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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/05/01 17:30:47 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@20d769617fa6ab561d7ed2b7cd61ed2b6b4710ba)
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 d0cd4ee606 deploying docs (apache/tvm@20d769617fa6ab561d7ed2b7cd61ed2b6b4710ba)
d0cd4ee606 is described below
commit d0cd4ee6061703a6915c35e21c356fab122c90b4
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed May 1 17:30:41 2024 +0000
deploying docs (apache/tvm@20d769617fa6ab561d7ed2b7cd61ed2b6b4710ba)
---
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docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 22 +-
docs/tutorial/tensor_expr_get_started.html | 45 +-
351 files changed, 13853 insertions(+), 14137 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 6ed9bef6b5..b7a404d517 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 42.892 seconds)
+ **Total running time of the script:** ( 1 minutes 34.269 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index 84bf98fa93..8b8a06881e 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -232,7 +232,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
- 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 952ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 925ms/step
Keras top-1 id: 285, class name: Egyptian cat
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 d0d159ed5b..a59c3e5df0 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] 19%|#8 | 7.72M/41.5M [00:00<00:00, 52.5MB/s] 31%|### | 12.7M/41.5M [00:00<00:00, 43.2MB/s] 41%|#### | 16.8M/41.5M [00:00<00:00, 32.1MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 35.3MB/s] 65%|######5 | 27.0M/41.5M [00:00<00:00, 38.9MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 42.2MB/s] 92%|#########2| 38.3M/41.5M [00:00<00:00, 47.7MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 42.7MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:00, 66.0MB/s] 30%|### | 12.6M/41.5M [00:00<00:00, 65.1MB/s] 45%|####5 | 18.8M/41.5M [00:00<00:00, 39.3MB/s] 56%|#####6 | 23.4M/41.5M [00:00<00:00, 34.7MB/s] 65%|######5 | 27.2M/41.5M [00:00<00:00, 35.5MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 36.2MB/s] 96%|#########6| 40.0M/41.5M [00:00<00:00, 47.2MB/s] 100%|##########| 41.5M/41.5M [00:00<00:00, 44.7MB/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 70123cad1b..da5532a62d 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.601 seconds)
+ **Total running time of the script:** ( 1 minutes 3.681 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 07fe9bf69f..afcd8ea377 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] 32%|###2 | 14.3M/44.7M [00:00<00:00, 101MB/s] 54%|#####3 | 23.9M/44.7M [00:00<00:00, 89.6MB/s] 73%|#######2 | 32.4M/44.7M [00:00<00:00, 50.5MB/s] 99%|#########8| 44.2M/44.7M [00:00<00:00, 63.3MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 66.3MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 63.1MB/s] 32%|###2 | 14.3M/44.7M [00:00<00:00, 52.8MB/s] 43%|####3 | 19.4M/44.7M [00:00<00:00, 46.6MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 46.0MB/s] 72%|#######1 | 32.0M/44.7M [00:00<00:00, 43.3MB/s] 87%|########6 | 38.7M/44.7M [00:00<00:00, 49.9MB/s] 98%|#########7| 43.7M/44.7M [00:00<00:00, 45.6MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 48.1MB/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 fbcd4368f4..655d2a3608 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -424,7 +424,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 37.013 seconds)
+ **Total running time of the script:** ( 1 minutes 28.931 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 c07248e038..5f84abba89 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
=================
-**07:12.880** total execution time for **how_to_compile_models** files:
+**06:40.197** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:42.892 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:34.269 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:37.013 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:28.931 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:09.601 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:03.681 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:47.277 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:44.649 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:43.131 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:39.200 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.754 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.729 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.898 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:26.387 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.284 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.501 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:03.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.850 | 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 31c60b7f6c..d62f355ec9 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)
- 4129.0682 4128.5797 4134.1013 4126.6861 2.3136
+ 3995.1092 3995.1356 4000.3554 3992.0123 2.6385
@@ -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 22.881 seconds)
+ **Total running time of the script:** ( 1 minutes 18.272 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 e20878b99e..73a8e9221c 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 6635520/102967424 [>.............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 22880256/102967424 [=====>........................] - ETA: 0s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 0s
50323456/102967424 [=============>................] - ETA: 0s 59039744/102967424 [================>.............] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 77684736/102967424 [=====================>........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 100646912/102967424 [============================>.] -
ETA: 0s 102967424/102967424 [==============================] - 1s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 7208960/102967424 [=>............................] - ETA: 0s 12632064/102967424 [==>...........................] - ETA: 0s 17702912/102967424 [====>.........................] - ETA: 0s 22740992/102967424 [=====>........................] - ETA: 0s 25157632/102967424 [======>.......................] - ETA: 0s 33546240/102967424 [========>.....................] - ETA: 0s 40517632/102967424 [==========>...................] - ETA: 0s
42770432/102967424 [===========>..................] - ETA: 0s 50323456/102967424 [=============>................] - ETA: 0s 55844864/102967424 [===============>..............] - ETA: 0s 60841984/102967424 [================>.............] - ETA: 0s 65814528/102967424 [==================>...........] - ETA: 0s 70828032/102967424 [===================>..........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 82010112/102967424 [======================>.......] -
ETA: 0s 87105536/102967424 [========================>.....] - ETA: 0s 92045312/102967424 [=========================>....] - ETA: 0s 95789056/102967424 [==========================>...] - ETA: 0s 102121472/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 1s 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 69d5d7aeb3..3e443b42e5 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)
- 15.2613 15.2470 15.4540 15.0844 0.1284
+ 13.8544 13.6712 14.4706 13.6148 0.3138
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 6fbd904a5d..fa32d8c8a0 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] 4%|3 | 6.60M/170M [00:00<00:02, 69.2MB/s] 9%|9 | 16.0M/170M [00:00<00:02, 58.8MB/s] 14%|#4 | 24.0M/170M [00:00<00:02, 61.1MB/s] 19%|#8 | 32.0M/170M [00:00<00:02, 51.9MB/s] 24%|##3 | 40.0M/170M [00:00<00:02, 56.6MB/s] 27%|##7 | 46.3M/170M [00:00<00:02, 44.9MB/s] 30%|### | 51.0M/170M [00:01<00:02, 43.4MB/s] 36%|###5 | 60.8M/170M [00:01<00:02, 56.8MB/s] 39%|###9 | 66.9M/170M [00:01<00:02, 53.2MB/s] 46%|####6 | 78.3M/170M [00:01<00:01, 66.2MB/s] 50%|##### | 85.1M/170M [00:01<00:01, 65.5MB/s] 55%|#####4 | 92.6M/170M [00:01<00:01, 68.8MB/s] 59%|#####8 | 99.4M/170M [00:01<00:01, 61.0MB/s] 62%|######2 | 106M/170M [00:02<00:01, 43.7MB/s] 66%|######5 | 112M/170M [00:02<00:01, 44.4MB/s] 71%|####### | 120M/170M [00:02<00:01, 47.0MB/s] 75%|#######5 | 128M/170M [00:02<00:00, 51.6MB/s]
80%|######## | 136M/170M [00:02<00:00, 48.0MB/s] 85%|########4 | 144M/170M [00:02<00:00, 55.0MB/s] 89%|########9 | 152M/170M [00:02<00:00, 59.0MB/s] 93%|#########3| 158M/170M [00:03<00:00, 60.4MB/s] 97%|#########6| 164M/170M [00:03<00:00, 51.8MB/s] 100%|#########9| 170M/170M [00:03<00:00, 42.3MB/s] 100%|##########| 170M/170M [00:03<00:00, 52.2MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 53.3MB/s] 8%|8 | 14.3M/170M [00:00<00:03, 47.0MB/s] 13%|#3 | 22.3M/170M [00:00<00:03, 40.0MB/s] 15%|#5 | 26.2M/170M [00:00<00:03, 39.0MB/s] 19%|#8 | 32.0M/170M [00:00<00:03, 37.1MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 39.9MB/s] 28%|##8 | 48.0M/170M [00:01<00:02, 49.0MB/s] 33%|###2 | 56.0M/170M [00:01<00:02, 55.1MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 50.6MB/s] 42%|####2 | 72.0M/170M [00:01<00:02, 50.1MB/s] 47%|####7 | 80.0M/170M [00:01<00:02, 46.9MB/s] 52%|#####1 | 88.0M/170M [00:01<00:01, 48.0MB/s] 57%|#####6 | 96.0M/170M [00:02<00:01, 51.2MB/s] 61%|######1 | 104M/170M [00:02<00:01, 49.3MB/s] 66%|######5 | 112M/170M [00:02<00:01, 52.7MB/s] 71%|####### | 120M/170M [00:02<00:00, 56.8MB/s] 75%|#######5 | 128M/170M [00:02<00:00, 50.8MB/s]
80%|######## | 136M/170M [00:02<00:00, 46.2MB/s] 85%|########4 | 144M/170M [00:03<00:00, 53.3MB/s] 88%|########8 | 150M/170M [00:03<00:00, 52.8MB/s] 91%|#########1| 155M/170M [00:03<00:00, 44.8MB/s] 94%|#########4| 160M/170M [00:03<00:00, 39.2MB/s] 98%|#########7| 166M/170M [00:03<00:00, 39.7MB/s] 100%|##########| 170M/170M [00:03<00:00, 46.6MB/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 53.791 seconds)
+ **Total running time of the script:** ( 3 minutes 25.792 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 d266afede3..4ed04f7716 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, 50.7MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 64.0MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 59%|#####8 | 7.99M/13.6M [00:00<00:00, 41.4MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 52.4MB/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)
- 89.4760 89.4854 90.1533 88.9613 0.2534
+ 86.1471 86.1470 87.3198 85.5411 0.2720
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 37.969 seconds)
+ **Total running time of the script:** ( 1 minutes 29.718 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 b80e708a68..06a40681bd 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)
- 111.1993 111.1829 112.1194 110.5506 0.2942
+ 103.5244 103.4213 105.2008 102.9670 0.4021
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 85324ecc42..ad168c0f9e 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
=================
-**10:42.478** total execution time for **how_to_deploy_models** files:
+**09:42.961** 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``) | 03:53.791 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:25.792 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:37.969 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.718 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:22.881 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.272 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:57.101 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.670 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:55.003 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.602 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.110 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:47.264 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:32.416 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.882 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:32.197 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.755 | 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``) | 00:00.007 | 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 de65cec1de..dc4dda83b0 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
=================
-**00:04.324** total execution time for **how_to_extend_tvm** files:
+**00:04.002** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:03.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.759 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.308 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.236 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+| :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 36df63dc0d..e1b5fa5d5e 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: 26012us [26012us] (48.55%; 48.55%)
- FoldScaleAxis: 27568us [11us] (51.45%; 51.45%)
- FoldConstant: 27557us [1856us] (51.43%; 99.96%)
