12/20) | 8.13 s
[Task 14/25] Current/Best: 16.17/ 20.99 GFLOPS | Progress: (16/20) | 9.75 s
[Task 14/25] Current/Best: 17.23/ 20.99 GFLOPS | Progress: (20/20) | 11.45 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.38/ 17.94 GFLOPS | Progress: (4/20) | 2.53 s
[Task 15/25] Current/Best: 13.55/ 18.35 GFLOPS | Progress: (8/20) | 3.84 s
[Task 15/25] Current/Best: 10.54/ 22.65 GFLOPS | Progress: (12/20) | 6.02 s
[Task 15/25] Current/Best: 20.80/ 22.65 GFLOPS | Progress: (16/20) | 8.99 s
[Task 15/25] Current/Best: 9.84/ 22.65 GFLOPS | Progress: (20/20) | 9.94 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) |
0.00 s
[Task 16/25] Current/Best: 20.81/ 20.81 GFLOPS | Progress: (4/20) | 2.76 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.49/ 17.49 GFLOPS | Progress: (4/20) | 6.06 s
[Task 1/25] Current/Best: 6.00/ 17.49 GFLOPS | Progress: (8/20) | 8.94 s
[Task 1/25] Current/Best: 11.55/ 22.81 GFLOPS | Progress: (12/20) | 11.45 s
[Task 1/25] Current/Best: 16.82/ 22.81 GFLOPS | Progress: (16/20) | 13.15 s
[Task 1/25] Current/Best: 11.58/ 23.91 GFLOPS | Progress: (20/20) | 14.90 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.22/ 12.97 GFLOPS | Progress: (4/20) | 3.84 s
[Task 2/25] Current/Best: 14.15/ 18.54 GFLOPS | Progress: (8/20) | 5.15 s
[Task 2/25] Current/Best: 21.26/ 21.26 GFLOPS | Progress: (12/20) | 6.46 s
[Task 2/25] Current/Best: 12.33/ 21.26 GFLOPS | Progress: (16/20) | 7.73 s
[Task 2/25] Current/Best: 19.60/ 21.26 GFLOPS | Progress: (20/20) | 9.36 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.50 GFLOPS | Progress: (4/20) | 5.83 s
[Task 3/25] Current/Best: 15.52/ 16.81 GFLOPS | Progress: (8/20) | 7.75 s
[Task 3/25] Current/Best: 14.82/ 16.81 GFLOPS | Progress: (12/20) | 9.48 s
[Task 3/25] Current/Best: 7.17/ 23.75 GFLOPS | Progress: (16/20) | 11.39 s
[Task 3/25] Current/Best: 12.47/ 23.75 GFLOPS | Progress: (20/20) | 16.02 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.51/ 20.11 GFLOPS | Progress: (4/20) | 2.34 s
[Task 4/25] Current/Best: 6.68/ 20.11 GFLOPS | Progress: (8/20) | 7.05 s
[Task 4/25] Current/Best: 21.28/ 21.28 GFLOPS | Progress: (12/20) | 12.07 s
[Task 4/25] Current/Best: 17.24/ 21.28 GFLOPS | Progress: (16/20) | 14.50 s
[Task 4/25] Current/Best: 13.19/ 21.28 GFLOPS | Progress: (20/20) | 16.60 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.50/ 10.23 GFLOPS | Progress: (4/20) | 2.54 s
[Task 5/25] Current/Best: 11.60/ 12.52 GFLOPS | Progress: (8/20) | 4.59 s
[Task 5/25] Current/Best: 11.33/ 18.08 GFLOPS | Progress: (12/20) | 7.77 s
[Task 5/25] Current/Best: 11.68/ 22.57 GFLOPS | Progress: (16/20) | 9.18 s
[Task 5/25] Current/Best: 12.04/ 22.57 GFLOPS | Progress: (20/20) | 11.07 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.16/ 19.94 GFLOPS | Progress: (4/20) | 4.05 s
[Task 6/25] Current/Best: 19.05/ 19.94 GFLOPS | Progress: (8/20) | 5.82 s
[Task 6/25] Current/Best: 13.28/ 19.94 GFLOPS | Progress: (12/20) | 7.78 s
[Task 6/25] Current/Best: 19.93/ 19.94 GFLOPS | Progress: (16/20) | 10.02 s
[Task 6/25] Current/Best: 3.69/ 19.94 GFLOPS | Progress: (20/20) | 12.54 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.13/ 12.10 GFLOPS | Progress: (4/20) | 3.62 s
[Task 7/25] Current/Best: 20.01/ 20.93 GFLOPS | Progress: (8/20) | 5.13 s
[Task 7/25] Current/Best: 16.00/ 20.93 GFLOPS | Progress: (12/20) | 7.06 s
[Task 7/25] Current/Best: 12.20/ 20.93 GFLOPS | Progress: (16/20) | 9.14 s
[Task 7/25] Current/Best: 6.25/ 21.44 GFLOPS | Progress: (20/20) | 11.61 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.01/ 14.23 GFLOPS | Progress: (4/20) | 2.90 s
[Task 8/25] Current/Best: 9.75/ 14.23 GFLOPS | Progress: (8/20) | 7.98 s
[Task 8/25] Current/Best: 12.93/ 14.23 GFLOPS | Progress: (12/20) | 14.54 s
[Task 8/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (16/20) | 16.65 s
[Task 8/25] Current/Best: 19.63/ 19.63 GFLOPS | Progress: (20/20) | 23.74 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.29/ 14.99 GFLOPS | Progress: (4/20) | 11.93 s
[Task 9/25] Current/Best: 23.29/ 23.29 GFLOPS | Progress: (8/20) | 13.64 s
[Task 9/25] Current/Best: 8.23/ 23.29 GFLOPS | Progress: (12/20) | 16.17 s
[Task 9/25] Current/Best: 17.82/ 23.29 GFLOPS | Progress: (16/20) | 18.93 s
[Task 9/25] Current/Best: 8.97/ 23.29 GFLOPS | Progress: (20/20) | 27.57 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 17.99/ 17.99 GFLOPS | Progress: (4/20) | 2.53 s
[Task 10/25] Current/Best: 15.50/ 17.99 GFLOPS | Progress: (8/20) | 4.21 s
[Task 10/25] Current/Best: 12.83/ 18.86 GFLOPS | Progress: (12/20) | 5.77 s
[Task 10/25] Current/Best: 19.07/ 20.50 GFLOPS | Progress: (16/20) | 6.89 s
[Task 10/25] Current/Best: 8.84/ 20.50 GFLOPS | Progress: (20/20
) | 8.42 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.22/ 18.12 GFLOPS | Progress: (4/20) | 3.26 s
[Task 11/25] Current/Best: 16.83/ 18.12 GFLOPS | Progress: (8/20) | 6.08 s
[Task 11/25] Current/Best: 16.93/ 18.12 GFLOPS | Progress: (12/20) | 8.12 s
[Task 11/25] Current/Best: 11.82/ 21.10 GFLOPS | Progress: (16/20) | 11.09 s
[Task 11/25] Current/Best: 19.49/ 21.55 GFLOPS | Progress: (20/20) | 13.21 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.79/ 17.97 GFLOPS | Progress: (4/20) | 5.73 s
[Task 12/25] Current/Best: 5.18/ 17.97 GFLOPS | Progress: (8/20) | 9.66 s
[Task 12/25] Current/Best: 18.51/ 18.90 GFLOPS | Progress: (12/20) | 11.67 s
[Task 12/25] Current/Best: 14.74/ 18.90 GFLOPS | Progress: (16/20) | 14.59 s
[Task 12/25] Current/Best: 15.11/ 19.07 GFLOPS | Progress: (20/20) | 16.51 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.70/ 17.31 GFLOPS | Progress: (4/20) | 3.71 s
[Task 13/25] Current/Best: 16.03/ 20.87 GFLOPS | Progress: (8/20) | 6.34 s
[Task 13/25] Current/Best: 19.30/ 21.17 GFLOPS | Progress: (12/20) | 9.44 s
[Task 13/25] Current/Best: 12.21/ 21.17 GFLOPS | Progress: (16/20) | 12.87 s
[Task 13/25] Current/Best: 18.68/ 21.17 GFLOPS | Progress: (20/20) | 15.14 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 12.45/ 13.11 GFLOPS | Progress: (4/20) | 3.30 s
[Task 14/25] Current/Best: 6.09/ 13.25 GFLOPS | Progress: (8/20) | 5.54 s
[Task 14/25] Current/Best: 20.20/ 20.20 GFLOPS | Progress: (12/20) | 8.27 s
[Task 14/25] Current/Best: 15.56/ 20.20 GFLOPS | Progress: (16/20) | 9.95 s Done.
+
[Task 14/25] Current/Best: 16.85/ 20.20 GFLOPS | Progress: (20/20) | 11.66 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.04/ 17.58 GFLOPS | Progress: (4/20) | 2.66 s
[Task 15/25] Current/Best: 14.34/ 17.95 GFLOPS | Progress: (8/20) | 4.05 s
[Task 15/25] Current/Best: 10.39/ 22.16 GFLOPS | Progress: (12/20) | 6.55 s
[Task 15/25] Current/Best: 20.43/ 22.16 GFLOPS | Progress: (16/20) | 9.61 s
[Task 15/25] Current/Best: 9.71/ 22.16 GFLOPS | Progress: (20/20) | 10.62 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.55/ 20.55 GFLOPS | Progress: (4/20) | 2.96 s
[Task 16/25] Current/Best: 3.04/ 20.55 GFLOPS | Progress: (8/20) | 4.58 s
[Task 16/25] Current/Best: 19.38/ 20.55 GFLOPS | Progress: (12/20) | 5.80 s
[Task 16/25] Current/Best: 17.58/ 20.55 GFLOPS | Progress: (16/20) |
7.18 s
[Task 16/25] Current/Best: 9.95/ 21.47 GFLOPS | Progress: (20/20) | 9.35 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 11.67/ 18.90 GFLOPS | Progress: (4/20) | 4.77 s
[Task 17/25] Current/Best: 14.37/ 23.30 GFLOPS | Progress: (8/20) | 7.56 s
[Task 17/25] Current/Best: 17.16/ 23.30 GFLOPS | Progress: (12/20) | 9.64 s
[Task 17/25] Current/Best: 17.51/ 23.30 GFLOPS | Progress: (16/20) | 11.85 s
[Task 17/25] Current/Best: 10.03/ 23.30 GFLOPS | Progress: (20/20) | 14.03 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.12/ 17.75 GFLOPS | Progress: (4/20) | 3.77 s
[Task 18/25] Current/Best: 10.37/ 17.75 GFLOPS | Progress: (8/20) | 7.49 s
[Task 18/25] Current/Best: 19.15/ 19.15 GFLOPS | Progress: (12/20) | 9.42 s
[Task 18/25] Current/Best: 10.03/ 19.15 GFLOPS | Progress: (16/20) | 13.33 s
[Task 18/25] Current/Best: 20.54/ 20.54 GFLOPS | Progress: (20/20) | 14.89 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 6.90/ 20.12 GFLOPS | Progress: (4/20) | 6.22 s
[Task 19/25] Current/Best: 2.61/ 20.12 GFLOPS | Progress: (8/20) | 9.57 s
[Task 19/25] Current/Best: 19.42/ 20.77 GFLOPS | Progress: (12/20) | 12.52 s
[Task 19/25] Current/Best: 14.70/ 21.11 GFLOPS | Progress: (16/20) | 15.48 s
[Task 19/25] Current/Best: 2.70/ 23.23 GFLOPS | Progress: (20/20) | 18.29 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.80/ 15.00 GFLOPS | Progress: (4/20) | 3.31 s Done.
Done.
-
[Task 16/25] Current/Best: 3.09/ 20.81 GFLOPS | Progress: (8/20) | 4.36 s
[Task 16/25] Current/Best: 20.01/ 20.81 GFLOPS | Progress: (12/20) | 5.54 s
[Task 16/25] Current/Best: 17.75/ 20.81 GFLOPS | Progress: (16/20) | 6.88 s
[Task 16/25] Current/Best: 10.21/ 22.39 GFLOPS | Progress: (20/20) | 8.97 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.95/ 19.23 GFLOPS | Progress: (4/20) | 4.61 s
[Task 17/25] Current/Best: 14.58/ 23.81 GFLOPS | Progress: (8/20) | 7.38 s
[Task 17/25] Current/Best: 17.15/ 23.81 GFLOPS | Progress: (12/20) | 9.41 s
[Task 17/25] Current/Best: 16.87/ 23.81 GFLOPS | Progress: (16/20) | 11.57 s
[Task 17/25] Current/Best: 10.22/ 23.81 GFLOPS | Progress: (20/20) | 13.67 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.53/ 18.39 GFLOPS | Progress: (4/20) | 3.64 s
[Task 18/25] Current/Best: 10.70/ 19.70 GFLOPS | Progress: (8/20) | 7.18 s
[Task 18/25] Current/Best: 19.81/ 19.81 GFLOPS | Progress: (12/20) | 9.08 s
[Task 18/25] Current/Best: 10.25/ 19.81 GFLOPS | Progress: (16/20) | 12.80 s
[Task 18/25] Current/Best: 21.07/ 21.07 GFLOPS | Progress: (20/20) | 14.26 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.25/ 20.82 GFLOPS | Progress: (4/20) | 5.89 s
[Task 19/25] Current/Best: 2.65/ 20.82 GFLOPS | Progress: (8/20) | 9.23 s
[Task 19/25] Current/Best: 20.89/ 22.23 GFLOPS | Progress: (12/20) | 12.18 s
[Task 19/25] Current/Best: 14.48/ 22.23 GFLOPS | Progress: (16/20) | 15.15 s
[Task 19/25] Current/Best: 2.74/ 24.22 GFLOPS | Progress: (20/20) | 17.97 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.33/ 15.50 GFLOPS | Progress: (4/20) | 3.20 s
[Task 20/25] Current/Best: 9.90/ 15.50 GFLOPS | Progress: (8/20) | 6.52 s
[Task 20/25] Current/Best: 2.35/ 16.89 GFLOPS | Progress: (12/20) | 10.30 s
[Task 20/25] Current/Best: 12.50/ 16.89 GFLOPS | Progress: (16/20) | 14.05 s
[Task 20/25] Current/Best: 12.72/ 22.66 GFLOPS | Progress: (20/20) | 16.09 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.52/ 18.09 GFLOPS | Progress: (4/20) | 3.12 s
[Task 21/25] Current/Best: 14.82/ 18.09 GFLOPS | Progress: (8/20) | 4.70 s
[Task 21/25] Current/Best: 1.64/ 18.09 GFLOPS | Progress: (12/20) | 6.76 s
[Task 21/25] Current/Best: 18.32/ 18.32 GFLOPS | Progress: (16/20) | 10.16 s
[Task 21/25] Current/Best: 4.54/ 18.32 GFLOPS | Progress: (20/20)
| 17.27 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.75/ 17.32 GFLOPS | Progress: (4/20) | 2.53 s
[Task 22/25] Current/Best: 8.75/ 22.18 GFLOPS | Progress: (8/20) | 4.55 s Done.
+
[Task 20/25] Current/Best: 9.95/ 15.00 GFLOPS | Progress: (8/20) | 6.83 s
[Task 20/25] Current/Best: 2.32/ 16.53 GFLOPS | Progress: (12/20) | 10.81 s
[Task 20/25] Current/Best: 12.53/ 16.53 GFLOPS | Progress: (16/20) | 14.77 s
[Task 20/25] Current/Best: 13.38/ 21.63 GFLOPS | Progress: (20/20) | 16.87 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.39/ 17.48 GFLOPS | Progress: (4/20) | 3.27 s
[Task 21/25] Current/Best: 14.35/ 17.48 GFLOPS | Progress: (8/20) | 4.90 s
[Task 21/25] Current/Best: 1.61/ 17.48 GFLOPS | Progress: (12/20) | 7.02 s
[Task 21/25] Current/Best: 18.21/ 18.21 GFLOPS | Progress: (16/20) | 10.54 s
[Task 21/25] Current/Best: 4.45/ 18.21 GFLOPS | Progress: (20/20) | 17.96 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 16.93 GFLOPS | Progress: (4/20
) | 2.64 s
[Task 22/25] Current/Best: 8.94/ 21.58 GFLOPS | Progress: (8/20) | 4.66 s
[Task 22/25] Current/Best: 19.76/ 21.58 GFLOPS | Progress: (12/20) | 7.05 s
[Task 22/25] Current/Best: 15.24/ 21.58 GFLOPS | Progress: (16/20) | 9.16 s
[Task 22/25] Current/Best: 14.82/ 21.58 GFLOPS | Progress: (20/20) | 10.91 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.41/ 20.23 GFLOPS | Progress: (4/20) | 3.21 s
[Task 23/25] Current/Best: 15.66/ 20.23 GFLOPS | Progress: (8/20) | 6.67 s
[Task 23/25] Current/Best: 20.70/ 21.34 GFLOPS | Progress: (12/20) | 8.55 s
[Task 23/25] Current/Best: 6.33/ 21.34 GFLOPS | Progress: (16/20) | 15.68 s
[Task 23/25] Current/Best: 7.65/ 21.34 GFLOPS | Progress: (20/20) | 19.95 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.65/ 8.65 GFLOPS | Progress: (4/20) | 11.76 s
[Task 24/25] Current/Best: 2.12/ 8.65 GFLOPS | Progress: (8/20) | 22.77 s
[Task 24/25] Current/Best: 4.44/ 8.65 GFLOPS | Progress: (12/20) | 34.29 s Done.
Done.
