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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/07/03 09:22:37 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@fb64be3f7807df18c2df6ebf5e68178e564ab0b4)
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 1f6ff3cabe deploying docs (apache/tvm@fb64be3f7807df18c2df6ebf5e68178e564ab0b4)
1f6ff3cabe is described below
commit 1f6ff3cabeaf3c3b4e4806a2e1370e8043972f95
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Mon Jul 3 09:22:30 2023 +0000
deploying docs (apache/tvm@fb64be3f7807df18c2df6ebf5e68178e564ab0b4)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_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_adreno.rst.txt | 4 +-
.../deploy_model_on_adreno_tvmc.rst.txt | 2 +-
.../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 | 2 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 22 +-
.../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 | 12 +-
.../tune_conv2d_layer_cuda.rst.txt | 2 +-
.../tune_network_cuda.rst.txt | 4 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 6 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 2 +-
.../work_with_microtvm/micro_autotune.rst.txt | 18 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 14 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../how_to/work_with_schedules/intrin_math.rst.txt | 2 +-
.../work_with_schedules/sg_execution_times.rst.txt | 16 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
.../frontend/deploy_classification.rst.txt | 4 +-
.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 4 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 56 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 22 +-
.../tutorial/tensor_expr_get_started.rst.txt | 43 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 14 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 17 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 26 +-
.../deploy_models/deploy_model_on_adreno.html | 4 +-
.../deploy_models/deploy_model_on_adreno_tvmc.html | 28 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 55 +-
docs/how_to/deploy_models/deploy_prequantized.html | 8 +-
.../deploy_models/deploy_prequantized_tflite.html | 2 +-
docs/how_to/deploy_models/deploy_quantized.html | 2 +-
docs/how_to/deploy_models/sg_execution_times.html | 26 +-
.../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 +-
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.../tune_conv2d_layer_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_cuda.html | 4 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autotvm/sg_execution_times.html | 6 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 2 +-
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docs/how_to/work_with_microtvm/micro_train.html | 16 +-
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.../how_to/work_with_relay/sg_execution_times.html | 8 +-
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.../work_with_schedules/sg_execution_times.html | 20 +-
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228 files changed, 8811 insertions(+), 8781 deletions(-)
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index 1d002474de..5e0af51e54 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 33.130 seconds)
+ **Total running time of the script:** ( 1 minutes 36.392 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 0773982460..8958f96418 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -116,7 +116,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipfb88b4db-4535-41f3-9fa1-fa610a3db641 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip31ca2403-cc98-4130-82a5-7e6a7d6904f5 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 b62b36b850..2ee6760dfb 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -121,7 +121,7 @@ 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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+
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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 da1509e916..dba4022e51 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.964 seconds)
+ **Total running time of the script:** ( 1 minutes 4.542 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 7d9dbbb5f3..a13acfe4d1 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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+
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diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index c915a93408..d71ab0afab 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 42.319 seconds)
+ **Total running time of the script:** ( 1 minutes 38.904 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 c40067e429..1bd3223709 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
@@ -5,26 +5,26 @@
Computation times
=================
-**07:28.865** total execution time for **how_to_compile_models** files:
+**07:32.200** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:42.319 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:38.904 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:33.130 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:36.392 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:03.964 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:04.542 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.333 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.241 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.513 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:38.436 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.179 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:36.114 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.842 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.094 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.789 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.982 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.894 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.573 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.900 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.923 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
index 8210defd06..d6a46dd0af 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -673,7 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4226.1398 4223.9429 4242.1782 4219.5818 6.8190
+ 4227.0359 4226.8018 4231.3196 4223.7070 2.7194
@@ -681,7 +681,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.206 seconds)
+ **Total running time of the script:** ( 1 minutes 21.230 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_model_on_adreno.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
index db723946b6..f2ba793dba 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
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+
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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 9557f6c638..1930f16f15 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
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.9040 14.8740 15.3718 14.6190 0.2038
+ 16.2984 16.0939 17.2630 15.8577 0.4872
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 616a3c7874..24c90e2b34 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
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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]
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+
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: 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).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: 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).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 43.796 seconds)
+ **Total running time of the script:** ( 3 minutes 39.558 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 2c2628976a..b39c7f3778 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -227,7 +227,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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+
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@@ -409,7 +409,7 @@ 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)
- 88.8350 88.7637 89.5505 88.4463 0.2754
+ 89.2295 89.1629 90.0341 88.7476 0.2662
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.881 seconds)
+ **Total running time of the script:** ( 1 minutes 29.730 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 11d7bc8203..5af8968893 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
@@ -423,7 +423,7 @@ 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)
- 109.7722 109.7262 113.7016 108.7968 0.5859
+ 109.2653 109.2123 112.3004 108.6312 0.4026
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 b917c98962..f982cc00de 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -257,7 +257,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 13.561 seconds)
+ **Total running time of the script:** ( 1 minutes 54.798 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.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 3ac22fb484..a30ef569e6 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
@@ -5,26 +5,26 @@
Computation times
=================
-**12:23.117** total execution time for **how_to_deploy_models** files:
+**12:09.680** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:43.796 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:39.558 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:13.561 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:54.798 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:26.881 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.730 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.206 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.230 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:57.344 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:58.108 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:50.847 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:52.252 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:50.109 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.554 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.265 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.648 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.101 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.796 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
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 755ed07425..cf00c87db7 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
@@ -463,7 +463,7 @@ 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.zipf4f1fe8d-e70a-44e8-8097-7424b0877a59 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip55ec5102-885a-4a33-8dfa-cc09f60ba92c 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 ea06a70d44..ac6314a48f 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
@@ -5,14 +5,14 @@
Computation times
=================
-**00:57.320** total execution time for **how_to_extend_tvm** files:
+**00:59.819** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:53.476 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:55.799 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.681 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.805 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.156 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.208 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
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 f7b8e91b9f..2a7ef9340b 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
@@ -220,10 +220,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 23401us [23401us] (48.50%; 48.50%)
- FoldScaleAxis: 24847us [8us] (51.50%; 51.50%)
- FoldConstant: 24839us [1778us] (51.48%; 99.97%)
- InferType: 23061us [23061us] (47.80%; 92.84%)
+ InferType: 23325us [23325us] (48.18%; 48.18%)
+ FoldScaleAxis: 25091us [9us] (51.82%; 51.82%)
+ FoldConstant: 25082us [1887us] (51.81%; 99.96%)
+ InferType: 23196us [23196us] (47.91%; 92.48%)
@@ -262,10 +262,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 22838us [22838us] (48.33%; 48.33%)
- FoldScaleAxis: 24411us [6us] (51.67%; 51.67%)
- FoldConstant: 24406us [1779us] (51.65%; 99.98%)
- InferType: 22627us [22627us] (47.89%; 92.71%)
+ InferType: 23315us [23315us] (48.21%; 48.21%)
+ FoldScaleAxis: 25044us [8us] (51.79%; 51.79%)
+ FoldConstant: 25036us [1887us] (51.77%; 99.97%)
+ InferType: 23149us [23149us] (47.87%; 92.46%)
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 f8a1349917..b7cac8cec2 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
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 33.752159 ms
+ Convolution: 53.543712 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 8d9fcbd9f5..a42eebf580 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
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.273619 ms
+ conv2d with tensor core: 12.273667 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 b644504a4d..4521cfe7ac 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -134,8 +134,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018277
- Baseline: 3.490719
+ Numpy running time: 0.019191
+ Baseline: 3.296524
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.290850
+ Opt1: 0.320876
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.301618
+ Opt2: 0.314365
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.120986
+ Opt3: 0.122655
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.107221
+ Opt4: 0.107496
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111988
+ Opt5: 0.112067
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.132691
+ Opt6: 0.133419
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 2e53252017..01dc571ade 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
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.376** total execution time for **how_to_optimize_operators** files:
+**00:34.797** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.182 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.447 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.986 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.025 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.208 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.325 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
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 45e45f0143..1e0f25f8e2 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
@@ -5,18 +5,18 @@
Computation times
=================
-**03:50.255** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:54.964** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:40.627 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:42.407 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:19.232 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:20.824 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.555 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.696 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.455 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.039 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.282 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.894 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.104 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
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 4cc0ca336b..6356898a60 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
@@ -767,7 +767,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.348 ms
+ Execution time of this operator: 0.347 ms
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 bd847dca4f..e94a9f78d3 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
@@ -647,7 +647,7 @@ 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)
- 8.0909 8.0905 8.0927 8.0894 0.0014
+ 8.1056 8.1025 8.1194 8.0950 0.0102
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.232 seconds)
+ **Total running time of the script:** ( 1 minutes 20.824 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_cuda.py:
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 14028eae42..b1a686ac88 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
@@ -666,7 +666,7 @@ 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)
- 767.7577 767.8364 770.3924 765.0442 2.1841
+ 758.6010 758.3941 759.4665 757.9425 0.6391
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 40.627 seconds)
+ **Total running time of the script:** ( 1 minutes 42.407 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
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 810789a0e2..eb63db4722 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
@@ -5,12 +5,12 @@
Computation times
=================
-**00:24.077** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.746** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.040 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.707 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 684eacb7c6..f686e7c874 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
@@ -326,7 +326,7 @@ and measure running time.