- InferType: 25701us [25701us] (47.97%; 93.27%)
+ InferType: 24624us [24624us] (48.41%; 48.41%)
+ FoldScaleAxis: 26241us [9us] (51.59%; 51.59%)
+ FoldConstant: 26231us [1802us] (51.57%; 99.97%)
+ InferType: 24430us [24430us] (48.03%; 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: 25934us [25934us] (48.36%; 48.36%)
- FoldScaleAxis: 27695us [10us] (51.64%; 51.64%)
- FoldConstant: 27686us [1989us] (51.62%; 99.97%)
- InferType: 25697us [25697us] (47.92%; 92.82%)
+ InferType: 24193us [24193us] (48.32%; 48.32%)
+ FoldScaleAxis: 25873us [7us] (51.68%; 51.68%)
+ FoldConstant: 25867us [1782us] (51.66%; 99.97%)
+ InferType: 24085us [24085us] (48.11%; 93.11%)
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 4c19579cc4..6fff91e809 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: 34.898303 ms
+ Convolution: 37.080863 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 1087b74b74..1777d01799 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.026739 ms
+ conv2d with tensor core: 12.223975 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 96f1bf9a21..1c01e4ae2f 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: 0.019675
- Baseline: 3.341123
+ Numpy running time: 0.013639
+ Baseline: 3.303306
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.327822
+ Opt1: 0.290055
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.322427
+ Opt2: 0.274088
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.125736
+ Opt3: 0.109759
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.108541
+ Opt4: 0.103649
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.112312
+ Opt5: 0.094723
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.134857
+ Opt6: 0.113168
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 1c83cdd2e1..54203f6bde 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
=================
-**00:35.345** total execution time for **how_to_optimize_operators** files:
+**00:31.681** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.973 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.574 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.080 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.965 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.292 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.142 | 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 65982c9631..bbd1d399ac 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
=================
-**03:55.971** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:38.012** 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``) | 01:42.735 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:34.451 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:16.384 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:11.552 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:21.631 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:19.722 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:18.351 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.664 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.768 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.523 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 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_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index c77baf9d66..91f5085cb7 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)
- 3.1990 3.1988 3.1995 3.1988 0.0003
+ 3.1932 3.1931 3.1944 3.1921 0.0009
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 16.384 seconds)
+ **Total running time of the script:** ( 1 minutes 11.552 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 5010496ae4..c761e827f4 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)
- 769.9586 769.9373 772.4536 767.4849 2.0285
+ 710.5981 711.4943 714.4102 705.8897 3.5357
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 42.735 seconds)
+ **Total running time of the script:** ( 1 minutes 34.451 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 e5b93fdb8b..39913d9789 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
@@ -5,16 +5,16 @@
Computation times
=================
-**00:24.310** total execution time for **how_to_tune_with_autotvm** files:
+**00:22.788** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.272 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.755 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.017 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+| :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 fcc41b3927..64edc6a5f2 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: 0.037118
+ Time cost of this operator: 0.037315
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 afaf29102c..569b589207 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 306.8 98.752 (1, 2, 10, 10, 3) 2 1 [306.8]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.901 0.934 (1, 6, 10, 10) 1 1 [2.901]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.976 0.314 (1, 1, 10, 10, 3) 1 1 [0.976]
- Total_time - 310.677 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.1 98.733 (1, 2, 10, 10, 3) 2 1 [296.1]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.874 0.958 (1, 6, 10, 10) 1 1 [2.874]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.924 0.308 (1, 1, 10, 10, 3) 1 1 [0.924]
+ Total_time - 299.898 - - - - -
@@ -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 176.7 98.444 (1, 1, 10, 10, 6) 2 1 [176.7]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.928 1.074 (1, 6, 10, 10) 1 1 [1.928]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.866 0.482 (1, 3, 10, 10, 1) 1 1 [0.866]
- Total_time - 179.494 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 97.562 97.339 (1, 6, 10, 10, 1) 2 1 [97.562]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.717 1.713 (1, 6, 10, 10) 1 1 [1.717]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.95 0.948 (1, 1, 10, 10, 3) 1 1 [0.95]
+ Total_time - 100.23 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.579 seconds)
+ **Total running time of the script:** ( 1 minutes 24.184 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 a125b852c7..cfb3024fb0 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
- 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 210MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 108MB/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 36.197 seconds)
+ **Total running time of the script:** ( 1 minutes 28.404 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 de17de9c4f..13bbebb193 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
@@ -218,7 +218,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpdwf0nhqt/images/random'
+ '/tmp/tmpxozargce/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpdwf0nhqt/images/target contains 8144 images
- /tmp/tmpdwf0nhqt/images/random contains 5000 images
+ /tmp/tmpxozargce/images/target contains 8144 images
+ /tmp/tmpxozargce/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 47s - loss: 0.2233 - accuracy: 0.9235 - val_loss: 0.1041 - val_accuracy: 0.9626 - 47s/epoch - 144ms/step
+ 328/328 - 42s - loss: 0.2212 - accuracy: 0.9254 - val_loss: 0.1258 - val_accuracy: 0.9554 - 42s/epoch - 129ms/step
Epoch 2/3
- 328/328 - 44s - loss: 0.1029 - accuracy: 0.9627 - val_loss: 0.0975 - val_accuracy: 0.9668 - 44s/epoch - 135ms/step
+ 328/328 - 40s - loss: 0.1001 - accuracy: 0.9629 - val_loss: 0.1408 - val_accuracy: 0.9479 - 40s/epoch - 121ms/step
Epoch 3/3
- 328/328 - 44s - loss: 0.0731 - accuracy: 0.9730 - val_loss: 0.1568 - val_accuracy: 0.9573 - 44s/epoch - 135ms/step
+ 328/328 - 40s - loss: 0.0634 - accuracy: 0.9762 - val_loss: 0.1085 - val_accuracy: 0.9668 - 40s/epoch - 122ms/step
- <keras.callbacks.History object at 0x7f30fd260730>
+ <keras.callbacks.History object at 0x7fd4e3476670>
@@ -861,7 +861,7 @@ 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 48.901 seconds)
+ **Total running time of the script:** ( 4 minutes 33.880 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 514f8989b7..d9114027c0 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
=================
-**08:26.158** total execution time for **how_to_work_with_microtvm** files:
+**07:55.655** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:48.901 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:33.880 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:36.197 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:28.404 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:28.579 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:24.184 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.814 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.654 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:11.101 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.203 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.567 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.331 | 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 a6153fad4c..46f8696f50 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
=================
-**00:41.571** total execution time for **how_to_work_with_relay** files:
+**00:38.125** 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``) | 00:35.601 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:32.756 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.884 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.376 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.079 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.987 | 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 61e6126bae..75a4278608 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 0x7f312573a670>
+ <function my_cuda_math_rule at 0x7fd4f3b94af0>
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 aa15567a2d..be0966532c 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
=================
-**00:05.392** total execution time for **how_to_work_with_schedules** files:
+**00:05.102** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.570 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.342 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.049 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.144 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.738 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.674 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.726 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.666 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.131 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.115 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.078 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.068 | 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_tedd.py` (``tedd.py``) | 00:00.061 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.033 | 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 4eb586ae23..1e87bfbf9b 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 29.72s!
+ yolov3-tiny inference graph built in 27.15s!
@@ -445,11 +445,6 @@ Download test image
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 3.294 seconds)
-
-
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
.. only:: html
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 6dda394b96..94690e6ef3 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
=================
-**01:03.294** total execution time for **topic_vta_tutorials_frontend** files:
+**00:59.577** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:03.294 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:59.577 | 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 c5bcbb9b68..369fa9f4f3 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
=================
-**00:03.455** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.231** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.951 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.760 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.504 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.471 | 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 4035f0626e..7060b7804c 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
=================
-**00:00.822** total execution time for **topic_vta_tutorials** files:
+**00:00.769** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.424 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.401 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.398 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.368 | 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 86739de82c..b3e288d42f 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: 94.986 ms
+ Execution time of this operator: 90.779 ms
@@ -423,7 +416,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
- .T
+
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 43.913 seconds)
