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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/02/23 20:00:51 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@faa66282155a591b5b64f9da254102d74614816a)
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 4eedf9e92d deploying docs (apache/tvm@faa66282155a591b5b64f9da254102d74614816a)
4eedf9e92d is described below
commit 4eedf9e92db987c1f634465a453efa1c1edd6da1
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Fri Feb 23 20:00:43 2024 +0000
deploying docs (apache/tvm@faa66282155a591b5b64f9da254102d74614816a)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 5 +
.../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 | 20 +-
.../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 +-
.../deploy_models/sg_execution_times.rst.txt | 20 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 6 +-
.../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 +-
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.../tune_conv2d_layer_cuda.rst.txt | 2 +-
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.../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 +-
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.../how_to/work_with_schedules/intrin_math.rst.txt | 2 +-
.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
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docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 62 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 20 +-
.../tutorial/tensor_expr_get_started.rst.txt | 44 +-
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docs/how_to/compile_models/from_oneflow.html | 17 +-
docs/how_to/compile_models/from_paddle.html | 1 +
docs/how_to/compile_models/from_pytorch.html | 14 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 24 +-
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.../deploy_models/deploy_model_on_adreno_tvmc.html | 35 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 57 +-
docs/how_to/deploy_models/deploy_prequantized.html | 10 +-
.../deploy_models/deploy_prequantized_tflite.html | 2 +-
docs/how_to/deploy_models/sg_execution_times.html | 20 +-
docs/how_to/extend_tvm/sg_execution_times.html | 6 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 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 +-
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docs/how_to/work_with_microtvm/micro_autotune.html | 18 +-
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docs/how_to/work_with_microtvm/micro_train.html | 16 +-
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334 files changed, 12083 insertions(+), 12033 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 d6bd6f6754..d1301736ac 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 34.245 seconds)
+ **Total running time of the script:** ( 1 minutes 36.054 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
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 3a2b3679f2..e466d1e1bc 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
- 0%| | 0.00/41.5M [00:00<?, ?B/s] 6%|6 | 2.66M/41.5M [00:00<00:02, 15.8MB/s] 15%|#5 | 6.33M/41.5M [00:00<00:01, 23.6MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 37.2MB/s] 43%|####3 | 17.9M/41.5M [00:00<00:00, 34.0MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 34.2MB/s] 76%|#######6 | 31.7M/41.5M [00:00<00:00, 42.9MB/s] 87%|########6 | 35.9M/41.5M [00:01<00:00, 38.8MB/s] 96%|#########5| 39.7M/41.5M [00:01<00:00, 37.3MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 34.0MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:01, 32.0MB/s] 23%|##2 | 9.39M/41.5M [00:00<00:01, 25.4MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 32.9MB/s] 43%|####2 | 17.7M/41.5M [00:00<00:00, 33.1MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 35.2MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 37.4MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 43.9MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 38.0MB/s]
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 8df75c9e33..a34c15c035 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -207,6 +207,11 @@ Look up prediction top 1 index in 1000 class synset.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 2.341 seconds)
+
+
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
.. only:: html
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 4ab9475f41..e5863edd41 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
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 74.0MB/s] 34%|###3 | 15.1M/44.7M [00:00<00:00, 66.3MB/s] 52%|#####1 | 23.0M/44.7M [00:00<00:00, 73.4MB/s] 69%|######8 | 30.6M/44.7M [00:00<00:00, 75.9MB/s] 90%|########9 | 40.0M/44.7M [00:00<00:00, 80.1MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 74.3MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 14%|#4 | 6.30M/44.7M [00:00<00:01, 36.5MB/s] 29%|##9 | 13.0M/44.7M [00:00<00:00, 51.2MB/s] 41%|#### | 18.3M/44.7M [00:00<00:00, 48.4MB/s] 52%|#####1 | 23.2M/44.7M [00:00<00:00, 35.0MB/s] 60%|###### | 27.0M/44.7M [00:00<00:00, 31.6MB/s] 85%|########4 | 37.9M/44.7M [00:00<00:00, 51.6MB/s] 98%|#########8| 43.8M/44.7M [00:01<00:00, 39.5MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 41.3MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 0a64e9ef15..6967f433cc 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 29.221 seconds)
+ **Total running time of the script:** ( 1 minutes 31.713 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 99e333eba0..1b87f8d3fb 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
@@ -5,24 +5,24 @@
Computation times
=================
-**06:31.195** total execution time for **how_to_compile_models** files:
+**06:43.320** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:34.245 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:36.054 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:29.221 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:31.713 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:58.971 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.341 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.074 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.336 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.392 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:38.453 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.637 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.680 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.337 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.568 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.539 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.331 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.779 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.845 | 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 8fd6e9fb6f..3c04bac413 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)
- 3990.0556 3989.0571 3998.5293 3987.5312 3.1488
+ 3997.5079 3996.3706 4015.0666 3990.9144 6.2842
@@ -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 16.859 seconds)
+ **Total running time of the script:** ( 1 minutes 18.260 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 f2bf966b3f..7896ea38b6 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
- 8192/102967424 [..............................] - ETA: 0s 7618560/102967424 [=>............................] - ETA: 0s 11796480/102967424 [==>...........................] - ETA: 0s 16769024/102967424 [===>..........................] - ETA: 0s 25157632/102967424 [======>.......................] - ETA: 0s 31031296/102967424 [========>.....................] - ETA: 0s 33546240/102967424 [========>.....................] - ETA: 0s 41934848/102967424 [===========>..................] - ETA: 0s
48578560/102967424 [=============>................] - ETA: 0s 57286656/102967424 [===============>..............] - ETA: 0s 62046208/102967424 [=================>............] - ETA: 0s 70541312/102967424 [===================>..........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 94806016/102967424 [==========================>...] -
ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 102883328/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 1s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 5521408/102967424 [>.............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 2s 15024128/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s
40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s 47439872/102967424 [============>.................] - ETA: 1s 50315264/102967424 [=============>................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 58712064/102967424 [================>.............] - ETA: 1s 65355776/102967424 [==================>...........] - ETA: 0s 67100672/102967424 [==================>...........] -
ETA: 0s 69296128/102967424 [===================>..........] - ETA: 0s 73744384/102967424 [====================>.........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424
[============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 3s 0us/step
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 6642d2e638..693a6051c5 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)
- 14.4100 14.3660 15.2845 14.0266 0.3226
+ 15.4868 15.6010 16.2016 14.8208 0.5304
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 922714a20e..f0cbd532f2 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
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 44.3MB/s] 8%|8 | 14.3M/170M [00:00<00:03, 51.1MB/s] 11%|#1 | 19.4M/170M [00:00<00:03, 47.0MB/s] 14%|#4 | 23.9M/170M [00:00<00:04, 34.8MB/s] 16%|#6 | 27.5M/170M [00:00<00:04, 34.2MB/s] 19%|#8 | 32.0M/170M [00:00<00:04, 30.8MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 39.5MB/s] 27%|##7 | 46.3M/170M [00:01<00:02, 44.5MB/s] 30%|##9 | 50.8M/170M [00:01<00:03, 39.1MB/s] 33%|###2 | 56.0M/170M [00:01<00:03, 36.2MB/s] 38%|###7 | 64.0M/170M [00:01<00:03, 36.7MB/s] 42%|####2 | 72.0M/170M [00:01<00:02, 41.0MB/s] 46%|####6 | 78.3M/170M [00:02<00:02, 36.6MB/s] 48%|####8 | 81.9M/170M [00:02<00:02, 35.5MB/s] 51%|##### | 86.3M/170M [00:02<00:02, 37.2MB/s] 53%|#####2 | 90.0M/170M [00:02<00:02, 37.0MB/s] 57%|#####6 | 96.0M/170M [00:02<00:02, 32.3MB/
s] 61%|######1 | 104M/170M [00:02<00:01, 35.5MB/s] 66%|######5 | 112M/170M [00:03<00:01, 45.0MB/s] 71%|####### | 120M/170M [00:03<00:01, 50.3MB/s] 75%|#######5 | 128M/170M [00:03<00:01, 43.5MB/s] 80%|######## | 136M/170M [00:03<00:00, 46.1MB/s] 85%|########4 | 144M/170M [00:03<00:00, 49.6MB/s] 88%|########8 | 150M/170M [00:03<00:00, 51.3MB/s] 92%|#########1| 155M/170M [00:03<00:00, 50.3MB/s] 94%|#########4| 160M/170M [00:04<00:00, 36.2MB/s] 98%|#########7| 166M/170M [00:04<00:00, 38.6MB/s] 100%|##########| 170M/170M [00:04<00:00, 40.5MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 4%|3 | 6.30M/170M [00:00<00:02, 62.4MB/s] 7%|7 | 12.3M/170M [00:00<00:03, 47.0MB/s] 10%|9 | 16.9M/170M [00:00<00:04, 32.3MB/s] 14%|#4 | 24.0M/170M [00:00<00:04, 34.5MB/s] 19%|#8 | 32.0M/170M [00:00<00:03, 40.4MB/s] 24%|##3 | 40.0M/170M [00:00<00:02, 49.1MB/s] 28%|##8 | 48.0M/170M [00:01<00:02, 45.6MB/s] 33%|###2 | 56.0M/170M [00:01<00:02, 49.7MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 49.9MB/s] 42%|####2 | 72.0M/170M [00:01<00:01, 53.5MB/s] 47%|####6 | 79.4M/170M [00:01<00:01, 59.0MB/s] 50%|##### | 85.4M/170M [00:01<00:01, 51.1MB/s] 53%|#####3 | 90.6M/170M [00:02<00:02, 37.3MB/s] 57%|#####6 | 96.0M/170M [00:02<00:02, 34.6MB/s] 60%|###### | 102M/170M [00:02<00:01, 38.2MB/s] 63%|######2 | 106M/170M [00:02<00:01, 36.4MB/s] 66%|######5 | 112M/170M [00:02<00:01, 41.1MB/s]
71%|####### | 120M/170M [00:02<00:01, 47.2MB/s] 74%|#######4 | 126M/170M [00:03<00:01, 44.3MB/s] 77%|#######6 | 131M/170M [00:03<00:01, 38.3MB/s] 80%|######## | 136M/170M [00:03<00:01, 34.9MB/s] 85%|########4 | 144M/170M [00:03<00:00, 41.1MB/s] 89%|########9 | 152M/170M [00:03<00:00, 40.6MB/s] 93%|#########3| 158M/170M [00:03<00:00, 37.7MB/s] 95%|#########5| 162M/170M [00:04<00:00, 34.5MB/s] 99%|#########8| 168M/170M [00:04<00:00, 37.5MB/s] 100%|##########| 170M/170M [00:04<00:00, 42.0MB/s]
/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).