Best config:
,None
- Time cost of this operator: 0.037052
+ Time cost of this operator: 0.037175
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 6759082fa0..95959d3f23 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
@@ -360,10 +360,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 302.4 98.746 (1, 2, 10, 10, 3) 2 1 [302.4]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.885 0.942 (1, 6, 10, 10) 1 1 [2.885]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.312 (1, 1, 10, 10, 3) 1 1 [0.955]
- Total_time - 306.24 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.3 98.751 (1, 2, 10, 10, 3) 2 1 [304.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.888 0.937 (1, 6, 10, 10) 1 1 [2.888]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.961 0.312 (1, 1, 10, 10, 3) 1 1 [0.961]
+ Total_time - 308.148 - - - - -
@@ -428,10 +428,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.2 97.405 (1, 6, 10, 10, 1) 2 1 [103.2]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.793 1.692 (1, 6, 10, 10) 1 1 [1.793]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.957 0.903 (1, 1, 10, 10, 3) 1 1 [0.957]
- Total_time - 105.949 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.0 97.417 (1, 6, 10, 10, 1) 2 1 [103.0]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.776 1.679 (1, 6, 10, 10) 1 1 [1.776]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.956 0.904 (1, 1, 10, 10, 3) 1 1 [0.956]
+ Total_time - 105.732 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.496 seconds)
+ **Total running time of the script:** ( 1 minutes 27.824 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_autotune.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
index d897c4462e..b04d78f45d 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
-
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 78.0MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 11.1MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 16.1MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -326,7 +326,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 27.491 seconds)
+ **Total running time of the script:** ( 1 minutes 31.702 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_pytorch.py:
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 e766c57610..1481d0213e 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
@@ -217,7 +217,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpe7v7i46o/images/random'
+ '/tmp/tmpm7ibuohw/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpe7v7i46o/images/target contains 8144 images
- /tmp/tmpe7v7i46o/images/random contains 5000 images
+ /tmp/tmpm7ibuohw/images/target contains 8144 images
+ /tmp/tmpm7ibuohw/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 40s - loss: 0.2265 - accuracy: 0.9249 - val_loss: 0.1584 - val_accuracy: 0.9445 - 40s/epoch - 123ms/step
+ 328/328 - 40s - loss: 0.2383 - accuracy: 0.9215 - val_loss: 0.1064 - val_accuracy: 0.9622 - 40s/epoch - 123ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.1007 - accuracy: 0.9655 - val_loss: 0.1083 - val_accuracy: 0.9611 - 35s/epoch - 108ms/step
+ 328/328 - 36s - loss: 0.0999 - accuracy: 0.9642 - val_loss: 0.1296 - val_accuracy: 0.9532 - 36s/epoch - 109ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0687 - accuracy: 0.9760 - val_loss: 0.1183 - val_accuracy: 0.9577 - 35s/epoch - 108ms/step
+ 328/328 - 36s - loss: 0.0712 - accuracy: 0.9732 - val_loss: 0.1031 - val_accuracy: 0.9668 - 36s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7f23af097700>
+ <keras.callbacks.History object at 0x7f566d706670>
@@ -860,7 +860,7 @@ 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 32.874 seconds)
+ **Total running time of the script:** ( 4 minutes 45.452 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 1ab02ac790..4e229bf955 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
@@ -5,20 +5,20 @@
Computation times
=================
-**07:55.934** total execution time for **how_to_work_with_microtvm** files:
+**08:15.894** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:32.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:45.452 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:27.491 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:31.702 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:25.496 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:27.824 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.466 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.863 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.105 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.284 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.502 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.769 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``) | 00:00.000 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
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 9909807a62..29b0e0275d 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
@@ -5,14 +5,14 @@
Computation times
=================
-**00:39.603** total execution time for **how_to_work_with_relay** files:
+**00:40.984** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.464 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.602 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.264 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.406 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.869 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.970 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.006 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
index 3c5daf44dc..e5c2ead9e3 100644
--- a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
@@ -278,7 +278,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f23ca39f820>
+ <function my_cuda_math_rule at 0x7f5920025820>
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 be9e6e09a8..c019cf2be5 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
@@ -5,22 +5,22 @@
Computation times
=================
-**00:08.973** total execution time for **how_to_work_with_schedules** files:
+**00:07.271** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.707 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.977 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.498 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.479 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.740 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.769 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.734 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.745 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.119 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.120 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.085 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.058 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.063 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.033 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
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 d574e4c4db..be5156a904 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:35.805** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:36.626** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.798 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:36.618 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
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 fed0711afe..a0b29b325f 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -293,7 +293,7 @@ The compilation steps are:
warnings.warn(
/workspace/vta/tutorials/frontend/deploy_classification.py:212: 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.
graph, lib, params = relay.build(
- resnet18_v1 inference graph built in 37.56s!
+ resnet18_v1 inference graph built in 39.10s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.771 seconds)
+ **Total running time of the script:** ( 1 minutes 6.451 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_classification.py:
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 d168157d20..6c08216723 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 26.63s!
+ yolov3-tiny inference graph built in 26.84s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.840 seconds)
+ **Total running time of the script:** ( 1 minutes 10.207 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
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 7419e6b521..ff3212db45 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
@@ -5,10 +5,10 @@
Computation times
=================
-**02:14.610** total execution time for **topic_vta_tutorials_frontend** files:
+**02:16.657** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:09.840 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:10.207 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:04.771 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:06.451 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
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 5ecd780f9b..d4c28a8a94 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:03.399** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.422** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.882 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.886 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.518 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.536 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 367538dee0..33f3e7f420 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:00.868** total execution time for **topic_vta_tutorials** files:
+**00:00.894** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.444 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.463 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.424 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.431 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 255811b4ed..b49ec0c3ea 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -325,7 +325,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 95.397 ms
+ Execution time of this operator: 95.914 ms
@@ -441,7 +441,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 40.015 seconds)
+ **Total running time of the script:** ( 1 minutes 29.688 seconds)
.. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index cfe30ea892..ab75443e0c 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 5.74/5.74 result: MeasureResult(costs=(0.0467725634,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.0018970966339111, timestamp=1688315918.8944511) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 2 GFLOPS: 10.32/10.32 result: MeasureResult(costs=(0.0260085756,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.704857587814331, timestamp=1688315919.5737717) [('tile_y', [-1, 32]), ('tile_x', [-1, 256])],None,85
- No: 3 GFLOPS: 10.45/10.45 result: MeasureResult(costs=(0.025699331000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6640698909759521, timestamp=1688315920.243475) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
- No: 4 GFLOPS: 11.85/11.85 result: MeasureResult(costs=(0.0226606738,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6798343658447266, timestamp=1688315920.861996) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
- No: 5 GFLOPS: 8.73/11.85 result: MeasureResult(costs=(0.030761312199999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7481791973114014, timestamp=1688315921.730237) [('tile_y', [-1, 512]), ('tile_x', [-1, 32])],None,59
- No: 6 GFLOPS: 11.76/11.85 result: MeasureResult(costs=(0.0228198682,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6319293975830078, timestamp=1688315922.3546555) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
- No: 7 GFLOPS: 10.25/11.85 result: MeasureResult(costs=(0.026194485400000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6807129383087158, timestamp=1688315923.0343387) [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
- No: 8 GFLOPS: 9.53/11.85 result: MeasureResult(costs=(0.028171416799999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7049129009246826, timestamp=1688315923.749099) [('tile_y', [-1, 1]), ('tile_x', [-1, 512])],None,90
- No: 9 GFLOPS: 11.09/11.85 result: MeasureResult(costs=(0.024199963999999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6233828067779541, timestamp=1688315924.482041) [('tile_y', [-1, 64]), ('tile_x', [-1, 256])],None,86
- No: 10 GFLOPS: 2.75/11.85 result: MeasureResult(costs=(0.0977713144,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7947039604187012, timestamp=1688315926.311388) [('tile_y', [-1, 16]), ('tile_x', [-1, 2])],None,14
+ No: 1 GFLOPS: 8.84/8.84 result: MeasureResult(costs=(0.030379357000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7616262435913086, timestamp=1688373387.8364036) [('tile_y', [-1, 128]), ('tile_x', [-1, 16])],None,47
+ No: 2 GFLOPS: 2.11/8.84 result: MeasureResult(costs=(0.12741309879999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3056640625, timestamp=1688373390.141824) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
+ No: 3 GFLOPS: 9.07/9.07 result: MeasureResult(costs=(0.029609836,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7428739070892334, timestamp=1688373390.885648) [('tile_y', [-1, 512]), ('tile_x', [-1, 256])],None,89
+ No: 4 GFLOPS: 3.60/9.07 result: MeasureResult(costs=(0.0744738764,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.447695255279541, timestamp=1688373392.337781) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
+ No: 5 GFLOPS: 9.14/9.14 result: MeasureResult(costs=(0.0293791154,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7374036312103271, timestamp=1688373393.2068305) [('tile_y', [-1, 2]), ('tile_x', [-1, 64])],None,61
+ No: 6 GFLOPS: 8.55/9.14 result: MeasureResult(costs=(0.031408737000000006,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7723984718322754, timestamp=1688373393.9734402) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 7 GFLOPS: 16.43/16.43 result: MeasureResult(costs=(0.0163345472,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5443055629730225, timestamp=1688373394.4971316) [('tile_y', [-1, 16]), ('tile_x', [-1, 64])],None,64
+ No: 8 GFLOPS: 0.44/16.43 result: MeasureResult(costs=(0.614587027,), error_no=MeasureErrorNo.NO_ERROR, all_cost=10.109237909317017, timestamp=1688373404.6213558) [('tile_y', [-1, 512]), ('tile_x', [-1, 1])],None,9
+ No: 9 GFLOPS: 9.93/16.43 result: MeasureResult(costs=(0.0270278026,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6783030033111572, timestamp=1688373405.7990975) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
+ No: 10 GFLOPS: 5.21/16.43 result: MeasureResult(costs=(0.0514811024,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.049487829208374, timestamp=1688373406.892491) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 307a13891e..dc8a9926b5 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -311,7 +311,7 @@ standard deviation.