+ **Total running time of the script:** ( 1 minutes 26.177 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 f4df9bb1d4..3ef6988f1a 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: 9.62/9.62 result: MeasureResult(costs=(0.0279179988,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7222738265991211, timestamp=1714510681.8931835) [('tile_y', [-1, 1]), ('tile_x', [-1, 512])],None,90
- No: 2 GFLOPS: 1.31/9.62 result: MeasureResult(costs=(0.20438574799999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.5401041507720947, timestamp=1714510685.4310079) [('tile_y', [-1, 2]), ('tile_x', [-1, 1])],None,1
- No: 3 GFLOPS: 9.89/9.89 result: MeasureResult(costs=(0.0271359058,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6925051212310791, timestamp=1714510686.135488) [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
- No: 4 GFLOPS: 8.60/9.89 result: MeasureResult(costs=(0.031213139,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.809532880783081, timestamp=1714510686.905747) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
- No: 5 GFLOPS: 3.21/9.89 result: MeasureResult(costs=(0.083600965,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.622483253479004, timestamp=1714510688.660179) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
- No: 6 GFLOPS: 3.64/9.89 result: MeasureResult(costs=(0.073845331,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4764862060546875, timestamp=1714510690.1110413) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
- No: 7 GFLOPS: 2.13/9.89 result: MeasureResult(costs=(0.12602459559999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.30586314201355, timestamp=1714510692.4021127) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
- No: 8 GFLOPS: 0.87/9.89 result: MeasureResult(costs=(0.30842449860000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.178284406661987, timestamp=1714510697.5764136) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 9 GFLOPS: 1.01/9.89 result: MeasureResult(costs=(0.2661657066,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.489276170730591, timestamp=1714510702.1782653) [('tile_y', [-1, 64]), ('tile_x', [-1, 2])],None,16
- No: 10 GFLOPS: 7.31/9.89 result: MeasureResult(costs=(0.036697670599999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8282434940338135, timestamp=1714510703.0339603) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
+ No: 1 GFLOPS: 4.78/4.78 result: MeasureResult(costs=(0.0561594824,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.182835340499878, timestamp=1714580914.5191505) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 2 GFLOPS: 1.57/4.78 result: MeasureResult(costs=(0.1706294294,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.9698126316070557, timestamp=1714580917.502353) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
+ No: 3 GFLOPS: 9.91/9.91 result: MeasureResult(costs=(0.027099640799999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7900910377502441, timestamp=1714580918.191337) [('tile_y', [-1, 512]), ('tile_x', [-1, 64])],None,69
+ No: 4 GFLOPS: 11.71/11.71 result: MeasureResult(costs=(0.0229242498,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6184628009796143, timestamp=1714580918.8185418) [('tile_y', [-1, 1]), ('tile_x', [-1, 512])],None,90
+ No: 5 GFLOPS: 12.75/12.75 result: MeasureResult(costs=(0.021057696,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.614720344543457, timestamp=1714580919.5605943) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 6 GFLOPS: 13.70/13.70 result: MeasureResult(costs=(0.0196007028,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5801792144775391, timestamp=1714580920.1349947) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+ No: 7 GFLOPS: 16.92/16.92 result: MeasureResult(costs=(0.0158611386,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5352499485015869, timestamp=1714580920.6445868) [('tile_y', [-1, 16]), ('tile_x', [-1, 64])],None,64
+ No: 8 GFLOPS: 3.90/16.92 result: MeasureResult(costs=(0.06874425240000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3655414581298828, timestamp=1714580921.994091) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
+ No: 9 GFLOPS: 10.46/16.92 result: MeasureResult(costs=(0.0256584264,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6371355056762695, timestamp=1714580922.7420337) [('tile_y', [-1, 2]), ('tile_x', [-1, 64])],None,61
+ No: 10 GFLOPS: 6.31/16.92 result: MeasureResult(costs=(0.0425164608,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9081261157989502, timestamp=1714580923.6766965) [('tile_y', [-1, 16]), ('tile_x', [-1, 4])],None,24
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 0befc9a5fd..60bb4897cf 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': 499.7243982199507, 'median': 499.81111324996164, 'std': 2.514632711037611}
+ {'mean': 466.96538297999723, 'median': 466.461504650033, 'std': 2.1149238319919585}
@@ -582,29 +582,30 @@ 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: 7.36/ 17.74 GFLOPS | Progress: (4/20) | 9.38 s [Task 1/25] Current/Best: 13.22/ 17.74 GFLOPS | Progress: (8/20) | 12.58 s [Task 1/25] Current/Best: 12.70/ 18.87 GFLOPS | Progress: (12/20) | 16.13 s [Task 1/25] Current/Best: 5.40/ 18.87 GFLOPS | Progress: (16/20) | 20.57 s [Task 1/25] Current/Best: 10.95/ 18.87 GFLOPS | Progress: (20/20) | 23.98 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 16.48/ 16.48 GFLOPS | Progress: (4/20) | 4.57 s [Task 2/25] Current/Best: 16.19/ 16.48 GFLOPS | Progress: (8/20) | 6.29 s [Task 2/25] Current/Best: 13.96/ 20.46 GFLOPS | Progress: (12/20) | 8.33 s [Task 2/25] Current/Best: 2.06/ 20.46 GFLOPS | Progress: (16/20) | 11.18 s [Task 2/25] Current/Best: 15.45/ 20.46 GFLOPS | Progress: (20/20) | 12.74 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 6.02/ 23.24 GFLOPS | Progress: (4/20) | 5.32 s [Task 3/25] Current/Best: 21.62/ 23.24 GFLOPS | Progress: (8/20) | 7.80 s [Task 3/25] Current/Best: 13.51/ 23.24 GFLOPS | Progress: (12/20) | 10.48 s [Task 3/25] Current/Best: 23.94/ 23.94 GFLOPS | Progress: (16/20) | 12.98 s [Task 3/25] Current/Best: 8.14/ 23.94 GFLOPS | Progress: (20/20) | 15.69 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 6.42/ 14.24 GFLOPS | Progress: (4/20) | 5.06 s [Task 4/25] Current/Best: 7.30/ 14.24 GFLOPS | Progress: (8/20) | 6.86 s [Task 4/25] Current/Best: 16.86/ 17.15 GFLOPS | Progress: (12/20) | 8.78 s [Task 4/25] Current/Best: 15.52/ 17.15 GFLOPS | Progress: (16/20) | 11.49 s [Task 4/25] Current/Best: 15.41/ 17.68 GFLOPS | Progress: (20/20) | 14.67 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 7.18/ 20.61 GFLOPS | Progress: (4/20) | 4.81 s [Task 5/25] Current/Best: 15.49/ 20.61 GFLOPS | Progress: (8/20) | 6.99 s [Task 5/25] Current/Best: 9.84/ 20.61 GFLOPS | Progress: (12/20) | 9.31 s [Task 5/25] Current/Best: 5.75/ 20.61 GFLOPS | Progress: (16/20) | 11.47 s [Task 5/25] Current/Best: 10.56/ 20.61 GFLOPS | Progress: (20/20) | 14.61 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 5.53/ 16.79 GFLOPS | Progress: (4/20) | 5.26 s [Task 6/25] Current/Best: 10.48/ 16.79 GFLOPS | Progress: (8/20) | 8.42 s [Task 6/25] Current/Best: 3.50/ 16.79 GFLOPS | Progress: (12/20) | 12.42 s [Task 6/25] Current/Best: 4.92/ 21.37 GFLOPS | Progress: (16/20) | 15.00 s [Task 6/25] Current/Best: 15.55/ 21.37 GFLOPS | Progress: (20/20) | 18.48 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 9.18/ 16.19 GFLOPS | Progress: (4/20) | 6.11 s [Task 7/25] Current/Best: 22.12/ 22.12 GFLOPS | Progress: (8/20) | 8.92 s [Task 7/25] Current/Best: 9.63/ 22.12 GFLOPS | Progress: (12/20) | 11.63 s [Task 7/25] Current/Best: 12.47/ 22.12 GFLOPS | Progress: (16/20) | 14.42 s [Task 7/25] Current/Best: 7.82/ 22.12 GFLOPS | Progress: (20/20) | 16.82 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 3.06/ 14.55 GFLOPS | Progress: (4/20) | 6.57 s [Task 8/25] Current/Best: 6.72/ 14.55 GFLOPS | Progress: (8/20) | 10.15 s [Task 8/25] Current/Best: 6.59/ 18.41 GFLOPS | Progress: (12/20) | 14.61 s [Task 8/25] Current/Best: 14.29/ 18.41 GFLOPS | Progress: (16/20) | 17.72 s [Task 8/25] Current/Best: 7.82/ 18.41 GFLOPS | Progress: (20/20) | 22.39 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 8.59/ 15.50 GFLOPS | Progress: (4/20) | 9.79 s [Task 9/25] Current/Best: 10.61/ 15.50 GFLOPS | Progress: (8/20) | 15.71 s [Task 9/25] Current/Best: 16.33/ 16.33 GFLOPS | Progress: (12/20) | 17.89 s [Task 9/25] Current/Best: 14.66/ 16.33 GFLOPS | Progress: (16/20) | 26.11 s [Task 9/25] Current/Best: 6.47/ 16.64 GFLOPS | Progress: (20/20) | 32.16 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 12.86/ 16.77 GFLOPS | Progress: (4/20) | 5.09 s [Task 10/25] Current/Best: 6.25/ 16.86 GFLOPS | Progress: (8/20) | 7.13 s [Task 10/25] Current/Best: 5.07/ 21.57 GFLOPS | Progress: (12/20) | 9.13 s [Task 10/25] Current/Best: 5.17/ 21.57 GFLOPS | Progress: (16/20) | 11.88 s [Task 10/25] Current/Best: 2.61/ 21.57 GFLOPS | Progress: (20/20) | 15.71 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 19.69/ 22.42 GFLOPS | Progress: (4/20) | 5.05 s [Task 11/25] Current/Best: 19.49/ 22.42 GFLOPS | Progress: (8/20) | 7.76 s [Task 11/25] Current/Best: 9.52/ 22.42 GFLOPS | Progress: (12/20) | 11.10 s [Task 11/25] Current/Best: 4.92/ 22.42 GFLOPS | Progress: (16/20) | 14.38 s [Task 11/25] Current/Best: 11.87/ 22.49 GFLOPS | Progress: (20/20) | 17.07 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 20.95/ 20.95 GFLOPS | Progress: (4/20) | 7.08 s [Task 12/25] Current/Best: 13.44/ 20.95 GFLOPS | Progress: (8/20) | 9.33 s [Task 12/25] Current/Best: 15.90/ 20.95 GFLOPS | Progress: (12/20) | 12.95 s [Task 12/25] Current/Best: 5.69/ 20.95 GFLOPS | Progress: (16/20) | 16.29 s [Task 12/25] Current/Best: 16.48/ 20.95 GFLOPS | Progress: (20/20) | 20.66 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 7.22/ 17.82 GFLOPS | Progress: (4/20) | 5.58 s [Task 13/25] Current/Best: 9.49/ 17.82 GFLOPS | Progress: (8/20) | 8.28 s [Task 13/25] Current/Best: 21.92/ 21.92 GFLOPS | Progress: (12/20) | 10.38 s [Task 13/25] Current/Best: 8.07/ 21.92 GFLOPS | Progress: (16/20) | 14.52 s [Task 13/25] Current/Best: 17.63/ 22.81 GFLOPS | Progress: (20/20) | 17.79 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 9.82/ 20.80 GFLOPS | Progress: (4/20) | 6.40 s [Task 14/25] Current/Best: 7.65/ 20.80 GFLOPS | Progress: (8/20) | 11.37 s [Task 14/25] Current/Best: 9.53/ 20.80 GFLOPS | Progress: (12/20) | 21.67 s [Task 14/25] Current/Best: 3.74/ 20.80 GFLOPS | Progress: (16/20) | 24.77 s [Task 14/25] Current/Best: 6.75/ 20.80 GFLOPS | Progress: (20/20) | 27.66 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 15.46/ 15.46 GFLOPS | Progress: (4/20) | 6.71 s [Task 15/25] Current/Best: 7.90/ 17.19 GFLOPS | Progress: (8/20) | 13.42 s [Task 15/25] Current/Best: 8.32/ 22.31 GFLOPS | Progress: (12/20) | 18.87 s [Task 15/25] Current/Best: 15.77/ 22.31 GFLOPS | Progress: (16/20) | 22.62 s [Task 15/25] Current/Best: 14.38/ 22.31 GFLOPS | Progress: (20/20) | 24.44 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 15.14/ 20.84 GFLOPS | Progress: (4/20) | 4.40 s [Task 16/25] Current/Best: 12.09/ 20.84 GFLOPS | Progress: (8/20) | 7.72 s [Task 16/25] Current/Best: 7.96/ 20.84 GFLOPS | Progress: (12/20) | 9.62 s [Task 16/25] Current/Best: 13.79/ 20.84 GFLOPS | Progress: (16/20) | 12.17 s [Task 16/25] Current/Best: 15.09/ 20.84 GFLOPS | Progress: (20/20) | 14.59 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 12.93/ 19.89 GFLOPS | Progress: (4/20) | 5.02 s [Task 17/25] Current/Best: 14.90/ 20.30 GFLOPS | Progress: (8/20) | 7.34 s [Task 17/25] Current/Best: 22.32/ 22.32 GFLOPS | Progress: (12/20) | 10.97 s [Task 17/25] Current/Best: 1.54/ 22.47 GFLOPS | Progress: (16/20) | 14.97 s [Task 17/25] Current/Best: 6.18/ 22.47 GFLOPS | Progress: (20/20) | 17.41 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 3.00/ 18.97 GFLOPS | Progress: (4/20) | 5.52 s [Task 18/25] Current/Best: 13.05/ 18.97 GFLOPS | Progress: (8/20) | 8.39 s [Task 18/25] Current/Best: 7.18/ 18.97 GFLOPS | Progress: (12/20) | 10.88 s [Task 18/25] Current/Best: 9.30/ 19.09 GFLOPS | Progress: (16/20) | 15.89 s [Task 18/25] Current/Best: 17.84/ 19.09 GFLOPS | Progress: (20/20) | 18.55 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 17.97/ 19.48 GFLOPS | Progress: (4/20) | 7.65 s [Task 19/25] Current/Best: 5.09/ 21.00 GFLOPS | Progress: (8/20) | 11.19 s [Task 19/25] Current/Best: 16.80/ 21.84 GFLOPS | Progress: (12/20) | 14.32 s [Task 19/25] Current/Best: 9.12/ 21.84 GFLOPS | Progress: (16/20) | 19.84 s [Task 19/25] Current/Best: 11.47/ 22.04 GFLOPS | Progress: (20/20) | 23.73 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 9.11/ 18.04 GFLOPS | Progress: (4/20) | 6.40 s [Task 20/25] Current/Best: 9.81/ 18.04 GFLOPS | Progress: (8/20) | 18.15 s [Task 20/25] Current/Best: 14.06/ 18.04 GFLOPS | Progress: (12/20) | 21.97 s [Task 20/25] Current/Best: 5.59/ 19.73 GFLOPS | Progress: (16/20) | 25.19 s [Task 20/25] Current/Best: 5.10/ 19.73 GFLOPS | Progress: (20/20) | 37.26 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 5.49/ 13.47 GFLOPS | Progress: (4/20) | 14.43 s Done.