-
[Task 22/25] Current/Best: 20.34/ 22.18 GFLOPS | Progress: (12/20) | 6.95 s
[Task 22/25] Current/Best: 15.32/ 22.18 GFLOPS | Progress: (16/20) | 9.05 s
[Task 22/25] Current/Best: 14.08/ 22.18 GFLOPS | Progress: (20/20) | 10.76 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 18.01/ 21.21 GFLOPS | Progress: (4/20) | 3.12 s
[Task 23/25] Current/Best: 14.27/ 21.21 GFLOPS | Progress: (8/20) | 6.52 s
[Task 23/25] Current/Best: 21.39/ 22.23 GFLOPS | Progress: (12/20) | 8.29 s
[Task 23/25] Current/Best: 6.54/ 22.23 GFLOPS | Progress: (16/20) | 15.25 s
[Task 23/25] Current/Best: 8.07/ 22.23 GFLOPS | Progress: (20/20) | 19.36 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.58/ 8.58 GFLOPS | Progress: (4/20) | 11.67 s
[Task 24/25] Current/Best: 2.16/ 8.58 GFLOPS | Progress: (8/20) | 22.63 s
[Task 24/25] Current/Best: 4.34/ 8.58 GFLOPS | Progress: (12/20) | 34.06 s
[Task 24/25] Current/Best: 6.30/ 9.10 GFLOPS | Progress: (16/20) | 39.59 s
[Task 24/25] Current/Best: 3.47/ 9.10 GFLOPS | Progress: (20/20) | 45.53 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
-
[Task 25/25] Current/Best: 1.57/ 2.95 GFLOPS | Progress: (4/20) | 11.55 s
[Task 25/25] Current/Best: 6.13/ 8.06 GFLOPS | Progress: (8/20) | 22.76 s
[Task 25/25] Current/Best: 6.06/ 8.06 GFLOPS | Progress: (12/20) | 34.15 s
[Task 25/25] Current/Best: 5.89/ 8.96 GFLOPS | Progress: (16/20) | 35.83 s
[Task 25/25] Current/Best: 2.92/ 9.22 GFLOPS | Progress: (20/20) | 46.50 s
+
[Task 24/25] Current/Best: 6.66/ 8.68 GFLOPS | Progress: (16/20) | 40.38 s
[Task 24/25] Current/Best: 3.27/ 8.98 GFLOPS | Progress: (20/20) | 46.51 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.88 GFLOPS | Progress: (4/20) | 11.55 s
[Task 25/25] Current/Best: 5.61/ 7.67 GFLOPS | Progress: (8/20) | 22.81 s
[Task 25/25] Current/Best: 5.86/ 7.67 GFLOPS | Progress: (12/20) | 34.09 s
[Task 25/25] Current/Best: 5.68/ 8.54 GFLOPS | Progress: (16/20) | 35.97 s
[Task 25/25] Current/Best: 2.88/ 8.54 GFLOPS | Progress: (20/20) | 46.65 s
The output from this tuning process will look something like this:
@@ -660,8 +660,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 404.41845570999703, 'median': 404.4244133500001, 'std': 1.1108665845633878}
- unoptimized: {'mean': 485.34359255000254, 'median': 485.3246965500034, 'std': 1.1821005350493337}
+ optimized: {'mean': 413.06094868999025, 'median': 413.1940400000076, 'std': 0.8286562565105212}
+ unoptimized: {'mean': 495.57246422999015, 'median': 495.29582899999696, 'std': 0.6631744310948293}
@@ -681,7 +681,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 10 minutes 12.309 seconds)
+ **Total running time of the script:** ( 10 minutes 26.240 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 563ac8d46..5d32e6d9a 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
- 1.215e-07 secs/op
+ 1.264e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 6444432b6..38bfd6db2 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -233,7 +233,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0xc68d9b0)), stage(b, placeholder(b, 0x216ddfb0)), 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, 0x20e57850)), stage(b, placeholder(b, 0x23df6aa0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(mi [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 1bf6202e9..69513ec07 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
=================
-**13:05.827** total execution time for **tutorial** files:
+**13:40.940** total execution time for **tutorial** files:
-- **10:12.309**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **01:00.464**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:59.946**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:27.630**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:23.166**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:01.302**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.696**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.195**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.032**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.029**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
-- **00:00.029**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
-- **00:00.029**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
+- **10:26.240**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:17.200**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **01:01.906**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:28.372**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:25.226**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:00.832**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.744**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.220**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.051**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.050**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
+- **00:00.050**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.049**: :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 a6ff98ea3..11946a45f 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -252,7 +252,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000009
+ Numpy running time: 0.000008
naive: 0.000006
@@ -344,7 +344,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000007
+ parallel: 0.000006
@@ -397,7 +397,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000025
+ vector: 0.000024
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [(stride: int32*n: int32)], [], type="auto"),
@@ -447,10 +447,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.922829999846727e-06 1.0
- naive 6.465899999999999e-06 0.7246467768758419
- parallel 6.6949e-06 0.7503112801784863
- vector 2.45213e-05 2.7481527722058154
+ numpy 8.198289997380925e-06 1.0
+ naive 5.8254e-06 0.7105628127159466
+ parallel 6.0816e-06 0.7418132320206854
+ vector 2.4459900000000003e-05 2.9835368116783023
@@ -839,7 +839,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017991
+ Numpy running time: 0.018439
@@ -897,7 +897,7 @@ optimizations.
/workspace/python/tvm/driver/build_module.py:264: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- none: 3.415491
+ none: 3.449056
@@ -996,7 +996,7 @@ schedule.
.. code-block:: none
- blocking: 0.288683
+ blocking: 0.323903
@@ -1088,7 +1088,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.324753
+ vectorization: 0.350504
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1160,7 +1160,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.113943
+ loop permutation: 0.118218
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1257,7 +1257,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.108356
+ array packing: 0.109189
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1348,7 +1348,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.109372
+ block caching: 0.111873
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1432,7 +1432,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.142428
+ parallelization: 0.144885
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1511,13 +1511,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.4154911798 1.0
- blocking 0.2886831157 0.08452169849166594
- vectorization 0.3247531953 0.09508242832558464
- loop permutation 0.1139430135 0.033360652246413394
- array packing 0.1083559747 0.03172485859159647
- block caching 0.10937236170000002 0.032022440094957146
- parallelization 0.14242816809999997 0.04170063999648217
+ none 3.4490564284 1.0
+ blocking 0.32390314130000003 0.0939106529637896
+ vectorization 0.3505040656 0.10162317517159239
+ loop permutation 0.1182177817 0.03427539796872521
+ array packing 0.1091890503 0.031657658425337
+ block caching 0.11187268549999999 0.032435736504287105
+ parallelization 0.14488473600000001 0.042007064542928144
@@ -1554,7 +1554,7 @@ the computation for specific platforms.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 0.464 seconds)
+ **Total running time of the script:** ( 1 minutes 1.906 seconds)
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index c6a0ea553..95a8f0167 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-9d6599c928ec4de1aede59927fcc5f651096e358
+70884e957aa5c8de9c02c25a14d30563d7300cb9
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index 3ee08d73e..a23ad67c1 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.zip67d7d724-c04c-4721-915c-d096871322ec 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.zip6d599b92-18c1-40d6-a64d-b9431e592e8c 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 2faf1960b..93d055ca5 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -406,79 +406,42 @@ 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 5638707ad..db8186301 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -469,7 +469,7 @@ A quick solution is</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 5.874 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.165 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 b6b373cc8..3c15e782b 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -387,9 +387,10 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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</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 ecb236aed..47e3415a9 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -612,7 +612,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.631 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.652 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 faab5dd94..8a7efed7c 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>05:24.515</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:30.301</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:05.874</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
-<li><p><strong>01:01.631</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>00:56.444</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>00:37.554</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>00:23.872</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
-<li><p><strong>00:22.405</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
-<li><p><strong>00:21.090</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>00:19.078</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
-<li><p><strong>00:13.722</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>00:02.846</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
+<li><p><strong>01:07.165</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>01:04.652</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>00:59.961</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>00:31.997</strong>: <a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></li>
+<li><p><strong>00:24.519</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>00:23.147</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>00:22.287</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>00:19.985</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
+<li><p><strong>00:13.820</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>00:02.767</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 e91536c28..437688027 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -627,7 +627,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.3281 15.3607 15.5444 15.1472 0.1172
+ 16.3197 16.2442 16.9251 16.1694 0.2110
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 565247a2e..db0ece84f 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,13 +409,15 @@ 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').
@@ -513,7 +515,7 @@ 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> ( 2 minutes 55.302 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 1.783 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 379b9f970..95bb6ae35 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,7 +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>
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@@ -544,7 +544,7 @@ 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)
- 88.9107 88.8655 89.4770 88.5857 0.1836
+ 90.3297 90.2424 91.1498 90.1341 0.2079
</pre></div>
</div>
<div class="admonition note">
@@ -583,7 +583,7 @@ 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 4.883 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.638 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 b1575e380..f9d2e21a3 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -545,7 +545,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 118.3542 117.7878 141.4023 116.8825 2.5086
+ 121.9273 121.9259 122.8546 121.2343 0.3097
</pre></div>
</div>
<div class="admonition note">
@@ -573,7 +573,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 57.635 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 59.432 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 912f210b6..25fdb269c 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -482,7 +482,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.581 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.219 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">
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<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index 40b05acff..7ee934e8c 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,24 +415,23 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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</pre></div>
</div>
<p>Create TVM runtime and do inference
@@ -477,7 +476,7 @@ 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 14.928 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 23.827 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 549c7d873..693e2c821 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:15.614</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:37.779</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:55.302</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>02:14.928</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
-<li><p><strong>01:57.635</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>01:12.581</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
-<li><p><strong>01:04.883</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
-<li><p><strong>00:28.414</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>00:21.674</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>00:00.195</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>03:01.783</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>02:23.827</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>01:59.432</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>01:12.219</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
+<li><p><strong>01:07.638</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
+<li><p><strong>00:29.918</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
+<li><p><strong>00:22.755</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>00:00.207</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 68b71e303..26a06a971 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -590,7 +590,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipc7c5c4bd-3b6e-48f6-98a8-bca21dacb4b4 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.zipa6f0d07b-9158-4cb5-baa5-bef7f9e22e30 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index ccf1cec00..cd408ee8e 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>00:39.585</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:40.986</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:35.913</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>00:02.360</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>00:01.104</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
-<li><p><strong>00:00.208</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
+<li><p><strong>00:37.152</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>00:02.477</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>00:01.140</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>00:00.217</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 effea8798..899da6157 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: 6595us [6595us] (45.61%; 45.61%)
-FoldScaleAxis: 7866us [6us] (54.39%; 54.39%)
- FoldConstant: 7860us [1599us] (54.35%; 99.93%)
- InferType: 6261us [6261us] (43.30%; 79.66%)
+InferType: 6878us [6878us] (45.75%; 45.75%)
+FoldScaleAxis: 8154us [6us] (54.25%; 54.25%)
+ FoldConstant: 8148us [1620us] (54.21%; 99.93%)
+ InferType: 6528us [6528us] (43.43%; 80.12%)
</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: 6198us [6198us] (44.48%; 44.48%)
-FoldScaleAxis: 7735us [5us] (55.52%; 55.52%)
- FoldConstant: 7730us [1582us] (55.48%; 99.94%)
- InferType: 6149us [6149us] (44.13%; 79.54%)
+InferType: 6589us [6589us] (44.80%; 44.80%)
+FoldScaleAxis: 8118us [5us] (55.20%; 55.20%)
+ FoldConstant: 8112us [1658us] (55.16%; 99.94%)
+ InferType: 6454us [6454us] (43.89%; 79.56%)
</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 13ca2e397..d20063f28 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: 54.131989 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 51.575460 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 7811b8ca5..21343bb88 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: 6.614921 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 10.396211 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 5a04b3f5b..074e89ee9 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: 0.018521
-Baseline: 3.399815
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019390
+Baseline: 3.448056
</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: 0.290889
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.317239
</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: 0.331642
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.345401
</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: 0.114744
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.120997
</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: 0.109034
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110805
</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: 0.109441
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111641
</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: 0.142529
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145231
</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 d30321467..d6549591e 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>00:34.606</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.774</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:31.993</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>00:01.390</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>00:01.223</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
+<li><p><strong>00:32.978</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>00:01.532</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>00:01.264</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 55f30c082..19b645634 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>05:16.718</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>05:11.887</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:32.299</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>01:19.117</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>00:42.185</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>00:26.270</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>00:08.595</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>00:08.252</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>02:32.057</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
+<li><p><strong>01:21.484</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>00:43.568</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>00:16.939</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>00:08.956</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>00:08.884</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 28428c924..c7bfbda94 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
@@ -470,359 +470,78 @@ 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" = 16;
- allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [4032]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [6144]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
- conv2d_nchw_1[7] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 56;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [7]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [84]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [768]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope="local", align=4)[0] = 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
- for (rc.outer.outer: int32, 0, 8) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_4: int32 = (rc.outer.outer*3136)
- let cse_var_3: int32 = (ry.outer.outer*7)
- let cse_var_2: int32 = (rc.outer.outer*576)
- let cse_var_1: int32 = (ry.outer.outer*3)
+ for (rc.outer.outer: int32, 0, 128) {
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_1: int32 = (rc.outer.outer*36)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [4032], [], 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" = 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_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1008)] = @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)) + 776)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1120)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1120), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1120), 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 + 1120), 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 + 1232)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1232), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1232), 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 + 1232), 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 + 1344)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1344), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1344), 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 + 1344), 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 + 1456)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1456), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1456), 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 + 1456), 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 + 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1680)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1680), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1680), 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 + 1680), 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 + 1792)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1792), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1792), 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 + 1792), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 1904)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 1904), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 1904), 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 + 1904), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2016)] = @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)) + 1560)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2128)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2128), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2128), 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 + 2128), 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 + 2240)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2240), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2240), 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 + 2240), 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 + 2352)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2352), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2352), 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 + 2352), 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 + 2464)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2464), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2464), 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 + 2464), 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 + 2576)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2576), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2576), 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 + 2576), 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 + 2688)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2688), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2688), 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 + 2688), 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 + 2800)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2800), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2800), 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 + 2800), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 2912)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 2912), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 2912), 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 + 2912), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3024)] = @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)) + 2344)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3136)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3136), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3136), 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 + 3136), 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 + 3248)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3248), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3248), 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 + 3248), 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 + 3360)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3360), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3360), 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 + 3360), 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 + 3472)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3472), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3472), 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 + 3472), 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 + 3584)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3584), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3584), 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 + 3584), 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 + 3696)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3696), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3696), 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 + 3696), 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 + 3808)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3808), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3808), 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 + 3808), 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" = 112;
- pad_temp.shared_1[(threadIdx.x_1 + 3920)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 3920), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 3920), 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 + 3920), 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, [6144], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*147456) + 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" = 112;
- kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 7), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 192), 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) + 21), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 448)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 28), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 560), 192), 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) + 42), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 784)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 49), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 784), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 192), 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) + 63), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 1120)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 70), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1120), 192), 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), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1232), 192), 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) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 1456)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 91), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1456), 192), 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 + 1568)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 98), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1568), 192), 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 + 1680)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 105), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 1792)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 112), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1792), 192), 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 + 1904)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 119), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1904), 192), 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 + 2016)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 126), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 2128)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 133), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2128), 192), 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 + 2240)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 140), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2240), 192), 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 + 2352)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 147), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 2464)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 154), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2464), 192), 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 + 2576)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 161), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2576), 192), 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 + 2688)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 2800)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 175), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2800), 192), 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 + 2912)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 182), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 2912), 192), 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 + 3024)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 189), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 3136)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 196), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3136), 192), 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 + 3248)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 203), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3248), 192), 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 + 3360)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 210), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 3472)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 217), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3472), 192), 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 + 3584)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 224), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3584), 192), 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 + 3696)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 231), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 3808)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 238), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3808), 192), 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 + 3920)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 245), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 3920), 192), 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 + 4032)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 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 + 4144)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 259), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4144), 192), 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 + 4256)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 266), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4256), 192), 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 + 4368)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 273), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 4480)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 280), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4480), 192), 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 + 4592)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 287), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4592), 192), 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 + 4704)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 294), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*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 + 4816)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 301), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4816), 192), 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 + 4928)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 308), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 4928), 192), 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 + 5040)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 315), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 16), 64)*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 + 5152)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 322), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5152), 192), 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 + 5264)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 329), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5264), 192), 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 + 5376)] = kernel[((((((blockIdx.x*147456) + cse_var_2) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 129024)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- kernel.shared_1[(threadIdx.x_2 + 5488)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 343), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5488), 192), 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 + 5600)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 350), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5600), 192), 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 + 5712)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 357), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 48), 64)*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 + 5824)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 364), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5824), 192), 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 + 5936)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 371), 12)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 5936), 192), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
- if @tir.likely((threadIdx.x_2 < 96), dtype=bool) {
- kernel.shared_1[(threadIdx.x_2 + 6048)] = kernel[((((((blockIdx.x*147456) + (floordiv((floordiv(threadIdx.x_2, 16) + 378), 12)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 32), 64)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
+ if @tir.likely((threadIdx.x_1 < 42), dtype=bool) {
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [84], [], scope="shared")[(threadIdx.x_1*2)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1*2), 21), 7) + floormod(blockIdx.x, 7))) && ((floordiv(floormod((threadIdx.x_1*2), 21), 7) + floormod(blockIdx.x, 7)) < 8)) && (1 <= (rx.outer.outer + floormod((threadIdx.x_1*2), 7)))) && ((rx.outer.outer + floormod((threadIdx.x_1*2), 7)) < 8)), data[((((((rc.outer.outer*1 [...]
+ }
+ if @tir.likely((threadIdx.x_1 < 42), dtype=bool) {
+ pad_temp.shared_1[((threadIdx.x_1*2) + 1)] = @tir.if_then_else(((((1 <= (floordiv(floormod(((threadIdx.x_1*2) + 1), 21), 7) + floormod(blockIdx.x, 7))) && ((floordiv(floormod(((threadIdx.x_1*2) + 1), 21), 7) + floormod(blockIdx.x, 7)) < 8)) && (1 <= (rx.outer.outer + floormod(((threadIdx.x_1*2) + 1), 7)))) && ((rx.outer.outer + floormod(((threadIdx.x_1*2) + 1), 7)) < 8)), data[((((((rc.outer.outer*196) + (floordiv(((threadIdx.x_1*2) + 1), [...]