.. code-block:: none
- {'mean': 492.90568887000063, 'median': 492.4394915999983, 'std': 2.6454448350320123}
+ {'mean': 494.6835584499968, 'median': 494.85915825007396, 'std': 1.3994030296421438}
@@ -582,31 +582,29 @@ 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: 20.41/ 20.41 GFLOPS | Progress: (4/20) | 9.58 s
[Task 1/25] Current/Best: 12.40/ 22.91 GFLOPS | Progress: (8/20) | 12.40 s
[Task 1/25] Current/Best: 10.26/ 22.91 GFLOPS | Progress: (12/20) | 16.62 s
[Task 1/25] Current/Best: 21.29/ 22.91 GFLOPS | Progress: (16/20) | 22.90 s
[Task 1/25] Current/Best: 11.92/ 22.91 GFLOPS | Progress: (20/20) | 25.63 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 5.74/ 21.68 GFLOPS | Progress: (4/20) | 4.24 s
[Task 2/25] Current/Best: 15.18/ 21.68 GFLOPS | Progress: (8/20) | 5.68 s
[Task 2/25] Current/Best: 17.19/ 21.68 GFLOPS | Progress: (12/20) | 7.37 s
[Task 2/25] Current/Best: 5.07/ 21.68 GFLOPS | Progress: (16/20) | 8.93 s
[Task 2/25] Current/Best: 18.33/ 21.68 GFLOPS | Progress: (20/20) | 10.92 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 11.76/ 21.29 GFLOPS | Progress: (4/20) | 6.71 s
[Task 3/25] Current/Best: 12.41/ 24.29 GFLOPS | Progress: (8/20) | 8.92 s
[Task 3/25] Current/Best: 12.27/ 24.29 GFLOPS | Progress: (12/20) | 11.25 s
[Task 3/25] Current/Best: 13.56/ 24.29 GFLOPS | Progress: (16/20) | 14.46 s
[Task 3/25] Current/Best: 23.25/ 24.29 GFLOPS | Progress: (20/20) | 16.51 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 7.68/ 16.15 GFLOPS | Progress: (4/20) | 10.97 s
[Task 4/25] Current/Best: 10.28/ 19.79 GFLOPS | Progress: (8/20) | 15.96 s
[Task 4/25] Current/Best: 11.22/ 19.79 GFLOPS | Progress: (12/20) | 18.12 s
[Task 4/25] Current/Best: 3.48/ 19.79 GFLOPS | Progress: (16/20) | 27.00 s
[Task 4/25] Current/Best: 13.78/ 21.02 GFLOPS | Progress: (20/20) | 29.20 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 16.76/ 16.76 GFLOPS | Progress: (4/20) | 5.09 s
[Task 5/25] Current/Best: 7.94/ 16.76 GFLOPS | Progress: (8/20) | 7.63 s
[Task 5/25] Current/Best: 4.15/ 16.76 GFLOPS | Progress: (12/20) | 10.95 s
[Task 5/25] Current/Best: 11.41/ 19.39 GFLOPS | Progress: (16/20) | 12.93 s
[Task 5/25] Current/Best: 13.18/ 19.39 GFLOPS | Progress: (20/20) | 15.41 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.36/ 12.36 GFLOPS | Progress: (4/20) | 5.58 s
[Task 6/25] Current/Best: 13.66/ 17.43 GFLOPS | Progress: (8/20) | 8.01 s
[Task 6/25] Current/Best: 10.52/ 17.43 GFLOPS | Progress: (12/20) | 11.25 s
[Task 6/25] Current/Best: 7.07/ 20.85 GFLOPS | Progress: (16/20) | 13.93 s
[Task 6/25] Current/Best: 4.85/ 20.85 GFLOPS | Progress: (20/20) | 16.52 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 7.91/ 20.53 GFLOPS | Progress: (4/20) | 5.77 s
[Task 7/25] Current/Best: 23.31/ 23.31 GFLOPS | Progress: (8/20) | 7.94 s
[Task 7/25] Current/Best: 15.08/ 23.31 GFLOPS | Progress: (12/20) | 10.98 s
[Task 7/25] Current/Best: 20.04/ 23.31 GFLOPS | Progress: (16/20) | 13.43 s
[Task 7/25] Current/Best: 18.87/ 23.31 GFLOPS | Progress: (20/20) | 16.48 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 2.48/ 12.50 GFLOPS | Progress: (4/20) | 5.95 s
[Task 8/25] Current/Best: 3.22/ 17.74 GFLOPS | Progress: (8/20) | 10.87 s
[Task 8/25] Current/Best: 13.19/ 17.74 GFLOPS | Progress: (12/20) | 14.73 s
[Task 8/25] Current/Best: 14.17/ 17.74 GFLOPS | Progress: (16/20) | 17.73 s
[Task 8/25] Current/Best: 8.06/ 17.74 GFLOPS | Progress: (20/20) | 24.48 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 3.45/ 11.38 GFLOPS | Progress: (4/20) | 13.67 s
[Task 9/25] Current/Best: 12.36/ 17.27 GFLOPS | Progress: (8/20) | 18.34 s
[Task 9/25] Current/Best: 22.14/ 22.14 GFLOPS | Progress: (12/20) | 20.05 s
[Task 9/25] Current/Best: 13.83/ 22.77 GFLOPS | Progress: (16/20) | 23.06 s
[Task 9/25] Current/Best: 11.36/ 22.77 GFLOPS | Progress: (20/20) | 27.83 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 16.17/ 16.17 GFLOPS | Progress: (4/20) | 4.82 s
[Task 10/25] Current/Best: 15.10/ 16.17 GFLOPS | Progress: (8/20) | 7.00 s
[Task 10/25] Current/Best: 15.56/ 16.17 GFLOPS | Progress: (12/20) | 10.15 s
[Task 10/25] Current/Best: 11.06/ 17.41 GFLOPS | Progress: (16/20) | 12.81 s
[Task 10/25] Current/Best: 4.66/ 22.03 GFLOPS | Progress: (20/20) | 14.67 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 24.01/ 24.01 GFLOPS | Progress: (4/20) | 5.04 s
[Task 11/25] Current/Best: 1.58/ 24.01 GFLOPS | Progress: (8/20) | 8.82 s
[Task 11/25] Current/Best: 12.51/ 24.01 GFLOPS | Progress: (12/20) | 12.12 s
[Task 11/25] Current/Best: 21.01/ 24.01 GFLOPS | Progress: (16/20) | 14.75 s
[Task 11/25] Current/Best: 19.49/ 24.01 GFLOPS | Progress: (20/20) | 16.69 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 16.24/ 16.24 GFLOPS | Progress: (4/20) | 5.80 s
[Task 12/25] Current/Best: 13.58/ 19.68 GFLOPS | Progress: (8/20) | 8.16 s
[Task 12/25] Current/Best: 14.12/ 19.68 GFLOPS | Progress: (12/20) | 13.99 s
[Task 12/25] Current/Best: 18.14/ 19.68 GFLOPS | Progress: (16/20) | 15.90 s
[Task 12/25] Current/Best: 13.90/ 20.18 GFLOPS | Progress: (20/20) | 18.59 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 11.62/ 19.54 GFLOPS | Progress: (4/20) | 5.37 s
[Task 13/25] Current/Best: 13.17/ 19.54 GFLOPS | Progress: (8/20) | 7.61 s
[Task 13/25] Current/Best: 22.10/ 23.62 GFLOPS | Progress: (12/20) | 10.12 s
[Task 13/25] Current/Best: 15.22/ 23.62 GFLOPS | Progress: (16/20) | 12.79 s
[Task 13/25] Current/Best: 10.34/ 23.62 GFLOPS | Progress: (20/20) | 15.99 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 10.04/ 22.51 GFLOPS | Progress: (4/20) | 8.15 s
[Task 14/25] Current/Best: 10.96/ 22.51 GFLOPS | Progress: (8/20) | 19.60 s
[Task 14/25] Current/Best: 1.60/ 22.51 GFLOPS | Progress: (12/20) | 24.33 s
[Task 14/25] Current/Best: 6.74/ 22.51 GFLOPS | Progress: (16/20) | 34.30 s
[Task 14/25] Current/Best: 13.48/ 22.51 GFLOPS | Progress: (20/20) | 36.96 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 5.88/ 12.05 GFLOPS | Progress: (4/20) | 13.97 s
[Task 15/25] Current/Best: 15.44/ 21.13 GFLOPS | Progress: (8/20) | 16.78 s
[Task 15/25] Current/Best: 7.97/ 21.13 GFLOPS | Progress: (12/20) | 26.09 s
[Task 15/25] Current/Best: 12.37/ 21.13 GFLOPS | Progress: (16/20) | 27.82 s
[Task 15/25] Current/Best: 17.79/ 21.95 GFLOPS | Progress: (20/20) | 30.15 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 21.75/ 21.75 GFLOPS | Progress: (4/20) | 5.94 s
[Task 16/25] Current/Best: 10.18/ 23.46 GFLOPS | Progress: (8/20) | 9.16 s
[Task 16/25] Current/Best: 1.58/ 23.46 GFLOPS | Progress: (12/20) | 12.15 s
[Task 16/25] Current/Best: 15.24/ 23.46 GFLOPS | Progress: (16/20) | 14.07 s
[Task 16/25] Current/Best: 10.93/ 23.46 GFLOPS | Progress: (20/
20) | 17.30 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 6.17/ 22.48 GFLOPS | Progress: (4/20) | 5.60 s
[Task 17/25] Current/Best: 11.33/ 22.48 GFLOPS | Progress: (8/20) | 8.81 s
[Task 17/25] Current/Best: 17.12/ 22.48 GFLOPS | Progress: (12/20) | 11.07 s
[Task 17/25] Current/Best: 6.16/ 23.17 GFLOPS | Progress: (16/20) | 14.13 s
[Task 17/25] Current/Best: 18.78/ 23.17 GFLOPS | Progress: (20/20) | 17.10 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 7.70/ 10.45 GFLOPS | Progress: (4/20) | 7.91 s
[Task 18/25] Current/Best: 6.00/ 15.25 GFLOPS | Progress: (8/20) | 14.10 s
[Task 18/25] Current/Best: 10.67/ 17.06 GFLOPS | Progress: (12/20) | 17.66 s
[Task 18/25] Current/Best: 7.75/ 18.61 GFLOPS | Progress: (16/20) | 22.86 s
[Task 18/25] Current/Best: 15.27/ 18.61 GFLOPS | Progress: (20/20) | 25.37 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 17.73/ 17.73 GFLOPS | Progress: (4/20) | 6.20 s
[Task 19/25] Current/Best: 9.96/ 18.98 GFLOPS | Progress: (8/20) | 9.21 s
[Task 19/25] Current/Best: 17.92/ 18.98 GFLOPS | Progress: (12/20) | 13.07 s
[Task 19/25] Current/Best: 9.54/ 18.98 GFLOPS | Progress: (16/20) | 17.42 s
[Task 19/25] Current/Best: 19.59/ 19.59 GFLOPS | Progress: (20/20) | 22.02 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 6.08/ 17.73 GFLOPS | Progress: (4/20) | 14.62 s
[Task 20/25] Current/Best: 13.43/ 20.62 GFLOPS | Progress: (8/20) | 18.30 s
[Task 20/25] Current/Best: 16.34/ 20.62 GFLOPS | Progress: (12/20) | 23.51 s
[Task 20/25] Current/Best: 17.21/ 20.62 GFLOPS | Progress: (16/20) | 25.78 s
[Task 20/25] Current/Best: 7.41/ 20.62 GFLOPS | Progress: (20/20) | 30.07 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 7.55/ 22.38 GFLOPS | Progress: (4/20) | 5.37 s
[Task 21/25] Current/Best: 10.05/ 22.38 GFLOPS | Progress: (8/20) | 9.57 s
[Task 21/25] Current/Best: 5.38/ 22.38 GFLOPS | Progress: (12/20) | 12.95 s
[Task 21/25] Current/Best: 9.36/ 22.38 GFLOPS | Progress: (16/20) | 15.03 s
[Task 21/25] Current/Best: 4.98/ 22.38 GFLOPS | Progress: (20/
20) | 19.43 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 21.42/ 21.42 GFLOPS | Progress: (4/20) | 5.54 s
[Task 22/25] Current/Best: 18.58/ 21.42 GFLOPS | Progress: (8/20) | 10.23 s
[Task 22/25] Current/Best: 11.93/ 21.42 GFLOPS | Progress: (12/20) | 12.04 s
[Task 22/25] Current/Best: 9.75/ 21.42 GFLOPS | Progress: (16/20) | 13.77 s
[Task 22/25] Current/Best: 10.29/ 21.42 GFLOPS | Progress: (20/20) | 16.98 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 2.66/ 12.32 GFLOPS | Progress: (4/20) | 7.16 s
[Task 23/25] Current/Best: 11.20/ 22.86 GFLOPS | Progress: (8/20) | 10.23 s
[Task 23/25] Current/Best: 9.85/ 22.86 GFLOPS | Progress: (12/20) | 14.50 s
[Task 23/25] Current/Best: 6.14/ 23.31 GFLOPS | Progress: (16/20) | 16.86 s
[Task 23/25] Current/Best: 10.74/ 23.31 GFLOPS | Progress: (20/20) | 20.80 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 7.57/ 9.84 GFLOPS | Progress: (4/20) | 13.86 s
[Task 24/25] Current/Best: 2.93/ 9.84 GFLOPS | Progress: (8/20) | 17.34 s
[Task 24/25] Current/Best: 5.43/ 9.84 GFLOPS | Progress: (12/20) | 26.63 s
[Task 24/25] Current/Best: 4.00/ 9.84 GFLOPS | Progress: (16/20) | 37.64 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 23.72/ 23.72 GFLOPS | Progress: (4/20) | 9.54 s
[Task 1/25] Current/Best: 11.89/ 23.72 GFLOPS | Progress: (8/20) | 14.46 s