- [Task 21/25] Current/Best: 8.00/ 13.47 GFLOPS | Progress: (8/20) | 26.04 s [Task 21/25] Current/Best: 7.13/ 19.74 GFLOPS | Progress: (12/20) | 29.71 s [Task 21/25] Current/Best: 12.29/ 19.74 GFLOPS | Progress: (16/20) | 40.92 s [Task 21/25] Current/Best: 8.04/ 21.42 GFLOPS | Progress: (20/20) | 47.20 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 18.60/ 18.60 GFLOPS | Progress: (4/20) | 5.95 s [Task 22/25] Current/Best: 8.57/ 19.97 GFLOPS | Progress: (8/20) | 8.79 s [Task 22/25] Current/Best: 1.55/ 19.97 GFLOPS | Progress: (12/20) | 12.88 s [Task 22/25] Current/Best: 19.06/ 20.18 GFLOPS | Progress: (16/20) | 14.65 s [Task 22/25] Current/Best: 7.15/ 20.18 GFLOPS | Progress: (20/20) | 17.46 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 19.07/ 19.07 GFLOPS | Progress: (4/20) | 7.15 s [Task 23/25] Current/Best: 21.14/ 21.14 GFLOPS | Progress: (8/20) | 9.86 s [Task 23/25] Current/Best: 11.46/ 21.14 GFLOPS | Progress: (12/20) | 15.06 s [Task 23/25] Current/Best: 8.98/ 21.14 GFLOPS | Progress: (16/20) | 20.43 s [Task 23/25] Current/Best: 12.01/ 21.14 GFLOPS | Progress: (20/20) | 23.66 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 2.36/ 5.37 GFLOPS | Progress: (4/20) | 9.84 s [Task 24/25] Current/Best: 3.19/ 5.75 GFLOPS | Progress: (8/20) | 20.92 s [Task 24/25] Current/Best: 3.68/ 5.75 GFLOPS | Progress: (12/20) | 26.28 s [Task 24/25] Current/Best: 5.59/ 5.75 GFLOPS | Progress: (16/20) | 29.32 s [Task 24/25] Current/Best: 1.54/ 5.75 GFLOPS | Progress: (20/20) | 37.41 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 8.06/ 9.73 GFLOPS | Progress: (4/20) | 7.74 s [Task 25/25] Current/Best: 2.98/ 9.73 GFLOPS | Progress: (8/20) | 9.94 s [Task 25/25] Current/Best: 8.89/ 9.73 GFLOPS | Progress: (12/20) | 20.64 s [Task 25/25] Current/Best: 7.77/ 9.73 GFLOPS | Progress: (16/20) | 26.41 s [Task 25/25] Current/Best: 7.72/ 9.73 GFLOPS | Progress: (20/2
0) | 32.26 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 18.44/ 18.44 GFLOPS | Progress: (4/20) | 7.87 s [Task 1/25] Current/Best: 11.78/ 18.44 GFLOPS | Progress: (8/20) | 15.40 s [Task 1/25] Current/Best: 5.36/ 23.01 GFLOPS | Progress: (12/20) | 18.00 s [Task 1/25] Current/Best: 16.07/ 24.25 GFLOPS | Progress: (16/20) | 20.51 s [Task 1/25] Current/Best: 24.80/ 24.80 GFLOPS | Progress: (20/20) | 23.06 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 17.15/ 17.15 GFLOPS | Progress: (4/20) | 4.26 s [Task 2/25] Current/Best: 8.18/ 17.15 GFLOPS | Progress: (8/20) | 6.33 s [Task 2/25] Current/Best: 7.18/ 17.83 GFLOPS | Progress: (12/20) | 7.71 s [Task 2/25] Current/Best: 12.25/ 17.83 GFLOPS | Progress: (16/20) | 9.32 s [Task 2/25] Current/Best: 19.61/ 19.61 GFLOPS | Progress: (20/20) | 10.78 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 18.74/ 23.39 GFLOPS | Progress: (4/20) | 4.52 s [Task 3/25] Current/Best: 8.37/ 23.39 GFLOPS | Progress: (8/20) | 6.75 s [Task 3/25] Current/Best: 10.27/ 23.39 GFLOPS | Progress: (12/20) | 9.37 s [Task 3/25] Current/Best: 5.45/ 23.39 GFLOPS | Progress: (16/20) | 12.14 s [Task 3/25] Current/Best: 19.75/ 23.39 GFLOPS | Progress: (20/20) | 14.25 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 18.55/ 18.55 GFLOPS | Progress: (4/20) | 4.36 s [Task 4/25] Current/Best: 20.04/ 20.04 GFLOPS | Progress: (8/20) | 6.93 s [Task 4/25] Current/Best: 19.42/ 20.04 GFLOPS | Progress: (12/20) | 8.65 s [Task 4/25] Current/Best: 17.52/ 20.04 GFLOPS | Progress: (16/20) | 16.32 s [Task 4/25] Current/Best: 12.51/ 20.04 GFLOPS | Progress: (20/20) | 18.27 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 15.27/ 15.27 GFLOPS | Progress: (4/20) | 4.84 s [Task 5/25] Current/Best: 21.65/ 21.65 GFLOPS | Progress: (8/20) | 6.73 s [Task 5/25] Current/Best: 18.41/ 21.65 GFLOPS | Progress: (12/20) | 8.91 s [Task 5/25] Current/Best: 18.22/ 21.65 GFLOPS | Progress: (16/20) | 10.94 s [Task 5/25] Current/Best: 6.36/ 21.65 GFLOPS | Progress: (20/20) | 13.78 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 6.06/ 20.88 GFLOPS | Progress: (4/20) | 4.76 s [Task 6/25] Current/Best: 14.49/ 20.88 GFLOPS | Progress: (8/20) | 7.61 s [Task 6/25] Current/Best: 20.15/ 20.88 GFLOPS | Progress: (12/20) | 9.42 s [Task 6/25] Current/Best: 12.04/ 20.88 GFLOPS | Progress: (16/20) | 13.24 s [Task 6/25] Current/Best: 15.42/ 20.88 GFLOPS | Progress: (20/20) | 15.58 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 21.77/ 21.77 GFLOPS | Progress: (4/20) | 4.90 s [Task 7/25] Current/Best: 17.10/ 21.77 GFLOPS | Progress: (8/20) | 7.80 s [Task 7/25] Current/Best: 12.71/ 21.77 GFLOPS | Progress: (12/20) | 10.13 s [Task 7/25] Current/Best: 19.41/ 23.74 GFLOPS | Progress: (16/20) | 12.22 s [Task 7/25] Current/Best: 6.95/ 23.74 GFLOPS | Progress: (20/20) | 14.58 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 2.92/ 20.18 GFLOPS | Progress: (4/20) | 5.49 s [Task 8/25] Current/Best: 15.70/ 20.18 GFLOPS | Progress: (8/20) | 17.09 s [Task 8/25] Current/Best: 15.86/ 20.18 GFLOPS | Progress: (12/20) | 22.12 s [Task 8/25] Current/Best: 13.70/ 20.18 GFLOPS | Progress: (16/20) | 26.75 s [Task 8/25] Current/Best: 10.13/ 20.18 GFLOPS | Progress: (20/20) | 38.21 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 13.08/ 14.35 GFLOPS | Progress: (4/20) | 4.34 s [Task 9/25] Current/Best: 7.96/ 20.79 GFLOPS | Progress: (8/20) | 14.81 s [Task 9/25] Current/Best: 11.48/ 20.79 GFLOPS | Progress: (12/20) | 19.80 s [Task 9/25] Current/Best: 9.61/ 20.79 GFLOPS | Progress: (16/20) | 22.31 s [Task 9/25] Current/Best: 16.45/ 21.11 GFLOPS | Progress: (20/
20) | 24.13 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 20.41/ 20.41 GFLOPS | Progress: (4/20) | 4.74 s [Task 10/25] Current/Best: 16.62/ 20.41 GFLOPS | Progress: (8/20) | 7.00 s [Task 10/25] Current/Best: 14.86/ 20.41 GFLOPS | Progress: (12/20) | 8.96 s [Task 10/25] Current/Best: 17.39/ 20.41 GFLOPS | Progress: (16/20) | 10.85 s [Task 10/25] Current/Best: 10.27/ 20.41 GFLOPS | Progress: (20/20) | 13.00 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 18.25/ 23.18 GFLOPS | Progress: (4/20) | 5.10 s [Task 11/25] Current/Best: 19.00/ 23.18 GFLOPS | Progress: (8/20) | 7.70 s [Task 11/25] Current/Best: 12.82/ 23.21 GFLOPS | Progress: (12/20) | 9.80 s [Task 11/25] Current/Best: 3.21/ 23.21 GFLOPS | Progress: (16/20) | 12.49 s [Task 11/25] Current/Best: 12.67/ 23.21 GFLOPS | Progress: (20/20) | 15.24 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 14.85/ 15.77 GFLOPS | Progress: (4/20) | 5.20 s [Task 12/25] Current/Best: 17.50/ 17.50 GFLOPS | Progress: (8/20) | 7.65 s [Task 12/25] Current/Best: 12.57/ 18.54 GFLOPS | Progress: (12/20) | 11.03 s [Task 12/25] Current/Best: 4.76/ 18.54 GFLOPS | Progress: (16/20) | 14.19 s [Task 12/25] Current/Best: 15.19/ 18.54 GFLOPS | Progress: (20/20) | 16.92 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 11.61/ 21.26 GFLOPS | Progress: (4/20) | 5.80 s [Task 13/25] Current/Best: 12.58/ 21.26 GFLOPS | Progress: (8/20) | 7.91 s [Task 13/25] Current/Best: 12.25/ 21.26 GFLOPS | Progress: (12/20) | 11.42 s [Task 13/25] Current/Best: 3.19/ 21.26 GFLOPS | Progress: (16/20) | 15.57 s [Task 13/25] Current/Best: 10.28/ 21.26 GFLOPS | Progress: (20/20) | 19.83 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 15.53/ 21.49 GFLOPS | Progress: (4/20) | 5.03 s [Task 14/25] Current/Best: 12.36/ 21.49 GFLOPS | Progress: (8/20) | 10.43 s [Task 14/25] Current/Best: 9.87/ 21.49 GFLOPS | Progress: (12/20) | 16.86 s [Task 14/25] Current/Best: 11.68/ 21.49 GFLOPS | Progress: (16/20) | 22.28 s [Task 14/25] Current/Best: 5.76/ 21.49 GFLOPS | Progress: (20/20) | 34.18 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 5.74/ 15.25 GFLOPS | Progress: (4/20) | 15.38 s [Task 15/25] Current/Best: 14.48/ 18.88 GFLOPS | Progress: (8/20) | 17.67 s [Task 15/25] Current/Best: 7.10/ 18.88 GFLOPS | Progress: (12/20) | 19.60 s [Task 15/25] Current/Best: 10.54/ 20.52 GFLOPS | Progress: (16/20) | 22.87 s [Task 15/25] Current/Best: 18.87/ 20.52 GFLOPS | Progress: (20
/20) | 30.92 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 17.03/ 17.76 GFLOPS | Progress: (4/20) | 4.07 s [Task 16/25] Current/Best: 7.62/ 17.76 GFLOPS | Progress: (8/20) | 6.22 s [Task 16/25] Current/Best: 19.97/ 19.97 GFLOPS | Progress: (12/20) | 8.33 s [Task 16/25] Current/Best: 20.95/ 20.95 GFLOPS | Progress: (16/20) | 10.07 s [Task 16/25] Current/Best: 17.75/ 20.95 GFLOPS | Progress: (20/20) | 11.92 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 21.27/ 21.27 GFLOPS | Progress: (4/20) | 5.11 s [Task 17/25] Current/Best: 9.48/ 23.38 GFLOPS | Progress: (8/20) | 7.14 s [Task 17/25] Current/Best: 3.20/ 23.38 GFLOPS | Progress: (12/20) | 10.18 s [Task 17/25] Current/Best: 23.90/ 23.90 GFLOPS | Progress: (16/20) | 12.25 s [Task 17/25] Current/Best: 10.12/ 23.90 GFLOPS | Progress: (20/20) | 14.71 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 17.85/ 19.04 GFLOPS | Progress: (4/20) | 4.96 s [Task 18/25] Current/Best: 12.59/ 19.42 GFLOPS | Progress: (8/20) | 7.31 s [Task 18/25] Current/Best: 4.38/ 19.42 GFLOPS | Progress: (12/20) | 11.52 s [Task 18/25] Current/Best: 16.48/ 22.40 GFLOPS | Progress: (16/20) | 13.93 s [Task 18/25] Current/Best: 15.46/ 22.40 GFLOPS | Progress: (20/20) | 19.24 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 14.16/ 14.16 GFLOPS | Progress: (4/20) | 7.89 s Done.