+ }
}
- for (rx.outer.inner: int32, 0, 3) {
- for (xx.outer.inner: int32, 0, 7) {
- let cse_var_5: int32 = (xx.outer.inner + 7)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1: Buffer(kernel.shared, float32, [768], [], scope="shared")[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*294912) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 16), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 4), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 32), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 8), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 73728)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 64), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 16), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 80), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 20), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 384)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 147456)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 112), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 28), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 512)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 128), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 32), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 576)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 221184)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 160), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 40), 12), 3)*9)) + (floormod((threadIdx.x_2 + 1), 3)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
+ kernel.shared_1[(threadIdx.x_2 + 704)] = kernel[((((((floordiv(blockIdx.x, 7)*294912) + (floordiv((floordiv(threadIdx.x_2, 4) + 176), 3)*4608)) + cse_var_1) + (floordiv(floormod((threadIdx.x_2 + 44), 12), 3)*9)) + (floormod((threadIdx.x_2 + 2), 3)*3)) + rx.outer.outer)]
+ for (rc.inner: int32, 0, 4) {
+ for (ry.inner: int32, 0, 3) {
+ let cse_var_2: int32 = ((rc.inner*21) + (ry.inner*7))
{
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[(((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*384) + rx.outer.inner)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[(((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 192)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 3)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 195)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 6)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 198)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 9)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 201)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 12)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 204)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 15)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 207)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 18)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 210)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 21)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 213)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 24)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 504)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 216)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 27)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 567)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 219)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 30)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 630)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 222)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 33)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 693)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 225)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 36)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 756)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 228)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 39)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 819)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 231)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 42)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 882)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 234)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 45)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 945)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 237)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 48)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1008)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 240)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 51)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1071)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 243)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 54)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1134)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 246)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 57)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1197)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 249)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 60)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1260)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 252)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 63)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1323)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 255)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 66)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1386)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 258)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 69)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1449)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 261)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 72)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1512)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 264)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 75)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1575)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 267)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 78)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1638)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 270)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 81)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1701)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 273)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 84)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1764)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 276)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 87)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1827)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 279)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 90)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1890)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 282)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 93)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 1953)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 285)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2016)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 96)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2016)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 288)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2079)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 99)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2079)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 291)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2142)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 102)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2142)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 294)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2205)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 105)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2205)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 297)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2268)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 108)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2268)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 300)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2331)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 111)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2331)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 303)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2394)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 114)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2394)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 306)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2457)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 117)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2457)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 309)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2520)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 120)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2520)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 312)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2583)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 123)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2583)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 315)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2646)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 126)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2646)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 318)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2709)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 129)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2709)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 321)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2772)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 132)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2772)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 324)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2835)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 135)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2835)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 327)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2898)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 138)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2898)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 330)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2961)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 141)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 2961)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 333)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3024)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 144)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3024)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 336)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3087)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 147)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3087)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 339)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3150)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 150)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3150)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 342)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3213)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 153)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3213)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 345)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3276)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 156)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3276)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 348)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3339)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 159)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3339)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 351)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3402)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 162)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3402)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 354)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3465)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 165)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3465)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 357)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3528)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 168)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3528)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 360)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3591)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 171)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3591)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 363)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3654)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 174)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3654)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 366)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3717)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 177)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3717)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 369)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3780)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 180)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3780)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 372)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3843)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 183)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3843)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 375)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3906)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 186)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3906)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 378)]))
- conv2d_nchw_1[xx.outer.inner] = (conv2d_nchw_1[xx.outer.inner] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3969)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 189)]))
- conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((floormod(threadIdx.x, 7)*9) + xx.outer.inner) + rx.outer.inner) + 3969)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*384) + rx.outer.inner) + 381)]))
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[cse_var_2]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(cse_var_2 + 1)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(cse_var_2 + 2)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(cse_var_2 + 3)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[(cse_var_2 + 4)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[(cse_var_2 + 5)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[(cse_var_2 + 6)]*kernel.shared_1[(((threadIdx.x*12) + (rc.inner*3)) + ry.inner)]))
}
}
}
}
}
}
- for (i1.inner: int32, 0, 2) {
- for (i3.inner: int32, 0, 7) {
- compute[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[((i1.inner*7) + i3.inner)] + bias[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
- }
- }
+ compute[(((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[1] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[2] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[3] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[4] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[5] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
+ compute[((((floordiv(blockIdx.x, 7)*3136) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[6] + bias[((floordiv(blockIdx.x, 7)*64) + threadIdx.x)]), 0f32)
}
}
</pre></div>
@@ -859,7 +578,7 @@ 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: 0.373 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.383 ms
</pre></div>
</div>
</div>
@@ -889,37 +608,37 @@ conv2d_nchw_nn_o_i, conv2d_nchw_nn_i = s[conv2d_nchw].split(conv2d_nchw_nn, fact
conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_i, factor=1)
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
-conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=2)
+conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=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_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=64)
conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
-conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
+conv2d_nchw_yy_o_o_o_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=7)
+conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
-conv2d_nchw_xx_o_o_o_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=64)
+conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=4)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
-conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
+conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=3)
conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
-conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=3)
+conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=1)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2d_nc [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
+compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
-compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+compute_i2_o_o_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=7)
+compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
-compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -938,14 +657,14 @@ s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=2)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
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", 512)
+s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 16)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -963,260 +682,59 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[14];
- __shared__ float pad_temp_shared[4032];
- __shared__ float kernel_shared[6144];
+extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[7];
+ __shared__ float pad_temp_shared[84];
+ __shared__ float kernel_shared[768];
conv2d_nchw[0] = 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;
- for (int rc_outer_outer = 0; rc_outer_outer < 8; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
+ for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
__syncthreads();
- 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 * 3136) + ((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((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 * 3136) + (((((int)threadIdx.x) + 896) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1008)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 776)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1120)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1120) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1232)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1232) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1344)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1344) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1456)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1456) / 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 * 3136) + (((((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) + 1680)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1680) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1792)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1792) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 1904)] = (((((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 * 3136) + (((((int)threadIdx.x) + 1904) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2016)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 1560)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2128)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2128) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2240)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2240) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2352)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2352) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2464)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2464) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2576)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2576) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2688)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2688) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2800)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2800) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 2912)] = (((((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 * 3136) + (((((int)threadIdx.x) + 2912) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3024)] = (((((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 * 3136) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 2344)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3136)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3136) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3248)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3248) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3360)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3360) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3472)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3472) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3584)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3584) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3696)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3696) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3808)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3808) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
- pad_temp_shared[(((int)threadIdx.x) + 3920)] = (((((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 * 3136) + (((((int)threadIdx.x) + 3920) / 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) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 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) + 336) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 448) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 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) + 672) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 784)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 784) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 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) + 1008) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1120)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1120) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 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) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
- kernel_shared[(((int)threadIdx.x) + 1456)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1456) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1568)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1568) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1680)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1680) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1792) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1904)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 1904) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2016)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2016) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2128)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2128) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2240) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2352)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2352) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2464)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2464) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2576)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2576) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
- kernel_shared[(((int)threadIdx.x) + 2800)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2800) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2912)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 2912) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3024)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3024) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3136)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3136) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3248)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3248) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3360)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3360) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3472)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3472) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3584)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3584) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3696)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3696) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3808)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3808) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3920)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 3920) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4032)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 96768)];
- kernel_shared[(((int)threadIdx.x) + 4144)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4144) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4256)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4256) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4368)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4368) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4480)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4480) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4592)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4592) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4704)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4704) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 32) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4816)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4816) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 16) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 4928)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 4928) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 128) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5040)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5040) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 16) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5152)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5152) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 160) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5264)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5264) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 80) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5376)] = kernel[((((((((int)blockIdx.x) * 147456) + (rc_outer_outer * 576)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 129024)];
- kernel_shared[(((int)threadIdx.x) + 5488)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5488) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 112) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5600)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5600) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 32) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5712)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5712) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) / 3) + 48) & 63) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5824)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5824) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 64) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 5936)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 5936) / 192) * 4608)) + (rc_outer_outer * 576)) + ((((((int)threadIdx.x) + 176) % 192) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- if (((int)threadIdx.x) < 96) {
- kernel_shared[(((int)threadIdx.x) + 6048)] = kernel[((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) + 6048) / 192) * 4608)) + (rc_outer_outer * 576)) + (((((int)threadIdx.x) / 3) + 32) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+ if (((int)threadIdx.x) < 42) {
+ pad_temp_shared[(((int)threadIdx.x) * 2)] = (((((1 <= ((((((int)threadIdx.x) * 2) % 21) / 7) + (((int)blockIdx.x) % 7))) && (((((((int)threadIdx.x) * 2) % 21) / 7) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (rx_outer_outer + ((((int)threadIdx.x) * 2) % 7)))) && ((rx_outer_outer + ((((int)threadIdx.x) * 2) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) * 2) / 21) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + rx_outer_outer) + [...]
}
+ if (((int)threadIdx.x) < 42) {
+ pad_temp_shared[((((int)threadIdx.x) * 2) + 1)] = (((((1 <= (((((((int)threadIdx.x) * 2) + 1) % 21) / 7) + (((int)blockIdx.x) % 7))) && ((((((((int)threadIdx.x) * 2) + 1) % 21) / 7) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (rx_outer_outer + (((((int)threadIdx.x) * 2) + 1) % 7)))) && ((rx_outer_outer + (((((int)threadIdx.x) * 2) + 1) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + ((((((int)threadIdx.x) * 2) + 1) / 21) * 49)) + ((((int)blockId [...]
+ }
+ kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 64) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 128) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 192)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 73728)];
+ kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 256) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 320) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 384)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 147456)];
+ kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 448) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 512)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 512) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 576)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 221184)];
+ kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 640) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 1) % 3) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 704)] = kernel[(((((((((int)blockIdx.x) / 7) * 294912) + (((((int)threadIdx.x) + 704) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (((((int)threadIdx.x) + 2) % 3) * 3)) + rx_outer_outer)];
__syncthreads();
- for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
- for (int xx_outer_inner = 0; xx_outer_inner < 7; ++xx_outer_inner) {
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner)] * kernel_shared[(((((int)threadIdx.x) / 7) * 384) + rx_outer_inner)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 192)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 3)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 195)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 6)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 198)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 9)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 201)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 12)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 204)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 15)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 207)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 18)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 210)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 21)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 213)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 504)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 24)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 504)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 216)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 567)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 27)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 567)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 219)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 630)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 30)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 630)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 222)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 693)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 33)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 693)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 225)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 756)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 36)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 756)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 228)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 819)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 39)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 819)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 231)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 882)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 42)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 882)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 234)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 945)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 45)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 945)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 237)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 48)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1008)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 240)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 51)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1071)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 243)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 54)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1134)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 246)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 57)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1197)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 249)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 60)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1260)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 252)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 63)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1323)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 255)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 66)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1386)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 258)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 69)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1449)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 261)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 72)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1512)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 264)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 75)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1575)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 267)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 78)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1638)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 270)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 81)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1701)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 273)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 84)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1764)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 276)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 87)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1827)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 279)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 90)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1890)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 282)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 93)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 1953)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 285)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2016)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 96)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2016)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 288)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2079)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 99)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2079)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 291)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2142)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 102)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2142)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 294)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2205)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 105)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2205)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 297)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2268)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 108)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2268)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 300)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2331)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 111)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2331)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 303)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2394)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 114)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2394)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 306)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2457)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 117)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2457)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 309)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2520)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 120)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2520)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 312)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2583)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 123)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2583)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 315)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2646)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 126)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2646)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 318)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2709)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 129)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2709)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 321)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2772)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 132)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2772)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 324)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2835)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 135)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2835)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 327)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2898)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 138)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2898)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 330)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2961)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 141)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 2961)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 333)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3024)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 144)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3024)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 336)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3087)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 147)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3087)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 339)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3150)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 150)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3150)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 342)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3213)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 153)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3213)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 345)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3276)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 156)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3276)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 348)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3339)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 159)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3339)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 351)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3402)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 162)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3402)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 354)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3465)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 165)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3465)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 357)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3528)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 168)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3528)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 360)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3591)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 171)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3591)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 363)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3654)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 174)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3654)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 366)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3717)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 177)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3717)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 369)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3780)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 180)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3780)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 372)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3843)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 183)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3843)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 375)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3906)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 186)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3906)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 378)]));
- conv2d_nchw[xx_outer_inner] = (conv2d_nchw[xx_outer_inner] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3969)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 189)]));
- conv2d_nchw[(xx_outer_inner + 7)] = (conv2d_nchw[(xx_outer_inner + 7)] + (pad_temp_shared[(((((((int)threadIdx.x) % 7) * 9) + xx_outer_inner) + rx_outer_inner) + 3969)] * kernel_shared[((((((int)threadIdx.x) / 7) * 384) + rx_outer_inner) + 381)]));
+ for (int rc_inner = 0; rc_inner < 4; ++rc_inner) {
+ for (int ry_inner = 0; ry_inner < 3; ++ry_inner) {
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rc_inner * 21) + (ry_inner * 7))] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 1)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 2)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 3)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 4)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 5)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[(((rc_inner * 21) + (ry_inner * 7)) + 6)] * kernel_shared[(((((int)threadIdx.x) * 12) + (rc_inner * 3)) + ry_inner)]));
}
}
}
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- for (int i3_inner = 0; i3_inner < 7; ++i3_inner) {
- compute[(((((((int)blockIdx.x) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[(((((int)blockIdx.x) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
- }
- }
+ compute[((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[1] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[2] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[3] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[4] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[5] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) / 7) * 3136) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[6] + bias[(((((int)blockIdx.x) / 7) * 64) + ((int)threadIdx.x))]), 0.000000e+00f);
}
</pre></div>
</div>
@@ -1253,7 +771,7 @@ 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 32.299 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 32.057 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-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 c58c275b2..e5142cdce 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -878,7 +878,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.9812 9.9720 10.0057 9.9658 0.0175
+ 10.0095 10.0044 10.0492 9.9750 0.0305
</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 1564e3ab8..74c094060 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -897,7 +897,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 748.7067 748.2991 750.5136 747.3074 1.3403
+ 769.7185 769.6068 770.5845 768.9642 0.6662
</pre></div>
</div>
</div>
@@ -919,7 +919,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.117 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.484 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 aa6e22cbf..e825fa0b3 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,30 +600,32 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_7: placeholder_16: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (i.outer.inner: int32, 0, 4) {
- for (i.inner.init: int32, 0, 8) {
- for (j.init: int32, 0, 16) {
- compute_5: Buffer(compute_4, float32, [512], [])[(((i.outer.inner*128) + (i.inner.init*16)) + j.init)] = 0f32
+ preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], []), compute_1: compute_3: Buffer(compute_2, 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, 32) {
+ for (j.init: int32, 0, 16) {
+ compute_5: Buffer(compute_4, float32, [2048], [])[((((i.outer.inner*1024) + (i.inner.init*32)) + (nb_j.inner*16)) + j.init)] = 0f32
+ }
}
- }
- for (elem_idx: int32, 0, let cse_var_1: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 64), 2) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- for (i.inner: int32, 0, 8) {
- for (j: int32, 0, 16) {
- let cse_var_3: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 64), 2)
- let cse_var_2: int32 = (((i.outer.inner*128) + (i.inner*16)) + j)
- compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 64)*8192) + (i.outer.inner*2048)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_1: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
+ for (i.inner: int32, 0, 32) {
+ for (j: int32, 0, 16) {
+ let cse_var_3: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_2: int32 = ((((i.outer.inner*1024) + (i.inner*32)) + (nb_j.inner*16)) + j)
+ compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*8192)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_5: int32 = floormod(i0.outer.i1.outer.fused, 64)
- let cse_var_6: int32 = (cse_var_5*8)
- let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 64)*16384) + (i0.inner*512)) + cse_var_6)
- compute[ramp(cse_var_4, 1, 8)] = max((compute_5[ramp((((i0.inner*16) + cse_var_6) - (floordiv(cse_var_5, 2)*16)), 1, 8)] + placeholder_4[ramp(cse_var_4, 1, 8)]), broadcast(0f32, 8))
+ for (i0.inner: int32, 0, 64) {
+ for (i1.inner: int32, 0, 32) {
+ let cse_var_4: int32 = ((((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)) + i1.inner)
+ compute[cse_var_4] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_4]), 0f32)
+ }
}
}
}
@@ -662,7 +664,7 @@ 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: 3.013 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.656 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 7313cb81c..073be0281 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:44.633</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:45.406</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:43.803</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>00:00.216</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>00:00.213</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>00:00.201</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>00:00.200</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>00:44.480</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>00:00.243</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>00:00.229</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
+<li><p><strong>00:00.227</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>00:00.226</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>
</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 4269dafb5..8596f0da4 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: 92.34/92.34 result: MeasureResult(costs=(0.0025069530416666668,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.607266902923584, timestamp=1654534569.8616302) [('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: 0.00/92.34 result: Traceback (most recent call last):
+No: 6 GFLOPS: 93.25/93.25 result: MeasureResult(costs=(0.002482552083333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6437036991119385, timestamp=1654592794.6007924) [('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: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/93.25 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: 0.00/92.34 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/93.25 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: 0.00/92.34 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/93.25 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: 0x00007f12b294cfa2
+ 12: 0x00007fc885257fa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2667,7 +2667,7 @@ Traceback (most recent call last):
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
-No: 20 GFLOPS: 144.88/144.88 result: MeasureResult(costs=(0.00159785495,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4223277568817139, timestamp=1654534596.2005908) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+No: 20 GFLOPS: 143.47/143.47 result: MeasureResult(costs=(0.0016135417399999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3977861404418945, timestamp=1654592821.1712134) [('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: 0.001968
+Time cost of this operator: 0.002017
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 4b86d6108..ccf975f43 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -556,10 +556,10 @@ the tuned operator.</p>
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.6 98.74 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.026 0.968 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.913 0.292 (1, 1, 10, 10, 3) 1 1
-Total_time - 312.539 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.3 98.745 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.073 0.965 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.921 0.289 (1, 1, 10, 10, 3) 1 1
+Total_time - 318.294 - - - -
</pre></div>
</div>
</div>
@@ -611,10 +611,10 @@ 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 78.65 96.621 (1, 6, 10, 10, 1) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.825 2.242 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.926 1.137 (1, 1, 10, 10, 3) 1 1
-Total_time - 81.401 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 190.4 98.416 (1, 1, 10, 10, 6) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.1 1.085 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.964 0.499 (1, 3, 10, 10, 1) 1 1
+Total_time - 193.464 - - - -
</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/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 2d25f2a86..05ac46814 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -552,8 +552,8 @@ objects to other stuff? We can display some examples from our datasets using <co
</div>
<img alt="../../_images/sphx_glr_micro_train_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_micro_train_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>/tmp/tmp1lta35dh/images/target contains 8144 images
-/tmp/tmp1lta35dh/images/random contains 5000 images
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpy4bdfu_a/images/target contains 8144 images
+/tmp/tmpy4bdfu_a/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -666,11 +666,11 @@ the time on our validation set).</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>Epoch 1/3
-328/328 - 53s - loss: 0.2230 - accuracy: 0.9233 - val_loss: 0.1561 - val_accuracy: 0.9566
+328/328 - 54s - loss: 0.2123 - accuracy: 0.9228 - val_loss: 0.1276 - val_accuracy: 0.9603
Epoch 2/3
-328/328 - 51s - loss: 0.1036 - accuracy: 0.9618 - val_loss: 0.1126 - val_accuracy: 0.9637
+328/328 - 52s - loss: 0.1009 - accuracy: 0.9640 - val_loss: 0.1362 - val_accuracy: 0.9603
Epoch 3/3
-328/328 - 51s - loss: 0.0630 - accuracy: 0.9757 - val_loss: 0.1042 - val_accuracy: 0.9664
+328/328 - 52s - loss: 0.0695 - accuracy: 0.9728 - val_loss: 0.1461 - val_accuracy: 0.9573
</pre></div>
</div>
</div>
@@ -959,7 +959,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 29.896 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 36.088 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.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">micro_train.py</span></code></a></p>
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 5f7148c61..a2db0411a 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,14 +300,14 @@
<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>05:15.036</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>05:23.790</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>04:29.896</strong>: <a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></li>
-<li><p><strong>00:40.961</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>00:03.572</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>00:00.206</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
-<li><p><strong>00:00.201</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>00:00.201</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
+<li><p><strong>04:36.088</strong>: <a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></li>
+<li><p><strong>00:43.346</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>00:03.732</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>00:00.210</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>00:00.208</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>00:00.206</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
</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 729d5089d..25bc6101a 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>00:11.872</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:12.465</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:09.955</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>00:01.702</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>00:00.214</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>00:10.112</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>00:02.125</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>00:00.228</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 76a5e7ec8..0d8f85ea8 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>00:05.641</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.963</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.083</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>00:01.173</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>00:00.722</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>00:00.697</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>00:00.310</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>00:00.230</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>00:00.215</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>00:00.211</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
+<li><p><strong>00:02.189</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>00:01.207</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>00:00.766</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>00:00.748</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>00:00.319</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>00:00.252</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>00:00.248</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>00:00.234</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 d5de816d2..b1f039e73 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], [])} {
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+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpvvh2m_cg/input0.cc'\nsource_filename = \"/tmp/tmpvvh2m_cg/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/reference/api/doxygen/classes.html b/docs/reference/api/doxygen/classes.html
index c68da64ed..6c3df74b3 100644
--- a/docs/reference/api/doxygen/classes.html
+++ b/docs/reference/api/doxygen/classes.html
@@ -132,12 +132,12 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html">AttrInitVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1EinsumAttrs.html">EinsumAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MatchBufferRegion.html">MatchBufferRegion</a> (<a class="el" h [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNonDefaultVisitor.html">AttrNonDefaultVisitor</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_1tir_1_1MatchBufferRegionNode.html">MatchBufferRegionNode</a> (<a class="el" href="namespace [...]