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 26.433 seconds)
+ **Total running time of the script:** ( 3 minutes 36.601 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 84157387ff..77cb1a5763 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
- 0%| | 0.00/13.6M [00:00<?, ?B/s] 59%|#####8 | 7.99M/13.6M [00:00<00:00, 53.9MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 63.9MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 47%|####6 | 6.30M/13.6M [00:00<00:00, 22.6MB/s] 62%|######2 | 8.46M/13.6M [00:00<00:00, 16.9MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 19.0MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 21.1MB/s]
@@ -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)
- 86.1678 86.1296 87.5604 85.7649 0.2445
+ 86.1570 86.0927 86.6830 85.9208 0.1804
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.944 seconds)
+ **Total running time of the script:** ( 1 minutes 29.839 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 d3982147d6..54174be29c 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)
- 106.9944 106.9412 112.0087 105.6538 0.6166
+ 109.1151 109.0472 109.9507 108.4506 0.3736
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 26f727a2d3..98cd6bc479 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
@@ -5,24 +5,24 @@
Computation times
=================
-**09:31.786** total execution time for **how_to_deploy_models** files:
+**09:56.614** 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``) | 03:26.433 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:36.601 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:25.944 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.839 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:16.859 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.260 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.226 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:51.933 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:49.640 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.895 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.913 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:48.308 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.012 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.918 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.751 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.850 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.010 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
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 342deb71c4..7e884bafe4 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
@@ -5,12 +5,12 @@
Computation times
=================
-**00:04.054** total execution time for **how_to_extend_tvm** files:
+**00:04.042** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.857 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.808 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.190 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.227 | 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 |
+-------------------------------------------------------------------------------------------+-----------+--------+
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 d8ee0dde7b..f3fed8a60f 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: 25280us [25280us] (48.53%; 48.53%)
- FoldScaleAxis: 26811us [7us] (51.47%; 51.47%)
- FoldConstant: 26804us [1792us] (51.46%; 99.97%)
- InferType: 25012us [25012us] (48.02%; 93.31%)
+ InferType: 24834us [24834us] (48.40%; 48.40%)
+ FoldScaleAxis: 26480us [9us] (51.60%; 51.60%)
+ FoldConstant: 26471us [1820us] (51.59%; 99.97%)
+ InferType: 24651us [24651us] (48.04%; 93.13%)
@@ -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: 25097us [25097us] (48.47%; 48.47%)
- FoldScaleAxis: 26676us [7us] (51.53%; 51.53%)
- FoldConstant: 26669us [1840us] (51.51%; 99.97%)
- InferType: 24829us [24829us] (47.96%; 93.10%)
+ InferType: 24637us [24637us] (47.90%; 47.90%)
+ FoldScaleAxis: 26795us [7us] (52.10%; 52.10%)
+ FoldConstant: 26788us [1825us] (52.08%; 99.97%)
+ InferType: 24963us [24963us] (48.53%; 93.19%)
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 2d281ac5b8..c70e2d3d4a 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: 53.549087 ms
+ Convolution: 53.525470 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 35d5dac521..9c29fa270f 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: 11.068355 ms
+ conv2d with tensor core: 12.076163 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 3cd12f2a66..3e01306a1b 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: 0.014256
- Baseline: 3.411588
+ Numpy running time: 0.017821
+ Baseline: 3.272619
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.280367
+ Opt1: 0.294679
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.271664
+ Opt2: 0.280125
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.112447
+ Opt3: 0.114635
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.104239
+ Opt4: 0.109832
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.098505
+ Opt5: 0.108423
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.116369
+ Opt6: 0.128806
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 60814e49e2..7e2325b095 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
=================
-**00:32.109** total execution time for **how_to_optimize_operators** files:
+**00:33.330** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:29.082 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.197 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.763 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.856 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.264 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.276 | 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 3ad49e8bfc..c65dc13fdb 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
=================
-**03:30.335** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:36.713** 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``) | 01:31.495 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:34.674 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:10.868 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:12.054 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:17.278 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:17.785 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.852 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.637 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.743 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.462 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.099 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.100 | 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 e4320ab4cc..d85b3f249a 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
@@ -766,7 +766,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.337 ms
+ Execution time of this operator: 0.334 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 7b36b1f053..441b379df3 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
@@ -633,7 +633,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.2118 3.2109 3.2141 3.2104 0.0016
+ 3.1978 3.1977 3.1989 3.1968 0.0009
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 10.868 seconds)
+ **Total running time of the script:** ( 1 minutes 12.054 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 289db6b2d2..071b9fb1e8 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
@@ -655,7 +655,7 @@ 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)
- 722.2385 722.5255 724.6662 719.5238 2.1092
+ 733.1481 733.2826 733.9315 732.2301 0.7011
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 31.495 seconds)
+ **Total running time of the script:** ( 1 minutes 34.674 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 1f0356b4f1..4d7bbdd21d 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
=================
-**00:22.696** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.466** 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``) | 00:22.664 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.430 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.017 | 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_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 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 367f59d666..4f83e7047e 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: 0.037088
+ Time cost of this operator: 0.036944
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 1906a62c4f..11741ca586 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 302.6 98.715 (1, 2, 10, 10, 3) 2 1 [302.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.962 0.966 (1, 6, 10, 10) 1 1 [2.962]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.319 (1, 1, 10, 10, 3) 1 1 [0.978]
- Total_time - 306.54 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 295.8 98.705 (1, 2, 10, 10, 3) 2 1 [295.8]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.955 0.986 (1, 6, 10, 10) 1 1 [2.955]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.925 0.309 (1, 1, 10, 10, 3) 1 1 [0.925]
+ Total_time - 299.68 - - - - -
@@ -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 104.9 97.559 (1, 6, 10, 10, 1) 2 1 [104.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.777 1.652 (1, 6, 10, 10) 1 1 [1.777]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.848 0.789 (1, 3, 10, 10, 1) 1 1 [0.848]
- Total_time - 107.525 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.875 97.55 (1, 6, 10, 10, 1) 2 1 [100.875]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.719 1.662 (1, 6, 10, 10) 1 1 [1.719]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.815 0.788 (1, 3, 10, 10, 1) 1 1 [0.815]
+ Total_time - 103.409 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.424 seconds)
+ **Total running time of the script:** ( 1 minutes 24.864 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 52903d3cac..26354b4f7c 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] 61%|###### | 2.09M/3.42M [00:00<00:00, 11.9MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 18.7MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 12.2MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 19.1MB/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 26.353 seconds)
+ **Total running time of the script:** ( 1 minutes 28.700 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 b1fe36184f..5c6c3bc55e 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/tmppv7imotm/images/random'
+ '/tmp/tmpyf3y9ll5/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmppv7imotm/images/target contains 8144 images
- /tmp/tmppv7imotm/images/random contains 5000 images
+ /tmp/tmpyf3y9ll5/images/target contains 8144 images
+ /tmp/tmpyf3y9ll5/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 39s - loss: 0.2166 - accuracy: 0.9249 - val_loss: 0.1258 - val_accuracy: 0.9554 - 39s/epoch - 118ms/step
+ 328/328 - 39s - loss: 0.2237 - accuracy: 0.9241 - val_loss: 0.1079 - val_accuracy: 0.9622 - 39s/epoch - 120ms/step
Epoch 2/3
- 328/328 - 34s - loss: 0.0964 - accuracy: 0.9662 - val_loss: 0.1384 - val_accuracy: 0.9498 - 34s/epoch - 105ms/step
+ 328/328 - 35s - loss: 0.1036 - accuracy: 0.9622 - val_loss: 0.1080 - val_accuracy: 0.9619 - 35s/epoch - 105ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0661 - accuracy: 0.9753 - val_loss: 0.1024 - val_accuracy: 0.9653 - 35s/epoch - 105ms/step
+ 328/328 - 34s - loss: 0.0649 - accuracy: 0.9763 - val_loss: 0.1127 - val_accuracy: 0.9626 - 34s/epoch - 104ms/step
- <keras.callbacks.History object at 0x7f2295556e20>
+ <keras.callbacks.History object at 0x7f4848b8e6d0>
@@ -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 46.713 seconds)
+ **Total running time of the script:** ( 4 minutes 49.455 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 08ec3238da..1f3648b741 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
=================
-**08:08.371** total execution time for **how_to_work_with_microtvm** files:
+**08:11.391** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:46.713 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:49.455 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.424 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:28.700 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:26.353 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:24.864 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.701 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.974 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:09.123 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.951 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.057 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.447 | 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 71bedc804c..d228930207 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
=================
-**00:37.575** total execution time for **how_to_work_with_relay** files:
+**00:38.317** 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``) | 00:32.555 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.265 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.128 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.178 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.884 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.868 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.007 | 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 960020b438..0c8b80cf5a 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
@@ -281,7 +281,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f2299d63550>
+ <function my_cuda_math_rule at 0x7f4865992b80>
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 9b2035e717..2d325c461c 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
=================
-**00:05.468** total execution time for **how_to_work_with_schedules** files:
+**00:05.381** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.449 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.500 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.342 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.173 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.706 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.713 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.685 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.701 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.121 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.124 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.069 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.071 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.066 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.067 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
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 13f8889af2..4e75187304 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 26.74s!
+ yolov3-tiny inference graph built in 26.89s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 0.381 seconds)
+ **Total running time of the script:** ( 1 minutes 0.020 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 bc41656364..96213b4374 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
@@ -5,8 +5,8 @@
Computation times
=================
-**01:00.381** total execution time for **topic_vta_tutorials_frontend** files:
+**01:00.020** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:00.381 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:00.020 | 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 44883f3c28..46a9bc4204 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
=================
-**00:03.272** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.192** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.785 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.706 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.487 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.486 | 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 66f49ee5e5..e7bcc29579 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
=================
-**00:00.791** total execution time for **topic_vta_tutorials** files:
+**00:00.794** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.412 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.379 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.382 | 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 dba3b383dc..e778079e28 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,13 +207,6 @@ trials, we can load the best schedule from the log file and apply it.