[Task 1/25] Current/Best: 13.17/ 23.72 GFLOPS | Progress: (12/20) | 17.57 s
[Task 1/25] Current/Best: 13.79/ 23.72 GFLOPS | Progress: (16/20) | 21.21 s
[Task 1/25] Current/Best: 23.44/ 23.72 GFLOPS | Progress: (20/20) | 23.99 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 14.93/ 15.03 GFLOPS | Progress: (4/20) | 4.76 s
[Task 2/25] Current/Best: 12.41/ 16.97 GFLOPS | Progress: (8/20) | 6.88 s
[Task 2/25] Current/Best: 15.12/ 16.97 GFLOPS | Progress: (12/20) | 8.40 s
[Task 2/25] Current/Best: 12.60/ 20.06 GFLOPS | Progress: (16/20) | 9.79 s
[Task 2/25] Current/Best: 13.13/ 20.06 GFLOPS | Progress: (20/20) | 12.20 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 6.22/ 23.41 GFLOPS | Progress: (4/20) | 5.29 s
[Task 3/25] Current/Best: 5.41/ 23.41 GFLOPS | Progress: (8/20) | 7.98 s
[Task 3/25] Current/Best: 11.69/ 23.41 GFLOPS | Progress: (12/20) | 10.54 s
[Task 3/25] Current/Best: 1.62/ 23.41 GFLOPS | Progress: (16/20) | 14.31 s
[Task 3/25] Current/Best: 14.02/ 23.41 GFLOPS | Progress: (20/20) | 17.95 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 10.19/ 18.13 GFLOPS | Progress: (4/20) | 7.30 s
[Task 4/25] Current/Best: 14.86/ 18.13 GFLOPS | Progress: (8/20) | 9.25 s
[Task 4/25] Current/Best: 16.90/ 18.13 GFLOPS | Progress: (12/20) | 15.39 s
[Task 4/25] Current/Best: 15.32/ 18.13 GFLOPS | Progress: (16/20) | 17.30 s
[Task 4/25] Current/Best: 13.18/ 18.13 GFLOPS | Progress: (20/20) | 21.93 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 11.35/ 17.00 GFLOPS | Progress: (4/20) | 4.87 s
[Task 5/25] Current/Best: 14.55/ 17.00 GFLOPS | Progress: (8/20) | 6.77 s
[Task 5/25] Current/Best: 16.25/ 17.00 GFLOPS | Progress: (12/20) | 8.92 s
[Task 5/25] Current/Best: 13.20/ 21.95 GFLOPS | Progress: (16/20) | 10.79 s
[Task 5/25] Current/Best: 18.47/ 21.95 GFLOPS | Progress: (20/20) | 12.71 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 17.71/ 17.71 GFLOPS | Progress: (4/20) | 5.19 s
[Task 6/25] Current/Best: 15.75/ 17.71 GFLOPS | Progress: (8/20) | 7.49 s
[Task 6/25] Current/Best: 7.90/ 17.71 GFLOPS | Progress: (12/20) | 10.38 s
[Task 6/25] Current/Best: 4.85/ 17.71 GFLOPS | Progress: (16/20) | 14.20 s
[Task 6/25] Current/Best: 11.30/ 17.71 GFLOPS | Progress: (20/20) | 17.70 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 15.55/ 15.55 GFLOPS | Progress: (4/20) | 5.83 s
[Task 7/25] Current/Best: 7.77/ 16.80 GFLOPS | Progress: (8/20) | 8.43 s
[Task 7/25] Current/Best: 17.44/ 20.15 GFLOPS | Progress: (12/20) | 10.64 s
[Task 7/25] Current/Best: 18.53/ 20.15 GFLOPS | Progress: (16/20) | 12.89 s
[Task 7/25] Current/Best: 1.59/ 20.15 GFLOPS | Progress: (20/20) | 16.75 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 9.27/ 17.08 GFLOPS | Progress: (4/20) | 11.60 s
[Task 8/25] Current/Best: 17.51/ 17.51 GFLOPS | Progress: (8/20) | 14.12 s
[Task 8/25] Current/Best: 11.49/ 17.51 GFLOPS | Progress: (12/20) | 16.99 s
[Task 8/25] Current/Best: 12.51/ 20.69 GFLOPS | Progress: (16/20) | 19.47 s
[Task 8/25] Current/Best: 9.76/ 20.69 GFLOPS | Progress: (20/20) | 25.68 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 19.53/ 19.53 GFLOPS | Progress: (4/20) | 13.98 s
[Task 9/25] Current/Best: 6.36/ 19.53 GFLOPS | Progress: (8/20) | 16.98 s
[Task 9/25] Current/Best: 15.67/ 19.53 GFLOPS | Progress: (12/20) | 24.40 s
[Task 9/25] Current/Best: 1.95/ 19.53 GFLOPS | Progress: (16/20) | 26.57 s
[Task 9/25] Current/Best: 12.54/ 19.53 GFLOPS | Progress: (20/20) | 36.25 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 10/25] Current/Best: 6.01/ 14.26 GFLOPS | Progress: (4/20) | 5.84 s
[Task 10/25] Current/Best: 16.21/ 17.64 GFLOPS | Progress: (8/20) | 8.63 s
[Task 10/25] Current/Best: 5.70/ 17.64 GFLOPS | Progress: (12/20) | 10.49 s
[Task 10/25] Current/Best: 15.82/ 17.64 GFLOPS | Progress: (16/20) | 12.60 s
[Task 10/25] Current/Best: 4.10/ 18.61 GFLOPS | Progress: (20/20) | 14.85 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 7.63/ 16.33 GFLOPS | Progress: (4/20) | 5.36 s
[Task 11/25] Current/Best: 19.62/ 19.62 GFLOPS | Progress: (8/20) | 7.60 s
[Task 11/25] Current/Best: 6.20/ 19.88 GFLOPS | Progress: (12/20) | 10.21 s
[Task 11/25] Current/Best: 11.92/ 20.34 GFLOPS | Progress: (16/20) | 12.63 s
[Task 11/25] Current/Best: 17.55/ 20.34 GFLOPS | Progress: (20/20) | 15.15 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 9.91/ 12.84 GFLOPS | Progress: (4/20) | 9.86 s
[Task 12/25] Current/Best: 9.73/ 18.47 GFLOPS | Progress: (8/20) | 12.47 s
[Task 12/25] Current/Best: 5.36/ 18.47 GFLOPS | Progress: (12/20) | 15.35 s
[Task 12/25] Current/Best: 4.10/ 18.81 GFLOPS | Progress: (16/20) | 17.80 s
[Task 12/25] Current/Best: 6.17/ 18.81 GFLOPS | Progress: (20/20) | 20.39 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.33/ 18.84 GFLOPS | Progress: (4/20) | 5.67 s
[Task 13/25] Current/Best: 18.63/ 18.84 GFLOPS | Progress: (8/20) | 9.12 s
[Task 13/25] Current/Best: 20.61/ 20.92 GFLOPS | Progress: (12/20) | 12.37 s
[Task 13/25] Current/Best: 17.97/ 20.92 GFLOPS | Progress: (16/20) | 15.22 s
[Task 13/25] Current/Best: 7.09/ 20.92 GFLOPS | Progress: (20/20) | 18.84 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 15.24/ 15.24 GFLOPS | Progress: (4/20) | 6.45 s
[Task 14/25] Current/Best: 10.70/ 15.24 GFLOPS | Progress: (8/20) | 17.85 s
[Task 14/25] Current/Best: 9.17/ 15.30 GFLOPS | Progress: (12/20) | 29.29 s
[Task 14/25] Current/Best: 10.09/ 15.30 GFLOPS | Progress: (16/20) | 38.89 s
[Task 14/25] Current/Best: 11.95/ 21.57 GFLOPS | Progress: (20/20) | 46.41 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 11.23/ 11.23 GFLOPS | Progress: (4/20) | 13.47 s
[Task 15/25] Current/Best: 8.05/ 12.33 GFLOPS | Progress: (8/20) | 24.63 s
[Task 15/25] Current/Best: 7.04/ 22.36 GFLOPS | Progress: (12/20) | 35.33 s
[Task 15/25] Current/Best: 8.68/ 22.36 GFLOPS | Progress: (16/20) | 37.25 s
[Task 15/25] Current/Best: 12.81/ 22.36 GFLOPS | Progress: (20
/20) | 39.63 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 9.17/ 18.44 GFLOPS | Progress: (4/20) | 4.81 s
[Task 16/25] Current/Best: 9.57/ 20.25 GFLOPS | Progress: (8/20) | 8.53 s
[Task 16/25] Current/Best: 11.34/ 20.39 GFLOPS | Progress: (12/20) | 10.74 s
[Task 16/25] Current/Best: 14.90/ 20.39 GFLOPS | Progress: (16/20) | 12.56 s
[Task 16/25] Current/Best: 6.66/ 20.39 GFLOPS | Progress: (20/20) | 14.82 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 22.15/ 22.15 GFLOPS | Progress: (4/20) | 4.93 s
[Task 17/25] Current/Best: 10.36/ 23.37 GFLOPS | Progress: (8/20) | 7.76 s
[Task 17/25] Current/Best: 11.42/ 23.37 GFLOPS | Progress: (12/20) | 11.54 s
[Task 17/25] Current/Best: 3.10/ 23.37 GFLOPS | Progress: (16/20) | 14.55 s
[Task 17/25] Current/Best: 9.63/ 23.37 GFLOPS | Progress: (20/20) | 17.82 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 10.32/ 18.78 GFLOPS | Progress: (4/20) | 4.76 s
[Task 18/25] Current/Best: 18.24/ 18.78 GFLOPS | Progress: (8/20) | 7.61 s
[Task 18/25] Current/Best: 19.87/ 19.87 GFLOPS | Progress: (12/20) | 11.58 s
[Task 18/25] Current/Best: 16.75/ 19.87 GFLOPS | Progress: (16/20) | 13.60 s
[Task 18/25] Current/Best: 11.83/ 19.87 GFLOPS | Progress: (20/20) | 17.18 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 10.37/ 10.98 GFLOPS | Progress: (4/20) | 7.42 s
[Task 19/25] Current/Best: 6.14/ 16.81 GFLOPS | Progress: (8/20) | 10.35 s
[Task 19/25] Current/Best: 9.97/ 17.70 GFLOPS | Progress: (12/20) | 15.53 s
[Task 19/25] Current/Best: 7.34/ 21.31 GFLOPS | Progress: (16/20) | 20.18 s
[Task 19/25] Current/Best: 11.12/ 22.31 GFLOPS | Progress: (20/20) | 24.45 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 19.61/ 19.61 GFLOPS | Progress: (4/20) | 14.57 s
[Task 20/25] Current/Best: 9.59/ 21.08 GFLOPS | Progress: (8/20) | 22.04 s
[Task 20/25] Current/Best: 6.63/ 21.08 GFLOPS | Progress: (12/20) | 25.18 s
[Task 20/25] Current/Best: 12.51/ 21.08 GFLOPS | Progress: (16/20) | 34.09 s
[Task 20/25] Current/Best: 9.29/ 21.08 GFLOPS | Progress: (20/20) | 38.43 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
-
[Task 24/25] Current/Best: 3.17/ 9.84 GFLOPS | Progress: (20/20) | 41.46 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 6.79/ 6.79 GFLOPS | Progress: (4/20) | 5.09 s
[Task 25/25] Current/Best: 8.24/ 8.24 GFLOPS | Progress: (8/20) | 6.49 s
[Task 25/25] Current/Best: 7.94/ 8.24 GFLOPS | Progress: (12/20) | 17.53 s
[Task 25/25] Current/Best: 4.99/ 8.24 GFLOPS | Progress: (16/20) | 20.32 s
[Task 25/25] Current/Best: 5.81/ 9.30 GFLOPS | Progress: (20/20) | 25.53 s
+ Done.
+
[Task 21/25] Current/Best: 11.36/ 11.36 GFLOPS | Progress: (4/20) | 13.71 s
[Task 21/25] Current/Best: 8.68/ 13.44 GFLOPS | Progress: (8/20) | 18.31 s
[Task 21/25] Current/Best: 7.95/ 20.28 GFLOPS | Progress: (12/20) | 20.31 s
[Task 21/25] Current/Best: 7.88/ 20.28 GFLOPS | Progress: (16/20) | 24.50 s
[Task 21/25] Current/Best: 5.35/ 20.28 GFLOPS | Progress: (20/20) | 35.72 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 17.97/ 17.97 GFLOPS | Progress: (4/20) | 5.90 s
[Task 22/25] Current/Best: 13.27/ 18.57 GFLOPS | Progress: (8/20) | 8.16 s
[Task 22/25] Current/Best: 12.80/ 18.57 GFLOPS | Progress: (12/20) | 10.62 s
[Task 22/25] Current/Best: 13.58/ 18.57 GFLOPS | Progress: (16/20) | 12.51 s
[Task 22/25] Current/Best: 18.33/ 19.08 GFLOPS | Progress: (20/20) | 14.41 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 11.95/ 13.71 GFLOPS | Progress: (4/20) | 7.59 s
[Task 23/25] Current/Best: 19.14/ 19.14 GFLOPS | Progress: (8/20) | 10.79 s
[Task 23/25] Current/Best: 19.48/ 19.92 GFLOPS | Progress: (12/20) | 13.28 s
[Task 23/25] Current/Best: 3.09/ 19.92 GFLOPS | Progress: (16/20) | 17.78 s
[Task 23/25] Current/Best: 22.14/ 22.36 GFLOPS | Progress: (20/20) | 20.13 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 7.99/ 9.64 GFLOPS | Progress: (4/20) | 13.89 s
[Task 24/25] Current/Best: 6.48/ 9.64 GFLOPS | Progress: (8/20) | 18.04 s
[Task 24/25] Current/Best: 3.32/ 9.64 GFLOPS | Progress: (12/20) | 25.24 s
[Task 24/25] Current/Best: 3.78/ 9.64 GFLOPS | Progress: (16/20) | 35.96 s
[Task 24/25] Current/Best: 3.03/ 9.64 GFLOPS | Progress: (20/20) | 40.14 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.54/ 4.28 GFLOPS | Progress: (4/20) | 5.02 s