<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_1EnvFuncNode.html">EnvFuncNode</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</ [...]
-<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_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_1MatmulAttrs.html">MatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.htm [...]
-<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_1EQNode.html">EQNode</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_1re [...]
-<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="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_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="classtvm_1_1ErrorReporter.html">ErrorReporter</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 valig [...]
-<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_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_1MaxPool1DAttrs.html">MaxPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay< [...]
-<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_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_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="namespa [...]
+<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_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_1MatmulAttrs.html">MatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.htm [...]
+<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_1EQNode.html">EQNode</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_1re [...]
+<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="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_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="classtvm_1_1ErrorReporter.html">ErrorReporter</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 valig [...]
+<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_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_1MaxPool1DAttrs.html">MaxPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay< [...]
+<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_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_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="namespa [...]
<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_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="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html" [...]
<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_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_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html [...]
<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_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_1auto__scheduler_1_1MeasureCallback.html">MeasureCallback</a> ( [...]
@@ -201,10 +201,10 @@ $(function() {
<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 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="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </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="unionTVMValue.html">TVMValue</a>   </td></tr>
<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_o"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  o  </div></td></tr></table>
-</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_1Type.html">Type</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<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="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="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm: [...]
-<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_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" hr [...]
-<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="classtvm_1_1tir_1_1GE.html">GE</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_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_1Type.html">Type</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<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="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="classtvm_1_1tir_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1tir.html">tv [...]
+<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_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" hr [...]
+<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="classtvm_1_1tir_1_1GE.html">GE</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>) &# [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::ru [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html">Builder</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_ [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html">BuilderInput</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespa [...]
@@ -252,8 +252,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1CompileError.html">CompileError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadAttrs</a> (<a class="el" h [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pass.html">Pass</a> (<a class="el" href="namespacetvm [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html">ComputeAtStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a class="el" href="nam [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a class="el" href="nam [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</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_1IndexMap.html">IndexMap</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a class [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</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_1IndexMapNode.html">IndexMapNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html">PassInstr [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</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_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNod [...]
@@ -272,8 +272,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">Pattern [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html">PatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm: [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> (<a class="el" href="namespacetvm_1_1rela [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a cla [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a cl [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a class="el" href="namespacetvm_1_1te.ht [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerType.html">PointerType</a> (<a class="el" href="namespacetvm.html">tvm< [...]
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode-members.html
index b07ae1d43..a21eecaf6 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode-members.html
@@ -90,7 +90,7 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#acf145edd9c5a047166dd8f29f65ab75e">FInitializeWithTuneContext</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a802c0ead40a90b4bf5c0962a8d4bbdee">FNotifyRunnerResults</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#ad4730dca4fcd0cfbd73fc6c9ed11fe4a">FPostTuning</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">FPreTuning</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">FPreTuning</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#ad565f2d8d7b6908f92b34aea6f478fd3">GenerateMeasureCandidates</a>() final</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></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>
@@ -105,7 +105,7 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">operator=</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 class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</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>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a1357c198f8d2aca2c8fa9358b92e1e2a">PostTuning</a>() final</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#aedcd9c6cc9d8d2a0da10b345ff5d5a61">PreTuning</a>(const Array< tir::Schedule > &design_spaces) final</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a53127523582ffdb12f40c9433a577adf">PreTuning</a>(const Array< tir::Schedule > &design_spaces, const Optional< Database > &database, const Optional< CostModel > &cost_model) final</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html">tvm::meta_schedule::PySearchStrategyNode</a></td><td class="entry"><spa [...]
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</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#a55549a6c23987890246248682560a03d">RefCounterType</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#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</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">static</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html
index 661fac87f..29fc9bcd3 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html
@@ -86,7 +86,7 @@ Inheritance diagram for tvm::meta_schedule::PySearchStrategyNode:</div>
<div class="dynheader">
Collaboration diagram for tvm::meta_schedule::PySearchStrategyNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1PySearchStrategyNode__coll__graph.svg" width="1531" height="1015"><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_1meta__schedule_1_1PySearchStrategyNode__coll__graph.svg" width="1563" height="1015"><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">
@@ -95,9 +95,9 @@ Public Types</h2></td></tr>
<tr class="memitem:acf145edd9c5a047166dd8f29f65ab75e"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#acf145edd9c5a047166dd8f29f65ab75e">FInitializeWithTuneContext</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a>< void(const <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html">TuneConte [...]
<tr class="memdesc:acf145edd9c5a047166dd8f29f65ab75e"><td class="mdescLeft"> </td><td class="mdescRight">The function type of <code>InitializeWithTuneContext</code> method. <a href="#acf145edd9c5a047166dd8f29f65ab75e">More...</a><br /></td></tr>
<tr class="separator:acf145edd9c5a047166dd8f29f65ab75e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a62a1fce99946955bdb2116cd01a61b9c"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">FPreTuning</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a>< void(const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="clas [...]
-<tr class="memdesc:a62a1fce99946955bdb2116cd01a61b9c"><td class="mdescLeft"> </td><td class="mdescRight">The function type of <code>PreTuning</code> method. <a href="#a62a1fce99946955bdb2116cd01a61b9c">More...</a><br /></td></tr>
-<tr class="separator:a62a1fce99946955bdb2116cd01a61b9c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a528df580fe251d7fe9eec1e68f8d2385"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">FPreTuning</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a>< void(const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="clas [...]
+<tr class="memdesc:a528df580fe251d7fe9eec1e68f8d2385"><td class="mdescLeft"> </td><td class="mdescRight">The function type of <code>PreTuning</code> method. <a href="#a528df580fe251d7fe9eec1e68f8d2385">More...</a><br /></td></tr>
+<tr class="separator:a528df580fe251d7fe9eec1e68f8d2385"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad4730dca4fcd0cfbd73fc6c9ed11fe4a"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#ad4730dca4fcd0cfbd73fc6c9ed11fe4a">FPostTuning</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a>< void()></td></tr>
<tr class="memdesc:ad4730dca4fcd0cfbd73fc6c9ed11fe4a"><td class="mdescLeft"> </td><td class="mdescRight">The function type of <code>PostTuning</code> method. <a href="#ad4730dca4fcd0cfbd73fc6c9ed11fe4a">More...</a><br /></td></tr>
<tr class="separator:ad4730dca4fcd0cfbd73fc6c9ed11fe4a"><td class="memSeparator" colspan="2"> </td></tr>
@@ -121,9 +121,9 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:afcc701ca7cbb2a80ebe59428bd422946"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#afcc701ca7cbb2a80ebe59428bd422946">InitializeWithTuneContext</a> (const <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html">TuneContext</a> &context) final</td></tr>
<tr class="memdesc:afcc701ca7cbb2a80ebe59428bd422946"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the search strategy with tuning context. <a href="#afcc701ca7cbb2a80ebe59428bd422946">More...</a><br /></td></tr>
<tr class="separator:afcc701ca7cbb2a80ebe59428bd422946"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aedcd9c6cc9d8d2a0da10b345ff5d5a61"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#aedcd9c6cc9d8d2a0da10b345ff5d5a61">PreTuning</a> (const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > &design_spaces) final</td></tr>
-<tr class="memdesc:aedcd9c6cc9d8d2a0da10b345ff5d5a61"><td class="mdescLeft"> </td><td class="mdescRight">Pre-tuning for the search strategy. <a href="#aedcd9c6cc9d8d2a0da10b345ff5d5a61">More...</a><br /></td></tr>
-<tr class="separator:aedcd9c6cc9d8d2a0da10b345ff5d5a61"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a53127523582ffdb12f40c9433a577adf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a53127523582ffdb12f40c9433a577adf">PreTuning</a> (const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > &design_spaces, const <a class="el" href="classtv [...]
+<tr class="memdesc:a53127523582ffdb12f40c9433a577adf"><td class="mdescLeft"> </td><td class="mdescRight">Pre-tuning for the search strategy. <a href="#a53127523582ffdb12f40c9433a577adf">More...</a><br /></td></tr>
+<tr class="separator:a53127523582ffdb12f40c9433a577adf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1357c198f8d2aca2c8fa9358b92e1e2a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a1357c198f8d2aca2c8fa9358b92e1e2a">PostTuning</a> () final</td></tr>
<tr class="memdesc:a1357c198f8d2aca2c8fa9358b92e1e2a"><td class="mdescLeft"> </td><td class="mdescRight">Post-tuning for the search strategy. <a href="#a1357c198f8d2aca2c8fa9358b92e1e2a">More...</a><br /></td></tr>
<tr class="separator:a1357c198f8d2aca2c8fa9358b92e1e2a"><td class="memSeparator" colspan="2"> </td></tr>
@@ -169,7 +169,7 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a32818326baaa42c2de89704fcec48915"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#acf145edd9c5a047166dd8f29f65ab75e">FInitializeWithTuneContext</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a32818326baaa42c2de89704fcec48915">f_initialize_with_tune_context</a></td></tr>
<tr class="memdesc:a32818326baaa42c2de89704fcec48915"><td class="mdescLeft"> </td><td class="mdescRight">The packed function to the <code>InitializeWithTuneContext</code> method. <a href="#a32818326baaa42c2de89704fcec48915">More...</a><br /></td></tr>
<tr class="separator:a32818326baaa42c2de89704fcec48915"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:addb53ffefebe51c9f2227349833f51de"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">FPreTuning</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#addb53ffefebe51c9f2227349833f51de">f_pre_tuning</a></td></tr>
+<tr class="memitem:addb53ffefebe51c9f2227349833f51de"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">FPreTuning</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#addb53ffefebe51c9f2227349833f51de">f_pre_tuning</a></td></tr>
<tr class="memdesc:addb53ffefebe51c9f2227349833f51de"><td class="mdescLeft"> </td><td class="mdescRight">The packed function to the <code>PreTuning</code> method. <a href="#addb53ffefebe51c9f2227349833f51de">More...</a><br /></td></tr>
<tr class="separator:addb53ffefebe51c9f2227349833f51de"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a215e220c292bbafa7a0eeaf46a9a3f38"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#ad4730dca4fcd0cfbd73fc6c9ed11fe4a">FPostTuning</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a215e220c292bbafa7a0eeaf46a9a3f38">f_post_tuning</a></td></tr>
@@ -325,14 +325,14 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="a62a1fce99946955bdb2116cd01a61b9c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a62a1fce99946955bdb2116cd01a61b9c">◆ </a></span>FPreTuning</h2>
+<a id="a528df580fe251d7fe9eec1e68f8d2385"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a528df580fe251d7fe9eec1e68f8d2385">◆ </a></span>FPreTuning</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">using <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">tvm::meta_schedule::PySearchStrategyNode::FPreTuning</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a><void(const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a>>&)></td>
+ <td class="memname">using <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">tvm::meta_schedule::PySearchStrategyNode::FPreTuning</a> = <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html">runtime::TypedPackedFunc</a><void( const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a>>&, const <a class="el" href=" [...]
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-<a id="aedcd9c6cc9d8d2a0da10b345ff5d5a61"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aedcd9c6cc9d8d2a0da10b345ff5d5a61">◆ </a></span>PreTuning()</h2>
+<a id="a53127523582ffdb12f40c9433a577adf"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a53127523582ffdb12f40c9433a577adf">◆ </a></span>PreTuning()</h2>
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<div class="memproto">
@@ -512,13 +512,29 @@ Additional Inherited Members</h2></td></tr>
<td class="memname">void tvm::meta_schedule::PySearchStrategyNode::PreTuning </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > & </td>
- <td class="paramname"><em>design_spaces</em></td><td>)</td>
+ <td class="paramname"><em>design_spaces</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > & </td>
+ <td class="paramname"><em>database</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
<td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> > & </td>
+ <td class="paramname"><em>cost_model</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">final</span><span class="mlabel">virtual</span></span> </td>
+<span class="mlabels"><span class="mlabel">final</span><span class="mlabel">virtual</span></span> </td>
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<p>Pre-tuning for the search strategy. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramname">design_spaces</td><td>The design spaces for pre-tuning. </td></tr>
+ <tr><td class="paramname">design_spaces</td><td>The design spaces used during tuning process. </td></tr>
+ <tr><td class="paramname">database</td><td>The database used during tuning process. </td></tr>
+ <tr><td class="paramname">cost_model</td><td>The cost model used during tuning process. </td></tr>
</table>
</dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>Pre-tuning is supposed to be called before the tuning process and after the initialization. Because the search strategy is stateful, we can always call pretuning and reset the search strategy. </dd></dl>
-<p>Implements <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#abd1485c82a7df42a54904de7822f0fbf">tvm::meta_schedule::SearchStrategyNode</a>.</p>
+<p>Implements <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#ad88e1545e88dc1934c25f4b417077aff">tvm::meta_schedule::SearchStrategyNode</a>.</p>
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- <td class="memname"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">FPreTuning</a> tvm::meta_schedule::PySearchStrategyNode::f_pre_tuning</td>
+ <td class="memname"><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">FPreTuning</a> tvm::meta_schedule::PySearchStrategyNode::f_pre_tuning</td>
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<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html#ad1a54354c9ed7d43da04c587b5484b96">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html">tvm::meta_schedule::PyTaskSchedulerNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#ae4c0d72966283dc88478f853c93afdf9">builder</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
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+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">database</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html
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+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html
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@@ -193,18 +193,18 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a13c74317fb8dccd7fbdb728336b09501 inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html">Runner</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a13c74317fb8dccd7fbdb728336b09501">runner</a> {nullptr}</td></tr>
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+<tr class="memdesc:adb95e507922150ac24d3813326f2cde1 inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="mdescLeft"> </td><td class="mdescRight">The database of the scheduler. <a href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">More...</a><br /></td></tr>
+<tr class="separator:adb95e507922150ac24d3813326f2cde1 inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a40cb157f85bff6952dbef9e2804bc3ef inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a40cb157f85bff6952dbef9e2804bc3ef"> [...]