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- *E
-
-
@@ -325,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 90.137 ms
+ Execution time of this operator: 90.640 ms
@@ -423,7 +416,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
- *E
+
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 45.176 seconds)
+ **Total running time of the script:** ( 1 minutes 18.141 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 8cc77cd0c6..1a82d25f7b 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: 12.97/12.97 result: MeasureResult(costs=(0.0206897024,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6065568923950195, timestamp=1708711204.2834284) [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
- No: 2 GFLOPS: 8.85/12.97 result: MeasureResult(costs=(0.030322223400000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7664384841918945, timestamp=1708711205.025699) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
- No: 3 GFLOPS: 11.74/12.97 result: MeasureResult(costs=(0.0228620754,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6322653293609619, timestamp=1708711205.654316) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
- No: 4 GFLOPS: 10.41/12.97 result: MeasureResult(costs=(0.025780368600000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6807208061218262, timestamp=1708711206.3231006) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
- No: 5 GFLOPS: 7.60/12.97 result: MeasureResult(costs=(0.0353249162,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8130064010620117, timestamp=1708711207.2685528) [('tile_y', [-1, 1]), ('tile_x', [-1, 8])],None,30
- No: 6 GFLOPS: 15.72/15.72 result: MeasureResult(costs=(0.0170793576,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.541215181350708, timestamp=1708711207.8074367) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 7 GFLOPS: 12.14/15.72 result: MeasureResult(costs=(0.02210255,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6154811382293701, timestamp=1708711208.4237812) [('tile_y', [-1, 128]), ('tile_x', [-1, 512])],None,97
- No: 8 GFLOPS: 3.76/15.72 result: MeasureResult(costs=(0.07138183940000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.403984785079956, timestamp=1708711209.8256555) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
- No: 9 GFLOPS: 8.21/15.72 result: MeasureResult(costs=(0.0326766628,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7399241924285889, timestamp=1708711210.6809258) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
- No: 10 GFLOPS: 1.06/15.72 result: MeasureResult(costs=(0.25376674920000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.292267322540283, timestamp=1708711215.0028558) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+ No: 1 GFLOPS: 1.33/1.33 result: MeasureResult(costs=(0.2013247616,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.468808889389038, timestamp=1708714842.0373058) [('tile_y', [-1, 2]), ('tile_x', [-1, 1])],None,1
+ No: 2 GFLOPS: 10.90/10.90 result: MeasureResult(costs=(0.0246285358,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6603114604949951, timestamp=1708714842.6921093) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
+ No: 3 GFLOPS: 9.42/10.90 result: MeasureResult(costs=(0.028484055599999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7727892398834229, timestamp=1708714843.409493) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
+ No: 4 GFLOPS: 3.73/10.90 result: MeasureResult(costs=(0.071988223,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4212782382965088, timestamp=1708714844.8193448) [('tile_y', [-1, 256]), ('tile_x', [-1, 8])],None,38
+ No: 5 GFLOPS: 10.77/10.90 result: MeasureResult(costs=(0.024921979200000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6991176605224609, timestamp=1708714845.6331995) [('tile_y', [-1, 4]), ('tile_x', [-1, 128])],None,72
+ No: 6 GFLOPS: 10.95/10.95 result: MeasureResult(costs=(0.024513937399999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6674833297729492, timestamp=1708714846.2902129) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 7 GFLOPS: 3.75/10.95 result: MeasureResult(costs=(0.0715921416,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4155042171478271, timestamp=1708714847.6954935) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 8 GFLOPS: 12.16/12.16 result: MeasureResult(costs=(0.022084209799999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6331312656402588, timestamp=1708714848.3085713) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
+ No: 9 GFLOPS: 1.63/12.16 result: MeasureResult(costs=(0.1648254654,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.8599839210510254, timestamp=1708714851.2793043) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
+ No: 10 GFLOPS: 11.56/12.16 result: MeasureResult(costs=(0.02322755,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6039812564849854, timestamp=1708714851.9130669) [('tile_y', [-1, 16]), ('tile_x', [-1, 256])],None,84
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 94b5089902..afa08cec4b 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': 500.5685418899884, 'median': 501.0803453499648, 'std': 3.744422612045327}
+ {'mean': 477.34598311997615, 'median': 476.0032203499577, 'std': 3.6898097033551043}
@@ -582,31 +582,31 @@ the tuning data to.
.. code-block:: none
- [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 11.63/ 12.77 GFLOPS | Progress: (4/20) | 9.58 s [Task 1/25] Current/Best: 12.07/ 17.88 GFLOPS | Progress: (8/20) | 12.89 s [Task 1/25] Current/Best: 16.16/ 22.63 GFLOPS | Progress: (12/20) | 14.94 s [Task 1/25] Current/Best: 8.10/ 22.63 GFLOPS | Progress: (16/20) | 17.72 s [Task 1/25] Current/Best: 22.98/ 22.98 GFLOPS | Progress: (20/20) | 19.99 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 14.55/ 17.32 GFLOPS | Progress: (4/20) | 4.10 s [Task 2/25] Current/Best: 12.80/ 20.92 GFLOPS | Progress: (8/20) | 6.80 s [Task 2/25] Current/Best: 15.75/ 20.92 GFLOPS | Progress: (12/20) | 8.80 s [Task 2/25] Current/Best: 17.76/ 20.92 GFLOPS | Progress: (16/20) | 10.21 s [Task 2/25] Current/Best: 6.63/ 20.92 GFLOPS | Progress: (20/20) | 12.31 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 13.50/ 21.68 GFLOPS | Progress: (4/20) | 6.03 s [Task 3/25] Current/Best: 14.29/ 21.68 GFLOPS | Progress: (8/20) | 8.84 s [Task 3/25] Current/Best: 9.75/ 21.68 GFLOPS | Progress: (12/20) | 11.23 s [Task 3/25] Current/Best: 10.03/ 21.68 GFLOPS | Progress: (16/20) | 13.38 s [Task 3/25] Current/Best: 17.44/ 21.68 GFLOPS | Progress: (20/20) | 15.47 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 18.13/ 18.13 GFLOPS | Progress: (4/20) | 6.49 s [Task 4/25] Current/Best: 12.61/ 18.13 GFLOPS | Progress: (8/20) | 8.39 s [Task 4/25] Current/Best: 6.27/ 18.49 GFLOPS | Progress: (12/20) | 14.49 s [Task 4/25] Current/Best: 17.05/ 18.49 GFLOPS | Progress: (16/20) | 16.44 s [Task 4/25] Current/Best: 5.91/ 19.54 GFLOPS | Progress: (20/20) | 22.61 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 20.56/ 23.22 GFLOPS | Progress: (4/20) | 4.20 s [Task 5/25] Current/Best: 12.51/ 23.22 GFLOPS | Progress: (8/20) | 7.29 s [Task 5/25] Current/Best: 10.62/ 23.22 GFLOPS | Progress: (12/20) | 9.64 s [Task 5/25] Current/Best: 8.36/ 23.22 GFLOPS | Progress: (16/20) | 11.79 s [Task 5/25] Current/Best: 10.74/ 23.22 GFLOPS | Progress: (20/20) | 14.66 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 4.14/ 13.77 GFLOPS | Progress: (4/20) | 5.67 s [Task 6/25] Current/Best: 5.86/ 14.58 GFLOPS | Progress: (8/20) | 8.45 s [Task 6/25] Current/Best: 4.50/ 14.58 GFLOPS | Progress: (12/20) | 11.77 s [Task 6/25] Current/Best: 16.77/ 16.77 GFLOPS | Progress: (16/20) | 14.88 s [Task 6/25] Current/Best: 14.94/ 16.77 GFLOPS | Progress: (20/20) | 17.33 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 15.78/ 15.78 GFLOPS | Progress: (4/20) | 5.63 s [Task 7/25] Current/Best: 19.57/ 19.57 GFLOPS | Progress: (8/20) | 8.63 s [Task 7/25] Current/Best: 18.22/ 19.57 GFLOPS | Progress: (12/20) | 11.31 s [Task 7/25] Current/Best: 8.15/ 19.57 GFLOPS | Progress: (16/20) | 13.90 s [Task 7/25] Current/Best: 17.38/ 19.97 GFLOPS | Progress: (20/20) | 16.09 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 13.03/ 13.48 GFLOPS | Progress: (4/20) | 13.96 s [Task 8/25] Current/Best: 12.19/ 21.66 GFLOPS | Progress: (8/20) | 17.31 s [Task 8/25] Current/Best: 3.96/ 21.66 GFLOPS | Progress: (12/20) | 24.23 s [Task 8/25] Current/Best: 18.79/ 21.66 GFLOPS | Progress: (16/20) | 32.23 s [Task 8/25] Current/Best: 8.49/ 21.66 GFLOPS | Progress: (20/20) | 40.80 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 13.22/ 14.56 GFLOPS | Progress: (4/20) | 6.00 s [Task 9/25] Current/Best: 12.73/ 17.78 GFLOPS | Progress: (8/20) | 8.26 s [Task 9/25] Current/Best: 12.23/ 17.78 GFLOPS | Progress: (12/20) | 11.22 s [Task 9/25] Current/Best: 6.26/ 17.78 GFLOPS | Progress: (16/20) | 13.10 s [Task 9/25] Current/Best: 7.09/ 17.78 GFLOPS | Progress: (20/
20) | 17.45 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 2.62/ 17.06 GFLOPS | Progress: (4/20) | 4.90 s [Task 10/25] Current/Best: 14.05/ 17.06 GFLOPS | Progress: (8/20) | 8.38 s [Task 10/25] Current/Best: 13.37/ 20.28 GFLOPS | Progress: (12/20) | 11.09 s [Task 10/25] Current/Best: 16.64/ 20.28 GFLOPS | Progress: (16/20) | 13.05 s [Task 10/25] Current/Best: 15.52/ 20.28 GFLOPS | Progress: (20/20) | 15.07 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 8.44/ 19.51 GFLOPS | Progress: (4/20) | 5.13 s [Task 11/25] Current/Best: 17.36/ 19.51 GFLOPS | Progress: (8/20) | 7.49 s [Task 11/25] Current/Best: 10.11/ 19.51 GFLOPS | Progress: (12/20) | 9.87 s [Task 11/25] Current/Best: 20.23/ 20.23 GFLOPS | Progress: (16/20) | 11.93 s [Task 11/25] Current/Best: 11.93/ 20.23 GFLOPS | Progress: (20/20) | 14.64 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 14.73/ 17.85 GFLOPS | Progress: (4/20) | 4.79 s [Task 12/25] Current/Best: 12.64/ 22.15 GFLOPS | Progress: (8/20) | 6.70 s [Task 12/25] Current/Best: 14.97/ 22.15 GFLOPS | Progress: (12/20) | 14.47 s [Task 12/25] Current/Best: 14.04/ 22.15 GFLOPS | Progress: (16/20) | 17.25 s [Task 12/25] Current/Best: 6.83/ 22.15 GFLOPS | Progress: (20/20) | 19.85 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (4/20) | 5.20 s [Task 13/25] Current/Best: 19.57/ 19.57 GFLOPS | Progress: (8/20) | 8.82 s [Task 13/25] Current/Best: 11.39/ 19.57 GFLOPS | Progress: (12/20) | 13.73 s [Task 13/25] Current/Best: 18.74/ 19.57 GFLOPS | Progress: (16/20) | 16.29 s [Task 13/25] Current/Best: 12.57/ 19.81 GFLOPS | Progress: (20/20) | 19.19 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 11.12/ 13.15 GFLOPS | Progress: (4/20) | 6.41 s [Task 14/25] Current/Best: 16.36/ 16.36 GFLOPS | Progress: (8/20) | 9.81 s [Task 14/25] Current/Best: 6.05/ 16.36 GFLOPS | Progress: (12/20) | 15.55 s [Task 14/25] Current/Best: 13.17/ 16.36 GFLOPS | Progress: (16/20) | 22.33 s [Task 14/25] Current/Best: 14.50/ 16.36 GFLOPS | Progress: (20/20) | 25.23 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 11.52/ 17.77 GFLOPS | Progress: (4/20) | 6.85 s [Task 15/25] Current/Best: 15.04/ 17.77 GFLOPS | Progress: (8/20) | 9.26 s [Task 15/25] Current/Best: 13.84/ 18.63 GFLOPS | Progress: (12/20) | 20.30 s [Task 15/25] Current/Best: 9.92/ 20.96 GFLOPS | Progress: (16/20) | 31.45 s [Task 15/25] Current/Best: 6.63/ 21.25 GFLOPS | Progress: (20/20) | 41.20 s [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- Done.