[Task 25/25] Current/Best: 8.81/ 8.81 GFLOPS | Progress: (8/20) | 16.00 s
[Task 25/25] Current/Best: 2.99/ 8.83 GFLOPS | Progress: (12/20) | 22.70 s
[Task 25/25] Current/Best: 2.95/ 8.83 GFLOPS | Progress: (16/20) | 24.51 s
[Task 25/25] Current/Best: 1.55/ 8.83 GFLOPS | Progress: (20
/20) | 26.06 s
@@ -675,6 +673,8 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
+ Done.
+ Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 399.4270909700117, 'median': 398.9459565499601, 'std': 1.4575875342480034}
- unoptimized: {'mean': 492.90568887000063, 'median': 492.4394915999983, 'std': 2.6454448350320123}
+ optimized: {'mean': 422.1827813800337, 'median': 422.24054694997903, 'std': 1.3756395959746879}
+ unoptimized: {'mean': 494.6835584499968, 'median': 494.85915825007396, 'std': 1.3994030296421438}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 20.080 seconds)
+ **Total running time of the script:** ( 14 minutes 5.928 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 82e6b90a51..8eff72f5fd 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -274,7 +274,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.198e-07 secs/op
+ 1.252e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index efba9b8d72..0f4b9517d6 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -270,7 +270,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x23372350)), stage(b, placeholder(b, 0x1d045650)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T [...]
+ [stage(a, placeholder(a, 0x22c5f080)), stage(b, placeholder(b, 0xca15980)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T. [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 8a3f60ba99..329783dc31 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,32 +5,32 @@
Computation times
=================
-**17:02.809** total execution time for **tutorial** files:
+**17:47.800** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:20.080 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 14:05.928 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:40.015 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:29.688 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.956 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.437 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.683 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:44.161 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:15.064 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:26.548 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.001 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.008 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.840 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.848 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.170 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.182 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 123b819855..26b9f7997e 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.170829994720407e-06 1.0
- naive 6.6549000000000004e-06 0.9280515651465361
- parallel 7.1701999999999996e-06 0.9999121447976216
- vector 3.93058e-05 5.4813459570146374
+ numpy 7.324990001507103e-06 1.0
+ naive 6.797300000000001e-06 0.9279603110176899
+ parallel 6.965e-06 0.9508545402201183
+ vector 3.93611e-05 5.373536345019109
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018542
+ Numpy running time: 0.018858
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.535464
+ none: 3.296294
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.303797
+ blocking: 0.318920
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.287021
+ vectorization: 0.311501
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1230,7 +1230,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.115764
+ loop permutation: 0.120140
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1321,7 +1321,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.106303
+ array packing: 0.106518
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1404,7 +1404,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.111185
+ block caching: 0.111063
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1478,7 +1478,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.132624
+ parallelization: 0.132473
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1548,13 +1548,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.5354641136 1.0
- blocking 0.3037971866 0.08592851655073296
- vectorization 0.2870212005 0.0811834574691071
- loop permutation 0.11576403929999998 0.03274366125077785
- array packing 0.10630339019999999 0.03006773277405895
- block caching 0.11118493 0.0314484679882058
- parallelization 0.1326235131 0.037512334686083296
+ none 3.2962942796 1.0
+ blocking 0.31892042099999995 0.09675119814809144
+ vectorization 0.3115012256 0.09450042962723587
+ loop permutation 0.1201396723 0.036446889175980594
+ array packing 0.10651778429999999 0.032314403771293666
+ block caching 0.1110634201 0.033693417722848874
+ parallelization 0.13247334679999997 0.040188567998872785
@@ -1594,11 +1594,6 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 1.956 seconds)
-
-
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index 4a8e83fb83..ea52bba270 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-683dfb0c04d9f2296940e89c60c2277aca095ccd
+fb64be3f7807df18c2df6ebf5e68178e564ab0b4
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 02b99f2172..3c27922f18 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -595,7 +595,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 33.130 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.392 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index bd9a60e43a..eb48962f7f 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -449,7 +449,7 @@
<span class="nb">print</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipfb88b4db-4535-41f3-9fa1-fa610a3db641 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip31ca2403-cc98-4130-82a5-7e6a7d6904f5 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 69d238ae10..9ab546c46e 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,13 +459,13 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<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 7f5c6b81e9..2f7bb4b7be 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -494,7 +494,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</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 3.964 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.542 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python 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 d2e12c2762..99c18fb395 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,14 +442,15 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 7ae7649c6c..df934a0c46 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -662,7 +662,7 @@ 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 42.319 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.904 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python 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 62fb9a3039..a5cedbca5a 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>07:28.865</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:32.200</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>01:42.319</p></td>
+<td><p>01:38.904</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>01:33.130</p></td>
+<td><p>01:36.392</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>01:03.964</p></td>
+<td><p>01:04.542</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:43.333</p></td>
+<td><p>00:43.241</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:37.513</p></td>
+<td><p>00:38.436</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:35.179</p></td>
+<td><p>00:36.114</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><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></td>
-<td><p>00:28.842</p></td>
+<tr class="row-odd"><td><p><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></td>
+<td><p>00:29.094</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><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></td>
-<td><p>00:28.789</p></td>
+<tr class="row-even"><td><p><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></td>
+<td><p>00:28.982</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:12.894</p></td>
+<td><p>00:13.573</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:02.900</p></td>
+<td><p>00:02.923</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 57e751ab05..c394daec03 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -840,10 +840,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4226.1398 4223.9429 4242.1782 4219.5818 6.8190
+ 4227.0359 4226.8018 4231.3196 4223.7070 2.7194
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.206 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.230 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.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_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 33380c12da..c8be41cde5 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -448,32 +448,30 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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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 cac2f92026..8a33e340f4 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -672,7 +672,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.9040 14.8740 15.3718 14.6190 0.2038
+ 16.2984 16.0939 17.2630 15.8577 0.4872
</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 53a5e5ee24..26de49b4fe 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,33 +464,32 @@ be unstable.</p>
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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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: 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).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: 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).