<tr class="memdesc:a40cb157f85bff6952dbef9e2804bc3ef inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="mdescLeft"> </td><td class="mdescRight">The cost model of the scheduler. <a href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a40cb157f85bff6952dbef9e2804bc3ef">More...</a><br /></td></tr>
<tr class="separator:a40cb157f85bff6952dbef9e2804bc3ef inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="separator:aa8e6efd489ccdc6f81495da417da842a inherit pub_attribs_classtvm_1_1meta__schedule_1_1TaskSchedulerNode"><td class="memSeparator" colspan="2"> </td></tr>
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+<text text-anchor="start" x="638" y="-348.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="733.5" y="-337.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TaskSchedulerNode</text>
+<polyline fill="none" stroke="#000000" points="630,-330.5 837,-330.5 "/>
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+<text text-anchor="start" x="638" y="-307.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ num_trials_already</text>
+<text text-anchor="start" x="638" y="-296.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="630,-289.5 837,-289.5 "/>
+<text text-anchor="start" x="638" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~TaskSchedulerNode()</text>
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+<text text-anchor="start" x="638" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Tune()</text>
+<text text-anchor="start" x="638" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ InitializeTask()</text>
+<text text-anchor="start" x="638" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TouchTask()</text>
+<text text-anchor="start" x="638" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ JoinRunningTask()</text>
+<text text-anchor="start" x="638" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ NextTaskId()</text>
+<text text-anchor="start" x="638" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
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<title>Node6</title>
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-<text text-anchor="start" x="279.5" y="-597" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
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-<text text-anchor="start" x="279.5" y="-553" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyDatabase()</text>
+<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1PackedFunc.html" target="_top" xlink:title="Packed function is a type-erased function. The arguments are passed by packed format. ">
+<polygon fill="#ffffff" stroke="#000000" points="271.5,-535 271.5,-669 425.5,-669 425.5,-535 271.5,-535"/>
+<text text-anchor="middle" x="348.5" y="-657" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::PackedFunc</text>
+<polyline fill="none" stroke="#000000" points="271.5,-650 425.5,-650 "/>
+<text text-anchor="middle" x="348.5" y="-638" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="271.5,-631 425.5,-631 "/>
+<text text-anchor="start" x="279.5" y="-619" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
+<text text-anchor="start" x="279.5" y="-608" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
+<text text-anchor="start" x="279.5" y="-597" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator()()</text>
+<text text-anchor="start" x="279.5" y="-586" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ CallPacked()</text>
+<text text-anchor="start" x="279.5" y="-575" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="279.5" y="-564" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="279.5" y="-553" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="279.5" y="-542" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
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+<path fill="none" stroke="#404040" d="M364.9345,-534.624C377.9212,-493.5852 399.7773,-442.5726 434.5,-408 484.6798,-358.0371 557.1036,-325.639 618.1042,-305.5619"/>
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<g id="node5" class="node">
<title>Node7</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
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-<text text-anchor="start" x="755.5" y="-1024.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
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-<text text-anchor="start" x="755.5" y="-1005.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
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+<polyline fill="none" stroke="#000000" points="744.5,-1036.5 878.5,-1036.5 "/>
+<text text-anchor="start" x="752.5" y="-1024.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
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+<text text-anchor="start" x="752.5" y="-1005.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="752.5" y="-994.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="752.5" y="-983.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="752.5" y="-972.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="752.5" y="-961.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="752.5" y="-950.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="752.5" y="-939.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="752.5" y="-928.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="752.5" y="-917.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="752.5" y="-906.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="752.5" y="-895.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="752.5" y="-884.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="752.5" y="-873.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="752.5" y="-862.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="752.5" y="-851.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="752.5" y="-840.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
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<g id="node7" class="node">
<title>Node9</title>
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-<text text-anchor="start" x="437.5" y="-619" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
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-<text text-anchor="start" x="437.5" y="-542" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::TuneContext \>\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="451.5" y="-684.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="start" x="451.5" y="-673.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
+<text text-anchor="middle" x="507.5" y="-662.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TuneContext ></text>
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+<polyline fill="none" stroke="#000000" points="443.5,-636.5 571.5,-636.5 "/>
+<text text-anchor="start" x="451.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-613.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-602.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-547.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-536.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="451.5" y="-525.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="451.5" y="-514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
</a>
</g>
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<!-- Node7->Node9 -->
<g id="edge8" class="edge">
<title>Node7->Node9</title>
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+<path fill="none" stroke="#191970" d="M735.2612,-911.2453C685.2357,-886.0118 621.6876,-846.9665 580.5,-796 557.3157,-767.3112 540.9941,-730.4952 529.7565,-696.6593"/>
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<g id="node8" class="node">
<title>Node10</title>
-<g id="a_node8"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::TuneContext \>\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="609.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-613.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-602.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-547.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-536.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="609.5" y="-525.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="609.5" y="-514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
+<g id="a_node8"><a xlink:href="classtvm_1_1runtime_1_1Optional.html" target="_top" xlink:title="{tvm::runtime::Optional\l\< tvm::meta_schedule::\lCostModel \>\n|+ _type_is_nullable\l|+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ operator=()\l+ operator=()\land 15 more...\l}">
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+<text text-anchor="start" x="597.5" y="-684.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
+<text text-anchor="start" x="597.5" y="-673.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule::</text>
+<text text-anchor="middle" x="656.5" y="-662.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">CostModel ></text>
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+<text text-anchor="start" x="597.5" y="-643.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
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+<text text-anchor="start" x="597.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-613.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-602.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-547.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-536.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="597.5" y="-525.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="597.5" y="-514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
</a>
</g>
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<!-- Node7->Node10 -->
<g id="edge10" class="edge">
<title>Node7->Node10</title>
-<path fill="none" stroke="#191970" d="M751.4864,-824.1794C746.9651,-814.7047 742.5841,-805.2328 738.5,-796 724.3792,-764.0771 710.4788,-728.4932 698.659,-696.6059"/>
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<g id="node9" class="node">
<title>Node11</title>
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-<text text-anchor="start" x="755.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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-<text text-anchor="start" x="755.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="755.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="755.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="755.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="755.5" y="-547.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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-<text text-anchor="start" x="755.5" y="-514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
+<g id="a_node9"><a xlink:href="classtvm_1_1meta__schedule_1_1Builder.html" target="_top" xlink:title="Managed reference to BuilderNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="741.5,-551.5 741.5,-652.5 881.5,-652.5 881.5,-551.5 741.5,-551.5"/>
+<text text-anchor="start" x="749.5" y="-640.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="811.5" y="-629.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
+<polyline fill="none" stroke="#000000" points="741.5,-622.5 881.5,-622.5 "/>
+<text text-anchor="middle" x="811.5" y="-610.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="741.5,-603.5 881.5,-603.5 "/>
+<text text-anchor="start" x="749.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
+<text text-anchor="start" x="749.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_NOTNULLABLE_OBJECT</text>
+<text text-anchor="start" x="749.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_REF_METHODS()</text>
+<text text-anchor="start" x="749.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyBuilder()</text>
</a>
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<!-- Node7->Node11 -->
<g id="edge12" class="edge">
<title>Node7->Node11</title>
-<path fill="none" stroke="#191970" d="M814.5,-823.1125C814.5,-781.5481 814.5,-735.6904 814.5,-696.5637"/>
-<polygon fill="none" stroke="#191970" points="811.0001,-823.3 814.5,-833.3001 818.0001,-823.3001 811.0001,-823.3"/>
+<path fill="none" stroke="#191970" d="M811.5,-823.2362C811.5,-764.7209 811.5,-697.6715 811.5,-652.8669"/>
+<polygon fill="none" stroke="#191970" points="808.0001,-823.3 811.5,-833.3001 815.0001,-823.3001 808.0001,-823.3"/>
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<!-- Node12 -->
<g id="node10" class="node">
<title>Node12</title>
-<g id="a_node10"><a xlink:href="classtvm_1_1meta__schedule_1_1Builder.html" target="_top" xlink:title="Managed reference to BuilderNode. ">
+<g id="a_node10"><a xlink:href="classtvm_1_1meta__schedule_1_1Runner.html" target="_top" xlink:title="Managed reference to RunnerNode. ">
<polygon fill="#ffffff" stroke="#000000" points="899.5,-551.5 899.5,-652.5 1039.5,-652.5 1039.5,-551.5 899.5,-551.5"/>
<text text-anchor="start" x="907.5" y="-640.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="969.5" y="-629.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
+<text text-anchor="middle" x="969.5" y="-629.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Runner</text>
<polyline fill="none" stroke="#000000" points="899.5,-622.5 1039.5,-622.5 "/>
<text text-anchor="middle" x="969.5" y="-610.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
<polyline fill="none" stroke="#000000" points="899.5,-603.5 1039.5,-603.5 "/>
<text text-anchor="start" x="907.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
<text text-anchor="start" x="907.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_NOTNULLABLE_OBJECT</text>
<text text-anchor="start" x="907.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_REF_METHODS()</text>
-<text text-anchor="start" x="907.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyBuilder()</text>
+<text text-anchor="start" x="907.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyRunner()</text>
</a>
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<!-- Node7->Node12 -->
<g id="edge14" class="edge">
<title>Node7->Node12</title>
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<g id="node11" class="node">
<title>Node13</title>
-<g id="a_node11"><a xlink:href="classtvm_1_1meta__schedule_1_1Runner.html" target="_top" xlink:title="Managed reference to RunnerNode. ">
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-<text text-anchor="start" x="1065.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
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-<text text-anchor="start" x="1065.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyRunner()</text>
+<g id="a_node11"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::MeasureCallback \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 24 more...\l}">
+<polygon fill="#ffffff" stroke="#000000" points="1057.5,-507.5 1057.5,-696.5 1185.5,-696.5 1185.5,-507.5 1057.5,-507.5"/>
+<text text-anchor="start" x="1065.5" y="-684.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="start" x="1065.5" y="-673.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
+<text text-anchor="middle" x="1121.5" y="-662.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCallback ></text>
+<polyline fill="none" stroke="#000000" points="1057.5,-655.5 1185.5,-655.5 "/>
+<text text-anchor="middle" x="1121.5" y="-643.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="1057.5,-636.5 1185.5,-636.5 "/>
+<text text-anchor="start" x="1065.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-613.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-602.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-591.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-580.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-569.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-558.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-547.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-536.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1065.5" y="-525.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1065.5" y="-514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
</a>
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<!-- Node7->Node13 -->
<g id="edge16" class="edge">
<title>Node7->Node13</title>
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+<text text-anchor="start" x="1211.5" y="-624.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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@@ -128,7 +128,7 @@ Public Member Functions</h2></td></tr>
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<tr class="memdesc:a95eb75dce8960913ed0d390ba38c612f"><td class="mdescLeft"> </td><td class="mdescRight">Create a search strategy with customized methods on the python-side. <a href="#a95eb75dce8960913ed0d390ba38c612f">More...</a><br /></td></tr>
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<td class="paramname"><em>f_pre_tuning</em>, </td>
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<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#a82e813659dd7cab4b03e43d0710619e7">PostTuning</a>()=0</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html">tvm::meta_schedule::SearchStrategyNode</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#ad88e1545e88dc1934c25f4b417077aff">PreTuning</a>(const Array< tir::Schedule > &design_spaces, const Optional< Database > &database, const Optional< CostModel > &cost_model)=0</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html">tvm::meta_schedule::SearchStrategyNode</a></td><td class="entry"><span class="m [...]
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</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>
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<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</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">static</span></td></tr>
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+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1SearchStrategyNode.html
@@ -96,9 +96,9 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:a76f812f41229a0a8a3e43b5fa052b26f"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#a76f812f41229a0a8a3e43b5fa052b26f">InitializeWithTuneContext</a> (const <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html">TuneContext</a> &context)=0</td></tr>
<tr class="memdesc:a76f812f41229a0a8a3e43b5fa052b26f"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the search strategy with tuning context. <a href="#a76f812f41229a0a8a3e43b5fa052b26f">More...</a><br /></td></tr>
<tr class="separator:a76f812f41229a0a8a3e43b5fa052b26f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abd1485c82a7df42a54904de7822f0fbf"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#abd1485c82a7df42a54904de7822f0fbf">PreTuning</a> (const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > &design_spaces)=0</td></tr>
-<tr class="memdesc:abd1485c82a7df42a54904de7822f0fbf"><td class="mdescLeft"> </td><td class="mdescRight">Pre-tuning for the search strategy. <a href="#abd1485c82a7df42a54904de7822f0fbf">More...</a><br /></td></tr>
-<tr class="separator:abd1485c82a7df42a54904de7822f0fbf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad88e1545e88dc1934c25f4b417077aff"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#ad88e1545e88dc1934c25f4b417077aff">PreTuning</a> (const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > &design_spaces, const <a class="el" href="c [...]
+<tr class="memdesc:ad88e1545e88dc1934c25f4b417077aff"><td class="mdescLeft"> </td><td class="mdescRight">Pre-tuning for the search strategy. <a href="#ad88e1545e88dc1934c25f4b417077aff">More...</a><br /></td></tr>
+<tr class="separator:ad88e1545e88dc1934c25f4b417077aff"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a82e813659dd7cab4b03e43d0710619e7"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#a82e813659dd7cab4b03e43d0710619e7">PostTuning</a> ()=0</td></tr>
<tr class="memdesc:a82e813659dd7cab4b03e43d0710619e7"><td class="mdescLeft"> </td><td class="mdescRight">Post-tuning for the search strategy. <a href="#a82e813659dd7cab4b03e43d0710619e7">More...</a><br /></td></tr>
<tr class="separator:a82e813659dd7cab4b03e43d0710619e7"><td class="memSeparator" colspan="2"> </td></tr>
@@ -382,8 +382,8 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="abd1485c82a7df42a54904de7822f0fbf"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abd1485c82a7df42a54904de7822f0fbf">◆ </a></span>PreTuning()</h2>
+<a id="ad88e1545e88dc1934c25f4b417077aff"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad88e1545e88dc1934c25f4b417077aff">◆ </a></span>PreTuning()</h2>
<div class="memitem">
<div class="memproto">
@@ -395,8 +395,24 @@ Additional Inherited Members</h2></td></tr>
<td class="memname">virtual void tvm::meta_schedule::SearchStrategyNode::PreTuning </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Schedule.html">tir::Schedule</a> > & </td>
- <td class="paramname"><em>design_spaces</em></td><td>)</td>
+ <td class="paramname"><em>design_spaces</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > & </td>
+ <td class="paramname"><em>database</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> > & </td>
+ <td class="paramname"><em>cost_model</em> </td>
+ </tr>
+ <tr>
<td></td>
+ <td>)</td>
+ <td></td><td></td>
</tr>
</table>
</td>
@@ -409,13 +425,15 @@ Additional Inherited Members</h2></td></tr>
<p>Pre-tuning for the search strategy. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramname">design_spaces</td><td>The design spaces for pre-tuning. </td></tr>
+ <tr><td class="paramname">design_spaces</td><td>The design spaces used during tuning process. </td></tr>
+ <tr><td class="paramname">database</td><td>The database used during tuning process. </td></tr>
+ <tr><td class="paramname">cost_model</td><td>The cost model used during tuning process. </td></tr>
</table>
</dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>Pre-tuning is supposed to be called before the tuning process and after the initialization. Because the search strategy is stateful, we can always call pretuning and reset the search strategy. </dd></dl>
-<p>Implemented in <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#aedcd9c6cc9d8d2a0da10b345ff5d5a61">tvm::meta_schedule::PySearchStrategyNode</a>.</p>
+<p>Implemented in <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a53127523582ffdb12f40c9433a577adf">tvm::meta_schedule::PySearchStrategyNode</a>.</p>
</div>
</div>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler-members.html
index 858281993..4313de37d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler-members.html
@@ -79,15 +79,15 @@ $(function() {
<tr><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 class="even"><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><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 class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a2d889bee3692995fd5a2876bfdb946a4">GradientBased</a>(Array< TuneContext > tasks, Array< FloatImm > task_weights, Builder builder, Runner runner, Database database, int max_trials, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, PackedFunc logging_func, double alpha, int window_size, support::LinearCongruentialEngi [...]
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#aa21e7e5400949a2fae93b542185a9600">GradientBased</a>(Array< TuneContext > tasks, Array< FloatImm > task_weights, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func, double alpha, int window_size, support::Line [...]
<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>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a3deeeac5827a88f375b8c6ae1039c219">operator-></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#a4744bf4a1b48f202d41b51dc5e08e6ee">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>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#affdf1b8cdb36e140de7b3ad7064e4617">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>
- <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a06366c04bd5cda14ac92ceb8b007cdcf">PyTaskScheduler</a>(Array< TuneContext > tasks, Builder builder, Runner runner, Database database, int max_trials, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, PackedFunc logging_func, PyTaskSchedulerNode::FTune f_tune, PyTaskSchedulerNode::FInitializeTask f_initialize_task, PyTaskSchedulerNode: [...]
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#adc37ee4209d129b8329292bd3c4da1af">RoundRobin</a>(Array< TuneContext > tasks, Builder builder, Runner runner, Database database, int max_trials, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, PackedFunc logging_func)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">tvm::meta_s [...]
+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a81ba808d521f750ab3703873fbc801b5">PyTaskScheduler</a>(Array< TuneContext > tasks, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func, PyTaskSchedulerNode::FTune f_tune, PyTaskSchedulerNode::FInitializeTask f_initialize_task, Py [...]
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a084b8b6743f0d63938c1bb8de37df6a1">RoundRobin</a>(Array< TuneContext > tasks, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler [...]
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#ae31a5b9f40781d60a2901994ead700e8">same_as</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>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a4d644a4e1e4d21f1d62d661c354d7738">TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS</a>(TaskScheduler, ObjectRef, TaskSchedulerNode)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">tvm::meta_schedule::TaskScheduler</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a4e7cdb1574b93a59e784d70aa47b8da7">unique</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_1meta__schedule_1_1TaskScheduler.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler.html
index 7fe7b827c..5220f6ad0 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskScheduler.html
@@ -128,15 +128,15 @@ Public Member Functions</h2></td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
-<tr class="memitem:adc37ee4209d129b8329292bd3c4da1af"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#adc37ee4209d129b8329292bd3c4da1af">RoundRobin</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Tu [...]
-<tr class="memdesc:adc37ee4209d129b8329292bd3c4da1af"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler that fetches tasks in a round-robin fashion. <a href="#adc37ee4209d129b8329292bd3c4da1af">More...</a><br /></td></tr>
-<tr class="separator:adc37ee4209d129b8329292bd3c4da1af"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2d889bee3692995fd5a2876bfdb946a4"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a2d889bee3692995fd5a2876bfdb946a4">GradientBased</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_ [...]
-<tr class="memdesc:a2d889bee3692995fd5a2876bfdb946a4"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler that fetches tasks in a gradient based fashion. <a href="#a2d889bee3692995fd5a2876bfdb946a4">More...</a><br /></td></tr>
-<tr class="separator:a2d889bee3692995fd5a2876bfdb946a4"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a06366c04bd5cda14ac92ceb8b007cdcf"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a06366c04bd5cda14ac92ceb8b007cdcf">PyTaskScheduler</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_ [...]
-<tr class="memdesc:a06366c04bd5cda14ac92ceb8b007cdcf"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler with customized methods on the python-side. <a href="#a06366c04bd5cda14ac92ceb8b007cdcf">More...</a><br /></td></tr>
-<tr class="separator:a06366c04bd5cda14ac92ceb8b007cdcf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a084b8b6743f0d63938c1bb8de37df6a1"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a084b8b6743f0d63938c1bb8de37df6a1">RoundRobin</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Tu [...]
+<tr class="memdesc:a084b8b6743f0d63938c1bb8de37df6a1"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler that fetches tasks in a round-robin fashion. <a href="#a084b8b6743f0d63938c1bb8de37df6a1">More...</a><br /></td></tr>
+<tr class="separator:a084b8b6743f0d63938c1bb8de37df6a1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa21e7e5400949a2fae93b542185a9600"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#aa21e7e5400949a2fae93b542185a9600">GradientBased</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_ [...]
+<tr class="memdesc:aa21e7e5400949a2fae93b542185a9600"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler that fetches tasks in a gradient based fashion. <a href="#aa21e7e5400949a2fae93b542185a9600">More...</a><br /></td></tr>
+<tr class="separator:aa21e7e5400949a2fae93b542185a9600"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a81ba808d521f750ab3703873fbc801b5"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html">TaskScheduler</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a81ba808d521f750ab3703873fbc801b5">PyTaskScheduler</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_ [...]
