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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/03/21 02:44:39 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@e257fb8a41159a2558dc1fccb5e3dd3c45001820)
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tqchen pushed a commit to branch asf-site
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The following commit(s) were added to refs/heads/asf-site by this push:
new 4d83329470 deploying docs (apache/tvm@e257fb8a41159a2558dc1fccb5e3dd3c45001820)
4d83329470 is described below
commit 4d8332947087a8ff5927c563d9d95ea79cfc3675
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Thu Mar 21 02:44:29 2024 +0000
deploying docs (apache/tvm@e257fb8a41159a2558dc1fccb5e3dd3c45001820)
---
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418 files changed, 53381 insertions(+), 51668 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 8b0abbbf52..0226a6ab33 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 35.903 seconds)
+ **Total running time of the script:** ( 1 minutes 39.662 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index d5ea29b0fa..b8ea73bd2b 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] 15%|#5 | 6.33M/41.5M [00:00<00:01, 20.9MB/s] 20%|## | 8.41M/41.5M [00:00<00:01, 21.2MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:01, 24.4MB/s] 40%|#### | 16.6M/41.5M [00:00<00:01, 21.2MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 25.7MB/s] 60%|#####9 | 24.8M/41.5M [00:01<00:00, 21.7MB/s] 73%|#######3 | 30.4M/41.5M [00:01<00:00, 29.5MB/s] 81%|########1 | 33.6M/41.5M [00:01<00:00, 27.4MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 27.2MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 25.7MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:01, 26.6MB/s] 26%|##6 | 10.8M/41.5M [00:00<00:00, 34.0MB/s] 35%|###4 | 14.5M/41.5M [00:00<00:00, 31.8MB/s] 43%|####2 | 17.8M/41.5M [00:00<00:00, 28.3MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 30.1MB/s] 61%|###### | 25.3M/41.5M [00:00<00:00, 25.8MB/s] 77%|#######7 | 32.0M/41.5M [00:01<00:00, 36.2MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 29.0MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 30.5MB/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 3377bb5014..5bbab0a4b7 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 2.458 seconds)
+ **Total running time of the script:** ( 1 minutes 5.459 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 46c0411b2a..8281157418 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:01, 36.5MB/s] 32%|###2 | 14.3M/44.7M [00:00<00:00, 40.9MB/s] 41%|####1 | 18.3M/44.7M [00:00<00:00, 37.4MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 42.9MB/s] 68%|######7 | 30.3M/44.7M [00:00<00:00, 38.1MB/s] 76%|#######6 | 34.1M/44.7M [00:00<00:00, 34.6MB/s] 90%|########9 | 40.0M/44.7M [00:01<00:00, 32.2MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 38.9MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 14%|#4 | 6.30M/44.7M [00:00<00:00, 42.8MB/s] 23%|##3 | 10.4M/44.7M [00:00<00:00, 39.5MB/s] 32%|###2 | 14.3M/44.7M [00:00<00:00, 37.4MB/s] 41%|####1 | 18.4M/44.7M [00:00<00:00, 39.2MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 38.0MB/s] 62%|######1 | 27.6M/44.7M [00:00<00:00, 31.5MB/s] 72%|#######1 | 32.0M/44.7M [00:01<00:00, 29.0MB/s] 90%|########9 | 40.0M/44.7M [00:01<00:00, 31.9MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 36.6MB/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 c0e8f9f5a5..736137cbf7 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 30.569 seconds)
+ **Total running time of the script:** ( 1 minutes 32.692 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 c46b5940f8..879e29b70c 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**06:43.241** total execution time for **how_to_compile_models** files:
+**06:55.944** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:35.903 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:39.662 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:30.569 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:32.692 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.458 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:05.459 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.905 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:44.893 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:38.966 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:40.265 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.705 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.097 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.883 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.937 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.016 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.041 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.836 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.897 | 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 95f299ad7c..49c46c2d8c 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)