- [Task 16/25] Current/Best: 5.48/ 15.76 GFLOPS | Progress: (4/20) | 4.90 s [Task 16/25] Current/Best: 11.94/ 15.76 GFLOPS | Progress: (8/20) | 6.71 s [Task 16/25] Current/Best: 14.87/ 18.60 GFLOPS | Progress: (12/20) | 8.29 s [Task 16/25] Current/Best: 14.50/ 22.43 GFLOPS | Progress: (16/20) | 11.44 s [Task 16/25] Current/Best: 9.61/ 22.43 GFLOPS | Progress: (20/20) | 14.03 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 7.39/ 19.88 GFLOPS | Progress: (4/20) | 6.57 s [Task 17/25] Current/Best: 9.39/ 23.01 GFLOPS | Progress: (8/20) | 9.74 s [Task 17/25] Current/Best: 19.63/ 23.01 GFLOPS | Progress: (12/20) | 11.86 s [Task 17/25] Current/Best: 5.34/ 23.01 GFLOPS | Progress: (16/20) | 14.37 s [Task 17/25] Current/Best: 11.98/ 23.01 GFLOPS | Progress: (20/20) | 17.31 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 5.88/ 18.64 GFLOPS | Progress: (4/20) | 5.42 s [Task 18/25] Current/Best: 12.54/ 18.64 GFLOPS | Progress: (8/20) | 11.48 s [Task 18/25] Current/Best: 11.23/ 18.64 GFLOPS | Progress: (12/20) | 14.15 s [Task 18/25] Current/Best: 13.56/ 18.84 GFLOPS | Progress: (16/20) | 17.67 s [Task 18/25] Current/Best: 10.14/ 18.84 GFLOPS | Progress: (20/20) | 23.37 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 10.74/ 12.01 GFLOPS | Progress: (4/20) | 6.44 s [Task 19/25] Current/Best: 10.45/ 22.30 GFLOPS | Progress: (8/20) | 10.25 s [Task 19/25] Current/Best: 17.07/ 22.30 GFLOPS | Progress: (12/20) | 13.04 s [Task 19/25] Current/Best: 9.71/ 22.30 GFLOPS | Progress: (16/20) | 17.72 s [Task 19/25] Current/Best: 7.96/ 22.30 GFLOPS | Progress: (20/20) | 20.94 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 16.77/ 18.79 GFLOPS | Progress: (4/20) | 5.11 s [Task 20/25] Current/Best: 4.64/ 18.79 GFLOPS | Progress: (8/20) | 10.40 s [Task 20/25] Current/Best: 11.85/ 18.79 GFLOPS | Progress: (12/20) | 14.92 s [Task 20/25] Current/Best: 11.91/ 18.79 GFLOPS | Progress: (16/20) | 18.06 s [Task 20/25] Current/Best: 6.69/ 18.79 GFLOPS | Progress: (20/20) | 20.77 s Done.
- [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 8.65/ 12.67 GFLOPS | Progress: (4/20) | 13.44 s [Task 21/25] Current/Best: 21.42/ 21.42 GFLOPS | Progress: (8/20) | 23.34 s [Task 21/25] Current/Best: 9.69/ 21.65 GFLOPS | Progress: (12/20) | 25.77 s [Task 21/25] Current/Best: 2.82/ 23.48 GFLOPS | Progress: (16/20) | 32.56 s [Task 21/25] Current/Best: 7.86/ 23.48 GFLOPS | Progress: (20/20) | 43.79 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 2.79/ 15.30 GFLOPS | Progress: (4/20) | 5.60 s [Task 22/25] Current/Best: 10.32/ 19.07 GFLOPS | Progress: (8/20) | 7.56 s [Task 22/25] Current/Best: 18.67/ 20.59 GFLOPS | Progress: (12/20) | 10.40 s [Task 22/25] Current/Best: 6.35/ 20.59 GFLOPS | Progress: (16/20) | 12.75 s [Task 22/25] Current/Best: 18.13/ 20.59 GFLOPS | Progress: (20/
20) | 15.21 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 14.60/ 22.57 GFLOPS | Progress: (4/20) | 5.49 s [Task 23/25] Current/Best: 17.89/ 22.57 GFLOPS | Progress: (8/20) | 11.17 s [Task 23/25] Current/Best: 22.07/ 22.57 GFLOPS | Progress: (12/20) | 14.11 s [Task 23/25] Current/Best: 10.67/ 22.57 GFLOPS | Progress: (16/20) | 17.51 s [Task 23/25] Current/Best: 5.48/ 22.57 GFLOPS | Progress: (20/20) | 20.55 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 2.32/ 5.71 GFLOPS | Progress: (4/20) | 13.52 s [Task 24/25] Current/Best: 3.20/ 5.71 GFLOPS | Progress: (8/20) | 24.24 s [Task 24/25] Current/Best: 0.87/ 5.71 GFLOPS | Progress: (12/20) | 35.32 s [Task 24/25] Current/Best: 4.19/ 6.13 GFLOPS | Progress: (16/20) | 46.29 s [Task 24/25] Current/Best: 10.78/ 10.78 GFLOPS | Progress: (20/20) | 56.55 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- Done.
- [Task 25/25] Current/Best: 6.36/ 7.78 GFLOPS | Progress: (4/20) | 9.53 s [Task 25/25] Current/Best: 2.78/ 7.78 GFLOPS | Progress: (8/20) | 12.70 s [Task 25/25] Current/Best: 7.74/ 9.42 GFLOPS | Progress: (12/20) | 23.64 s [Task 25/25] Current/Best: 7.49/ 9.42 GFLOPS | Progress: (16/20) | 34.66 s [Task 25/25] Current/Best: 5.69/ 9.42 GFLOPS | Progress: (20/20) | 37.59 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 6.59/ 22.22 GFLOPS | Progress: (4/20) | 13.23 s [Task 1/25] Current/Best: 12.63/ 22.22 GFLOPS | Progress: (8/20) | 15.93 s [Task 1/25] Current/Best: 20.10/ 22.67 GFLOPS | Progress: (12/20) | 18.39 s [Task 1/25] Current/Best: 9.04/ 22.67 GFLOPS | Progress: (16/20) | 20.80 s [Task 1/25] Current/Best: 7.38/ 22.67 GFLOPS | Progress: (20/20) | 23.52 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 7.22/ 20.66 GFLOPS | Progress: (4/20) | 3.94 s [Task 2/25] Current/Best: 20.45/ 20.66 GFLOPS | Progress: (8/20) | 5.57 s [Task 2/25] Current/Best: 19.58/ 20.66 GFLOPS | Progress: (12/20) | 7.02 s [Task 2/25] Current/Best: 13.24/ 20.66 GFLOPS | Progress: (16/20) | 8.72 s [Task 2/25] Current/Best: 16.30/ 20.66 GFLOPS | Progress: (20/20) | 10.18 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 10.84/ 20.64 GFLOPS | Progress: (4/20) | 4.68 s [Task 3/25] Current/Best: 14.96/ 20.64 GFLOPS | Progress: (8/20) | 7.39 s [Task 3/25] Current/Best: 7.49/ 23.11 GFLOPS | Progress: (12/20) | 10.10 s [Task 3/25] Current/Best: 14.53/ 23.11 GFLOPS | Progress: (16/20) | 12.53 s [Task 3/25] Current/Best: 23.51/ 23.51 GFLOPS | Progress: (20/20) | 14.59 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 8.50/ 12.30 GFLOPS | Progress: (4/20) | 7.16 s [Task 4/25] Current/Best: 3.96/ 17.01 GFLOPS | Progress: (8/20) | 9.32 s [Task 4/25] Current/Best: 7.79/ 17.01 GFLOPS | Progress: (12/20) | 11.53 s [Task 4/25] Current/Best: 8.26/ 17.43 GFLOPS | Progress: (16/20) | 19.15 s [Task 4/25] Current/Best: 7.81/ 17.43 GFLOPS | Progress: (20/20) | 22.39 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 3.72/ 8.82 GFLOPS | Progress: (4/20) | 5.23 s [Task 5/25] Current/Best: 13.82/ 15.19 GFLOPS | Progress: (8/20) | 7.36 s [Task 5/25] Current/Best: 3.35/ 15.58 GFLOPS | Progress: (12/20) | 9.59 s [Task 5/25] Current/Best: 15.88/ 15.88 GFLOPS | Progress: (16/20) | 11.68 s [Task 5/25] Current/Best: 12.13/ 18.26 GFLOPS | Progress: (20/20) | 13.58 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 5.96/ 15.62 GFLOPS | Progress: (4/20) | 7.88 s [Task 6/25] Current/Best: 16.46/ 18.09 GFLOPS | Progress: (8/20) | 12.24 s [Task 6/25] Current/Best: 11.46/ 18.09 GFLOPS | Progress: (12/20) | 15.00 s [Task 6/25] Current/Best: 14.77/ 18.09 GFLOPS | Progress: (16/20) | 17.89 s [Task 6/25] Current/Best: 16.51/ 18.09 GFLOPS | Progress: (20/20) | 21.27 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 18.59/ 18.59 GFLOPS | Progress: (4/20) | 5.12 s [Task 7/25] Current/Best: 5.79/ 20.27 GFLOPS | Progress: (8/20) | 7.89 s [Task 7/25] Current/Best: 13.52/ 22.10 GFLOPS | Progress: (12/20) | 10.00 s [Task 7/25] Current/Best: 6.06/ 22.10 GFLOPS | Progress: (16/20) | 12.78 s [Task 7/25] Current/Best: 9.02/ 22.10 GFLOPS | Progress: (20/20) | 15.23 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 12.86/ 22.33 GFLOPS | Progress: (4/20) | 4.85 s [Task 8/25] Current/Best: 3.16/ 22.33 GFLOPS | Progress: (8/20) | 7.95 s [Task 8/25] Current/Best: 13.18/ 22.33 GFLOPS | Progress: (12/20) | 15.79 s [Task 8/25] Current/Best: 13.75/ 22.33 GFLOPS | Progress: (16/20) | 18.51 s [Task 8/25] Current/Best: 13.68/ 22.33 GFLOPS | Progress: (20/20) | 20.93 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 7.91/ 16.29 GFLOPS | Progress: (4/20) | 4.55 s [Task 9/25] Current/Best: 9.83/ 20.44 GFLOPS | Progress: (8/20) | 12.55 s [Task 9/25] Current/Best: 15.56/ 20.44 GFLOPS | Progress: (12/20) | 17.53 s [Task 9/25] Current/Best: 18.08/ 24.16 GFLOPS | Progress: (16/20) | 23.18 s [Task 9/25] Current/Best: 15.04/ 24.16 GFLOPS | Progress: (20/20) | 28.48 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 5.73/ 20.55 GFLOPS | Progress: (4/20) | 4.52 s [Task 10/25] Current/Best: 20.98/ 20.98 GFLOPS | Progress: (8/20) | 7.76 s [Task 10/25] Current/Best: 16.52/ 20.98 GFLOPS | Progress: (12/20) | 9.80 s [Task 10/25] Current/Best: 18.59/ 20.98 GFLOPS | Progress: (16/20) | 11.94 s [Task 10/25] Current/Best: 6.02/ 20.98 GFLOPS | Progress: (20/20) | 14.12 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 12.40/ 23.07 GFLOPS | Progress: (4/20) | 4.70 s [Task 11/25] Current/Best: 16.08/ 23.07 GFLOPS | Progress: (8/20) | 8.18 s [Task 11/25] Current/Best: 14.36/ 23.07 GFLOPS | Progress: (12/20) | 10.42 s [Task 11/25] Current/Best: 3.20/ 23.07 GFLOPS | Progress: (16/20) | 13.34 s [Task 11/25] Current/Best: 3.09/ 23.07 GFLOPS | Progress: (20/20) | 16.14 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 9.55/ 14.36 GFLOPS | Progress: (4/20) | 5.64 s [Task 12/25] Current/Best: 11.16/ 14.60 GFLOPS | Progress: (8/20) | 9.59 s [Task 12/25] Current/Best: 12.43/ 14.60 GFLOPS | Progress: (12/20) | 12.27 s [Task 12/25] Current/Best: 13.58/ 14.60 GFLOPS | Progress: (16/20) | 16.05 s [Task 12/25] Current/Best: 19.16/ 19.16 GFLOPS | Progress: (20/20) | 20.11 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 12.54/ 16.35 GFLOPS | Progress: (4/20) | 5.04 s [Task 13/25] Current/Best: 1.62/ 22.69 GFLOPS | Progress: (8/20) | 8.87 s [Task 13/25] Current/Best: 19.85/ 22.69 GFLOPS | Progress: (12/20) | 11.31 s [Task 13/25] Current/Best: 16.68/ 22.69 GFLOPS | Progress: (16/20) | 14.01 s [Task 13/25] Current/Best: 9.44/ 22.69 GFLOPS | Progress: (20/20) | 17.07 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 12.47/ 12.86 GFLOPS | Progress: (4/20) | 4.85 s [Task 14/25] Current/Best: 4.87/ 13.20 GFLOPS | Progress: (8/20) | 12.27 s [Task 14/25] Current/Best: 14.56/ 14.56 GFLOPS | Progress: (12/20) | 19.29 s [Task 14/25] Current/Best: 14.07/ 17.41 GFLOPS | Progress: (16/20) | 22.90 s [Task 14/25] Current/Best: 8.97/ 20.11 GFLOPS | Progress: (20/20) | 31.20 s Done.