+ Done.
+ [Task 19/25] Current/Best: 19.90/ 19.90 GFLOPS | Progress: (8/20) | 10.96 s [Task 19/25] Current/Best: 13.16/ 23.22 GFLOPS | Progress: (12/20) | 14.31 s [Task 19/25] Current/Best: 3.20/ 23.22 GFLOPS | Progress: (16/20) | 18.21 s [Task 19/25] Current/Best: 12.40/ 23.22 GFLOPS | Progress: (20/20) | 20.72 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 11.28/ 21.87 GFLOPS | Progress: (4/20) | 9.93 s [Task 20/25] Current/Best: 17.04/ 21.87 GFLOPS | Progress: (8/20) | 12.50 s [Task 20/25] Current/Best: 12.41/ 21.87 GFLOPS | Progress: (12/20) | 23.90 s [Task 20/25] Current/Best: 5.31/ 21.87 GFLOPS | Progress: (16/20) | 37.44 s [Task 20/25] Current/Best: 9.39/ 21.87 GFLOPS | Progress: (20/20) | 49.73 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 20.65/ 20.65 GFLOPS | Progress: (4/20) | 6.80 s [Task 21/25] Current/Best: 9.01/ 20.65 GFLOPS | Progress: (8/20) | 10.39 s [Task 21/25] Current/Best: 8.23/ 20.65 GFLOPS | Progress: (12/20) | 18.92 s [Task 21/25] Current/Best: 19.65/ 20.65 GFLOPS | Progress: (16/20) | 21.42 s [Task 21/25] Current/Best: 13.56/ 20.75 GFLOPS | Progress: (20/
20) | 23.54 s Done.
+ [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 7.50/ 22.05 GFLOPS | Progress: (4/20) | 5.20 s [Task 22/25] Current/Best: 10.97/ 22.05 GFLOPS | Progress: (8/20) | 6.78 s [Task 22/25] Current/Best: 10.93/ 22.05 GFLOPS | Progress: (12/20) | 10.45 s [Task 22/25] Current/Best: 18.24/ 22.05 GFLOPS | Progress: (16/20) | 12.38 s [Task 22/25] Current/Best: 21.46/ 22.05 GFLOPS | Progress: (20/20) | 15.77 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 7.90/ 21.42 GFLOPS | Progress: (4/20) | 5.55 s [Task 23/25] Current/Best: 3.19/ 23.62 GFLOPS | Progress: (8/20) | 9.40 s [Task 23/25] Current/Best: 12.35/ 23.62 GFLOPS | Progress: (12/20) | 12.63 s [Task 23/25] Current/Best: 22.71/ 23.62 GFLOPS | Progress: (16/20) | 15.53 s [Task 23/25] Current/Best: 13.05/ 23.62 GFLOPS | Progress: (20/20) | 20.99 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.43/ 10.14 GFLOPS | Progress: (4/20) | 5.44 s [Task 24/25] Current/Best: 7.09/ 10.14 GFLOPS | Progress: (8/20) | 11.31 s [Task 24/25] Current/Best: 4.14/ 10.14 GFLOPS | Progress: (12/20) | 14.52 s [Task 24/25] Current/Best: 3.69/ 10.14 GFLOPS | Progress: (16/20) | 25.22 s [Task 24/25] Current/Best: 8.27/ 10.14 GFLOPS | Progress: (20/20) | 35.90 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 1.60/ 5.65 GFLOPS | Progress: (4/20) | 6.64 s [Task 25/25] Current/Best: 5.98/ 7.69 GFLOPS | Progress: (8/20) | 8.67 s [Task 25/25] Current/Best: 8.12/ 9.91 GFLOPS | Progress: (12/20) | 16.08 s [Task 25/25] Current/Best: 5.93/ 9.91 GFLOPS | Progress: (16/20) | 17.58 s [Task 25/25] Current/Best: 3.10/ 9.91 GFLOPS | Progress: (20/2
0) | 23.70 s Done.
+
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
Done.
Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 408.56563000994356, 'median': 408.11876299994765, 'std': 2.2131291369284547}
- unoptimized: {'mean': 499.7243982199507, 'median': 499.81111324996164, 'std': 2.514632711037611}
+ optimized: {'mean': 381.52636587003144, 'median': 380.5707524999889, 'std': 4.7648286567901526}
+ unoptimized: {'mean': 466.96538297999723, 'median': 466.461504650033, 'std': 2.1149238319919585}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 56.326 seconds)
+ **Total running time of the script:** ( 13 minutes 8.184 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 be0b867357..d688bd660c 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
- 1.249e-07 secs/op
+ 1.234e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index c60353fc7b..cf46fe8e62 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, 0x25a6f020)), stage(b, placeholder(b, 0x10c486a0)), 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, 0x101c2d40)), stage(b, placeholder(b, 0x101c5060)), 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 4f717625c3..ba6e96f945 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**17:57.136** total execution time for **tutorial** files:
+**16:32.770** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:56.326 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:08.184 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:43.913 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:26.177 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.360 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.269 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:46.068 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:42.624 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:28.458 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:16.545 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.911 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.937 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.880 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.853 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.219 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.180 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.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 2844a43260..53f36e281d 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,7 +285,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000008
+ Numpy running time: 0.000006
naive: 0.000006
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000008
+ parallel: 0.000006
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000040
+ vector: 0.000039
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.190910011762754e-06 1.0
- naive 5.7887999999999995e-06 0.7067346597248478
- parallel 7.6516e-06 0.93415749764211
- vector 3.99433e-05 4.876539962304367
+ numpy 5.633540004055249e-06 1.0
+ naive 5.6422e-06 1.001537220990446
+ parallel 5.8766e-06 1.043145161970943
+ vector 3.87928e-05 6.886043228960011
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.019484
+ Numpy running time: 0.013454
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.342058
+ none: 3.305080
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.320073
+ blocking: 0.282700
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.320353
+ vectorization: 0.261385
# 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: 0.122648
+ loop permutation: 0.108928
# 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: 0.104932
+ array packing: 0.102553
# 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: 0.111302
+ block caching: 0.093818
# 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: 0.133448
+ parallelization: 0.112566
# 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 3.3420582105000003 1.0
- blocking 0.32007252360000005 0.09577107980776746
- vectorization 0.3203525687 0.09585487400953215
- loop permutation 0.1226484036 0.03669846420228891
- array packing 0.104931814 0.0313973627599686
- block caching 0.11130191240000001 0.03330340328912114
- parallelization 0.1334483614 0.039929993134390974
+ none 3.3050804812 1.0
+ blocking 0.2827004809 0.08553512766423099
+ vectorization 0.2613854285 0.07908594964232062
+ loop permutation 0.1089276653 0.032957643821263566
+ array packing 0.10255307840000001 0.031028920168009132
+ block caching 0.0938183161 0.02838609124154722
+ parallelization 0.1125664412 0.03405860820645725
@@ -1594,11 +1594,6 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 0.360 seconds)
-
-
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index a71de271cd..8c9886d9db 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-a320b63198f14fa273e5104e506363bb1a85d9ba
+20d769617fa6ab561d7ed2b7cd61ed2b6b4710ba
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 5dacb44804..e98afa306f 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 42.892 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.269 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_keras.html b/docs/how_to/compile_models/from_keras.html
index a25e1650da..6e7f82457d 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -525,7 +525,7 @@ Tensorflow is also required since it’s used as the default backend of keras.</
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 952ms/step
+1/1 [==============================] - 1s 925ms/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index e15ea710fb..79d7dcd3e7 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -468,14 +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
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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 98ddc165c2..34d23aa42f 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -503,7 +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 9.601 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.681 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 79fe420ac4..91fa2fa06e 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -451,11 +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 98178fa8c9..7b9479193d 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -668,7 +668,7 @@ 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 37.013 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.931 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 52755c25fa..a611323df3 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>07:12.880</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:40.197</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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<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 class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
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</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<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>00:11.501</p></td>
<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>00:03.031</p></td>
+<td><p>00:02.850</p></td>
<td><p>0.0 MB</p></td>
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</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index bf419a772d..65af5e2bd0 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)
- 4129.0682 4128.5797 4134.1013 4126.6861 2.3136
+ 3995.1092 3995.1356 4000.3554 3992.0123 2.6385
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.881 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.272 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index aab065f988..6d5bcfe4fc 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
@@ -458,21 +458,25 @@ 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>
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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 63a30867c7..b4f5e8d0b4 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)
- 15.2613 15.2470 15.4540 15.0844 0.1284
+ 13.8544 13.6712 14.4706 13.6148 0.3138
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 885c635ab6..c4769cec56 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -474,30 +474,30 @@ 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).