- 3993.5226 3992.8541 3998.6957 3990.8793 2.1687
+ 4125.8090 4125.9218 4127.8100 4123.8300 1.3218
@@ -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 18.439 seconds)
+ **Total running time of the script:** ( 1 minutes 20.899 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 18cbcbe1c8..6edd8f4906 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: 1s 8380416/102967424 [=>............................] - ETA: 2s 16769024/102967424 [===>..........................] - ETA: 1s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s
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ETA: 0s 78315520/102967424 [=====================>........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 91414528/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 2s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 0s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s
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ETA: 0s 72556544/102967424 [====================>.........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 77692928/102967424 [=====================>........] - ETA: 0s 82124800/102967424 [======================>.......] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 86073344/102967424 [========================>.....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 97714176/102967424
[===========================>..] - ETA: 0s 100368384/102967424 [============================>.] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 3s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index cf332b1eb9..9826aee0de 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.5436 14.4490 15.2599 14.3580 0.2558
+ 15.8191 15.7680 16.2768 15.4724 0.2436
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 e414c873de..86c1e8b926 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 42.8MB/s] 8%|8 | 14.3M/170M [00:00<00:04, 33.3MB/s] 10%|# | 17.5M/170M [00:00<00:05, 26.8MB/s] 14%|#4 | 24.0M/170M [00:00<00:05, 28.2MB/s] 18%|#7 | 30.3M/170M [00:01<00:04, 29.4MB/s] 19%|#9 | 33.1M/170M [00:01<00:06, 23.8MB/s] 24%|##3 | 40.0M/170M [00:01<00:04, 30.1MB/s] 27%|##7 | 46.3M/170M [00:01<00:03, 36.3MB/s] 30%|##9 | 50.2M/170M [00:01<00:03, 34.2MB/s] 33%|###2 | 56.0M/170M [00:01<00:03, 38.1MB/s] 37%|###6 | 62.3M/170M [00:01<00:02, 43.7MB/s] 39%|###9 | 66.8M/170M [00:02<00:02, 40.3MB/s] 42%|####2 | 72.0M/170M [00:02<00:02, 42.4MB/s] 46%|####6 | 78.3M/170M [00:02<00:02, 42.8MB/s] 49%|####8 | 82.5M/170M [00:02<00:02, 42.1MB/s] 52%|#####1 | 88.0M/170M [00:02<00:02, 38.1MB/s] 56%|#####5 | 94.3M/170M [00:02<00:01, 40.5MB/
s] 58%|#####7 | 98.3M/170M [00:02<00:02, 36.3MB/s] 61%|######1 | 104M/170M [00:03<00:02, 32.4MB/s] 66%|######5 | 112M/170M [00:03<00:01, 41.1MB/s] 71%|####### | 120M/170M [00:03<00:01, 47.7MB/s] 74%|#######4 | 126M/170M [00:03<00:01, 42.1MB/s] 77%|#######6 | 131M/170M [00:03<00:00, 41.7MB/s] 79%|#######9 | 135M/170M [00:03<00:00, 39.2MB/s] 82%|########1 | 139M/170M [00:03<00:00, 38.5MB/s] 85%|########4 | 144M/170M [00:04<00:00, 38.6MB/s] 88%|########8 | 150M/170M [00:04<00:00, 36.4MB/s] 91%|######### | 154M/170M [00:04<00:00, 35.4MB/s] 94%|#########4| 160M/170M [00:04<00:00, 32.1MB/s] 99%|#########8| 168M/170M [00:04<00:00, 36.3MB/s] 100%|##########| 170M/170M [00:04<00:00, 36.9MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 4%|3 | 6.30M/170M [00:00<00:03, 45.4MB/s] 6%|6 | 10.6M/170M [00:00<00:05, 32.9MB/s] 8%|8 | 14.3M/170M [00:00<00:06, 24.0MB/s] 10%|9 | 16.8M/170M [00:00<00:07, 22.4MB/s] 13%|#3 | 22.3M/170M [00:00<00:05, 28.6MB/s] 15%|#4 | 25.2M/170M [00:01<00:06, 23.7MB/s] 18%|#7 | 30.3M/170M [00:01<00:05, 27.4MB/s] 19%|#9 | 33.1M/170M [00:01<00:06, 23.2MB/s] 24%|##3 | 40.0M/170M [00:01<00:04, 31.6MB/s] 27%|##7 | 46.3M/170M [00:01<00:03, 38.2MB/s] 30%|##9 | 50.3M/170M [00:01<00:04, 31.1MB/s] 33%|###2 | 56.0M/170M [00:01<00:03, 34.6MB/s] 38%|###7 | 64.0M/170M [00:02<00:03, 37.0MB/s] 42%|####2 | 72.0M/170M [00:02<00:02, 41.2MB/s] 46%|####6 | 78.3M/170M [00:02<00:02, 35.5MB/s] 48%|####8 | 81.9M/170M [00:02<00:02, 31.7MB/s] 52%|#####1 | 88.0M/170M [00:02<00:02, 33.6MB/
s] 56%|#####5 | 94.3M/170M [00:03<00:02, 31.1MB/s] 57%|#####7 | 97.4M/170M [00:03<00:02, 28.5MB/s] 60%|###### | 102M/170M [00:03<00:02, 31.3MB/s] 62%|######2 | 105M/170M [00:03<00:02, 25.4MB/s] 66%|######5 | 112M/170M [00:03<00:02, 29.4MB/s] 70%|######9 | 118M/170M [00:04<00:01, 29.5MB/s] 71%|#######1 | 121M/170M [00:04<00:02, 24.8MB/s] 75%|#######5 | 128M/170M [00:04<00:01, 30.2MB/s] 79%|#######9 | 134M/170M [00:04<00:01, 26.5MB/s] 81%|######## | 137M/170M [00:04<00:01, 20.8MB/s] 85%|########4 | 144M/170M [00:05<00:01, 26.7MB/s] 88%|########8 | 150M/170M [00:05<00:00, 30.4MB/s] 90%|######### | 154M/170M [00:05<00:00, 26.7MB/s] 93%|#########3| 158M/170M [00:05<00:00, 28.8MB/s] 95%|#########4| 161M/170M [00:05<00:00, 24.0MB/s] 99%|#########8| 168M/170M [00:05<00:00, 30.5MB/s] 100%|##########| 170M/170M [00:05<00:00, 29.7MB/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 34.091 seconds)