+ [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 19.20/ 19.20 GFLOPS | Progress: (4/20) | 4.38 s [Task 15/25] Current/Best: 8.96/ 19.20 GFLOPS | Progress: (8/20) | 12.06 s [Task 15/25] Current/Best: 20.82/ 20.82 GFLOPS | Progress: (12/20) | 13.68 s [Task 15/25] Current/Best: 5.00/ 20.82 GFLOPS | Progress: (16/20) | 16.99 s [Task 15/25] Current/Best: 12.83/ 20.82 GFLOPS | Progress: (20/20) | 19.83 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 16.24/ 21.29 GFLOPS | Progress: (4/20) | 4.81 s [Task 16/25] Current/Best: 15.26/ 21.29 GFLOPS | Progress: (8/20) | 7.57 s [Task 16/25] Current/Best: 17.51/ 21.29 GFLOPS | Progress: (12/20) | 9.13 s [Task 16/25] Current/Best: 10.21/ 21.29 GFLOPS | Progress: (16/20) | 12.41 s [Task 16/25] Current/Best: 15.15/ 21.29 GFLOPS | Progress: (20/20) | 14.43 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 20.93/ 20.93 GFLOPS | Progress: (4/20) | 5.22 s [Task 17/25] Current/Best: 9.98/ 20.93 GFLOPS | Progress: (8/20) | 8.02 s [Task 17/25] Current/Best: 9.60/ 20.93 GFLOPS | Progress: (12/20) | 12.05 s [Task 17/25] Current/Best: 9.49/ 23.85 GFLOPS | Progress: (16/20) | 14.35 s [Task 17/25] Current/Best: 10.10/ 23.85 GFLOPS | Progress: (20/20) | 18.53 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 19.44/ 19.44 GFLOPS | Progress: (4/20) | 5.83 s [Task 18/25] Current/Best: 18.21/ 22.32 GFLOPS | Progress: (8/20) | 8.03 s [Task 18/25] Current/Best: 13.79/ 22.32 GFLOPS | Progress: (12/20) | 11.30 s [Task 18/25] Current/Best: 19.00/ 22.32 GFLOPS | Progress: (16/20) | 13.52 s [Task 18/25] Current/Best: 12.54/ 22.32 GFLOPS | Progress: (20/20) | 17.77 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 20.70/ 20.70 GFLOPS | Progress: (4/20) | 6.42 s [Task 19/25] Current/Best: 17.70/ 21.69 GFLOPS | Progress: (8/20) | 9.82 s [Task 19/25] Current/Best: 6.36/ 23.26 GFLOPS | Progress: (12/20) | 13.16 s [Task 19/25] Current/Best: 14.63/ 23.26 GFLOPS | Progress: (16/20) | 15.64 s [Task 19/25] Current/Best: 1.60/ 23.26 GFLOPS | Progress: (20/20) | 20.30 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 21.52/ 21.52 GFLOPS | Progress: (4/20) | 14.05 s [Task 20/25] Current/Best: 13.96/ 21.52 GFLOPS | Progress: (8/20) | 17.61 s [Task 20/25] Current/Best: 20.86/ 21.52 GFLOPS | Progress: (12/20) | 20.27 s [Task 20/25] Current/Best: 20.49/ 21.52 GFLOPS | Progress: (16/20) | 31.81 s [Task 20/25] Current/Best: 10.25/ 21.52 GFLOPS | Progress: (20/20) | 41.29 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 17.36/ 18.59 GFLOPS | Progress: (4/20) | 4.39 s [Task 21/25] Current/Best: 11.14/ 20.02 GFLOPS | Progress: (8/20) | 6.11 s [Task 21/25] Current/Best: 8.68/ 20.02 GFLOPS | Progress: (12/20) | 13.13 s [Task 21/25] Current/Best: 17.93/ 20.02 GFLOPS | Progress: (16/20) | 15.53 s [Task 21/25] Current/Best: 10.55/ 20.02 GFLOPS | Progress: (20/
20) | 18.17 s Done.
+ [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 16.22/ 16.22 GFLOPS | Progress: (4/20) | 4.57 s [Task 22/25] Current/Best: 21.49/ 21.49 GFLOPS | Progress: (8/20) | 15.76 s Done.
+ [Task 22/25] Current/Best: 13.63/ 21.49 GFLOPS | Progress: (12/20) | 19.10 s [Task 22/25] Current/Best: 9.77/ 21.49 GFLOPS | Progress: (16/20) | 21.55 s [Task 22/25] Current/Best: 8.32/ 21.69 GFLOPS | Progress: (20/20) | 23.88 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 9.40/ 16.93 GFLOPS | Progress: (4/20) | 9.73 s [Task 23/25] Current/Best: 23.25/ 23.25 GFLOPS | Progress: (8/20) | 12.00 s [Task 23/25] Current/Best: 21.31/ 23.25 GFLOPS | Progress: (12/20) | 15.40 s [Task 23/25] Current/Best: 11.96/ 23.25 GFLOPS | Progress: (16/20) | 18.34 s [Task 23/25] Current/Best: 9.47/ 23.25 GFLOPS | Progress: (20/20) | 21.69 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 6.80/ 9.21 GFLOPS | Progress: (4/20) | 13.56 s [Task 24/25] Current/Best: 2.30/ 9.21 GFLOPS | Progress: (8/20) | 25.66 s [Task 24/25] Current/Best: 5.85/ 9.21 GFLOPS | Progress: (12/20) | 36.63 s [Task 24/25] Current/Best: 7.30/ 10.00 GFLOPS | Progress: (16/20) | 40.76 s [Task 24/25] Current/Best: 8.05/ 10.00 GFLOPS | Progress: (20/20) | 51.76 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 9.58/ 9.58 GFLOPS | Progress: (4/20) | 13.53 s [Task 25/25] Current/Best: 1.60/ 9.58 GFLOPS | Progress: (8/20) | 20.10 s [Task 25/25] Current/Best: 4.04/ 9.58 GFLOPS | Progress: (12/20) | 22.09 s Done.