+<tr class="memdesc:a81ba808d521f750ab3703873fbc801b5"><td class="mdescLeft"> </td><td class="mdescRight">Create a task scheduler with customized methods on the python-side. <a href="#a81ba808d521f750ab3703873fbc801b5">More...</a><br /></td></tr>
+<tr class="separator:a81ba808d521f750ab3703873fbc801b5"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
Additional Inherited Members</h2></td></tr>
@@ -171,8 +171,8 @@ Additional Inherited Members</h2></td></tr>
<div class="textblock"><p>Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedulerNode</a>. </p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedulerNode</a> </dd></dl>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a id="a2d889bee3692995fd5a2876bfdb946a4"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2d889bee3692995fd5a2876bfdb946a4">◆ </a></span>GradientBased()</h2>
+<a id="aa21e7e5400949a2fae93b542185a9600"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aa21e7e5400949a2fae93b542185a9600">◆ </a></span>GradientBased()</h2>
<div class="memitem">
<div class="memproto">
@@ -207,15 +207,9 @@ Additional Inherited Members</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > </td>
<td class="paramname"><em>database</em>, </td>
</tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>max_trials</em>, </td>
- </tr>
<tr>
<td class="paramkey"></td>
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<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> >> </td>
<td class="paramname"><em>measure_callbacks</em>, </td>
</tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>max_trials</em>, </td>
+ </tr>
<tr>
<td class="paramkey"></td>
<td></td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a06366c04bd5cda14ac92ceb8b007cdcf">◆ </a></span>PyTaskScheduler()</h2>
+<a id="a81ba808d521f750ab3703873fbc801b5"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a81ba808d521f750ab3703873fbc801b5">◆ </a></span>PyTaskScheduler()</h2>
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@@ -317,15 +317,9 @@ Additional Inherited Members</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > </td>
<td class="paramname"><em>database</em>, </td>
</tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>max_trials</em>, </td>
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<td class="paramkey"></td>
<td></td>
@@ -338,6 +332,12 @@ Additional Inherited Members</h2></td></tr>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> >> </td>
<td class="paramname"><em>measure_callbacks</em>, </td>
</tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>max_trials</em>, </td>
+ </tr>
<tr>
<td class="paramkey"></td>
<td></td>
@@ -410,8 +410,8 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#adc37ee4209d129b8329292bd3c4da1af">◆ </a></span>RoundRobin()</h2>
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+<h2 class="memtitle"><span class="permalink"><a href="#a084b8b6743f0d63938c1bb8de37df6a1">◆ </a></span>RoundRobin()</h2>
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@@ -440,15 +440,9 @@ Additional Inherited Members</h2></td></tr>
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<td class="paramkey"></td>
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- <td class="paramtype"><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > </td>
<td class="paramname"><em>database</em>, </td>
</tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
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<td class="paramname"><em>measure_callbacks</em>, </td>
</tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>max_trials</em>, </td>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode-members.html
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<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a16a711126d33a3e3d260bd9ee6104345">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#ae4c0d72966283dc88478f853c93afdf9">builder</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a40cb157f85bff6952dbef9e2804bc3ef">cost_model</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a9bc942d4fa3918753a1b036bb9373deb">database</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">database</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">tvm::meta_schedule::TaskSchedulerNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</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#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>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html
index d0b10f1b4..fd8b31764 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html
@@ -85,7 +85,7 @@ Inheritance diagram for tvm::meta_schedule::TaskSchedulerNode:</div>
<div class="dynheader">
Collaboration diagram for tvm::meta_schedule::TaskSchedulerNode:</div>
<div class="dyncontent">
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+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1TaskSchedulerNode__coll__graph.svg" width="1795" height="1463"><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">
@@ -147,18 +147,18 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a13c74317fb8dccd7fbdb728336b09501"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html">Runner</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a13c74317fb8dccd7fbdb728336b09501">runner</a> {nullptr}</td></tr>
<tr class="memdesc:a13c74317fb8dccd7fbdb728336b09501"><td class="mdescLeft"> </td><td class="mdescRight">The runner of the scheduler. <a href="#a13c74317fb8dccd7fbdb728336b09501">More...</a><br /></td></tr>
<tr class="separator:a13c74317fb8dccd7fbdb728336b09501"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9bc942d4fa3918753a1b036bb9373deb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a9bc942d4fa3918753a1b036bb9373deb">database</a> {nullptr}</td></tr>
-<tr class="memdesc:a9bc942d4fa3918753a1b036bb9373deb"><td class="mdescLeft"> </td><td class="mdescRight">The database of the scheduler. <a href="#a9bc942d4fa3918753a1b036bb9373deb">More...</a><br /></td></tr>
-<tr class="separator:a9bc942d4fa3918753a1b036bb9373deb"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a8285eabc6e268c8e600f1f20f5d70585"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a8285eabc6e268c8e600f1f20f5d70585">max_trials</a></td></tr>
-<tr class="memdesc:a8285eabc6e268c8e600f1f20f5d70585"><td class="mdescLeft"> </td><td class="mdescRight">The maximum number of trials allowed. <a href="#a8285eabc6e268c8e600f1f20f5d70585">More...</a><br /></td></tr>
-<tr class="separator:a8285eabc6e268c8e600f1f20f5d70585"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:adb95e507922150ac24d3813326f2cde1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">database</a></td></tr>
+<tr class="memdesc:adb95e507922150ac24d3813326f2cde1"><td class="mdescLeft"> </td><td class="mdescRight">The database of the scheduler. <a href="#adb95e507922150ac24d3813326f2cde1">More...</a><br /></td></tr>
+<tr class="separator:adb95e507922150ac24d3813326f2cde1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a40cb157f85bff6952dbef9e2804bc3ef"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a40cb157f85bff6952dbef9e2804bc3ef">cost_model</a></td></tr>
<tr class="memdesc:a40cb157f85bff6952dbef9e2804bc3ef"><td class="mdescLeft"> </td><td class="mdescRight">The cost model of the scheduler. <a href="#a40cb157f85bff6952dbef9e2804bc3ef">More...</a><br /></td></tr>
<tr class="separator:a40cb157f85bff6952dbef9e2804bc3ef"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa8e6efd489ccdc6f81495da417da842a"><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_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#aa8e6efd489ccdc6f81495da417da842a">measure_callbacks</a></td></tr>
<tr class="memdesc:aa8e6efd489ccdc6f81495da417da842a"><td class="mdescLeft"> </td><td class="mdescRight">The list of measure callbacks of the scheduler. <a href="#aa8e6efd489ccdc6f81495da417da842a">More...</a><br /></td></tr>
<tr class="separator:aa8e6efd489ccdc6f81495da417da842a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8285eabc6e268c8e600f1f20f5d70585"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a8285eabc6e268c8e600f1f20f5d70585">max_trials</a></td></tr>
+<tr class="memdesc:a8285eabc6e268c8e600f1f20f5d70585"><td class="mdescLeft"> </td><td class="mdescRight">The maximum number of trials allowed. <a href="#a8285eabc6e268c8e600f1f20f5d70585">More...</a><br /></td></tr>
+<tr class="separator:a8285eabc6e268c8e600f1f20f5d70585"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a246e130e3928c57199f98dc3ce674d79"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a246e130e3928c57199f98dc3ce674d79">num_trials_already</a></td></tr>
<tr class="memdesc:a246e130e3928c57199f98dc3ce674d79"><td class="mdescLeft"> </td><td class="mdescRight">The number of trials already conducted. <a href="#a246e130e3928c57199f98dc3ce674d79">More...</a><br /></td></tr>
<tr class="separator:a246e130e3928c57199f98dc3ce674d79"><td class="memSeparator" colspan="2"> </td></tr>
@@ -540,14 +540,14 @@ Additional Inherited Members</h2></td></tr>
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- <td class="memname"><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html">Database</a> tvm::meta_schedule::TaskSchedulerNode::database {nullptr}</td>
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+<text text-anchor="start" x="638" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ JoinRunningTask()</text>
+<text text-anchor="start" x="638" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ NextTaskId()</text>
+<text text-anchor="start" x="638" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
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@@ -75,8 +75,8 @@
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<g id="edge1" class="edge">
<title>Node4->Node3</title>
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+<path fill="none" stroke="#191970" d="M190.0947,-275.8209C211.8669,-253.0032 236.3244,-231.4035 262.5,-215 376.3125,-143.6767 529.3972,-110.4738 629.7099,-95.5165"/>
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@@ -88,299 +88,306 @@
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<title>Node5</title>
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-<text text-anchor="start" x="279.5" y="-453" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="341.5" y="-442" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Database</text>
-<polyline fill="none" stroke="#000000" points="271.5,-435 411.5,-435 "/>
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-<text text-anchor="start" x="279.5" y="-404" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
-<text text-anchor="start" x="279.5" y="-393" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_NOTNULLABLE_OBJECT</text>
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-<text text-anchor="start" x="279.5" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ JSONDatabase()</text>
-<text text-anchor="start" x="279.5" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyDatabase()</text>
+<g id="a_node3"><a xlink:href="classtvm_1_1runtime_1_1PackedFunc.html" target="_top" xlink:title="Packed function is a type-erased function. The arguments are passed by packed format. ">
+<polygon fill="#ffffff" stroke="#000000" points="271.5,-342 271.5,-476 425.5,-476 425.5,-342 271.5,-342"/>
+<text text-anchor="middle" x="348.5" y="-464" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::PackedFunc</text>
+<polyline fill="none" stroke="#000000" points="271.5,-457 425.5,-457 "/>
+<text text-anchor="middle" x="348.5" y="-445" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="271.5,-438 425.5,-438 "/>
+<text text-anchor="start" x="279.5" y="-426" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
+<text text-anchor="start" x="279.5" y="-415" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
+<text text-anchor="start" x="279.5" y="-404" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator()()</text>
+<text text-anchor="start" x="279.5" y="-393" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ CallPacked()</text>
+<text text-anchor="start" x="279.5" y="-382" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="279.5" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="279.5" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="279.5" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
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-<text text-anchor="middle" x="477" y="-189" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +database</text>
+<path fill="none" stroke="#404040" d="M364.9345,-341.624C377.9212,-300.5852 399.7773,-249.5726 434.5,-215 484.6798,-165.0371 557.1036,-132.639 618.1042,-112.5619"/>
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+<text text-anchor="middle" x="505.5" y="-189" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +logging_func</text>
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<g id="node4" class="node">
<title>Node6</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="747.5,-640.5 747.5,-862.5 881.5,-862.5 881.5,-640.5 747.5,-640.5"/>
-<text text-anchor="middle" x="814.5" y="-850.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="747.5,-843.5 881.5,-843.5 "/>
-<text text-anchor="start" x="755.5" y="-831.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="747.5,-824.5 881.5,-824.5 "/>
-<text text-anchor="start" x="755.5" y="-812.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="755.5" y="-801.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="755.5" y="-790.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="755.5" y="-779.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="755.5" y="-768.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="755.5" y="-757.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="755.5" y="-746.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="755.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="755.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="755.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="755.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="755.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
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-<text text-anchor="start" x="755.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="755.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="755.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
+<polygon fill="#ffffff" stroke="#000000" points="744.5,-640.5 744.5,-862.5 878.5,-862.5 878.5,-640.5 744.5,-640.5"/>
+<text text-anchor="middle" x="811.5" y="-850.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="744.5,-843.5 878.5,-843.5 "/>
+<text text-anchor="start" x="752.5" y="-831.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="744.5,-824.5 878.5,-824.5 "/>
+<text text-anchor="start" x="752.5" y="-812.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="752.5" y="-801.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="752.5" y="-790.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="752.5" y="-779.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="752.5" y="-768.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="752.5" y="-757.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="752.5" y="-746.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="752.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="752.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="752.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="752.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="752.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="752.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="752.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="752.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="752.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
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<!-- Node6->Node5 -->
<g id="edge4" class="edge">
<title>Node6->Node5</title>
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<g id="node6" class="node">
<title>Node8</title>
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-<text text-anchor="middle" x="506.5" y="-464" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::PackedFunc</text>
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-<polyline fill="none" stroke="#000000" points="429.5,-438 583.5,-438 "/>
-<text text-anchor="start" x="437.5" y="-426" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PackedFunc()</text>
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-<text text-anchor="start" x="437.5" y="-404" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator()()</text>
-<text text-anchor="start" x="437.5" y="-393" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ CallPacked()</text>
-<text text-anchor="start" x="437.5" y="-382" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="437.5" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="437.5" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
-<text text-anchor="start" x="437.5" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::TuneContext \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 24 more...\l}">
+<polygon fill="#ffffff" stroke="#000000" points="443.5,-314.5 443.5,-503.5 571.5,-503.5 571.5,-314.5 443.5,-314.5"/>
+<text text-anchor="start" x="451.5" y="-491.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="start" x="451.5" y="-480.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
+<text text-anchor="middle" x="507.5" y="-469.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TuneContext ></text>
+<polyline fill="none" stroke="#000000" points="443.5,-462.5 571.5,-462.5 "/>
+<text text-anchor="middle" x="507.5" y="-450.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="443.5,-443.5 571.5,-443.5 "/>
+<text text-anchor="start" x="451.5" y="-431.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-420.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="451.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="451.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="451.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
</a>
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<g id="edge7" class="edge">
<title>Node6->Node8</title>
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<g id="node7" class="node">
<title>Node9</title>
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-<text text-anchor="middle" x="665.5" y="-450.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="601.5,-443.5 729.5,-443.5 "/>
-<text text-anchor="start" x="609.5" y="-431.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-420.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="609.5" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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-<text text-anchor="start" x="609.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1Optional.html" target="_top" xlink:title="{tvm::runtime::Optional\l\< tvm::meta_schedule::\lCostModel \>\n|+ _type_is_nullable\l|+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ operator=()\l+ operator=()\land 15 more...\l}">
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+<text text-anchor="start" x="597.5" y="-491.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
+<text text-anchor="start" x="597.5" y="-480.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule::</text>
+<text text-anchor="middle" x="656.5" y="-469.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">CostModel ></text>
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+<text text-anchor="start" x="597.5" y="-420.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="597.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="597.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="597.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
</a>
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<title>Node6->Node9</title>
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<g id="node8" class="node">
<title>Node10</title>
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-<text text-anchor="start" x="755.5" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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-<text text-anchor="start" x="755.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="755.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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+<g id="a_node8"><a xlink:href="classtvm_1_1meta__schedule_1_1Builder.html" target="_top" xlink:title="Managed reference to BuilderNode. ">
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+<text text-anchor="middle" x="811.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
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+<text text-anchor="middle" x="811.5" y="-417.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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+<text text-anchor="start" x="749.5" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
+<text text-anchor="start" x="749.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_NOTNULLABLE_OBJECT</text>
+<text text-anchor="start" x="749.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_REF_METHODS()</text>
+<text text-anchor="start" x="749.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyBuilder()</text>
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<!-- Node6->Node10 -->
<g id="edge11" class="edge">
<title>Node6->Node10</title>
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-<polygon fill="none" stroke="#191970" points="811.0001,-630.3 814.5,-640.3001 818.0001,-630.3001 811.0001,-630.3"/>
+<path fill="none" stroke="#191970" d="M811.5,-630.2362C811.5,-571.7209 811.5,-504.6715 811.5,-459.8669"/>
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<g id="node9" class="node">
<title>Node11</title>
-<g id="a_node9"><a xlink:href="classtvm_1_1meta__schedule_1_1Builder.html" target="_top" xlink:title="Managed reference to BuilderNode. ">
+<g id="a_node9"><a xlink:href="classtvm_1_1meta__schedule_1_1Runner.html" target="_top" xlink:title="Managed reference to RunnerNode. ">
<polygon fill="#ffffff" stroke="#000000" points="899.5,-358.5 899.5,-459.5 1039.5,-459.5 1039.5,-358.5 899.5,-358.5"/>
<text text-anchor="start" x="907.5" y="-447.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="969.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
+<text text-anchor="middle" x="969.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Runner</text>
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<text text-anchor="middle" x="969.5" y="-417.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
<polyline fill="none" stroke="#000000" points="899.5,-410.5 1039.5,-410.5 "/>
<text text-anchor="start" x="907.5" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
<text text-anchor="start" x="907.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_NOTNULLABLE_OBJECT</text>
<text text-anchor="start" x="907.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_REF_METHODS()</text>
-<text text-anchor="start" x="907.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyBuilder()</text>
+<text text-anchor="start" x="907.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PyRunner()</text>
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<title>Node6->Node11</title>
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<g id="node10" class="node">
<title>Node12</title>
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+<polygon fill="#ffffff" stroke="#000000" points="1057.5,-314.5 1057.5,-503.5 1185.5,-503.5 1185.5,-314.5 1057.5,-314.5"/>
+<text text-anchor="start" x="1065.5" y="-491.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="start" x="1065.5" y="-480.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
+<text text-anchor="middle" x="1121.5" y="-469.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCallback ></text>
+<polyline fill="none" stroke="#000000" points="1057.5,-462.5 1185.5,-462.5 "/>
+<text text-anchor="middle" x="1121.5" y="-450.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="1057.5,-443.5 1185.5,-443.5 "/>
+<text text-anchor="start" x="1065.5" y="-431.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-420.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<text text-anchor="start" x="1065.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1065.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<text text-anchor="start" x="1065.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1065.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1065.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
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<title>Node6->Node12</title>
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<g id="node11" class="node">
<title>Node13</title>
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-<text text-anchor="start" x="1223.5" y="-431.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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-<text text-anchor="start" x="1223.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<text text-anchor="start" x="1211.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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</a>