+ **Total running time of the script:** ( 3 minutes 43.156 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 37e8217891..bb348cb855 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] 47%|####6 | 6.30M/13.6M [00:00<00:00, 37.9MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 34.6MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 38.5MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 47%|####6 | 6.30M/13.6M [00:00<00:00, 34.3MB/s] 71%|####### | 9.58M/13.6M [00:00<00:00, 29.6MB/s] 91%|######### | 12.3M/13.6M [00:00<00:00, 18.9MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 23.9MB/s]
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 86.2873 86.2275 89.9730 85.8618 0.4318
+ 87.8449 87.8215 89.4329 87.4240 0.2779
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 29.511 seconds)
+ **Total running time of the script:** ( 1 minutes 30.090 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 bdc1e28329..a4ad06ba91 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)
- 107.3905 107.2979 112.5537 104.6349 0.8696
+ 108.7687 108.7454 113.4321 107.9973 0.5371
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 741265dc95..478373712f 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**09:52.762** total execution time for **how_to_deploy_models** files:
+**10:12.292** 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``) | 03:34.091 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:43.156 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.511 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:30.090 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.439 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.899 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.061 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:54.155 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.466 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.204 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:47.271 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.981 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.963 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.161 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.951 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.638 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.008 | 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 002121e84e..c8bdfde43c 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
=================
-**00:04.133** total execution time for **how_to_extend_tvm** files:
+**00:04.123** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.894 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.864 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.231 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.251 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 61d7bf2542..59a6b9541c 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: 24285us [24285us] (48.42%; 48.42%)
- FoldScaleAxis: 25872us [10us] (51.58%; 51.58%)
- FoldConstant: 25862us [1796us] (51.56%; 99.96%)
- InferType: 24066us [24066us] (47.98%; 93.06%)
+ InferType: 25156us [25156us] (48.16%; 48.16%)
+ FoldScaleAxis: 27073us [8us] (51.84%; 51.84%)
+ FoldConstant: 27065us [1885us] (51.82%; 99.97%)
+ InferType: 25181us [25181us] (48.21%; 93.04%)
@@ -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: 24615us [24615us] (48.18%; 48.18%)
- FoldScaleAxis: 26474us [10us] (51.82%; 51.82%)
- FoldConstant: 26464us [1903us] (51.80%; 99.96%)
- InferType: 24561us [24561us] (48.07%; 92.81%)
+ InferType: 25260us [25260us] (48.30%; 48.30%)
+ FoldScaleAxis: 27043us [10us] (51.70%; 51.70%)
+ FoldConstant: 27033us [1943us] (51.69%; 99.96%)
+ InferType: 25090us [25090us] (47.97%; 92.81%)
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 50dd03b216..850ee099f9 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.518337 ms
+ Convolution: 34.566398 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 a194b3e6ac..b60a074daf 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: 12.264816 ms
+ conv2d with tensor core: 12.273059 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 5a246e3660..bb1eef153c 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: 0.014200
- Baseline: 3.352059
+ Numpy running time: 0.017773
+ Baseline: 3.445178
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.295102
+ Opt1: 0.289836
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.278885
+ Opt2: 0.303563
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.113360
+ Opt3: 0.115953
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.105984
+ Opt4: 0.140538
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.095547
+ Opt5: 0.110639
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.112764
+ Opt6: 0.130842
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 735e62186d..ad42f06f7f 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:32.190** total execution time for **how_to_optimize_operators** files:
+**00:34.405** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:29.014 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.451 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.916 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.806 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.260 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.148 | 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 1f7a578978..0d396a42e1 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
=================
-**03:36.976** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:43.512** 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``) | 01:34.578 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:37.782 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:12.734 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.249 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:18.122 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:18.367 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.402 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.894 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.040 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.119 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.098 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.102 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index ae11619806..d85b3f249a 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -766,7 +766,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.341 ms
+ Execution time of this operator: 0.334 ms
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index ef9bdbb211..440b802df7 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)
- 3.2540 3.2515 3.2657 3.2447 0.0088
+ 3.2194 3.2182 3.2239 3.2160 0.0033
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.734 seconds)
+ **Total running time of the script:** ( 1 minutes 14.249 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 b25fce7402..6be25e4927 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)
- 709.7304 709.8460 710.9923 708.3529 1.0806
+ 771.4275 771.7011 772.5295 770.0519 1.0298
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.578 seconds)
+ **Total running time of the script:** ( 1 minutes 37.782 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 7bfe1cf9c6..51003d5714 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
@@ -5,14 +5,14 @@
Computation times
=================
-**00:22.645** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.884** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.609 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.849 | 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``) | 00:00.020 | 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 |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.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 df6d77bf56..03f51eeb25 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: 0.037276
+ Time cost of this operator: 0.037049
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 c00c04d005..a55be9dcb0 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 295.9 98.749 (1, 2, 10, 10, 3) 2 1 [295.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.809 0.937 (1, 6, 10, 10) 1 1 [2.809]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.939 0.313 (1, 1, 10, 10, 3) 1 1 [0.939]
- Total_time - 299.648 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 303.3 98.729 (1, 2, 10, 10, 3) 2 1 [303.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.927 0.953 (1, 6, 10, 10) 1 1 [2.927]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.977 0.318 (1, 1, 10, 10, 3) 1 1 [0.977]
+ Total_time - 307.204 - - - - -
@@ -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 97.375 97.325 (1, 6, 10, 10, 1) 2 1 [97.375]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.737 1.736 (1, 6, 10, 10) 1 1 [1.737]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.939 0.939 (1, 1, 10, 10, 3) 1 1 [0.939]
- Total_time - 100.051 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 135.6 98.1 (1, 6, 10, 10, 1) 2 1 [135.6]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.281 (1, 6, 10, 10) 1 1 [1.77]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.855 0.619 (1, 3, 10, 10, 1) 1 1 [0.855]
+ Total_time - 138.226 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.950 seconds)
+ **Total running time of the script:** ( 1 minutes 26.488 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 842f0e5722..1ff9cc6664 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, 86.9MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 17.9MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 28.0MB/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 28.906 seconds)
+ **Total running time of the script:** ( 1 minutes 30.247 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 d1b7992960..b50b306594 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -217,7 +217,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpux2aa2_k/images/random'