@@ -588,7 +588,7 @@ 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 53.791 seconds)</p>
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<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 d6fe26df84..dcf9e3b7d2 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -515,8 +515,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
0%| | 0.00/13.6M [00:00<?, ?B/s]
- 59%|#####8 | 7.99M/13.6M [00:00<00:00, 50.7MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 64.0MB/s]
+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 41.4MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 52.4MB/s]
</pre></div>
</div>
</div>
@@ -607,7 +607,7 @@ 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)
- 89.4760 89.4854 90.1533 88.9613 0.2534
+ 86.1471 86.1470 87.3198 85.5411 0.2720
</pre></div>
</div>
<div class="admonition note">
@@ -646,7 +646,7 @@ 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 37.969 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.718 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 51bebdfd4a..dc751cb2e7 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)
- 111.1993 111.1829 112.1194 110.5506 0.2942
+ 103.5244 103.4213 105.2008 102.9670 0.4021
</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 3f7436e92a..423ac16aaf 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>10:42.478</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>09:42.961</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>03:53.791</p></td>
+<td><p>03:25.792</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>01:37.969</p></td>
+<td><p>01:29.718</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>01:22.881</p></td>
+<td><p>01:18.272</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:57.101</p></td>
+<td><p>00:51.670</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:55.003</p></td>
+<td><p>00:50.602</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>00:51.110</p></td>
+<td><p>00:47.264</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>00:32.416</p></td>
+<td><p>00:29.882</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>00:32.197</p></td>
+<td><p>00:29.755</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>00:00.007</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 0a1f172dd2..327772cb8b 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>00:04.324</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.002</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,15 +369,15 @@
</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>00:03.008</p></td>
+<td><p>00:02.759</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>00:01.308</p></td>
+<td><p>00:01.236</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>
-<td><p>00:00.008</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 528667c7b0..8442621e7d 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: 26012us [26012us] (48.55%; 48.55%)
-FoldScaleAxis: 27568us [11us] (51.45%; 51.45%)
- FoldConstant: 27557us [1856us] (51.43%; 99.96%)
- InferType: 25701us [25701us] (47.97%; 93.27%)
+InferType: 24624us [24624us] (48.41%; 48.41%)
+FoldScaleAxis: 26241us [9us] (51.59%; 51.59%)
+ FoldConstant: 26231us [1802us] (51.57%; 99.97%)
+ InferType: 24430us [24430us] (48.03%; 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: 25934us [25934us] (48.36%; 48.36%)
-FoldScaleAxis: 27695us [10us] (51.64%; 51.64%)
- FoldConstant: 27686us [1989us] (51.62%; 99.97%)
- InferType: 25697us [25697us] (47.92%; 92.82%)
+InferType: 24193us [24193us] (48.32%; 48.32%)
+FoldScaleAxis: 25873us [7us] (51.68%; 51.68%)
+ FoldConstant: 25867us [1782us] (51.66%; 99.97%)
+ InferType: 24085us [24085us] (48.11%; 93.11%)
</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 22aec631c1..d83a62697b 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: 34.898303 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 37.080863 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 70c3314fd1..dec537d460 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: 12.026739 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.223975 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 c539bb142a..f0514907e5 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: 0.019675
-Baseline: 3.341123
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013639
+Baseline: 3.303306
</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: 0.327822
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.290055
</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: 0.322427
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.274088
</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: 0.125736
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.109759
</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: 0.108541
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.103649
</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: 0.112312
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.094723
</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: 0.134857
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.113168
</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 c0b426bc95..75239ac47a 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 @@
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<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>
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<table class="docutils align-default">
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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 cb9a492783..12260e0b12 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:
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</pre></div>
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To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
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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>
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<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>
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Best config:
,None
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--------- --- -------- ------- ----- ------ ------- ----------------
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@@ -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)
--------- --- -------- ------- ----- ------ ------- ----------------
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-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.928 1.074 (1, 6, 10, 10) 1 1 [1.928]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.866 0.482 (1, 3, 10, 10, 1) 1 1 [0.866]
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--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -475,7 +475,7 @@ 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
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/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,
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@@ -603,7 +603,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
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--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -544,7 +544,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
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@@ -604,8 +604,8 @@ objects to other stuff? We can display some examples from our datasets using <co
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+<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/tmpxozargce/images/target contains 8144 images
+/tmp/tmpxozargce/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -717,13 +717,13 @@ 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 - 47s - loss: 0.2233 - accuracy: 0.9235 - val_loss: 0.1041 - val_accuracy: 0.9626 - 47s/epoch - 144ms/step
+328/328 - 42s - loss: 0.2212 - accuracy: 0.9254 - val_loss: 0.1258 - val_accuracy: 0.9554 - 42s/epoch - 129ms/step
Epoch 2/3
-328/328 - 44s - loss: 0.1029 - accuracy: 0.9627 - val_loss: 0.0975 - val_accuracy: 0.9668 - 44s/epoch - 135ms/step
+328/328 - 40s - loss: 0.1001 - accuracy: 0.9629 - val_loss: 0.1408 - val_accuracy: 0.9479 - 40s/epoch - 121ms/step
Epoch 3/3
-328/328 - 44s - loss: 0.0731 - accuracy: 0.9730 - val_loss: 0.1568 - val_accuracy: 0.9573 - 44s/epoch - 135ms/step
+328/328 - 40s - loss: 0.0634 - accuracy: 0.9762 - val_loss: 0.1085 - val_accuracy: 0.9668 - 40s/epoch - 122ms/step
-<keras.callbacks.History object at 0x7f30fd260730>
+<keras.callbacks.History object at 0x7fd4e3476670>
</pre></div>
</div>
</div>
@@ -987,7 +987,7 @@ 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 48.901 seconds)</p>
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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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 ef43532a6b..7ffbc3b47f 100644
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:26.158</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>07:55.655</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:28.579</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="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>
-<td><p>00:12.814</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>00:11.101</p></td>
+<td><p>00:10.203</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>00:08.567</p></td>
+<td><p>00:07.331</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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 e619e119f3..37599b8e77 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>00:41.571</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:38.125</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -369,19 +369,19 @@
</colgroup>
<tbody>
<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>00:35.601</p></td>
+<td><p>00:32.756</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>00:03.884</p></td>
+<td><p>00:03.376</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
-<td><p>00:02.079</p></td>
+<td><p>00:01.987</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
+<td><p>00:00.006</p></td>
<td><p>0.0 MB</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 f3b09ad3ba..893c34e634 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 0x7f312573a670>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fd4f3b94af0>
</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 56eb21e4b3..717d7c6616 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>00:05.392</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.102</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,35 +369,35 @@
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<tbody>
<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>
-<td><p>00:02.570</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>
-<td><p>00:01.049</p></td>
+<td><p>00:01.144</p></td>
<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>
-<td><p>00:00.738</p></td>
+<td><p>00:00.674</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>00:00.666</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
-<td><p>00:00.131</p></td>
+<td><p>00:00.115</p></td>
<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>
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<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>00:00.061</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
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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 182d3145e4..9cac4f52ee 100644
--- a/docs/install/nnpack.html
+++ b/docs/install/nnpack.html
@@ -249,7 +249,17 @@
<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">
-<li class="toctree-l2"><a class="reference internal" href="from_source.html">Install from Source</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="from_source.html">Install from Source</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#developers-get-source-from-github">Developers: Get Source from Github</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#build-the-shared-library">Build the Shared Library</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#python-package-installation">Python Package Installation</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="from_source.html#install-contrib-libraries">Install Contrib Libraries</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#enable-c-tests">Enable C++ Tests</a></li>
+</ul>
+</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/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index 5e60392575..76cfb1f4bb 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -182,7 +182,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#acd70148b03897204efea4e579f812173">tvm::runtime::Array< T, typename ></a>
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<li>AgregateImpl()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a16a9dc37f218594b41ddeeb3ef803bec">tvm::runtime::Array< T, typename ></a>
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</li>
<li>align_corners
: <a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html#a529d4b973c43fc878b40da71d50e95ad">tvm::relay::GridSampleAttrs</a>
@@ -347,7 +347,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relay_1_1ErrorReporter.html#a90e1eea8f10d4e164d3a9fb76cefa6ac">tvm::relay::ErrorReporter</a>
</li>
<li>Append()
-: <a class="el" href="classtvm_1_1tir_1_1TraceNode.html#a6d101010417222bf43d131d9998e8824">tvm::tir::TraceNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1TraceNode.html#afaa0e3d25cf54e8a33bf4af4ded99006">tvm::tir::TraceNode</a>