@@ -584,7 +583,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 43.796 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 39.558 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python 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 9aaf408d9c..a2e7fd5935 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,8 +505,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 50.7MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 59.9MB/s]
</pre></div>
</div>
</div>
@@ -597,7 +597,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<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)
- 88.8350 88.7637 89.5505 88.4463 0.2754
+ 89.2295 89.1629 90.0341 88.7476 0.2662
</pre></div>
</div>
<div class="admonition note">
@@ -636,7 +636,7 @@ 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 26.881 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.730 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download sphx-glr-download-python 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 b5948adce2..2b5db2492d 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -590,7 +590,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<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)
- 109.7722 109.7262 113.7016 108.7968 0.5859
+ 109.2653 109.2123 112.3004 108.6312 0.4026
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index c5eb85e6ae..92401ee859 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -531,7 +531,7 @@ for calibration. But the accuracy might be impacted.</p>
warnings.warn(
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 13.561 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 54.798 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index c0591874ab..7726da54e2 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>12:23.117</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:09.680</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>03:43.796</p></td>
+<td><p>03:39.558</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>02:13.561</p></td>
+<td><p>01:54.798</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>01:26.881</p></td>
+<td><p>01:29.730</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:20.206</p></td>
+<td><p>01:21.230</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:57.344</p></td>
+<td><p>00:58.108</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:50.847</p></td>
+<tr class="row-even"><td><p><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></td>
+<td><p>00:52.252</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><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></td>
-<td><p>00:50.109</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
+<td><p>00:51.554</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:30.265</p></td>
+<td><p>00:31.648</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:30.101</p></td>
+<td><p>00:30.796</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
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 42f2a29b02..c9bbe21deb 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -629,7 +629,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
<span class="n">module</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="n">get_mobilenet</span><span class="p">()</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipf4f1fe8d-e70a-44e8-8097-7424b0877a59 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.zip55ec5102-885a-4a33-8dfa-cc09f60ba92c 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 8ed5a79254..ecb0ebd35d 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:57.320</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:59.819</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,19 +359,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:53.476</p></td>
+<td><p>00:55.799</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:02.681</p></td>
+<td><p>00:02.805</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:01.156</p></td>
+<td><p>00:01.208</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.008</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 167b55ce26..919d43a4f7 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -536,10 +536,10 @@ profile the execution time of each passes.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 23401us [23401us] (48.50%; 48.50%)
-FoldScaleAxis: 24847us [8us] (51.50%; 51.50%)
- FoldConstant: 24839us [1778us] (51.48%; 99.97%)
- InferType: 23061us [23061us] (47.80%; 92.84%)
+InferType: 23325us [23325us] (48.18%; 48.18%)
+FoldScaleAxis: 25091us [9us] (51.82%; 51.82%)
+ FoldConstant: 25082us [1887us] (51.81%; 99.96%)
+ InferType: 23196us [23196us] (47.91%; 92.48%)
</pre></div>
</div>
</div>
@@ -561,10 +561,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 22838us [22838us] (48.33%; 48.33%)
-FoldScaleAxis: 24411us [6us] (51.67%; 51.67%)
- FoldConstant: 24406us [1779us] (51.65%; 99.98%)
- InferType: 22627us [22627us] (47.89%; 92.71%)
+InferType: 23315us [23315us] (48.21%; 48.21%)
+FoldScaleAxis: 25044us [8us] (51.79%; 51.79%)
+ FoldConstant: 25036us [1887us] (51.77%; 99.97%)
+ InferType: 23149us [23149us] (47.87%; 92.46%)
</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 c47aa234f8..78bd32161f 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -585,7 +585,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 33.752159 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.543712 ms
</pre></div>
</div>
<div class="sphx-glr-footer 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 adeb4131fa..0012ca2dc6 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -867,7 +867,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.273619 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.273667 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 5a42527f15..d24d3f4188 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -482,8 +482,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018277
-Baseline: 3.490719
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019191
+Baseline: 3.296524
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -542,7 +542,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.290850
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.320876
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -599,7 +599,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.301618
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.314365
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -654,7 +654,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.120986
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.122655
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -731,7 +731,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107221
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107496
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -809,7 +809,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111988
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112067
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -889,7 +889,7 @@ class Module:
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.132691
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.133419
</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 9311beb689..aee600bc92 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:34.376</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.797</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -359,15 +359,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:31.182</p></td>
+<td><p>00:31.447</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:01.986</p></td>
+<td><p>00:02.025</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:01.208</p></td>
+<td><p>00:01.325</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
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 b85a14b11e..283f440af0 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>03:50.255</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:54.964</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -359,23 +359,23 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>01:40.627</p></td>
+<td><p>01:42.407</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>01:19.232</p></td>
+<td><p>01:20.824</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:17.555</p></td>
+<td><p>00:17.696</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:16.455</p></td>
+<td><p>00:17.039</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:16.282</p></td>
+<td><p>00:16.894</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
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 6f438aedc9..0b109f4cb6 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
@@ -1023,7 +1023,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.348 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.347 ms
</pre></div>
</div>
</div>
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 d454738f5d..5de9f40e5d 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -926,7 +926,7 @@ 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)
- 8.0909 8.0905 8.0927 8.0894 0.0014
+ 8.1056 8.1025 8.1194 8.0950 0.0102
</pre></div>
</div>
</div>
@@ -948,7 +948,7 @@ 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 19.232 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.824 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_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_network_cuda.py</span></code></a></p>
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 2e5944348d..7ad31417c4 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -945,7 +945,7 @@ 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)
- 767.7577 767.8364 770.3924 765.0442 2.1841
+ 758.6010 758.3941 759.4665 757.9425 0.6391
</pre></div>
</div>
</div>
@@ -967,7 +967,7 @@ 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 40.627 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.407 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python 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_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index bddb71707c..23eac10a53 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:24.077</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.746</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,11 +359,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:24.040</p></td>
+<td><p>00:23.707</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:00.021</p></td>
+<td><p>00:00.022</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
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 d9080058b4..3ec5e8798a 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -620,7 +620,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.037052
+Time cost of this operator: 0.037175
</pre></div>
</div>
<div class="sphx-glr-footer 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 17c577009f..7b7818fc0d 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -654,10 +654,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
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-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.885 0.942 (1, 6, 10, 10) 1 1 [2.885]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.312 (1, 1, 10, 10, 3) 1 1 [0.955]
-Total_time - 306.24 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.3 98.751 (1, 2, 10, 10, 3) 2 1 [304.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.888 0.937 (1, 6, 10, 10) 1 1 [2.888]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.961 0.312 (1, 1, 10, 10, 3) 1 1 [0.961]
+Total_time - 308.148 - - - - -
</pre></div>
</div>
</div>
@@ -709,13 +709,13 @@ 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 Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.2 97.405 (1, 6, 10, 10, 1) 2 1 [103.2]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.793 1.692 (1, 6, 10, 10) 1 1 [1.793]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.957 0.903 (1, 1, 10, 10, 3) 1 1 [0.957]
-Total_time - 105.949 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.0 97.417 (1, 6, 10, 10, 1) 2 1 [103.0]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.776 1.679 (1, 6, 10, 10) 1 1 [1.776]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.956 0.904 (1, 1, 10, 10, 3) 1 1 [0.956]
+Total_time - 105.732 - - - - -
</pre></div>
</div>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.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_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index fb6157e905..dcbff07cbf 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -465,7 +465,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 78.0MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 11.1MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 16.1MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -593,7 +594,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.491 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.702 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_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">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 276fae414c..758cf47ed2 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -533,7 +533,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpe7v7i46o/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpm7ibuohw/images/random'
</pre></div>
</div>
</div>
@@ -593,8 +593,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpe7v7i46o/images/target contains 8144 images
-/tmp/tmpe7v7i46o/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpm7ibuohw/images/target contains 8144 images
+/tmp/tmpm7ibuohw/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -706,13 +706,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 40s - loss: 0.2265 - accuracy: 0.9249 - val_loss: 0.1584 - val_accuracy: 0.9445 - 40s/epoch - 123ms/step
+328/328 - 40s - loss: 0.2383 - accuracy: 0.9215 - val_loss: 0.1064 - val_accuracy: 0.9622 - 40s/epoch - 123ms/step
Epoch 2/3
-328/328 - 35s - loss: 0.1007 - accuracy: 0.9655 - val_loss: 0.1083 - val_accuracy: 0.9611 - 35s/epoch - 108ms/step
+328/328 - 36s - loss: 0.0999 - accuracy: 0.9642 - val_loss: 0.1296 - val_accuracy: 0.9532 - 36s/epoch - 109ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0687 - accuracy: 0.9760 - val_loss: 0.1183 - val_accuracy: 0.9577 - 35s/epoch - 108ms/step
+328/328 - 36s - loss: 0.0712 - accuracy: 0.9732 - val_loss: 0.1031 - val_accuracy: 0.9668 - 36s/epoch - 108ms/step
-<keras.callbacks.History object at 0x7f23af097700>
+<keras.callbacks.History object at 0x7f566d706670>
</pre></div>
</div>
</div>
@@ -976,7 +976,7 @@ 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>
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index e2843d8398..23732ec987 100644
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. 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></td>
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 a2d605719b..4254187870 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:39.603</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
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<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
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<tr class="row-even"><td><p><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></td>
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<tr class="row-odd"><td><p><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></td>
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<tr class="row-even"><td><p><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></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 062eece2bf..1dc81d6542 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -559,7 +559,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<a href="../../reference/api/python/ir.html#tvm.ir.register_intrin_lowering" title="tvm.ir.register_intrin_lowering" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-function"><span class="n">register_intrin_lowering</span></a><span class="p">(</span><span class="s2">"tir.exp"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">f</span><span class="o">= [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f23ca39f820>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f5920025820>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
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@@ -350,7 +350,7 @@
<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>
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<tr class="row-odd"><td><p><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></td>
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<tr class="row-even"><td><p><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></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:00.119</p></td>
+<td><p>00:00.120</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
@@ -383,11 +383,11 @@
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><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></td>
-<td><p>00:00.058</p></td>
+<td><p>00:00.063</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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></td>
-<td><p>00:00.032</p></td>
+<td><p>00:00.033</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/reference/api/doxygen/analyzer_8h_source.html b/docs/reference/api/doxygen/analyzer_8h_source.html
index f0a31c0e62..f1d7599504 100644
--- a/docs/reference/api/doxygen/analyzer_8h_source.html
+++ b/docs/reference/api/doxygen/analyzer_8h_source.html
@@ -400,21 +400,22 @@ $(function() {
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<div class="line"><a name="l00618"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a23e266bcb0d9b5ca6cc01c691271db72"> 618</a></span>  <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a23e266bcb0d9b5ca6cc01c691271db72">transitive_comparisons</a>;</div>
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-<div class="line"><a name="l00633"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2"> 633</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f [...]