+ '/tmp/tmpzu9nmts7/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpux2aa2_k/images/target contains 8144 images
- /tmp/tmpux2aa2_k/images/random contains 5000 images
+ /tmp/tmpzu9nmts7/images/target contains 8144 images
+ /tmp/tmpzu9nmts7/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 39s - loss: 0.2136 - accuracy: 0.9245 - val_loss: 0.1003 - val_accuracy: 0.9615 - 39s/epoch - 120ms/step
+ 328/328 - 39s - loss: 0.2065 - accuracy: 0.9288 - val_loss: 0.1103 - val_accuracy: 0.9588 - 39s/epoch - 119ms/step
Epoch 2/3
- 328/328 - 34s - loss: 0.0962 - accuracy: 0.9647 - val_loss: 0.0976 - val_accuracy: 0.9630 - 34s/epoch - 105ms/step
+ 328/328 - 35s - loss: 0.0963 - accuracy: 0.9652 - val_loss: 0.1108 - val_accuracy: 0.9577 - 35s/epoch - 108ms/step
Epoch 3/3
- 328/328 - 34s - loss: 0.0623 - accuracy: 0.9753 - val_loss: 0.1254 - val_accuracy: 0.9532 - 34s/epoch - 105ms/step
+ 328/328 - 35s - loss: 0.0656 - accuracy: 0.9763 - val_loss: 0.0951 - val_accuracy: 0.9679 - 35s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7fc28e7b8b80>
+ <keras.callbacks.History object at 0x7f69d14d98e0>
@@ -860,7 +860,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 40.706 seconds)
+ **Total running time of the script:** ( 4 minutes 53.563 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 f7256cc9a3..77f301a4da 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
=================
-**08:04.092** total execution time for **how_to_work_with_microtvm** files:
+**08:18.871** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:40.706 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:53.563 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:28.906 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:30.247 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:25.950 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.488 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.207 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.293 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.775 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.959 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.547 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.321 | 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 38d9dbc745..7e429d2f3b 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
=================
-**00:38.879** total execution time for **how_to_work_with_relay** files:
+**00:38.592** 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``) | 00:33.833 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.325 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.228 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.157 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.811 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.103 | 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 26abf6c157..276c529efc 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 0x7fc2c61f8dc0>
+ <function my_cuda_math_rule at 0x7f6bae34f310>
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 139c073639..f0f83b3e16 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
=================
-**00:05.369** total execution time for **how_to_work_with_schedules** files:
+**00:05.119** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.556 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.407 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.079 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.053 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.724 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.689 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.718 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.678 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.123 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.121 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.070 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.072 | 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``) | 00:00.065 | 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``) | 00:00.034 | 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 3959d5050a..8ceb973bcc 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 27.17s!
+ yolov3-tiny inference graph built in 27.46s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 0.058 seconds)
+ **Total running time of the script:** ( 1 minutes 1.053 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 95347ea900..f2b7c40e26 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**01:00.058** total execution time for **topic_vta_tutorials_frontend** files:
+**01:01.053** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:00.058 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:01.053 | 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 b7a7ebd5f3..fd740629d9 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
=================