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<title>Node8->Node3</title>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode__inherit__graph.svg
index 5d4ddf990..38fd8ddb3 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TaskSchedulerNode__inherit__graph.svg
@@ -20,9 +20,9 @@
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<text text-anchor="start" x="9" y="-406.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ runner</text>
<text text-anchor="start" x="9" y="-395.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ database</text>
-<text text-anchor="start" x="9" y="-384.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ max_trials</text>
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-<text text-anchor="start" x="9" y="-362.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ measure_callbacks</text>
+<text text-anchor="start" x="9" y="-384.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ cost_model</text>
+<text text-anchor="start" x="9" y="-373.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ measure_callbacks</text>
+<text text-anchor="start" x="9" y="-362.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ max_trials</text>
<text text-anchor="start" x="9" y="-351.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ num_trials_already</text>
<text text-anchor="start" x="9" y="-340.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ logging_func</text>
<text text-anchor="start" x="9" y="-329.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode-members.html
index 86f3c94f7..ab011c16d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode-members.html
@@ -110,15 +110,14 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a7bdfdd48530bfe380c5f6c143158a07f">space_generator</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#ae2a4edfbc9e6246748fd0e10202cdb66">target</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a5cd36a027a0a4b1840bf3884948c6298">task_name</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a4107f4a73646a16829baea2a7eeddbd4">task_scheduler</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a89500f25fff207d09a8f3841453d2153">TVM_DECLARE_FINAL_OBJECT_INFO</a>(TuneContextNode, Object)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</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#a4bfc2586cb55f2af47728187b3256255">type_index_</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><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</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">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</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">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</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">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</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_1meta__schedule_1_1TuneContextNode.html#a0feacb165880521b85b82f6b4e6f4a8f">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a89500f25fff207d09a8f3841453d2153">TVM_DECLARE_FINAL_OBJECT_INFO</a>(TuneContextNode, Object)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</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#a4bfc2586cb55f2af47728187b3256255">type_index_</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#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</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">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</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">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</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">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</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_1meta__schedule_1_1TuneContextNode.html#a0feacb165880521b85b82f6b4e6f4a8f">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html">tvm::meta_schedule::TuneContextNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode.html
index 1f428a16b..099631589 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuneContextNode.html
@@ -79,13 +79,13 @@ $(function() {
<div class="dynheader">
Inheritance diagram for tvm::meta_schedule::TuneContextNode:</div>
<div class="dyncontent">
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+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1TuneContextNode__inherit__graph.svg" width="290" height="932"><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::meta_schedule::TuneContextNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1TuneContextNode__coll__graph.svg" width="3156" height="1959"><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_1meta__schedule_1_1TuneContextNode__coll__graph.svg" width="3186" height="1404"><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">
@@ -156,9 +156,6 @@ Public Attributes</h2></td></tr>
<tr class="memitem:aa7136d896f4145357ebb1b7639a25d65"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#aa7136d896f4145357ebb1b7639a25d65">num_threads</a></td></tr>
<tr class="memdesc:aa7136d896f4145357ebb1b7639a25d65"><td class="mdescLeft"> </td><td class="mdescRight">The number of threads to be used. <a href="#aa7136d896f4145357ebb1b7639a25d65">More...</a><br /></td></tr>
<tr class="separator:aa7136d896f4145357ebb1b7639a25d65"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a4107f4a73646a16829baea2a7eeddbd4"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">TaskSchedulerNode</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a4107f4a73646a16829baea2a7eeddbd4">task_scheduler</a></td></tr>
-<tr class="memdesc:a4107f4a73646a16829baea2a7eeddbd4"><td class="mdescLeft"> </td><td class="mdescRight">The task scheduler that owns the tune context. <a href="#a4107f4a73646a16829baea2a7eeddbd4">More...</a><br /></td></tr>
-<tr class="separator:a4107f4a73646a16829baea2a7eeddbd4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abc52e43954ee5c00fc3e8197b5e697b4"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#abc52e43954ee5c00fc3e8197b5e697b4">is_terminated</a></td></tr>
<tr class="memdesc:abc52e43954ee5c00fc3e8197b5e697b4"><td class="mdescLeft"> </td><td class="mdescRight">Whether the tuning task has been stopped or finished. <a href="#abc52e43954ee5c00fc3e8197b5e697b4">More...</a><br /></td></tr>
<tr class="separator:abc52e43954ee5c00fc3e8197b5e697b4"><td class="memSeparator" colspan="2"> </td></tr>
@@ -575,22 +572,6 @@ Additional Inherited Members</h2></td></tr>
<p>The name of the tuning task. </p>
-</div>
-</div>
-<a id="a4107f4a73646a16829baea2a7eeddbd4"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4107f4a73646a16829baea2a7eeddbd4">◆ </a></span>task_scheduler</h2>
-
-<div class="memitem">
-<div class="memproto">
- <table class="memname">
- <tr>
- <td class="memname">const <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html">TaskSchedulerNode</a>* tvm::meta_schedule::TuneContextNode::task_scheduler</td>
- </tr>
- </table>
-</div><div class="memdoc">
-
-<p>The task scheduler that owns the tune context. </p>
-
</div>
</div>
<hr/>The documentation for this class was generated from the following file:<ul>
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+<text text-anchor="start" x="8" y="-516.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
+<text text-anchor="start" x="8" y="-505.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
+<text text-anchor="start" x="8" y="-494.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
+<text text-anchor="start" x="8" y="-483.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
+<text text-anchor="start" x="8" y="-472.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
+<text text-anchor="start" x="8" y="-461.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
+<text text-anchor="start" x="8" y="-450.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="8" y="-439.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="8" y="-428.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
+<text text-anchor="start" x="8" y="-417.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="8" y="-406.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
+<text text-anchor="start" x="8" y="-395.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
+<text text-anchor="start" x="8" y="-384.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
+<polyline fill="none" stroke="#000000" points="0,-377.5 183,-377.5 "/>
+<text text-anchor="start" x="8" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
+<text text-anchor="start" x="8" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
+<text text-anchor="start" x="8" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="8" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
+<text text-anchor="start" x="8" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="8" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="8" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="8" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="8" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="8" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="8" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="8" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="8" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="8" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="8" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="8" y="-178.5" 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="991.9427,-665.8503 988.607,-675.9063 997.4857,-670.1253 991.9427,-665.8503"/>
+<path fill="none" stroke="#191970" d="M190.5277,-227.6767C211.9807,-205.769 236.212,-185.5604 262.5,-171 344.6786,-125.483 964.7571,-83.4537 1215.63,-68.1295"/>
+<polygon fill="none" stroke="#191970" points="187.7459,-225.5214 183.36,-235.1658 192.8029,-230.3615 187.7459,-225.5214"/>
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<!-- Node3->Node3 -->
<g id="edge2" class="edge">
<title>Node3->Node3</title>
-<path fill="none" stroke="#404040" d="M988.8625,-814.9248C999.5482,-808.6637 1006.5,-797.3555 1006.5,-781 1006.5,-770.0112 1003.3618,-761.3007 998.0615,-754.8687"/>
-<polygon fill="none" stroke="#404040" points="998.0184,-754.8322 990.8548,-754.0056 988.8625,-747.0752 996.0261,-747.9017 998.0184,-754.8322"/>
-<text text-anchor="middle" x="1032.5" y="-778.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
-</g>
-<!-- Node15 -->
-<g id="node14" class="node">
-<title>Node15</title>
-<g id="a_node14"><a xlink:href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" target="_top" xlink:title="The abstract interface of task schedulers. ">
-<polygon fill="#ffffff" stroke="#000000" points="1373.5,-182.5 1373.5,-349.5 1580.5,-349.5 1580.5,-182.5 1373.5,-182.5"/>
-<text text-anchor="start" x="1381.5" y="-337.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="1477" y="-326.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TaskSchedulerNode</text>
-<polyline fill="none" stroke="#000000" points="1373.5,-319.5 1580.5,-319.5 "/>
-<text text-anchor="start" x="1381.5" y="-307.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ max_trials</text>
-<text text-anchor="start" x="1381.5" y="-296.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ num_trials_already</text>
-<text text-anchor="start" x="1381.5" y="-285.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="1373.5,-278.5 1580.5,-278.5 "/>
-<text text-anchor="start" x="1381.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~TaskSchedulerNode()</text>
-<text text-anchor="start" x="1381.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
-<text text-anchor="start" x="1381.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Tune()</text>
-<text text-anchor="start" x="1381.5" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ InitializeTask()</text>
-<text text-anchor="start" x="1381.5" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TouchTask()</text>
-<text text-anchor="start" x="1381.5" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ JoinRunningTask()</text>
-<text text-anchor="start" x="1381.5" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ NextTaskId()</text>
-<text text-anchor="start" x="1381.5" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
-</a>
-</g>
-</g>
-<!-- Node3->Node15 -->
-<g id="edge23" class="edge">
-<title>Node3->Node15</title>
-<path fill="none" stroke="#191970" d="M995.5362,-644.8009C1017.1439,-622.5756 1041.5566,-601.965 1068,-587 1106.5404,-565.1889 1133.231,-598.7872 1166,-569 1230.9612,-509.9499 1157.9672,-441.1192 1220,-379 1240.5769,-358.3944 1255.817,-371.4444 1283,-361 1312.9309,-349.4998 1344.5088,-335.0846 1373.2948,-320.9842"/>
-<polygon fill="none" stroke="#191970" points="992.9784,-642.4116 988.6202,-652.0685 998.0493,-647.2371 992.9784,-642.4116"/>
+<path fill="none" stroke="#404040" d="M183.3625,-398.9248C194.0482,-392.6637 201,-381.3555 201,-365 201,-354.0112 197.8618,-345.3007 192.5615,-338.8687"/>
+<polygon fill="none" stroke="#404040" points="192.5184,-338.8322 185.3548,-338.0056 183.3625,-331.0752 190.5261,-331.9017 192.5184,-338.8322"/>
+<text text-anchor="middle" x="227" y="-362.5" 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_1Optional.html" target="_top" xlink:title="{tvm::runtime::Optional\l\< tvm::meta_schedule::\lSearchStrategy \>\n|+ _type_is_nullable\l|+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ operator=()\l+ operator=()\land 15 more...\l}">
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-<text text-anchor="start" x="8" y="-556.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
-<text text-anchor="start" x="8" y="-545.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule::</text>
-<text text-anchor="middle" x="67" y="-534.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">SearchStrategy ></text>
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-<text text-anchor="start" x="8" y="-515.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="0,-508.5 134,-508.5 "/>
-<text text-anchor="start" x="8" y="-496.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-441.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-430.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
-<text text-anchor="start" x="8" y="-408.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="8" y="-397.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="8" y="-386.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
+<polygon fill="#ffffff" stroke="#000000" points="271.5,-270.5 271.5,-459.5 405.5,-459.5 405.5,-270.5 271.5,-270.5"/>
+<text text-anchor="start" x="279.5" y="-447.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
+<text text-anchor="start" x="279.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule::</text>
+<text text-anchor="middle" x="338.5" y="-425.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">SearchStrategy ></text>
+<polyline fill="none" stroke="#000000" points="271.5,-418.5 405.5,-418.5 "/>
+<text text-anchor="start" x="279.5" y="-406.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="271.5,-399.5 405.5,-399.5 "/>
+<text text-anchor="start" x="279.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="279.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="279.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="279.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
</a>
</g>
</g>
<!-- Node4->Node2 -->
<g id="edge3" class="edge">
<title>Node4->Node2</title>
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-<text text-anchor="middle" x="87" y="-263.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +search_strategy</text>
+<path fill="none" stroke="#404040" d="M357.5637,-270.4336C368.8878,-235.203 386.6965,-197.4039 414.5,-171 444.0547,-142.933 461.6334,-150.479 501.5,-142 748.0051,-89.5725 1043.673,-71.412 1203.7016,-65.1873"/>
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+<text text-anchor="middle" x="546.5" y="-145" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +search_strategy</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="1158,-1012.5 1158,-1234.5 1292,-1234.5 1292,-1012.5 1158,-1012.5"/>
-<text text-anchor="middle" x="1225" y="-1222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="1158,-1215.5 1292,-1215.5 "/>
-<text text-anchor="start" x="1166" y="-1203.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="1158,-1196.5 1292,-1196.5 "/>
-<text text-anchor="start" x="1166" y="-1184.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="1166" y="-1173.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="1166" y="-1162.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="1166" y="-1151.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="1166" y="-1140.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
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-<text text-anchor="start" x="1166" y="-1118.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="1166" y="-1107.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="1166" y="-1096.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="1166" y="-1085.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="1166" y="-1074.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="1166" y="-1063.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
-<text text-anchor="start" x="1166" y="-1052.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
-<text text-anchor="start" x="1166" y="-1041.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="1166" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="1166" y="-1019.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
+<polygon fill="#ffffff" stroke="#000000" points="1328.5,-596.5 1328.5,-818.5 1462.5,-818.5 1462.5,-596.5 1328.5,-596.5"/>
+<text text-anchor="middle" x="1395.5" y="-806.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="1328.5,-799.5 1462.5,-799.5 "/>
+<text text-anchor="start" x="1336.5" y="-787.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="1328.5,-780.5 1462.5,-780.5 "/>
+<text text-anchor="start" x="1336.5" y="-768.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="1336.5" y="-757.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="1336.5" y="-746.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="1336.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="1336.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="1336.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="1336.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="1336.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="1336.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
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+<text text-anchor="start" x="1336.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="1336.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="1336.5" y="-636.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="1336.5" y="-625.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="1336.5" y="-614.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
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+<text text-anchor="start" x="583.5" y="-447.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
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<g id="a_node11"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::Postproc \>\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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<g id="a_node12"><a xlink:href="classtvm_1_1runtime_1_1Map.html" target="_top" xlink:title="{tvm::runtime::Map\<\l tvm::meta_schedule\l::Mutator, tvm::FloatImm \>\n||+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ operator=()\l+ operator=()\l+ at()\land 12 more...\l}">
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-<text text-anchor="start" x="931" y="-521" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
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+<polygon fill="#ffffff" stroke="#000000" points="1721.5,-276 1721.5,-454 1849.5,-454 1849.5,-276 1721.5,-276"/>
+<text text-anchor="start" x="1729.5" y="-442" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
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+<text text-anchor="start" x="1729.5" y="-393" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-382" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-327" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="1729.5" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1729.5" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1729.5" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
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+<text text-anchor="start" x="1875.5" y="-447.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="start" x="1875.5" y="-436.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
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+<text text-anchor="middle" x="1931.5" y="-406.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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+<text text-anchor="start" x="1875.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="1875.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1875.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="1875.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
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+<!-- Node5->Node15 -->
+<g id="edge23" class="edge">
+<title>Node5->Node15</title>
+<path fill="none" stroke="#191970" d="M1472.5524,-691.3219C1589.0553,-665.4859 1802.4237,-612.3654 1858.5,-559 1886.1324,-532.7034 1903.3975,-494.9228 1914.143,-459.6769"/>
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+<text text-anchor="start" x="2021.5" y="-371" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-327" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="2021.5" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="2021.5" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="2021.5" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
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-<text text-anchor="start" x="1877" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<text text-anchor="start" x="2168" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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+<text text-anchor="start" x="2168" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="2168" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
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-<text text-anchor="start" x="1573" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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+<text text-anchor="start" x="21" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="21" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="21" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="21" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="21" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1ObjectRef.html b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1ObjectRef.html
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--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1ObjectRef.html
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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::arith::IterSumExpr ></a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">tv [...]
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index ff2836ef3..accb988e3 100644
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<div class="ttc" id="classtvm_1_1runtime_1_1Object_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></div><div class="ttdoc">base class of all object containers. </div><div class="ttdef"><b>Definition:</b> object.h:167</div></div>
<div class="ttc" id="object_8h_html_aaaa3dc5b6dc33f84b2d28f9a81267212"><div class="ttname"><a href="object_8h.html#aaaa3dc5b6dc33f84b2d28f9a81267212">TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS</a></div><div class="ttdeci">#define TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(TypeName, ParentType, ObjectName)</div><div class="ttdef"><b>Definition:</b> object.h:744</div></div>
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+<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuneContext_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuneContext.html">tvm::meta_schedule::TuneContext</a></div><div class="ttdoc">Managed reference to TuneContextNode. </div><div class="ttdef"><b>Definition:</b> tune_context.h:102</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>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1FeatureExtractorNode_html_ad4e9fdab79326a5bd98745007bb29635"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html#ad4e9fdab79326a5bd98745007bb29635">tvm::meta_schedule::FeatureExtractorNode::ExtractFrom</a></div><div class="ttdeci">virtual Array< tvm::runtime::NDArray > ExtractFrom(const TuneContext &context, const Array< MeasureCandidate > &candidates)=0</div><div class="ttdoc">Extract [...]
<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyFeatureExtractorNode_html_ab994b701cd02fef4ebb9fecca29f23be"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyFeatureExtractorNode.html#ab994b701cd02fef4ebb9fecca29f23be">tvm::meta_schedule::PyFeatureExtractorNode::f_extract_from</a></div><div class="ttdeci">FExtractFrom f_extract_from</div><div class="ttdoc">The packed function to the ExtractFrom function. </div><div class="ttdef"><b>Definition:</b> feature_extractor.h:69< [...]