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-<div class="line"><a name="l00655"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c"> 655</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<Var, Range></a>& variables, <span class="keywordtype">bool</span> allow_o [...]
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-<div class="line"><a name="l00681"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#adb7ace66382583595724d79cc1504638"> 681</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#adb7ace66382583595724d79cc1504638">CanProveLess</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t upper_bound);</div>
-<div class="line"><a name="l00691"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a5b4350d33e93a9e6bb7f26e4c6569703"> 691</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a5b4350d33e93a9e6bb7f26e4c6569703">CanProveEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</span> <a class="code" href="clas [...]
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-<div class="line"><a name="l00721"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a7d8be689aff9dab648ca21f210d6d9c2"> 721</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a7d8be689aff9dab648ca21f210d6d9c2">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& cond, <a class="code" href="namespacetvm_1_1arith.html#a038b0205b1c66d2 [...]
-<div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  </div>
-<div class="line"><a name="l00736"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425"> 736</a></span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="keywordtype">int</span> step [...]
-<div class="line"><a name="l00737"></a><span class="lineno"> 737</span> };</div>
+<div class="line"><a name="l00636"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f"> 636</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">MarkGlobalNonNegValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& value);</div>
+<div class="line"><a name="l00649"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2"> 649</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f [...]
+<div class="line"><a name="l00662"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a20c4bae46db46362ba6bca4c1d368345"> 662</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a20c4bae46db46362ba6bca4c1d368345">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f [...]
+<div class="line"><a name="l00671"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c"> 671</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<Var, Range></a>& variables, <span class="keywordtype">bool</span> allow_o [...]
+<div class="line"><a name="l00684"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a5e97e3abc176f85a5b8e5d0d1cb9f5e1"> 684</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a5e97e3abc176f85a5b8e5d0d1cb9f5e1">CanProveGreaterEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t lower_bound);</div>
+<div class="line"><a name="l00697"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#adb7ace66382583595724d79cc1504638"> 697</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#adb7ace66382583595724d79cc1504638">CanProveLess</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t upper_bound);</div>
+<div class="line"><a name="l00707"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a5b4350d33e93a9e6bb7f26e4c6569703"> 707</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a5b4350d33e93a9e6bb7f26e4c6569703">CanProveEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</span> <a class="code" href="clas [...]
+<div class="line"><a name="l00724"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a4371d28a6b3306bdb4eec6c6a8317e03"> 724</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a4371d28a6b3306bdb4eec6c6a8317e03">CanProveLessEqualThanSymbolicShapeValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</span> [...]
+<div class="line"><a name="l00737"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a7d8be689aff9dab648ca21f210d6d9c2"> 737</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a7d8be689aff9dab648ca21f210d6d9c2">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& cond, <a class="code" href="namespacetvm_1_1arith.html#a038b0205b1c66d2 [...]
<div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  </div>
-<div class="line"><a name="l00739"></a><span class="lineno"> 739</span> } <span class="comment">// namespace arith</span></div>
-<div class="line"><a name="l00740"></a><span class="lineno"> 740</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00741"></a><span class="lineno"> 741</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_ARITH_ANALYZER_H_</span></div>
+<div class="line"><a name="l00752"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425"> 752</a></span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="keywordtype">int</span> step [...]
+<div class="line"><a name="l00753"></a><span class="lineno"> 753</span> };</div>
+<div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  </div>
+<div class="line"><a name="l00755"></a><span class="lineno"> 755</span> } <span class="comment">// namespace arith</span></div>
+<div class="line"><a name="l00756"></a><span class="lineno"> 756</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00757"></a><span class="lineno"> 757</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_ARITH_ANALYZER_H_</span></div>
<div class="ttc" id="aclasstvm_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="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:114</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range constainer</div><div class="ttdef"><b>Definition:</b> expr.h:715</div></div>
@@ -432,6 +433,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a675ba44dc7d70ef5606e4147919da9f2"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">tvm::arith::Analyzer::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const PrimExpr &expr, bool allow_override=false)</div><div class="ttdoc">Notify all the sub-analyzers that var is created and binded to expr.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a6cdf29adeceaa20b8c3dd7c26b92cd00"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a6cdf29adeceaa20b8c3dd7c26b92cd00">tvm::arith::Analyzer::canonical_simplify</a></div><div class="ttdeci">CanonicalSimplifier canonical_simplify</div><div class="ttdoc">sub-analyzer canonical simplify</div><div class="ttdef"><b>Definition:</b> analyzer.h:614</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a7d8be689aff9dab648ca21f210d6d9c2"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a7d8be689aff9dab648ca21f210d6d9c2">tvm::arith::Analyzer::CanProve</a></div><div class="ttdeci">bool CanProve(const PrimExpr &cond, ProofStrength strength=ProofStrength::kDefault)</div><div class="ttdoc">Whether can we prove condition.</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a98d3fc802f7de0ea645cea47aa20c66f"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">tvm::arith::Analyzer::MarkGlobalNonNegValue</a></div><div class="ttdeci">void MarkGlobalNonNegValue(const PrimExpr &value)</div><div class="ttdoc">Mark the value as non-negative value globally in analyzer.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a9b440f852f12ad0a4d8ed5ed97054425"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">tvm::arith::Analyzer::Simplify</a></div><div class="ttdeci">PrimExpr Simplify(const PrimExpr &expr, int steps=2)</div><div class="ttdoc">Simplify expr.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a9dccc7d98b8b9465390e10436d3a9178"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a9dccc7d98b8b9465390e10436d3a9178">tvm::arith::Analyzer::operator=</a></div><div class="ttdeci">Analyzer & operator=(const Analyzer &)=delete</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_acac92a9522deabe289fea99efbd9eaec"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#acac92a9522deabe289fea99efbd9eaec">tvm::arith::Analyzer::modular_set</a></div><div class="ttdeci">ModularSetAnalyzer modular_set</div><div class="ttdoc">sub-analyzer: modular set</div><div class="ttdef"><b>Definition:</b> analyzer.h:610</div></div>
diff --git a/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html b/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
index 28f873a855..6ba11df4a5 100644
--- a/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
@@ -85,11 +85,12 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a4371d28a6b3306bdb4eec6c6a8317e03">CanProveLessEqualThanSymbolicShapeValue</a>(const PrimExpr &lhs, const PrimExpr &shape)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a435eba3ac3a839d3c53b74acfdc10146">const_int_bound</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a0d054ea2ea5b7e99f0883c00672ec831">int_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acac92a9522deabe289fea99efbd9eaec">modular_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9dccc7d98b8b9465390e10436d3a9178">operator=</a>(const Analyzer &)=delete</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acc86c6e8c04cb0de4ff9d78e769924b2">rewrite_simplify</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a>(const PrimExpr &expr, int steps=2)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a23e266bcb0d9b5ca6cc01c691271db72">transitive_comparisons</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">MarkGlobalNonNegValue</a>(const PrimExpr &value)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acac92a9522deabe289fea99efbd9eaec">modular_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9dccc7d98b8b9465390e10436d3a9178">operator=</a>(const Analyzer &)=delete</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acc86c6e8c04cb0de4ff9d78e769924b2">rewrite_simplify</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a>(const PrimExpr &expr, int steps=2)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a23e266bcb0d9b5ca6cc01c691271db72">transitive_comparisons</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer.html b/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
index 815ec8335d..28dca7633d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
+++ b/docs/reference/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
@@ -81,7 +81,7 @@ $(function() {
<div class="dynheader">
Collaboration diagram for tvm::arith::Analyzer:</div>
<div class="dyncontent">