-**00:03.266** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.252** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.755 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.787 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.511 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.465 | 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 48fafbc500..a81eb74c87 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
=================
-**00:00.845** total execution time for **topic_vta_tutorials** files:
+**00:00.752** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.439 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.390 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.406 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.362 | 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 dbea5c0912..dc35e85c02 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,6 +207,13 @@ trials, we can load the best schedule from the log file and apply it.
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ *E
+
+
@@ -318,7 +325,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.376 ms
+ Execution time of this operator: 92.794 ms
@@ -434,7 +441,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.646 seconds)
+ **Total running time of the script:** ( 1 minutes 34.301 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 13e434402d..977752632c 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: 9.37/9.37 result: MeasureResult(costs=(0.0286365954,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7311263084411621, timestamp=1710973221.071847) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
- No: 2 GFLOPS: 12.20/12.20 result: MeasureResult(costs=(0.022009927999999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6055431365966797, timestamp=1710973221.68545) [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
- No: 3 GFLOPS: 0.53/12.20 result: MeasureResult(costs=(0.5093734628,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.454724788665771, timestamp=1710973230.138512) [('tile_y', [-1, 32]), ('tile_x', [-1, 1])],None,5
- No: 4 GFLOPS: 2.09/12.20 result: MeasureResult(costs=(0.12856295980000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3099756240844727, timestamp=1710973232.4535892) [('tile_y', [-1, 64]), ('tile_x', [-1, 4])],None,26
- No: 5 GFLOPS: 1.68/12.20 result: MeasureResult(costs=(0.15979629420000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.824359178543091, timestamp=1710973235.398528) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
- No: 6 GFLOPS: 3.88/12.20 result: MeasureResult(costs=(0.06909825039999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3769700527191162, timestamp=1710973236.7613351) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
- No: 7 GFLOPS: 12.22/12.22 result: MeasureResult(costs=(0.0219597602,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6350569725036621, timestamp=1710973237.3756354) [('tile_y', [-1, 64]), ('tile_x', [-1, 512])],None,96
- No: 8 GFLOPS: 2.10/12.22 result: MeasureResult(costs=(0.1279960514,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3110666275024414, timestamp=1710973239.6825972) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
- No: 9 GFLOPS: 2.16/12.22 result: MeasureResult(costs=(0.124173498,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.2077527046203613, timestamp=1710973242.0009887) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
- No: 10 GFLOPS: 12.99/12.99 result: MeasureResult(costs=(0.0206620138,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.553349494934082, timestamp=1710973242.595685) [('tile_y', [-1, 2]), ('tile_x', [-1, 512])],None,91
+ No: 1 GFLOPS: 1.07/1.07 result: MeasureResult(costs=(0.2505157094,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.312021970748901, timestamp=1710982940.176118) [('tile_y', [-1, 32]), ('tile_x', [-1, 2])],None,15
+ No: 2 GFLOPS: 0.52/1.07 result: MeasureResult(costs=(0.5149817528,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.5455961227417, timestamp=1710982948.6804638) [('tile_y', [-1, 64]), ('tile_x', [-1, 1])],None,6
+ No: 3 GFLOPS: 1.74/1.74 result: MeasureResult(costs=(0.1543614214,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.720386505126953, timestamp=1710982951.4087105) [('tile_y', [-1, 4]), ('tile_x', [-1, 1])],None,2
+ No: 4 GFLOPS: 14.74/14.74 result: MeasureResult(costs=(0.018210280000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6053571701049805, timestamp=1710982951.9582157) [('tile_y', [-1, 32]), ('tile_x', [-1, 64])],None,65
+ No: 5 GFLOPS: 9.51/14.74 result: MeasureResult(costs=(0.028212222400000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7202198505401611, timestamp=1710982953.0573308) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
+ No: 6 GFLOPS: 1.05/14.74 result: MeasureResult(costs=(0.2548135394,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.347085475921631, timestamp=1710982957.391401) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+ No: 7 GFLOPS: 2.10/14.74 result: MeasureResult(costs=(0.127617365,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.305999755859375, timestamp=1710982959.688716) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