diff --git a/docs/reference/api/doxygen/functions_d.html b/docs/reference/api/doxygen/functions_d.html
index c1b9681d5..561e17b20 100644
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+++ b/docs/reference/api/doxygen/functions_d.html
@@ -101,7 +101,7 @@ $(function() {
</li>
<li>database
: <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html#ae4c80b6dfe62636442a96bafb6887aa4">tvm::meta_schedule::ApplyHistoryBestNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a9bc942d4fa3918753a1b036bb9373deb">tvm::meta_schedule::TaskSchedulerNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">tvm::meta_schedule::TaskSchedulerNode</a>
</li>
<li>DataType()
: <a class="el" href="classtvm_1_1runtime_1_1DataType.html#aa27f63a2395ce1ac52e4d7740a31383b">tvm::runtime::DataType</a>
diff --git a/docs/reference/api/doxygen/functions_f.html b/docs/reference/api/doxygen/functions_f.html
index c834e6dff..c9c7923ef 100644
--- a/docs/reference/api/doxygen/functions_f.html
+++ b/docs/reference/api/doxygen/functions_f.html
@@ -417,7 +417,7 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1PyCostModelNode.html#ac5640a7bb280e0f29c342577709bafda">tvm::meta_schedule::PyCostModelNode</a>
</li>
<li>FPreTuning
-: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">tvm::meta_schedule::PySearchStrategyNode</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">tvm::meta_schedule::PySearchStrategyNode</a>
</li>
<li>FrameBuffer()
: <a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html#ab9a2ef141c8447bacedd6183bca25fa7">tvm::runtime::micro_rpc::FrameBuffer</a>
diff --git a/docs/reference/api/doxygen/functions_func_g.html b/docs/reference/api/doxygen/functions_func_g.html
index 734d99c7d..cbe766c40 100644
--- a/docs/reference/api/doxygen/functions_func_g.html
+++ b/docs/reference/api/doxygen/functions_func_g.html
@@ -355,7 +355,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a40b49fe5c05c5fe5f7a5c5c01bf651c8">tvm::runtime::vm::Instruction</a>
</li>
<li>GradientBased()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a2d889bee3692995fd5a2876bfdb946a4">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#aa21e7e5400949a2fae93b542185a9600">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>GreedyBase()
: <a class="el" href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a95d49572c346fb536671fc1923f39c2a">tvm::tir::usmp::algo::GreedyBase</a>
diff --git a/docs/reference/api/doxygen/functions_func_p.html b/docs/reference/api/doxygen/functions_func_p.html
index 7b17eaf1a..7a5233e1c 100644
--- a/docs/reference/api/doxygen/functions_func_p.html
+++ b/docs/reference/api/doxygen/functions_func_p.html
@@ -197,8 +197,8 @@ $(function() {
, <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html#abc2529d0b1cd485876e48037dd19dde1">tvm::auto_scheduler::SearchPolicyNode</a>
</li>
<li>PreTuning()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#aedcd9c6cc9d8d2a0da10b345ff5d5a61">tvm::meta_schedule::PySearchStrategyNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#abd1485c82a7df42a54904de7822f0fbf">tvm::meta_schedule::SearchStrategyNode</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a53127523582ffdb12f40c9433a577adf">tvm::meta_schedule::PySearchStrategyNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#ad88e1545e88dc1934c25f4b417077aff">tvm::meta_schedule::SearchStrategyNode</a>
</li>
<li>PrimExpr()
: <a class="el" href="classtvm_1_1PrimExpr.html#a756d3f8b17b019560946524951ae6118">tvm::PrimExpr</a>
@@ -307,7 +307,7 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html#af77ab4f9cb68cdb712b5b61995ffbc83">tvm::meta_schedule::SpaceGenerator</a>
</li>
<li>PyTaskScheduler()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a06366c04bd5cda14ac92ceb8b007cdcf">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a81ba808d521f750ab3703873fbc801b5">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>PythonBasedMeasureCallback()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html#a38db53470ab1e264f708e39429555f6f">tvm::auto_scheduler::PythonBasedMeasureCallback</a>
diff --git a/docs/reference/api/doxygen/functions_func_r.html b/docs/reference/api/doxygen/functions_func_r.html
index b5c641e38..7813efb9c 100644
--- a/docs/reference/api/doxygen/functions_func_r.html
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@@ -330,7 +330,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a3581ab0723c1ab1e74cf479c7c81a803">tvm::tir::usmp::algo::GreedyBase</a>
</li>
<li>RoundRobin()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#adc37ee4209d129b8329292bd3c4da1af">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a084b8b6743f0d63938c1bb8de37df6a1">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>RPCRunner()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html#a58f9c976e0b95dba69cf50b6fc284dca">tvm::auto_scheduler::RPCRunner</a>
diff --git a/docs/reference/api/doxygen/functions_func_s.html b/docs/reference/api/doxygen/functions_func_s.html
index 5056719e7..43e480d38 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
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, <a class="el" href="classtvm_1_1tir_1_1SeqStmtNode.html#a0b17eae7cf993b7e3c3e5b50b4fa5982">tvm::tir::SeqStmtNode</a>
</li>
<li>SizeVar()
-: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#a0f8cb8a92feb96343939d223db90f7cd">tvm::tir::SizeVar</a>
+: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#ac470249315d9e395ad581d35dd5dcb05">tvm::tir::SizeVar</a>
</li>
<li>Slice()
: <a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html#ab314819e8bcca6421e9a4f33e48578c3">tvm::te::Tensor::Slice</a>
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</li>
<li>Span()
: <a class="el" href="classtvm_1_1Span.html#a5216631b639e8c802263d87d3fe9e5f6">tvm::Span</a>
-, <a class="el" href="classtvm_1_1support_1_1Span.html#a3c22dd06856e7029e7107adf38eb72f5">tvm::support::Span< T, W ></a>
+, <a class="el" href="classtvm_1_1support_1_1Span.html#a77653730a2542edf93b7c4413a72f3ec">tvm::support::Span< T, W ></a>
</li>
<li>SpecializedCondition()
: <a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html#a48d119ee1c6033929a5592cfc2592e60">tvm::te::SpecializedCondition</a>
@@ -706,7 +706,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html#ac29b9295c432a87658392872c644864f">tvm::runtime::DeviceAPI</a>
</li>
<li>String()
-: <a class="el" href="classtvm_1_1runtime_1_1String.html#ac5d930b522e9fef9c07e51819d96d2f3">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#acf549b3c43142639879e0fc31ea5cd77">tvm::runtime::String</a>
</li>
<li>StringImm()
: <a class="el" href="classtvm_1_1tir_1_1StringImm.html#a0f2830290e055f677c5d5dea98aab726">tvm::tir::StringImm</a>
diff --git a/docs/reference/api/doxygen/functions_g.html b/docs/reference/api/doxygen/functions_g.html
index 692abf40a..349447684 100644
--- a/docs/reference/api/doxygen/functions_g.html
+++ b/docs/reference/api/doxygen/functions_g.html
@@ -370,7 +370,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a40b49fe5c05c5fe5f7a5c5c01bf651c8">tvm::runtime::vm::Instruction</a>
</li>
<li>GradientBased()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a2d889bee3692995fd5a2876bfdb946a4">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#aa21e7e5400949a2fae93b542185a9600">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>GreedyBase()
: <a class="el" href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a95d49572c346fb536671fc1923f39c2a">tvm::tir::usmp::algo::GreedyBase</a>
diff --git a/docs/reference/api/doxygen/functions_p.html b/docs/reference/api/doxygen/functions_p.html
index 3802cf747..97578fe81 100644
--- a/docs/reference/api/doxygen/functions_p.html
+++ b/docs/reference/api/doxygen/functions_p.html
@@ -382,8 +382,8 @@ $(function() {
: <a class="el" href="classtvm_1_1TargetKindNode.html#a47f02c66d0f972befdfb29ec592ecba0">tvm::TargetKindNode</a>
</li>
<li>PreTuning()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#aedcd9c6cc9d8d2a0da10b345ff5d5a61">tvm::meta_schedule::PySearchStrategyNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#abd1485c82a7df42a54904de7822f0fbf">tvm::meta_schedule::SearchStrategyNode</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a53127523582ffdb12f40c9433a577adf">tvm::meta_schedule::PySearchStrategyNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html#ad88e1545e88dc1934c25f4b417077aff">tvm::meta_schedule::SearchStrategyNode</a>
</li>
<li>PrimExpr()
: <a class="el" href="classtvm_1_1PrimExpr.html#a7f0ca30e951608a0b36a77a66d4d19e0">tvm::PrimExpr</a>
@@ -506,7 +506,7 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html#af77ab4f9cb68cdb712b5b61995ffbc83">tvm::meta_schedule::SpaceGenerator</a>
</li>
<li>PyTaskScheduler()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a06366c04bd5cda14ac92ceb8b007cdcf">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a81ba808d521f750ab3703873fbc801b5">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>PythonBasedMeasureCallback()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html#a38db53470ab1e264f708e39429555f6f">tvm::auto_scheduler::PythonBasedMeasureCallback</a>
diff --git a/docs/reference/api/doxygen/functions_r.html b/docs/reference/api/doxygen/functions_r.html
index 18a241386..856ad158e 100644
--- a/docs/reference/api/doxygen/functions_r.html
+++ b/docs/reference/api/doxygen/functions_r.html
@@ -543,7 +543,7 @@ $(function() {
, <a class="el" href="structtvm_1_1relay_1_1Resize3DAttrs.html#a6e15ec45b5788ebaf2101e14e7a366fb">tvm::relay::Resize3DAttrs</a>
</li>
<li>RoundRobin()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#adc37ee4209d129b8329292bd3c4da1af">tvm::meta_schedule::TaskScheduler</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html#a084b8b6743f0d63938c1bb8de37df6a1">tvm::meta_schedule::TaskScheduler</a>
</li>
<li>RPCRunner()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html#a58f9c976e0b95dba69cf50b6fc284dca">tvm::auto_scheduler::RPCRunner</a>
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index 542dc0855..38859b0c6 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -952,7 +952,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html#afc61714fbac246f72d02d0729fb9ba2d">tvm::tir::StmtSRefNode</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
</li>
<li>StmtSRef()
: <a class="el" href="classtvm_1_1tir_1_1StmtSRef.html#a31687ace5dc4fe487ffb87d658d86412">tvm::tir::StmtSRef</a>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index ddd9e2575..8f019f940 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#ab825b350cf478bf948d807b6fdf636a0">tvm::Target</a>
+: <a class="el" href="classtvm_1_1Target.html#a77f3d7cc97d8cfd7172af58b4e784d89">tvm::Target</a>
</li>
<li>target
: <a class="el" href="classtvm_1_1VirtualDeviceNode.html#a8b2d427d9e21886ccaeaae5e9cc55aaf">tvm::VirtualDeviceNode</a>
@@ -135,9 +135,6 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html#a92f0cdb010a27536d1c96a8a3f0d0237">tvm::meta_schedule::ExtractedTaskNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a5cd36a027a0a4b1840bf3884948c6298">tvm::meta_schedule::TuneContextNode</a>
</li>
-<li>task_scheduler
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a4107f4a73646a16829baea2a7eeddbd4">tvm::meta_schedule::TuneContextNode</a>
-</li>
<li>tasks
: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a70a2be68e991f43d42e53b76a584f547">tvm::meta_schedule::TaskSchedulerNode</a>
</li>
@@ -1275,7 +1272,7 @@ $(function() {
: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a0d72a6fa7263821c14bcd37837998ed9">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#a4abadc6786dd14a3aed6e2b5b342d1d6">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a36ca0d1876544463ee848766e70e5e96">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_type.html b/docs/reference/api/doxygen/functions_type.html
index 14bc3d4d0..8e62f51c5 100644
--- a/docs/reference/api/doxygen/functions_type.html
+++ b/docs/reference/api/doxygen/functions_type.html
@@ -194,7 +194,7 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1PyCostModelNode.html#ac5640a7bb280e0f29c342577709bafda">tvm::meta_schedule::PyCostModelNode</a>
</li>
<li>FPreTuning
-: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a62a1fce99946955bdb2116cd01a61b9c">tvm::meta_schedule::PySearchStrategyNode</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html#a528df580fe251d7fe9eec1e68f8d2385">tvm::meta_schedule::PySearchStrategyNode</a>
</li>
<li>FReprBytes
: <a class="el" href="classtvm_1_1ReflectionVTable.html#aa2daafb800ea82c05b325383b986218b">tvm::ReflectionVTable</a>
diff --git a/docs/reference/api/doxygen/functions_vars_d.html b/docs/reference/api/doxygen/functions_vars_d.html
index dc9d85e8e..6147f0c6e 100644
--- a/docs/reference/api/doxygen/functions_vars_d.html
+++ b/docs/reference/api/doxygen/functions_vars_d.html
@@ -97,7 +97,7 @@ $(function() {
</li>
<li>database
: <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html#ae4c80b6dfe62636442a96bafb6887aa4">tvm::meta_schedule::ApplyHistoryBestNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a9bc942d4fa3918753a1b036bb9373deb">tvm::meta_schedule::TaskSchedulerNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#adb95e507922150ac24d3813326f2cde1">tvm::meta_schedule::TaskSchedulerNode</a>
</li>
<li>datatype_fields
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a25adb383014a74a9c4ac805beefe86b6">tvm::runtime::vm::Instruction</a>
diff --git a/docs/reference/api/doxygen/functions_vars_t.html b/docs/reference/api/doxygen/functions_vars_t.html
index a3606dafc..9bc1762c4 100644
--- a/docs/reference/api/doxygen/functions_vars_t.html
+++ b/docs/reference/api/doxygen/functions_vars_t.html
@@ -105,9 +105,6 @@ $(function() {
: <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html#a92f0cdb010a27536d1c96a8a3f0d0237">tvm::meta_schedule::ExtractedTaskNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a5cd36a027a0a4b1840bf3884948c6298">tvm::meta_schedule::TuneContextNode</a>
</li>
-<li>task_scheduler
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a4107f4a73646a16829baea2a7eeddbd4">tvm::meta_schedule::TuneContextNode</a>
-</li>
<li>tasks
: <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a70a2be68e991f43d42e53b76a584f547">tvm::meta_schedule::TaskSchedulerNode</a>
</li>
diff --git a/docs/reference/api/doxygen/hierarchy.html b/docs/reference/api/doxygen/hierarchy.html
index ac7b2bd41..d8db6e815 100644
--- a/docs/reference/api/doxygen/hierarchy.html
+++ b/docs/reference/api/doxygen/hierarchy.html
@@ -895,323 +895,324 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<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_1IterMapResult.html" target="_self">tvm::arith::IterMapResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html" title="Result of DetectIterMap. ">IterMapResultNode</a> </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_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
-<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_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_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_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_103_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_103_" class="arrow" onclick="toggleFolder('107_103_')">►</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_103_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_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_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_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_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_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_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_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_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_108_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_108_" class="arrow" onclick="toggleFolder('107_108_')">►</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_108_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_108_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_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_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_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_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_111_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_111_" class="arrow" onclick="toggleFolder('107_111_')">►</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_111_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_111_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_116_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_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_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_118_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_118_" class="arrow" onclick="toggleFolder('107_118_')">►</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_118_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_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_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_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_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_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_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_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_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_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_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_123_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_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_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_125_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_125_" class="arrow" onclick="toggleFolder('107_125_')">►</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_125_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_125_0_" class="arrow" onclick="toggleFolder('107_125_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_125_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_125_0_0_" class="arrow" onclick="toggleFolder('107_125_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_125_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_125_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_125_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_125_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_125_0_2_" class="arrow" onclick="toggleFolder('107_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_125_0_34_" class="arrow" onclick="toggleFolder('107_125_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_125_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_125_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_125_1_" class="arrow" onclick="toggleFolder('107_125_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_125_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_125_1_0_" class="arrow" onclick="toggleFolder('107_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_125_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_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_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_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_1Diagnostic.html" target="_self">tvm::Diagnostic</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_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</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_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></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_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_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_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_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_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_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_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_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_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </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_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_136_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_136_" class="arrow" onclick="toggleFolder('107_136_')">►</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_136_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_1Source.html" target="_self">tvm::parser::Source</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_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</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_1PoolInfo.html" target="_self">tvm::PoolInfo</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_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_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_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></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_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
-<tr id="row_107_165_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_165_" class="arrow" onclick="toggleFolder('107_165_')">►</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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_165_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_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_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </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_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_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_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></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_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation 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_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </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_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
-<tr id="row_107_172_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_172_" class="arrow" onclick="toggleFolder('107_172_')">►</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_172_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_172_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_172_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_172_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_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_1RecClosure.html" target="_self">tvm::relay::RecClosure</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_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></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_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_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_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </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_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_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_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
-<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_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
-<tr id="row_107_178_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_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_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_180_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_180_" class="arrow" onclick="toggleFolder('107_180_')">►</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_180_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_1Metadata.html" target="_self">tvm::runtime::metadata::Metadata</a></td><td class="desc"></td></tr>
-<tr id="row_107_180_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_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_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_1runtime_1_1metadata_1_1TensorInfo.html" target="_self">tvm::runtime::metadata::TensorInfo</a></td><td class="desc"></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_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_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_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_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_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_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_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_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_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </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_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_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_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></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_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple 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_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </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_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_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_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_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_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </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_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></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_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_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_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_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_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_197_" class="even" style="display:none;"><td class="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_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_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_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_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_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_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_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_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_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_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_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_1Transform.html" target="_self">tvm::te::Transform</a></td><td class="desc"></td></tr>
-<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_199_" class="arrow" onclick="toggleFolder('107_199_')">►</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_199_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_199_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_199_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_199_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_199_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_199_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_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_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_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_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_211_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_211_" class="arrow" onclick="toggleFolder('107_211_')">►</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_211_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_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_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_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_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_222_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_222_" class="arrow" onclick="toggleFolder('107_222_')">►</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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_222_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_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_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_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_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_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_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_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_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</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_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</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_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</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_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
-<tr id="row_107_230_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_230_" class="arrow" onclick="toggleFolder('107_230_')">►</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_230_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_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_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_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_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_233_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_233_" class="arrow" onclick="toggleFolder('107_233_')">►</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_233_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_233_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_233_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_233_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_233_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_233_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_233_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_233_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_233_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_233_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_233_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_233_10_" class="arrow" onclick="toggleFolder('107_233_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_233_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_233_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_233_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_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_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_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_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_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_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_237_" class="even" style="display:none;"><td class="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_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::Database ></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::SearchStrategy ></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::meta_schedule::SpaceGenerator ></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::PrimExpr ></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::auto_scheduler::MeasureCallback > ></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::FloatImm > ></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::Integer > ></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::BuilderResult > ></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::MeasureCandidate > ></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::Array< tvm::meta_schedule::RunnerFuture > ></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::Map< tvm::runtime::String, 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::NDArray ></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::ObjectRef ></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::runtime::String ></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::Target ></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::IterVar ></a></td><td class="desc"></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_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
+<tr id="row_107_94_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_94_" class="arrow" onclick="toggleFolder('107_94_')">►</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_94_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_94_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_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_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_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_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_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_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_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_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_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_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_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_1IterMapResult.html" target="_self">tvm::arith::IterMapResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html" title="Result of DetectIterMap. ">IterMapResultNode</a> </td></tr>
+<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_1arith_1_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
+<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_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_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_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_104_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_104_" class="arrow" onclick="toggleFolder('107_104_')">►</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_104_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_112_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_117_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_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_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_119_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_119_" class="arrow" onclick="toggleFolder('107_119_')">►</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_119_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_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_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_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_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_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_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_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_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_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_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_124_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_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_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_126_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_126_" class="arrow" onclick="toggleFolder('107_126_')">►</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_126_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_126_0_" class="arrow" onclick="toggleFolder('107_126_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_126_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_0_" class="arrow" onclick="toggleFolder('107_126_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_126_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_126_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_126_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_126_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_2_" class="arrow" onclick="toggleFolder('107_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_34_" class="arrow" onclick="toggleFolder('107_126_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_126_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_126_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_126_1_" class="arrow" onclick="toggleFolder('107_126_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_126_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_1_0_" class="arrow" onclick="toggleFolder('107_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_126_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_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_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_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_1Diagnostic.html" target="_self">tvm::Diagnostic</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_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</a></td><td class="desc"></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_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></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_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_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_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_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_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_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_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_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_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </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_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_137_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_137_" class="arrow" onclick="toggleFolder('107_137_')">►</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_137_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_1parser_1_1Source.html" target="_self">tvm::parser::Source</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_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</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_1PoolInfo.html" target="_self">tvm::PoolInfo</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_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_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_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></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_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_107_166_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_166_" class="arrow" onclick="toggleFolder('107_166_')">►</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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_166_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_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_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </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_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_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_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></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_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation class </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_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </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_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_107_173_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_173_" class="arrow" onclick="toggleFolder('107_173_')">►</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_173_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_173_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_173_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_173_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_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_1RecClosure.html" target="_self">tvm::relay::RecClosure</a></td><td class="desc"></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_1relay_1_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></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_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_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_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </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_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_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_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
+<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_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_107_179_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_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_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_181_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_181_" class="arrow" onclick="toggleFolder('107_181_')">►</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_181_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_1Metadata.html" target="_self">tvm::runtime::metadata::Metadata</a></td><td class="desc"></td></tr>
+<tr id="row_107_181_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_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_181_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_1runtime_1_1metadata_1_1TensorInfo.html" target="_self">tvm::runtime::metadata::TensorInfo</a></td><td class="desc"></td></tr>
+<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_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_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_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_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_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_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_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_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_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </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_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_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_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></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_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </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_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </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_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_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_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_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_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </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_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></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_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_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_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_197_" class="even" style="display:none;"><td class="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_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_1IterVarAttr.html" target="_self">tvm::te::IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
+<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_199_" class="arrow" onclick="toggleFolder('107_199_')">►</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_199_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_199_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_199_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_199_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_199_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_200_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_200_" class="arrow" onclick="toggleFolder('107_200_')">►</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_200_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_200_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_200_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_200_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_200_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_200_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_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_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_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_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_212_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_212_" class="arrow" onclick="toggleFolder('107_212_')">►</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_212_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_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_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_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_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_223_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_223_" class="arrow" onclick="toggleFolder('107_223_')">►</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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_223_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_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_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_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_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_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_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_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_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</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_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</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_1tir_1_1usmp_1_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</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_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
+<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_1transform_1_1Pass.html" target="_self">tvm::transform::Pass</a></td><td class="desc"></td></tr>
+<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_1transform_1_1Sequential.html" target="_self">tvm::transform::Sequential</a></td><td class="desc"></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_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_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_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_234_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_234_" class="arrow" onclick="toggleFolder('107_234_')">►</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_234_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_234_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_234_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_234_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_234_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_234_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_234_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_234_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_234_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_234_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_234_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_234_10_" class="arrow" onclick="toggleFolder('107_234_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_234_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_234_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_234_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_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_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_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_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_237_" class="even" style="display:none;"><td class="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_238_" class="even" style="display:none;"><td class="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>
@@ -1311,7 +1312,7 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_194_" 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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String(const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision, const Array< String > &outputs)></a></td><td class="desc">< [...]
<tr id="row_195_"><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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< te::Schedule(const Attrs &attrs, const Array< te::Tensor > &outs, const Target &target)></a></td><td class="desc"></td></tr>
<tr id="row_196_" 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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void()></a></td><td class="desc"></td></tr>
-<tr id="row_197_"><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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const Array< tir::Schedule > &)></a></td><td class="desc"></td></tr>
+<tr id="row_197_"><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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const Array< tir::Schedule > &, const Optional< Database > &, const Optional< CostModel > &)></a></td><td class="desc"></td></tr>
<tr id="row_198_" 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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TaskScheduler &task_scheduler, int task_id, const Array< MeasureCandidate > &measure_candidates, const Array< BuilderResult > &builds, const Array< RunnerResult > &result [...]
<tr id="row_199_"><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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &)></a></td><td class="desc"></td></tr>
<tr id="row_200_" 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_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &, const Array< MeasureCandidate > &, const Array< RunnerResult > &)></a></td><td class="desc"></td></tr>
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index d40140c67..6c2153917 100644
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