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<table class="memberdecls">
@@ -94,6 +94,9 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:ae8568eeee02c68849e468f8c19a2701d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">Analyzer</a> ()</td></tr>
<tr class="memdesc:ae8568eeee02c68849e468f8c19a2701d"><td class="mdescLeft"> </td><td class="mdescRight">constructor <a href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">More...</a><br /></td></tr>
<tr class="separator:ae8568eeee02c68849e468f8c19a2701d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a98d3fc802f7de0ea645cea47aa20c66f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">MarkGlobalNonNegValue</a> (const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &value)</td></tr>
+<tr class="memdesc:a98d3fc802f7de0ea645cea47aa20c66f"><td class="mdescLeft"> </td><td class="mdescRight">Mark the value as non-negative value globally in analyzer. <a href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">More...</a><br /></td></tr>
+<tr class="separator:a98d3fc802f7de0ea645cea47aa20c66f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a675ba44dc7d70ef5606e4147919da9f2"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">Bind</a> (const <a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> &var, const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &expr, bool allow_override=false)</td></tr>
<tr class="memdesc:a675ba44dc7d70ef5606e4147919da9f2"><td class="mdescLeft"> </td><td class="mdescRight">Notify all the sub-analyzers that var is created and binded to expr. <a href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">More...</a><br /></td></tr>
<tr class="separator:a675ba44dc7d70ef5606e4147919da9f2"><td class="memSeparator" colspan="2"> </td></tr>
@@ -518,6 +521,30 @@ Public Attributes</h2></td></tr>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Whether we can prove lhs <= shape. </dd></dl>
+</div>
+</div>
+<a id="a98d3fc802f7de0ea645cea47aa20c66f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a98d3fc802f7de0ea645cea47aa20c66f">◆ </a></span>MarkGlobalNonNegValue()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void tvm::arith::Analyzer::MarkGlobalNonNegValue </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> & </td>
+ <td class="paramname"><em>value</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Mark the value as non-negative value globally in analyzer. </p>
+<p>Only call this function if the non-neg condition is global and not context-dependent.</p>
+<p>This function does best-effort propagations to the sub-analyzers</p>
+<dl class="section note"><dt>Note</dt><dd>We expose this function because non-negative global values, such as symbolic buffer shapes in function arguments are really important to ensure the best simplification, and usually they can be handled in a simpler way than the generic constraints.</dd></dl>
+<p>This function may call into the Update function of the sub-analyzers. </p>
+
</div>
</div>
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diff --git a/docs/reference/api/doxygen/functions_func_g.html b/docs/reference/api/doxygen/functions_func_g.html
index 0f8c89fa94..3a5fb66fc6 100644
--- a/docs/reference/api/doxygen/functions_func_g.html
+++ b/docs/reference/api/doxygen/functions_func_g.html
@@ -118,7 +118,7 @@ $(function() {
, <a class="el" href="classtvm_1_1TargetKind.html#a11c894b5e17dd940287648261d5efc0d">tvm::TargetKind</a>
, <a class="el" href="classtvm_1_1TargetTag.html#af90a7c152674c7dfe96f6e5aac2f9b25">tvm::TargetTag</a>
, <a class="el" href="classtvm_1_1tir_1_1InstructionKind.html#a6d165507be268ad41aa5221d48062da7">tvm::tir::InstructionKind</a>
-, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a273d3cfc07193e551292450bc2b0e56f">tvm::tir::LayoutAxis</a>
+, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a4b5df87bf9971c025f4c3399a369fd99">tvm::tir::LayoutAxis</a>
, <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a1d34e09f35c6ca2f179165a041037c4c">tvm::tir::ScheduleNode</a>
</li>
<li>get()
diff --git a/docs/reference/api/doxygen/functions_func_m.html b/docs/reference/api/doxygen/functions_func_m.html
index 7d6ec2f08d..9eac87dec2 100644
--- a/docs/reference/api/doxygen/functions_func_m.html
+++ b/docs/reference/api/doxygen/functions_func_m.html
@@ -105,6 +105,9 @@ $(function() {
<li>MapValuePathNode()
: <a class="el" href="classtvm_1_1MapValuePathNode.html#af9e0c40a725c015e71f755b086a852ff">tvm::MapValuePathNode</a>
</li>
+<li>MarkGlobalNonNegValue()
+: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">tvm::arith::Analyzer</a>
+</li>
<li>MarkGraphNode()
: <a class="el" href="classtvm_1_1SEqualHandlerDefault.html#afb033900262e701a1e0a7c598e4267ef">tvm::SEqualHandlerDefault</a>
, <a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html#a919cf7532dbf7dc7a4e97260e02bc947">tvm::SEqualReducer::Handler</a>
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</li>
<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
</li>
<li>Move()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a162dc8d73dc2306f066c3ee013ff096f">tvm::runtime::vm::Instruction</a>
diff --git a/docs/reference/api/doxygen/functions_func_s.html b/docs/reference/api/doxygen/functions_func_s.html
index a9dcdf935d..4491b08143 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
@@ -611,7 +611,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1Shuffle.html#a2d37f2f8afe5357f1a6ffc4c2b147ac4">tvm::tir::Shuffle</a>
</li>
<li>SIBuilder()
-: <a class="el" href="classtvm_1_1SIBuilder.html#a3c16b413d12243d3355a347e03fd778a">tvm::SIBuilder</a>
+: <a class="el" href="classtvm_1_1SIBuilder.html#aa50c19676d7755aa753baa2aabd8fc7c">tvm::SIBuilder</a>
</li>
<li>SilentMeasure()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html#a4beac97e57e780b2f41539403fbcaf38">tvm::auto_scheduler::ProgramMeasurerNode</a>
@@ -702,7 +702,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#a51432f38d9ec4792a2525023179ae604">tvm::te::Stage</a>
</li>
<li>SplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a184575a8029d77f7a3bee23d81141df5">tvm::auto_scheduler::SplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a64ed86582a56a2645b3e4eb44ecb31af">tvm::auto_scheduler::SplitStep</a>
</li>
<li>StableHashBytes()
: <a class="el" href="classtvm_1_1runtime_1_1String.html#a88aa19338db83ae838183e7478f09e60">tvm::runtime::String</a>
@@ -748,7 +748,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtDoc.html#adec8d59e41d8a4093fb310089bf2c3ba">tvm::script::printer::StmtDoc</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
</li>
<li>StmtSRef()
: <a class="el" href="classtvm_1_1tir_1_1StmtSRef.html#a31687ace5dc4fe487ffb87d658d86412">tvm::tir::StmtSRef</a>
@@ -787,7 +787,7 @@ $(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#a68df7bab89fca339e3918438dd80300d">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#a02fca36e3ff55cc1e83635b02a11fca3">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_m.html b/docs/reference/api/doxygen/functions_m.html
index ae30d45714..3f5bd68c2b 100644
--- a/docs/reference/api/doxygen/functions_m.html
+++ b/docs/reference/api/doxygen/functions_m.html
@@ -126,6 +126,9 @@ $(function() {
<li>MapValuePathNode()
: <a class="el" href="classtvm_1_1MapValuePathNode.html#af9e0c40a725c015e71f755b086a852ff">tvm::MapValuePathNode</a>
</li>
+<li>MarkGlobalNonNegValue()
+: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a98d3fc802f7de0ea645cea47aa20c66f">tvm::arith::Analyzer</a>
+</li>
<li>MarkGraphNode()
: <a class="el" href="classtvm_1_1SEqualHandlerDefault.html#afb033900262e701a1e0a7c598e4267ef">tvm::SEqualHandlerDefault</a>
, <a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html#a919cf7532dbf7dc7a4e97260e02bc947">tvm::SEqualReducer::Handler</a>
@@ -348,7 +351,7 @@ $(function() {
: <a class="el" href="classtvm_1_1DiagnosticContextNode.html#adea7e38a6e47cbab7fb5639f208aa536">tvm::DiagnosticContextNode</a>
</li>
<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
, <a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html#a21f639900c480510650969df9c74d17d">tvm::runtime::ModuleNode</a>
</li>
<li>module_alias
diff --git a/docs/reference/api/doxygen/functions_o.html b/docs/reference/api/doxygen/functions_o.html
index 1265545d62..074f274c0a 100644
--- a/docs/reference/api/doxygen/functions_o.html
+++ b/docs/reference/api/doxygen/functions_o.html
@@ -102,7 +102,7 @@ $(function() {
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<li>ObjectPtr
: <a class="el" href="classtvm_1_1runtime_1_1Object.html#a4193bb155125953e77ded93c0fb46965">tvm::runtime::Object</a>
-, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#ab9a13c742256492a2bb19070e41697f2">tvm::runtime::ObjectPtr< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a4193bb155125953e77ded93c0fb46965">tvm::runtime::ObjectPtr< T ></a>
</li>
<li>ObjectPtrHash
: <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#ab1db3e3b77985946e4379d31829030a5">tvm::runtime::ObjectPtr< T ></a>
diff --git a/docs/reference/api/doxygen/functions_p.html b/docs/reference/api/doxygen/functions_p.html
index bfe5ee5cd8..978c646af4 100644
--- a/docs/reference/api/doxygen/functions_p.html
+++ b/docs/reference/api/doxygen/functions_p.html
@@ -217,7 +217,7 @@ $(function() {
, <a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html#acfe3e9170d4c05ff59f3056b79ae58bb">tvm::relay::DFPatternCallbackNode</a>
</li>
<li>Pattern()
-: <a class="el" href="classtvm_1_1relay_1_1Pattern.html#ad2eae0030bb557fbee1b85517cd9c31e">tvm::relay::Pattern</a>
+: <a class="el" href="classtvm_1_1relay_1_1Pattern.html#aba2fe5ab04ab0b56ff855cfc572b16ff">tvm::relay::Pattern</a>
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<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>
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<li>target
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-: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#afa67abdb51145a49d42cd1464429d928">tvm::tir::ScheduleNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a66983e2dde6aeb18b443616398fff8bf">tvm::tir::ScheduleNode</a>
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-: <a class="el" href="classtvm_1_1relay_1_1Var.html#a06ef8ae1d07a5b8a3c25ca7775d17762">tvm::relay::Var</a>
+: <a class="el" href="classtvm_1_1relay_1_1Var.html#a45372a62057ee9332a391e29845505ff">tvm::relay::Var</a>
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