</li>
<li>Apply()
: <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html#a4801ac9f8d0718eaf3395eae1555026f">tvm::meta_schedule::MeasureCallbackNode</a>
@@ -452,7 +452,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a360b264ed892e620935b648e5a91a5ea">tvm::runtime::vm::Instruction</a>
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<li>Array()
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+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#af8b7450aea8633a51d3e8c75ed9fe2be">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a23070656da6784a7e4c33c4b0ea9de35">tvm::runtime::ArrayNode</a>
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<li>array
@@ -553,7 +553,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a410953f5ac23f6ca5caa257def8b08bd">tvm::runtime::ArrayNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a773d2e9099e34ab3bcf3b1870d0aee28">tvm::runtime::DenseMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map< K, V, typename, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a29503ec61af7a5bb9b030a00cfdff01a">tvm::runtime::MapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a07d50937020663f46ce9b1f31f066a7a">tvm::runtime::ShapeTuple</a>
, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#a0593c84ceb05afb1a3f87045a3dc3a59">tvm::runtime::SmallMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaeda6a88310d41a22ce884fb1570b0d2">tvm::runtime::String</a>
@@ -697,7 +697,6 @@ $(function() {
, <a class="el" href="structtvm_1_1relax_1_1LayerNormAttrs.html#ab02015a8e093b361ae6e68421a46b026">tvm::relax::LayerNormAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">tvm::relax::PermuteDimsAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1RMSNormAttrs.html#ac88533330cce287ba0084cf8249f4783">tvm::relax::RMSNormAttrs</a>
-, <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de">tvm::relax::StridedSliceAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyPerAxisAttrs.html#adcfa3541b09cf11be793e2cf08e3f802">tvm::relay::FixedPointMultiplyPerAxisAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html#a51c18ba5f84813d9d48f3c09a61a58d1">tvm::relay::SliceLikeAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#aefeb0aab0b9793cbf4b22cb9c1063d4e">tvm::relay::StridedSliceAttrs</a>
diff --git a/docs/reference/api/doxygen/functions_b.html b/docs/reference/api/doxygen/functions_b.html
index 16eaca99be..84f8213f4a 100644
--- a/docs/reference/api/doxygen/functions_b.html
+++ b/docs/reference/api/doxygen/functions_b.html
@@ -102,8 +102,7 @@ $(function() {
, <a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html#ad8d38a1e0ea91c357a55b141ae21fe68">tvm::relay::TakeAttrs</a>
</li>
<li>begin
-: <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26">tvm::relax::StridedSliceAttrs</a>
-, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#ade582278d58f236cccf69167cdd961f1">tvm::relay::StridedSliceAttrs</a>
+: <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#ade582278d58f236cccf69167cdd961f1">tvm::relay::StridedSliceAttrs</a>
, <a class="el" href="classtvm_1_1runtime_1_1Array.html#ae9734c8e6324f9be27fce8d45d0aa6f4">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a2860450a1004d1f41a9e3586f77cc7aa">tvm::runtime::ArrayNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a182df92fc8085f81b68933da80782098">tvm::runtime::DenseMapNode</a>
diff --git a/docs/reference/api/doxygen/functions_c.html b/docs/reference/api/doxygen/functions_c.html
index ce8b0e8cb9..775a98447d 100644
--- a/docs/reference/api/doxygen/functions_c.html
+++ b/docs/reference/api/doxygen/functions_c.html
@@ -97,7 +97,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html#ad2c24762f35f7f9ebe85d7c03cba1c8e">tvm::auto_scheduler::CacheWriteStep</a>
</li>
<li>CalcCapacityImpl()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#ad8746b19be1b83fe867869a597e6d479">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a9c7976c159330719285dbc5f0de07bd9">tvm::runtime::Array< T, typename ></a>
</li>
<li>Call()
: <a class="el" href="classtvm_1_1relax_1_1Call.html#ad290bc43f966baed02ef4e1fa11ca4ea">tvm::relax::Call</a>
diff --git a/docs/reference/api/doxygen/functions_e.html b/docs/reference/api/doxygen/functions_e.html
index ca82934f14..4c4abcecf7 100644
--- a/docs/reference/api/doxygen/functions_e.html
+++ b/docs/reference/api/doxygen/functions_e.html
@@ -167,8 +167,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Variant.html#ae1677dfc1f2cf943b7c8956ce0a8f028">tvm::runtime::Variant< V ></a>
</li>
<li>end
-: <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f">tvm::relax::StridedSliceAttrs</a>
-, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#a2492ceafc309edf983ba8c78fae5afe2">tvm::relay::StridedSliceAttrs</a>
+: <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#a2492ceafc309edf983ba8c78fae5afe2">tvm::relay::StridedSliceAttrs</a>
, <a class="el" href="classtvm_1_1runtime_1_1Array.html#a6b097149e69ea03fe3b812a3f5f7fcd9">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a2d43c95564271491432d279c12f7f94c">tvm::runtime::ArrayNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a467a2dacd260ee1e0fc5d233ba4b46d4">tvm::runtime::DenseMapNode</a>
@@ -276,7 +275,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a0fd88d16147aad4ed810846ce53d21c9">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a2e0612bed81949dd88fd440a467aa8c0">tvm::runtime::DenseMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a67431af5ae08050343eaf70629e5e310">tvm::runtime::Map< K, V, typename, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#afac316cac6b4fc06d81c66df46482ba6">tvm::runtime::MapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a3fda35a31720bc5d9c70d0b4fe26ecf0">tvm::runtime::MapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#aa8d46402a1b371bb9c711602942f1eab">tvm::runtime::SmallMapNode</a>
</li>
<li>Error()
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index d30ed2a353..c3425dc45a 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -187,7 +187,7 @@ $(function() {
, <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html#ae296d4462c2def1263a95a4c57ce9514">tvm::auto_scheduler::CacheWriteStepNode</a>
</li>
<li>ScopeDoc()
-: <a class="el" href="classtvm_1_1script_1_1printer_1_1ScopeDoc.html#a04590cd32fa1ed52f376549be09c214e">tvm::script::printer::ScopeDoc</a>
+: <a class="el" href="classtvm_1_1script_1_1printer_1_1ScopeDoc.html#aec595b3a2a9e9fb8fbc22c3f58a9dec7">tvm::script::printer::ScopeDoc</a>
</li>
<li>score_index
: <a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html#ac1f6c8ed8f47f543c20501af98cff87b">tvm::relay::GetValidCountsAttrs</a>
@@ -250,7 +250,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html#a1ce9920064b0c2ed42be0e9412fd3939">tvm::tir::StmtSRefNode</a>
</li>
<li>SeqExpr()
-: <a class="el" href="classtvm_1_1relax_1_1SeqExpr.html#a94be04d43a731061bb6b7d1d040c7f6d">tvm::relax::SeqExpr</a>
+: <a class="el" href="classtvm_1_1relax_1_1SeqExpr.html#a0986e35fc847fd3d59acf2acbe89a1f5">tvm::relax::SeqExpr</a>
</li>
<li>SeqStmt()
: <a class="el" href="classtvm_1_1tir_1_1SeqStmt.html#afe971b51fb6990008a118d4aa16c0920">tvm::tir::SeqStmt</a>
@@ -419,7 +419,7 @@ $(function() {
, <a class="el" href="structtvm_1_1WorkspacePoolInfoNode.html#af4e4be8a623b5f63e03e84a6b11b95ab">tvm::WorkspacePoolInfoNode</a>
</li>
<li>SEqualReducer()
-: <a class="el" href="classtvm_1_1SEqualReducer.html#abb85d3142af880fb96ae79423e5c382e">tvm::SEqualReducer</a>
+: <a class="el" href="classtvm_1_1SEqualReducer.html#a97eb296b45fee73c0ff6064c8eebdd7c">tvm::SEqualReducer</a>
</li>
<li>Sequential()
: <a class="el" href="classtvm_1_1transform_1_1Sequential.html#a99d7f2f204cb666b33e54c45f679d20c">tvm::transform::Sequential</a>
@@ -475,7 +475,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#aed802ed365a13a9cef9de4e9f601b62a">tvm::runtime::Registry</a>
</li>
<li>set_body_method()
-: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#ae1243626f5f81ca956343dbe8c22b9b3">tvm::runtime::Registry</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#ae5f7cc19067b609a79e1bb32b7b6f086">tvm::runtime::Registry</a>
</li>
<li>set_body_typed()
: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#a607848df01d8c33d7e2cf40e7dae42b1">tvm::runtime::Registry</a>
@@ -686,7 +686,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relax_1_1ShapeStructInfo.html#ac3f6bb64ae404b75793823205d555f84">tvm::relax::ShapeStructInfo</a>
</li>
<li>ShapeTuple()
-: <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a6836ad757cbcac207ddf025a74ac2ef2">tvm::runtime::ShapeTuple</a>
+: <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#aa54548a384457ae73d758886881b6136">tvm::runtime::ShapeTuple</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj_1_1FromStd.html#a1233caa001a4dd2bebeef49633724641">tvm::runtime::ShapeTupleObj::FromStd</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj.html#a1233caa001a4dd2bebeef49633724641">tvm::runtime::ShapeTupleObj</a>
</li>
@@ -969,7 +969,7 @@ $(function() {
: <a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html#ad6d089344fa741021584222ffa70a451">tvm::relay::MultiBoxPriorAttrs</a>
</li>
<li>SizeVar()
-: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#a0f8cb8a92feb96343939d223db90f7cd">tvm::tir::SizeVar</a>
+: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#ac470249315d9e395ad581d35dd5dcb05">tvm::tir::SizeVar</a>
</li>
<li>Slice()
: <a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html#ab314819e8bcca6421e9a4f33e48578c3">tvm::te::Tensor::Slice</a>
@@ -1085,7 +1085,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#aadcac01989e684f676cfdc74b6e6f415">tvm::te::Stage</a>
</li>
<li>SplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a184575a8029d77f7a3bee23d81141df5">tvm::auto_scheduler::SplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a64ed86582a56a2645b3e4eb44ecb31af">tvm::auto_scheduler::SplitStep</a>
</li>
<li>src
: <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html#a8ce159fc6db748e5092fa937de3fde53">tvm::arith::IntConstraintsTransformNode</a>
@@ -1214,7 +1214,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtDoc.html#adec8d59e41d8a4093fb310089bf2c3ba">tvm::script::printer::StmtDoc</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
</li>
<li>stmts
: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">tvm::script::ir_builder::tir::TIRFrameNode</a>
@@ -1267,7 +1267,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a93d1d23f24d903db844f75f51fe09a36">tvm::tir::ScheduleNode</a>
</li>
<li>StorageAlignStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#a99dbb8c55d9e7d78268b6d43fd348bc7">tvm::auto_scheduler::StorageAlignStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#af50b7c2f020f8e0a80f5bcc8e559b394">tvm::auto_scheduler::StorageAlignStep</a>
</li>
<li>StorageType
: <a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html#a67e86db3290b1d3bd4aca7e7a2faf187">tvm::runtime::SimpleObjAllocator::ArrayHandler< ArrayType, ElemType ></a>
@@ -1310,7 +1310,6 @@ $(function() {
, <a class="el" href="structtvm_1_1relax_1_1Pool1DAttrs.html#adb4b9e5f8e7eaf38ab8f86c884dea29b">tvm::relax::Pool1DAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1Pool2DAttrs.html#ae1256c332098d12a23a3afc6af45d947">tvm::relax::Pool2DAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1Pool3DAttrs.html#a9e276173050d0e3e4e5b710e48689687">tvm::relax::Pool3DAttrs</a>
-, <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9">tvm::relax::StridedSliceAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html#a07cc53b61ea1287df0fc6265b2e50c99">tvm::relay::AvgPool1DAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html#ac005587ae05168fa8e1f2093243ec922">tvm::relay::AvgPool2DAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html#a10d2de4e2d1d5979d8d6b9c6f7ef9757">tvm::relay::AvgPool3DAttrs</a>
diff --git a/docs/reference/api/doxygen/functions_vars_a.html b/docs/reference/api/doxygen/functions_vars_a.html
index 95fffb9048..5d0a7cc519 100644
--- a/docs/reference/api/doxygen/functions_vars_a.html
+++ b/docs/reference/api/doxygen/functions_vars_a.html
@@ -292,7 +292,6 @@ $(function() {
, <a class="el" href="structtvm_1_1relax_1_1LayerNormAttrs.html#ab02015a8e093b361ae6e68421a46b026">tvm::relax::LayerNormAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">tvm::relax::PermuteDimsAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1RMSNormAttrs.html#ac88533330cce287ba0084cf8249f4783">tvm::relax::RMSNormAttrs</a>
-, <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de">tvm::relax::StridedSliceAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyPerAxisAttrs.html#adcfa3541b09cf11be793e2cf08e3f802">tvm::relay::FixedPointMultiplyPerAxisAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html#a51c18ba5f84813d9d48f3c09a61a58d1">tvm::relay::SliceLikeAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#aefeb0aab0b9793cbf4b22cb9c1063d4e">tvm::relay::StridedSliceAttrs</a>
diff --git a/docs/reference/api/doxygen/functions_vars_b.html b/docs/reference/api/doxygen/functions_vars_b.html
index b9b2cafde6..e3e5c3ad94 100644
--- a/docs/reference/api/doxygen/functions_vars_b.html
+++ b/docs/reference/api/doxygen/functions_vars_b.html
@@ -92,8 +92,7 @@ $(function() {