+ [Task 25/25] Current/Best: 3.11/ 9.58 GFLOPS | Progress: (16/20) | 23.89 s [Task 25/25] Current/Best: 5.81/ 9.58 GFLOPS | Progress: (20/20) | 34.85 s
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621104
- class='n02123159 tiger cat' with probability=0.356378
+ class='n02123045 tabby, tabby cat' with probability=0.621105
+ class='n02123159 tiger cat' with probability=0.356377
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 392.7310230400326, 'median': 392.38115795005797, 'std': 2.756350245201662}
- unoptimized: {'mean': 500.5685418899884, 'median': 501.0803453499648, 'std': 3.744422612045327}
+ optimized: {'mean': 399.6946544999628, 'median': 398.9989166500891, 'std': 2.6101569794260713}
+ unoptimized: {'mean': 477.34598311997615, 'median': 476.0032203499577, 'std': 3.6898097033551043}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 50.784 seconds)
+ **Total running time of the script:** ( 13 minutes 21.357 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 025e5b6ecd..209933965f 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
- 1.191e-07 secs/op
+ 1.166e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index e6b86f286b..1c256e8781 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, 0x2e5d7f30)), stage(b, placeholder(b, 0x169d9ad0)), 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, 0x1452d1a0)), stage(b, placeholder(b, 0x2d4c0570)), 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 1a62b170ce..7d5683bde2 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**17:36.619** total execution time for **tutorial** files:
+**16:41.621** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:50.784 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:21.357 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:45.176 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:18.141 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.516 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.915 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:41.974 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:42.877 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:17.222 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:19.335 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.893 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.907 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.846 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.882 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.207 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
@@ -30,7 +30,7 @@ Computation times
+------------------------------------------------------------------------------------------+-----------+--------+
| :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_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.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 98d5130083..7fde9aa48f 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000006
- naive: 0.000006
+ Numpy running time: 0.000008
+ naive: 0.000007
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000006
+ parallel: 0.000008
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 6.403580009646248e-06 1.0
- naive 5.83e-06 0.910428227837832
- parallel 6.0997e-06 0.9525452935407245
- vector 3.78053e-05 5.9037756915742
+ numpy 7.778019989927998e-06 1.0
+ naive 7.459300000000001e-06 0.9590229916687386
+ parallel 7.9513e-06 1.0222781646609789
+ vector 3.8056e-05 4.892761917464845
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.015759
+ Numpy running time: 0.018074
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.418535
+ none: 3.267469
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.297310
+ blocking: 0.289837
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.279397
+ vectorization: 0.276952
# 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: 0.113207
+ loop permutation: 0.110477
# 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: 0.101588
+ array packing: 0.101136
# 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: 0.097111
+ block caching: 0.106988
# 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: 0.115826
+ parallelization: 0.126949
# 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 3.418535093 1.0
- blocking 0.2973095805 0.08696987815301038
- vectorization 0.279396823 0.08172998532971328
- loop permutation 0.11320676249999999 0.033115577117171925
- array packing 0.1015876327 0.029716714889958862
- block caching 0.0971110519 0.028407212229252958
- parallelization 0.1158260511 0.03388177916826785
+ none 3.2674689291 1.0
+ blocking 0.2898374286 0.0887039585958155
+ vectorization 0.27695195010000007 0.08476039286356257
+ loop permutation 0.1104774807 0.03381133320537196
+ array packing 0.1011364218 0.030952527474486888
+ block caching 0.10698775140000001 0.032743311021925764
+ parallelization 0.1269485695 0.03885226524096345
diff --git a/docs/commit_hash b/docs/commit_hash
index 45c70946a4..955dfc2e49 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-a6ab8cb8576df762a009c456c54059547bc643e8
+faa66282155a591b5b64f9da254102d74614816a
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 2333fc6174..472afb2821 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -604,7 +604,7 @@ 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 34.245 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.054 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_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index dd8f931e5c..499e2a9399 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -468,15 +468,14 @@ 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
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 6%|6 | 2.66M/41.5M [00:00<00:02, 15.8MB/s]
- 15%|#5 | 6.33M/41.5M [00:00<00:01, 23.6MB/s]
- 35%|###4 | 14.3M/41.5M [00:00<00:00, 37.2MB/s]
- 43%|####3 | 17.9M/41.5M [00:00<00:00, 34.0MB/s]
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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 12b26b40e5..152070b284 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -503,6 +503,7 @@ 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 2.341 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
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<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 28e04ceb30..194dae4487 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -451,12 +451,14 @@ 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 19bada6212..19829a57d9 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -671,7 +671,7 @@ 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 29.221 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.713 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
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<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 dc40a020f4..1d8a98c779 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>06:31.195</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:43.320</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -369,39 +369,39 @@
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<tbody>
<tr class="row-odd"><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>
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</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>
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+<td><p>01:02.341</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>
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<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>
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</tr>
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<td><p>0.0 MB</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><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>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:02.779</p></td>
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<td><p>0.0 MB</p></td>
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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 d9fa388468..03bfcd7888 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -850,10 +850,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3990.0556 3989.0571 3998.5293 3987.5312 3.1488
+ 3997.5079 3996.3706 4015.0666 3990.9144 6.2842
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.260 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">
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<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 080f95a55c..e392f8460c 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
@@ -458,24 +458,31 @@ 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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</pre></div>
</div>
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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 29d832f874..4b755a318f 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -682,7 +682,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.4100 14.3660 15.2845 14.0266 0.3226
+ 15.4868 15.6010 16.2016 14.8208 0.5304
</pre></div>
</div>
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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 5ab6e1fb2c..d150ec8112 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -474,34 +474,33 @@ 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).
@@ -592,7 +591,7 @@ 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 26.433 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 36.601 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 9b5eef5d34..cffe7fd21b 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -515,8 +515,10 @@ 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
0%| | 0.00/13.6M [00:00<?, ?B/s]
- 59%|#####8 | 7.99M/13.6M [00:00<00:00, 53.9MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 63.9MB/s]
+ 47%|####6 | 6.30M/13.6M [00:00<00:00, 22.6MB/s]
+ 62%|######2 | 8.46M/13.6M [00:00<00:00, 16.9MB/s]
+ 90%|########9 | 12.2M/13.6M [00:00<00:00, 19.0MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 21.1MB/s]
</pre></div>
</div>
</div>
@@ -607,7 +609,7 @@ 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)
- 86.1678 86.1296 87.5604 85.7649 0.2445
+ 86.1570 86.0927 86.6830 85.9208 0.1804
</pre></div>
</div>
<div class="admonition note">
@@ -646,7 +648,7 @@ 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 25.944 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.839 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 dd050186ab..4f3b7f20e6 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -600,7 +600,7 @@ 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)
- 106.9944 106.9412 112.0087 105.6538 0.6166
+ 109.1151 109.0472 109.9507 108.4506 0.3736
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index ad454b0674..88fa758ad0 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>09:31.786</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>09:56.614</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -369,39 +369,39 @@
</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>03:26.433</p></td>
+<td><p>03:36.601</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>01:25.944</p></td>
+<td><p>01:29.839</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>01:16.859</p></td>
+<td><p>01:18.260</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:50.226</p></td>
+<td><p>00:51.933</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>00:49.640</p></td>
+<td><p>00:51.895</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>00:44.913</p></td>
+<td><p>00:48.308</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:29.012</p></td>
+<td><p>00:29.918</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:28.751</p></td>
+<td><p>00:29.850</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:00.008</p></td>
+<td><p>00:00.010</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 05bafd03cb..88ecca2c44 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>00:04.054</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.042</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,11 +369,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><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>00:02.857</p></td>
+<td><p>00:02.808</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:01.190</p></td>
+<td><p>00:01.227</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index a79b6b4a56..8bb89fa316 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -545,10 +545,10 @@ 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: 25280us [25280us] (48.53%; 48.53%)
-FoldScaleAxis: 26811us [7us] (51.47%; 51.47%)
- FoldConstant: 26804us [1792us] (51.46%; 99.97%)
- InferType: 25012us [25012us] (48.02%; 93.31%)
+InferType: 24834us [24834us] (48.40%; 48.40%)
+FoldScaleAxis: 26480us [9us] (51.60%; 51.60%)
+ FoldConstant: 26471us [1820us] (51.59%; 99.97%)
+ InferType: 24651us [24651us] (48.04%; 93.13%)
</pre></div>
</div>
</div>
@@ -570,10 +570,10 @@ 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: 25097us [25097us] (48.47%; 48.47%)
-FoldScaleAxis: 26676us [7us] (51.53%; 51.53%)
- FoldConstant: 26669us [1840us] (51.51%; 99.97%)
- InferType: 24829us [24829us] (47.96%; 93.10%)
+InferType: 24637us [24637us] (47.90%; 47.90%)
+FoldScaleAxis: 26795us [7us] (52.10%; 52.10%)
+ FoldConstant: 26788us [1825us] (52.08%; 99.97%)
+ InferType: 24963us [24963us] (48.53%; 93.19%)
</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 5a1ae35dc3..a898e2aa8e 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -595,7 +595,7 @@ 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: 53.549087 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.525470 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 627f6ffdb2..3ef2f8d21f 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -877,7 +877,7 @@ 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: 11.068355 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.076163 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 f5d9dc4029..00567798a0 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -492,8 +492,8 @@ 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: 0.014256
-Baseline: 3.411588
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.017821
+Baseline: 3.272619
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -552,7 +552,7 @@ 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: 0.280367
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.294679
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -609,7 +609,7 @@ 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: 0.271664
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.280125
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -664,7 +664,7 @@ 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: 0.112447
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.114635
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -741,7 +741,7 @@ 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: 0.104239
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.109832
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -819,7 +819,7 @@ 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: 0.098505
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.108423
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -899,7 +899,7 @@ 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: 0.116369
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.128806
</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 5c5ad87154..b64044e7a8 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>
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+<p><strong>00:33.330</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,15 +369,15 @@
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<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>00:29.082</p></td>
+<td><p>00:30.197</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>00:01.763</p></td>
+<td><p>00:01.856</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>00:01.264</p></td>
+<td><p>00:01.276</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 735f77d38b..9522f02f1f 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>03:30.335</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:36.713</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -369,27 +369,27 @@
</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>01:31.495</p></td>
+<td><p>01:34.674</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>01:10.868</p></td>
+<td><p>01:12.054</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>00:17.278</p></td>
+<td><p>00:17.785</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>00:15.852</p></td>
+<td><p>00:16.637</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>00:14.743</p></td>
+<td><p>00:15.462</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>
-<td><p>00:00.099</p></td>
+<td><p>00:00.100</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
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 28c71739be..c521dbf338 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
@@ -1032,7 +1032,7 @@ 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: 0.337 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.334 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 7e55f4b1c4..1f73fca859 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -923,7 +923,7 @@ 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)
- 3.2118 3.2109 3.2141 3.2104 0.0016
+ 3.1978 3.1977 3.1989 3.1968 0.0009
</pre></div>
</div>
</div>
@@ -945,7 +945,7 @@ 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 10.868 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.054 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 3c4a9d614b..a665de3b71 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)
- 722.2385 722.5255 724.6662 719.5238 2.1092
+ 733.1481 733.2826 733.9315 732.2301 0.7011
</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 31.495 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.674 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 28b9786928..85dae5b19a 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>00:22.696</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.466</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -369,11 +369,11 @@
</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>00:22.664</p></td>
+<td><p>00:23.430</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>00:00.017</p></td>
+<td><p>00:00.021</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 eaacd9059e..ca42a08157 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -630,7 +630,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.037088
+Time cost of this operator: 0.036944
</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 57b600c57e..2bb537933b 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -664,10 +664,10 @@ 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)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 302.6 98.715 (1, 2, 10, 10, 3) 2 1 [302.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.962 0.966 (1, 6, 10, 10) 1 1 [2.962]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.319 (1, 1, 10, 10, 3) 1 1 [0.978]
-Total_time - 306.54 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 295.8 98.705 (1, 2, 10, 10, 3) 2 1 [295.8]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.955 0.986 (1, 6, 10, 10) 1 1 [2.955]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.925 0.309 (1, 1, 10, 10, 3) 1 1 [0.925]
+Total_time - 299.68 - - - - -
</pre></div>
</div>
</div>
@@ -719,13 +719,13 @@ 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 104.9 97.559 (1, 6, 10, 10, 1) 2 1 [104.9]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.777 1.652 (1, 6, 10, 10) 1 1 [1.777]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.848 0.789 (1, 3, 10, 10, 1) 1 1 [0.848]
-Total_time - 107.525 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.875 97.55 (1, 6, 10, 10, 1) 2 1 [100.875]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.719 1.662 (1, 6, 10, 10) 1 1 [1.719]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.815 0.788 (1, 3, 10, 10, 1) 1 1 [0.815]
+Total_time - 103.409 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.424 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 24.864 seconds)</p>
<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 0450c4c7a5..27224a0cc3 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -475,8 +475,8 @@ 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]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 11.9MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 18.7MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 12.2MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 19.1MB/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.
@@ -604,7 +604,7 @@ 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 26.353 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.700 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 ed450951af..a6f3501fd8 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -543,7 +543,7 @@ 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/tmppv7imotm/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpyf3y9ll5/images/random'
</pre></div>
</div>
</div>
@@ -603,8 +603,8 @@ 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/tmppv7imotm/images/target contains 8144 images
-/tmp/tmppv7imotm/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/tmpyf3y9ll5/images/target contains 8144 images
+/tmp/tmpyf3y9ll5/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -716,13 +716,13 @@ 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 - 39s - loss: 0.2166 - accuracy: 0.9249 - val_loss: 0.1258 - val_accuracy: 0.9554 - 39s/epoch - 118ms/step
+328/328 - 39s - loss: 0.2237 - accuracy: 0.9241 - val_loss: 0.1079 - val_accuracy: 0.9622 - 39s/epoch - 120ms/step
Epoch 2/3
-328/328 - 34s - loss: 0.0964 - accuracy: 0.9662 - val_loss: 0.1384 - val_accuracy: 0.9498 - 34s/epoch - 105ms/step
+328/328 - 35s - loss: 0.1036 - accuracy: 0.9622 - val_loss: 0.1080 - val_accuracy: 0.9619 - 35s/epoch - 105ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0661 - accuracy: 0.9753 - val_loss: 0.1024 - val_accuracy: 0.9653 - 35s/epoch - 105ms/step
+328/328 - 34s - loss: 0.0649 - accuracy: 0.9763 - val_loss: 0.1127 - val_accuracy: 0.9626 - 34s/epoch - 104ms/step
-<keras.callbacks.History object at 0x7f2295556e20>
+<keras.callbacks.History object at 0x7f4848b8e6d0>
</pre></div>
</div>
</div>
@@ -986,7 +986,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 46.713 seconds)</p>
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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-train-py">
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<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index d3c66b0b9b..b5d3e75c85 100644
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -360,7 +360,7 @@
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<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:08.371</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>08:11.391</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
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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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-<td><p>01:26.353</p></td>
+<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-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
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<tr class="row-odd"><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>
-<td><p>00:09.123</p></td>
+<td><p>00:08.951</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><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>
-<td><p>00:08.057</p></td>
+<td><p>00:07.447</p></td>
<td><p>0.0 MB</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 989436a599..d6be38c5a8 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>00:37.575</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:38.317</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
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@@ -369,19 +369,19 @@
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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>
-<td><p>00:32.555</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>
-<td><p>00:03.128</p></td>
+<td><p>00:03.178</p></td>
<td><p>0.0 MB</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>
-<td><p>00:00.007</p></td>
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diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index a72da03ffa..588884aad5 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -572,7 +572,7 @@ 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 0x7f2299d63550>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f4865992b80>
</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
index 0e125136b8..71ae49e927 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>
-<p><strong>00:05.468</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,35 +369,35 @@
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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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<td><p>0.0 MB</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="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
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+<td><p>00:00.713</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></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>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:00.069</p></td>
+<td><p>00:00.071</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>
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<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>
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+<td><p>00:00.032</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/install/nnpack.html b/docs/install/nnpack.html
index 61f3a9d084..262b273e47 100644
--- a/docs/install/nnpack.html
+++ b/docs/install/nnpack.html
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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Installing TVM</a><ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="from_source.html#developers-get-source-from-github">Developers: Get Source from Github</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="from_source.html#enable-c-tests">Enable C++ Tests</a></li>
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-</li>
+<li class="toctree-l2"><a class="reference internal" href="from_source.html">Install from Source</a></li>
<li class="toctree-l2"><a class="reference internal" href="docker.html">Docker Images</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#conditions">Conditions</a></li>
diff --git a/docs/reference/api/doxygen/namespacemembers_e.html b/docs/reference/api/doxygen/namespacemembers_e.html
index 768788ad91..9919fe31e8 100644
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+++ b/docs/reference/api/doxygen/namespacemembers_e.html
@@ -106,7 +106,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1relax.html#aff256c278f6c4493843a3b8fe5703092">tvm::relax</a>
</li>
<li>equal()
-: <a class="el" href="namespacetvm_1_1topi.html#a0e5aecf572e9eea859442ef181df0abc">tvm::topi</a>
+: <a class="el" href="namespacetvm_1_1topi.html#ab3048426a7157ce04d8e14bebd023b12">tvm::topi</a>
</li>
<li>EraseToWellDefined()
: <a class="el" href="namespacetvm_1_1relax.html#ae61c04eb7fcb890934bf2a2f221082d7">tvm::relax</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_f.html b/docs/reference/api/doxygen/namespacemembers_f.html
index f21c12ddec..3c76f880f5 100644
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+++ b/docs/reference/api/doxygen/namespacemembers_f.html
@@ -166,6 +166,9 @@ $(function() {
<li>FindEntryFunc()
: <a class="el" href="namespacetvm_1_1tir.html#af6c64bf0c1bf0a62f8828c48fb573233">tvm::tir</a>
</li>
+<li>FindImpureCall()
+: <a class="el" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">tvm::relax</a>
+</li>
<li>FInferStructInfo
: <a class="el" href="namespacetvm_1_1relax.html#a1a2fd4ee22f1a9f4ebd6360992f89e15">tvm::relax</a>
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diff --git a/docs/reference/api/doxygen/namespacemembers_func_f.html b/docs/reference/api/doxygen/namespacemembers_func_f.html
index 8783afcc10..5f9dd6607e 100644
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@@ -112,6 +112,9 @@ $(function() {
<li>FindEntryFunc()
: <a class="el" href="namespacetvm_1_1tir.html#af6c64bf0c1bf0a62f8828c48fb573233">tvm::tir</a>
</li>
+<li>FindImpureCall()
+: <a class="el" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">tvm::relax</a>
+</li>
<li>flatten()
: <a class="el" href="namespacetvm_1_1topi_1_1nn.html#a6a553de73860c1a295d6ee566a3916b4">tvm::topi::nn</a>
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<li>floor_divide()
-: <a class="el" href="namespacetvm_1_1topi.html#af0db5104dd5122151e0de3aaa35c43d7">tvm::topi</a>
+: <a class="el" href="namespacetvm_1_1topi.html#a6a04e135b576e7b4a17047168535c594">tvm::topi</a>
</li>
<li>floor_mod()
: <a class="el" href="namespacetvm_1_1topi.html#a1296f02c707cc93648bee68d473d26aa">tvm::topi</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1relax.html b/docs/reference/api/doxygen/namespacetvm_1_1relax.html
index a4cb34cbb9..3eb38d190f 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1relax.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1relax.html
@@ -776,6 +776,9 @@ Functions</h2></td></tr>
<tr class="memitem:a9a0625d588e3d480f49c80c7589fe5f6"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6">HasReshapePattern</a> (const <a class="el" href="classtvm_1_1tir_1_1PrimFunc.html">tir::PrimFunc</a> &func)</td></tr>
<tr class="memdesc:a9a0625d588e3d480f49c80c7589fe5f6"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given PrimFunc is essentially doing a reshape operation. The reshape operation also includes expand_dims, squeeze, flatten, etc. <a href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6">More...</a><br /></td></tr>
<tr class="separator:a9a0625d588e3d480f49c80c7589fe5f6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">FindImpureCall</a> (const <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9a [...]
+<tr class="memdesc:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given expression (likely a function body) contains any impure calls. <a href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">More...</a><br /></td></tr>
+<tr class="separator:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5a7dbb451882ba4b0f6ec19932d05004"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">ContainsImpureCall</a> (const <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a> &expr, const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="namespacetvm_1_1 [...]
<tr class="memdesc:a5a7dbb451882ba4b0f6ec19932d05004"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given expression (likely a function body) contains any impure calls. <a href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">More...</a><br /></td></tr>
<tr class="separator:a5a7dbb451882ba4b0f6ec19932d05004"><td class="memSeparator" colspan="2"> </td></tr>
@@ -2155,6 +2158,45 @@ template<typename T , typename FType > </div>
</table>
</div><div class="memdoc">
+</div>
+</div>
+<a id="ad6b9d59610bdd547afbdaaeb7dbe2d63"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad6b9d59610bdd547afbdaaeb7dbe2d63">◆ </a></span>FindImpureCall()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a>> tvm::relax::FindImpureCall </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a> & </td>
+ <td class="paramname"><em>expr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a> > & </td>
+ <td class="paramname"><em>own_name</em> = <code><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="namespacetvm_1_1relax.html#a8f6ef3d9aaba0138f730a520d7ebbfb3">Expr</a> >(nullptr)</code> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Check if the given expression (likely a function body) contains any impure calls. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">expr</td><td>The expression to be examined. <a class="el" href="classtvm_1_1relax_1_1If.html">If</a> expr is a function, we check the body. </td></tr>
+ <tr><td class="paramname">own_name</td><td>(Optional.) <a class="el" href="classtvm_1_1relax_1_1If.html">If</a> we are checking a recursive function body, the caller can pass the function's name so recursive calls can be ignored in the check (must be a <a class="el" href="classtvm_1_1relax_1_1Var.html">Var</a> or <a class="el" href="classtvm_1_1GlobalVar.html" title="Managed reference to GlobalVarNode.">GlobalVar</a>). </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The impure expression, if one exists within the given expression. Otherwise, NullOpt. </dd></dl>
+<dl class="section note"><dt>Note</dt><dd>Relies on <a class="el" href="classtvm_1_1relax_1_1StructInfo.html" title="Managed reference to StructInfoNode.">StructInfo</a> annotations, so ensure that the module has been normalized first. Also, an impure call in a <em>nested</em> function does <em>not</em> mean that the outer expression contains an impure call–it only does if the nested function is <em>later called</em>. </dd></dl>
+
</div>
</div>
<a id="adc02e3f6e603613190eff65a679ea38c"></a>
diff --git a/docs/reference/api/doxygen/relax_2analysis_8h.html b/docs/reference/api/doxygen/relax_2analysis_8h.html
index d2748f2777..6cd83c1bcb 100644
--- a/docs/reference/api/doxygen/relax_2analysis_8h.html
+++ b/docs/reference/api/doxygen/relax_2analysis_8h.html
@@ -213,6 +213,9 @@ Functions</h2></td></tr>
<tr class="memitem:a9a0625d588e3d480f49c80c7589fe5f6"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6">tvm::relax::HasReshapePattern</a> (const tir::PrimFunc &func)</td></tr>
<tr class="memdesc:a9a0625d588e3d480f49c80c7589fe5f6"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given PrimFunc is essentially doing a reshape operation. The reshape operation also includes expand_dims, squeeze, flatten, etc. <a href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6">More...</a><br /></td></tr>
<tr class="separator:a9a0625d588e3d480f49c80c7589fe5f6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="memItemLeft" align="right" valign="top">Optional< Expr > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">tvm::relax::FindImpureCall</a> (const Expr &expr, const Optional< Expr > &own_name=Optional< Expr >(nullptr))</td></tr>
+<tr class="memdesc:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given expression (likely a function body) contains any impure calls. <a href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">More...</a><br /></td></tr>