, <a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html#ad8d38a1e0ea91c357a55b141ae21fe68">tvm::relay::TakeAttrs</a>
</li>
<li>begin
-: <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26">tvm::relax::StridedSliceAttrs</a>
-, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#ade582278d58f236cccf69167cdd961f1">tvm::relay::StridedSliceAttrs</a>
+: <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#ade582278d58f236cccf69167cdd961f1">tvm::relay::StridedSliceAttrs</a>
</li>
<li>begin_
: <a class="el" href="classtvm_1_1support_1_1Span.html#acb5f2b0b878140f83bf73b909f0bdcc8">tvm::support::Span< T, W ></a>
diff --git a/docs/reference/api/doxygen/functions_vars_e.html b/docs/reference/api/doxygen/functions_vars_e.html
index 2eb10c2cbd..cc35b51cd0 100644
--- a/docs/reference/api/doxygen/functions_vars_e.html
+++ b/docs/reference/api/doxygen/functions_vars_e.html
@@ -102,8 +102,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunnerNode.html#a8243b1a607d4b24719d0e2d174097922">tvm::auto_scheduler::ProgramRunnerNode</a>
</li>
<li>end
-: <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f">tvm::relax::StridedSliceAttrs</a>
-, <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#a2492ceafc309edf983ba8c78fae5afe2">tvm::relay::StridedSliceAttrs</a>
+: <a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html#a2492ceafc309edf983ba8c78fae5afe2">tvm::relay::StridedSliceAttrs</a>
</li>
<li>end_
: <a class="el" href="classtvm_1_1support_1_1Span.html#a0f5635e38ba7bad57df9deed968baea5">tvm::support::Span< T, W ></a>
diff --git a/docs/reference/api/doxygen/functions_vars_s.html b/docs/reference/api/doxygen/functions_vars_s.html
index a4c4442594..c3abded890 100644
--- a/docs/reference/api/doxygen/functions_vars_s.html
+++ b/docs/reference/api/doxygen/functions_vars_s.html
@@ -456,7 +456,6 @@ $(function() {
, <a class="el" href="structtvm_1_1relax_1_1Pool1DAttrs.html#adb4b9e5f8e7eaf38ab8f86c884dea29b">tvm::relax::Pool1DAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1Pool2DAttrs.html#ae1256c332098d12a23a3afc6af45d947">tvm::relax::Pool2DAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1Pool3DAttrs.html#a9e276173050d0e3e4e5b710e48689687">tvm::relax::Pool3DAttrs</a>
-, <a class="el" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9">tvm::relax::StridedSliceAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html#a07cc53b61ea1287df0fc6265b2e50c99">tvm::relay::AvgPool1DAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html#ac005587ae05168fa8e1f2093243ec922">tvm::relay::AvgPool2DAttrs</a>
, <a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html#a10d2de4e2d1d5979d8d6b9c6f7ef9757">tvm::relay::AvgPool3DAttrs</a>
diff --git a/docs/reference/api/doxygen/index_8h_source.html b/docs/reference/api/doxygen/index_8h_source.html
index 41a1962e56..b73394965e 100644
--- a/docs/reference/api/doxygen/index_8h_source.html
+++ b/docs/reference/api/doxygen/index_8h_source.html
@@ -105,45 +105,29 @@ $(function() {
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> }; <span class="comment">// struct TakeAttrs</span></div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  </div>
<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html"> 42</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html">StridedSliceAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><StridedSliceAttrs> {</div>
-<div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de"> 43</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Integer></a> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de">axes</a>;</div>
-<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26"> 44</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<PrimExpr></a> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26">begin</a>;</div>
-<div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f"> 45</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<PrimExpr></a> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f">end</a>;</div>
-<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9"> 46</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<PrimExpr></a>> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9">strides</a>;</div>
-<div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba"> 47</a></span>  <span class="keywordtype">bool</span> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">assume_inbound</a>;</div>
-<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  </div>
-<div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2"> 49</a></span>  <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html">StridedSliceAttrs</a>, <span class="stringliteral">"relax.attrs.StridedSliceAttrs"</span>) {</div>
-<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de">axes</a>).describe(<span class="stringliteral">"Axes along which slicing is applied."</span>);</div>
-<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26">begin</a>).describe(<span class="stringliteral">"The indices to begin with in the slicing, inclusive."</span>);</div>
-<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f">end</a>).describe(<span class="stringliteral">"The indices indicating end of the slice, exclusive."</span>);</div>
-<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9">strides</a>).describe(</div>
-<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="stringliteral">"Specifies the stride values, it can be negative in that case, the input tensor will be "</span></div>
-<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="stringliteral">"reversed in that particular axis. If not specified, it by default is an list of ones of "</span></div>
-<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="stringliteral">"the same length as `axes`."</span>);</div>
-<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">assume_inbound</a>)</div>
-<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  .set_default(<span class="keyword">true</span>)</div>
-<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  .describe(</div>
-<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="stringliteral">"Whether to assume the indices are in bound. If it is set to false, "</span></div>
-<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="stringliteral">"out of bound indices will be clipped to the bound."</span>);</div>
-<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div>
-<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> }; <span class="comment">// struct StridedSliceAttrs</span></div>
-<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  </div>
-<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> } <span class="comment">// namespace relax</span></div>
-<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  </div>
-<div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ATTRS_INDEX_H_</span></div>
+<div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba"> 43</a></span>  <span class="keywordtype">bool</span> <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">assume_inbound</a>;</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  </div>
+<div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2"> 45</a></span>  <a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html">StridedSliceAttrs</a>, <span class="stringliteral">"relax.attrs.StridedSliceAttrs"</span>) {</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">assume_inbound</a>)</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  .set_default(<span class="keyword">true</span>)</div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  .describe(</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="stringliteral">"Whether to assume the indices are in bound. If it is set to false, "</span></div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="stringliteral">"out of bound indices will be clipped to the bound."</span>);</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> }; <span class="comment">// struct StridedSliceAttrs</span></div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  </div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> } <span class="comment">// namespace relax</span></div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  </div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ATTRS_INDEX_H_</span></div>
<div class="ttc" id="aclasstvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use "curiously recurring template pattern".</div><div class="ttdef"><b>Definition:</b> attrs.h:830</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs.</div><div class="ttdef"><b>Definition:</b> array.h:289</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Optional_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Optional.html">tvm::runtime::Optional</a></div><div class="ttdoc">Optional container that to represent to a Nullable variant of T.</div><div class="ttdef"><b>Definition:</b> optional.h:51</div></div>
<div class="ttc" id="air_2attrs_8h_html_a578da113eb199bad72e26c03ad24832f"><div class="ttname"><a href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a></div><div class="ttdeci">#define TVM_ATTR_FIELD(FieldName)</div><div class="ttdoc">Declare an attribute field.</div><div class="ttdef"><b>Definition:</b> attrs.h:76</div></div>
<div class="ttc" id="anamespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript.</div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="arelax_2expr_8h_html"><div class="ttname"><a href="relax_2expr_8h.html">expr.h</a></div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html">tvm::relax::StridedSliceAttrs</a></div><div class="ttdoc">Attributes used in strided_slice operator.</div><div class="ttdef"><b>Definition:</b> index.h:42</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_a19b7235e7972ea359ed83cf705aab0b9"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a19b7235e7972ea359ed83cf705aab0b9">tvm::relax::StridedSliceAttrs::strides</a></div><div class="ttdeci">Optional< Array< PrimExpr > > strides</div><div class="ttdef"><b>Definition:</b> index.h:46</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_a6d876207bd98e724c22e26a4d98c4d26"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a6d876207bd98e724c22e26a4d98c4d26">tvm::relax::StridedSliceAttrs::begin</a></div><div class="ttdeci">Array< PrimExpr > begin</div><div class="ttdef"><b>Definition:</b> index.h:44</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_a8168d3fe82679449981a4e26ba3b84ba"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">tvm::relax::StridedSliceAttrs::assume_inbound</a></div><div class="ttdeci">bool assume_inbound</div><div class="ttdef"><b>Definition:</b> index.h:47</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_ab9fc5ce40ea9d02d11b5738b190ead2f"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ab9fc5ce40ea9d02d11b5738b190ead2f">tvm::relax::StridedSliceAttrs::end</a></div><div class="ttdeci">Array< PrimExpr > end</div><div class="ttdef"><b>Definition:</b> index.h:45</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_ad5d33940ddb2acc06c515f0c1c4044a2"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2">tvm::relax::StridedSliceAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(StridedSliceAttrs, "relax.attrs.StridedSliceAttrs")</div><div class="ttdef"><b>Definition:</b> index.h:49</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_ad788b3a159215f320f7e50ea0f2f73de"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad788b3a159215f320f7e50ea0f2f73de">tvm::relax::StridedSliceAttrs::axes</a></div><div class="ttdeci">Array< Integer > axes</div><div class="ttdef"><b>Definition:</b> index.h:43</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_a8168d3fe82679449981a4e26ba3b84ba"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#a8168d3fe82679449981a4e26ba3b84ba">tvm::relax::StridedSliceAttrs::assume_inbound</a></div><div class="ttdeci">bool assume_inbound</div><div class="ttdef"><b>Definition:</b> index.h:43</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1StridedSliceAttrs_html_ad5d33940ddb2acc06c515f0c1c4044a2"><div class="ttname"><a href="structtvm_1_1relax_1_1StridedSliceAttrs.html#ad5d33940ddb2acc06c515f0c1c4044a2">tvm::relax::StridedSliceAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(StridedSliceAttrs, "relax.attrs.StridedSliceAttrs")</div><div class="ttdef"><b>Definition:</b> index.h:45</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1TakeAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1TakeAttrs.html">tvm::relax::TakeAttrs</a></div><div class="ttdoc">Attributes used in take operator.</div><div class="ttdef"><b>Definition:</b> index.h:33</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1TakeAttrs_html_a29a81a2d06a7b83afec686d487eeba95"><div class="ttname"><a href="structtvm_1_1relax_1_1TakeAttrs.html#a29a81a2d06a7b83afec686d487eeba95">tvm::relax::TakeAttrs::axis</a></div><div class="ttdeci">Optional< Integer > axis</div><div class="ttdef"><b>Definition:</b> index.h:34</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1TakeAttrs_html_a391491cb4f343df4bf29a3f665a03a5f"><div class="ttname"><a href="structtvm_1_1relax_1_1TakeAttrs.html#a391491cb4f343df4bf29a3f665a03a5f">tvm::relax::TakeAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(TakeAttrs, "relax.attrs.TakeAttrs")</div><div class="ttdef"><b>Definition:</b> index.h:36</div></div>
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