+<tr class="separator:ad6b9d59610bdd547afbdaaeb7dbe2d63"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5a7dbb451882ba4b0f6ec19932d05004"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">tvm::relax::ContainsImpureCall</a> (const Expr &expr, const Optional< Expr > &own_name=Optional< Expr >(nullptr))</td></tr>
<tr class="memdesc:a5a7dbb451882ba4b0f6ec19932d05004"><td class="mdescLeft"> </td><td class="mdescRight">Check if the given expression (likely a function body) contains any impure calls. <a href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">More...</a><br /></td></tr>
<tr class="separator:a5a7dbb451882ba4b0f6ec19932d05004"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/relax_2analysis_8h_source.html b/docs/reference/api/doxygen/relax_2analysis_8h_source.html
index e90ddb4d8d..6f16a1b1b5 100644
--- a/docs/reference/api/doxygen/relax_2analysis_8h_source.html
+++ b/docs/reference/api/doxygen/relax_2analysis_8h_source.html
@@ -207,33 +207,36 @@ $(function() {
<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  </div>
<div class="line"><a name="l00494"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6"> 494</a></span> TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="namespacetvm_1_1relax.html#a9a0625d588e3d480f49c80c7589fe5f6">HasReshapePattern</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1PrimFunc.html">tir::PrimFunc</a>& func);</div>
<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  </div>
-<div class="line"><a name="l00507"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004"> 507</a></span> TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">ContainsImpureCall</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1RelayExpr.html">Expr</a>& expr,</div>
-<div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>& own_name = <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>(<span class="keyword">nullptr</span>));</div>
-<div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  </div>
-<div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a05ac9dfb2e19397251bae6ca7f56ba69"> 521</a></span> TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="namespacetvm_1_1relax.html#a05ac9dfb2e19397251bae6ca7f56ba69">WellFormed</a>(<a class="code" href="classtvm_1_1IRModule.html">IRModule</a> m, <span class="keywordtype">bool</span> check_struct_info = <span class="keyword">true</span>);</div>
-<div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  </div>
-<div class="line"><a name="l00533"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a54d86282369773ba93d383a906317bb2"> 533</a></span> TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<tir::Block, Map<ObjectRef, tir::IndexMap></a>> <a class="code" href="namespacetvm_1_1relax.html#a54d86282369773ba93d383a906317bb2">SuggestLayoutTransforms</a>(</div>
-<div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1relax_1_1Function.html">Function</a>& fn, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<tir::IndexMap></a> write_buffer_transformations);</div>
-<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  </div>
-<div class="line"><a name="l00536"></a><span class="lineno"> 536</span> <span class="comment">/* \brief Collect variables whose value can be computed at compile-time</span></div>
-<div class="line"><a name="l00537"></a><span class="lineno"> 537</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00538"></a><span class="lineno"> 538</span> <span class="comment"> * If a function has the `kNumInput` attribute, then the first</span></div>
-<div class="line"><a name="l00539"></a><span class="lineno"> 539</span> <span class="comment"> * `kNumInput` parameters are provided at run-time, while all</span></div>
-<div class="line"><a name="l00540"></a><span class="lineno"> 540</span> <span class="comment"> * remaining parameters may be known at compile-time. This utility</span></div>
-<div class="line"><a name="l00541"></a><span class="lineno"> 541</span> <span class="comment"> * collects all variable bindings that only depend, directly or</span></div>
-<div class="line"><a name="l00542"></a><span class="lineno"> 542</span> <span class="comment"> * indirectly, on the parameters known at compile-time.</span></div>
-<div class="line"><a name="l00543"></a><span class="lineno"> 543</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00544"></a><span class="lineno"> 544</span> <span class="comment"> * \param func The relax::Function to analyze</span></div>
-<div class="line"><a name="l00545"></a><span class="lineno"> 545</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00546"></a><span class="lineno"> 546</span> <span class="comment"> * \return The set of variables that can be computed at compile-time,</span></div>
-<div class="line"><a name="l00547"></a><span class="lineno"> 547</span> <span class="comment"> * in order of their occurrence within the function.</span></div>
-<div class="line"><a name="l00548"></a><span class="lineno"> 548</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00549"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#ad8e27d47da2ae8d846b121289b1ecf10"> 549</a></span> TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Var></a> <a class="code" href="namespacetvm_1_1relax.html#ad8e27d47da2ae8d846b121289b1ecf10">ComputableAtCompileTime</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1relax_1_1Function.html">Function</a>& func);</div>
+<div class="line"><a name="l00508"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63"> 508</a></span> TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a> <a class="code" href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">FindImpureCall</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1RelayExpr.html">Expr</a>& expr,</div>
+<div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>& own_name = <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>(<span class="keyword">nullptr</span>));</div>
+<div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  </div>
+<div class="line"><a name="l00522"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004"> 522</a></span> TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="namespacetvm_1_1relax.html#a5a7dbb451882ba4b0f6ec19932d05004">ContainsImpureCall</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1RelayExpr.html">Expr</a>& expr,</div>
+<div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>& own_name = <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Expr></a>(<span class="keyword">nullptr</span>));</div>
+<div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  </div>
+<div class="line"><a name="l00536"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a05ac9dfb2e19397251bae6ca7f56ba69"> 536</a></span> TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="namespacetvm_1_1relax.html#a05ac9dfb2e19397251bae6ca7f56ba69">WellFormed</a>(<a class="code" href="classtvm_1_1IRModule.html">IRModule</a> m, <span class="keywordtype">bool</span> check_struct_info = <span class="keyword">true</span>);</div>
+<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  </div>
+<div class="line"><a name="l00548"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#a54d86282369773ba93d383a906317bb2"> 548</a></span> TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<tir::Block, Map<ObjectRef, tir::IndexMap></a>> <a class="code" href="namespacetvm_1_1relax.html#a54d86282369773ba93d383a906317bb2">SuggestLayoutTransforms</a>(</div>
+<div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1relax_1_1Function.html">Function</a>& fn, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<tir::IndexMap></a> write_buffer_transformations);</div>
<div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  </div>
-<div class="line"><a name="l00551"></a><span class="lineno"> 551</span> } <span class="comment">// namespace relax</span></div>
-<div class="line"><a name="l00552"></a><span class="lineno"> 552</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  </div>
-<div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ANALYSIS_H_</span></div>
+<div class="line"><a name="l00551"></a><span class="lineno"> 551</span> <span class="comment">/* \brief Collect variables whose value can be computed at compile-time</span></div>
+<div class="line"><a name="l00552"></a><span class="lineno"> 552</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="comment"> * If a function has the `kNumInput` attribute, then the first</span></div>
+<div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <span class="comment"> * `kNumInput` parameters are provided at run-time, while all</span></div>
+<div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="comment"> * remaining parameters may be known at compile-time. This utility</span></div>
+<div class="line"><a name="l00556"></a><span class="lineno"> 556</span> <span class="comment"> * collects all variable bindings that only depend, directly or</span></div>
+<div class="line"><a name="l00557"></a><span class="lineno"> 557</span> <span class="comment"> * indirectly, on the parameters known at compile-time.</span></div>
+<div class="line"><a name="l00558"></a><span class="lineno"> 558</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00559"></a><span class="lineno"> 559</span> <span class="comment"> * \param func The relax::Function to analyze</span></div>
+<div class="line"><a name="l00560"></a><span class="lineno"> 560</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00561"></a><span class="lineno"> 561</span> <span class="comment"> * \return The set of variables that can be computed at compile-time,</span></div>
+<div class="line"><a name="l00562"></a><span class="lineno"> 562</span> <span class="comment"> * in order of their occurrence within the function.</span></div>
+<div class="line"><a name="l00563"></a><span class="lineno"> 563</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00564"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax.html#ad8e27d47da2ae8d846b121289b1ecf10"> 564</a></span> TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Var></a> <a class="code" href="namespacetvm_1_1relax.html#ad8e27d47da2ae8d846b121289b1ecf10">ComputableAtCompileTime</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1relax_1_1Function.html">Function</a>& func);</div>
+<div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  </div>
+<div class="line"><a name="l00566"></a><span class="lineno"> 566</span> } <span class="comment">// namespace relax</span></div>
+<div class="line"><a name="l00567"></a><span class="lineno"> 567</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  </div>
+<div class="line"><a name="l00569"></a><span class="lineno"> 569</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ANALYSIS_H_</span></div>
<div class="ttc" id="aanalyzer_8h_html"><div class="ttname"><a href="analyzer_8h.html">analyzer.h</a></div><div class="ttdoc">Algebra expression simplifications.</div></div>
<div class="ttc" id="aclasstvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode.</div><div class="ttdef"><b>Definition:</b> module.h:365</div></div>
<div class="ttc" id="aclasstvm_1_1RelayExpr_html"><div class="ttname"><a href="classtvm_1_1RelayExpr.html">tvm::RelayExpr</a></div><div class="ttdoc">Managed reference to RelayExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:441</div></div>
@@ -280,6 +283,7 @@ $(function() {
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<div class="ttc" id="anamespacetvm_1_1relax_html_ab606681578effc49659261a0d11e0e01"><div class="ttname"><a href="namespacetvm_1_1relax.html#ab606681578effc49659261a0d11e0e01">tvm::relax::DefinableTIRVarsInStructInfo</a></div><div class="ttdeci">Array< tir::Var > DefinableTIRVarsInStructInfo(const StructInfo &sinfo)</div><div class="ttdoc">Get the TIR variables that appear in the input struct info.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_html_abfc2189e6a25f2df774b38c7d72f23f9"><div class="ttname"><a href="namespacetvm_1_1relax.html#abfc2189e6a25f2df774b38c7d72f23f9">tvm::relax::DeriveCallRetStructInfo</a></div><div class="ttdeci">StructInfo DeriveCallRetStructInfo(const FuncStructInfo &finfo, const Call &call, const BlockBuilder &ctx, arith::Analyzer *ana=nullptr)</div></div>
+<div class="ttc" id="anamespacetvm_1_1relax_html_ad6b9d59610bdd547afbdaaeb7dbe2d63"><div class="ttname"><a href="namespacetvm_1_1relax.html#ad6b9d59610bdd547afbdaaeb7dbe2d63">tvm::relax::FindImpureCall</a></div><div class="ttdeci">Optional< Expr > FindImpureCall(const Expr &expr, const Optional< Expr > &own_name=Optional< Expr >(nullptr))</div><div class="ttdoc">Check if the given expression (likely a function body) contains any impure calls.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_html_ad86a717c05debf2815aee7bcbe7d61e4"><div class="ttname"><a href="namespacetvm_1_1relax.html#ad86a717c05debf2815aee7bcbe7d61e4">tvm::relax::IsBaseOf</a></div><div class="ttdeci">bool IsBaseOf(const StructInfo &base, const StructInfo &derived, arith::Analyzer *ana=nullptr)</div><div class="ttdoc">Check the relation of two struct info to see if one subsumes another one.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_html_ad8e27d47da2ae8d846b121289b1ecf10"><div class="ttname"><a href="namespacetvm_1_1relax.html#ad8e27d47da2ae8d846b121289b1ecf10">tvm::relax::ComputableAtCompileTime</a></div><div class="ttdeci">Array< Var > ComputableAtCompileTime(const Function &func)</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_html_ae61c04eb7fcb890934bf2a2f221082d7"><div class="ttname"><a href="namespacetvm_1_1relax.html#ae61c04eb7fcb890934bf2a2f221082d7">tvm::relax::EraseToWellDefined</a></div><div class="ttdeci">StructInfo EraseToWellDefined(const StructInfo &info, std::function< Optional< PrimExpr >(const tir::Var &var)> f_shape_var_map=nullptr, std::function< Optional< Expr >(const Var &var)> f_var_map=nullptr, arith::Analyzer [...]
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