+ No: 8 GFLOPS: 15.12/15.12 result: MeasureResult(costs=(0.0177497648,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.81538987159729, timestamp=1710982960.228046) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+ No: 9 GFLOPS: 12.25/15.12 result: MeasureResult(costs=(0.0219052236,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.577690839767456, timestamp=1710982960.9146426) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
+ No: 10 GFLOPS: 9.38/15.12 result: MeasureResult(costs=(0.0286329548,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6880836486816406, timestamp=1710982961.6390758) [('tile_y', [-1, 512]), ('tile_x', [-1, 256])],None,89
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 4dc20da216..c0227e40d8 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': 467.1754210700601, 'median': 467.11238465031784, 'std': 0.5777047017991859}
+ {'mean': 469.0988353099965, 'median': 469.264257750001, 'std': 1.1990900522260681}
@@ -582,31 +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: 16.82/ 20.24 GFLOPS | Progress: (4/20) | 8.62 s [Task 1/25] Current/Best: 16.22/ 20.24 GFLOPS | Progress: (8/20) | 10.89 s [Task 1/25] Current/Best: 8.99/ 21.36 GFLOPS | Progress: (12/20) | 14.08 s [Task 1/25] Current/Best: 21.69/ 21.69 GFLOPS | Progress: (16/20) | 16.49 s [Task 1/25] Current/Best: 16.96/ 21.69 GFLOPS | Progress: (20/20) | 18.66 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 8.34/ 16.98 GFLOPS | Progress: (4/20) | 4.10 s [Task 2/25] Current/Best: 7.67/ 16.98 GFLOPS | Progress: (8/20) | 5.98 s [Task 2/25] Current/Best: 13.04/ 21.11 GFLOPS | Progress: (12/20) | 7.76 s [Task 2/25] Current/Best: 12.91/ 21.11 GFLOPS | Progress: (16/20) | 9.66 s [Task 2/25] Current/Best: 13.70/ 21.11 GFLOPS | Progress: (20/20) | 11.36 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 24.72/ 24.72 GFLOPS | Progress: (4/20) | 4.81 s [Task 3/25] Current/Best: 7.08/ 24.72 GFLOPS | Progress: (8/20) | 7.39 s [Task 3/25] Current/Best: 18.02/ 24.72 GFLOPS | Progress: (12/20) | 9.44 s [Task 3/25] Current/Best: 19.37/ 24.72 GFLOPS | Progress: (16/20) | 11.66 s [Task 3/25] Current/Best: 13.38/ 24.72 GFLOPS | Progress: (20/20) | 16.30 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 17.95/ 17.95 GFLOPS | Progress: (4/20) | 6.49 s [Task 4/25] Current/Best: 5.80/ 18.03 GFLOPS | Progress: (8/20) | 8.29 s [Task 4/25] Current/Best: 10.54/ 18.03 GFLOPS | Progress: (12/20) | 10.68 s [Task 4/25] Current/Best: 9.00/ 18.03 GFLOPS | Progress: (16/20) | 13.43 s [Task 4/25] Current/Best: 10.47/ 18.03 GFLOPS | Progress: (20/20) | 16.13 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 20.82/ 20.82 GFLOPS | Progress: (4/20) | 4.42 s [Task 5/25] Current/Best: 12.45/ 20.82 GFLOPS | Progress: (8/20) | 7.34 s [Task 5/25] Current/Best: 14.75/ 20.82 GFLOPS | Progress: (12/20) | 10.13 s [Task 5/25] Current/Best: 1.55/ 23.61 GFLOPS | Progress: (16/20) | 12.81 s [Task 5/25] Current/Best: 14.28/ 23.61 GFLOPS | Progress: (20/20) | 14.73 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 16.15/ 17.29 GFLOPS | Progress: (4/20) | 5.48 s [Task 6/25] Current/Best: 14.89/ 17.29 GFLOPS | Progress: (8/20) | 8.67 s [Task 6/25] Current/Best: 2.85/ 17.89 GFLOPS | Progress: (12/20) | 12.24 s [Task 6/25] Current/Best: 9.85/ 17.89 GFLOPS | Progress: (16/20) | 14.67 s [Task 6/25] Current/Best: 21.26/ 21.26 GFLOPS | Progress: (20/20) | 16.67 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 19.42/ 19.82 GFLOPS | Progress: (4/20) | 4.89 s [Task 7/25] Current/Best: 11.81/ 19.82 GFLOPS | Progress: (8/20) | 7.03 s [Task 7/25] Current/Best: 20.28/ 21.16 GFLOPS | Progress: (12/20) | 9.34 s [Task 7/25] Current/Best: 17.61/ 21.16 GFLOPS | Progress: (16/20) | 12.03 s [Task 7/25] Current/Best: 7.32/ 21.16 GFLOPS | Progress: (20/20) | 14.39 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 3.16/ 13.50 GFLOPS | Progress: (4/20) | 8.00 s [Task 8/25] Current/Best: 11.20/ 15.76 GFLOPS | Progress: (8/20) | 12.00 s [Task 8/25] Current/Best: 15.54/ 18.69 GFLOPS | Progress: (12/20) | 23.59 s [Task 8/25] Current/Best: 13.84/ 18.69 GFLOPS | Progress: (16/20) | 27.94 s [Task 8/25] Current/Best: 2.98/ 19.68 GFLOPS | Progress: (20/20) | 31.71 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 12.02/ 18.54 GFLOPS | Progress: (4/20) | 13.59 s [Task 9/25] Current/Best: 11.76/ 18.54 GFLOPS | Progress: (8/20) | 18.39 s [Task 9/25] Current/Best: 10.12/ 18.54 GFLOPS | Progress: (12/20) | 23.60 s [Task 9/25] Current/Best: 12.66/ 18.54 GFLOPS | Progress: (16/20) | 26.69 s [Task 9/25] Current/Best: 8.22/ 18.54 GFLOPS | Progress: (20
/20) | 31.06 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 13.14/ 16.84 GFLOPS | Progress: (4/20) | 4.59 s [Task 10/25] Current/Best: 16.93/ 17.05 GFLOPS | Progress: (8/20) | 6.23 s [Task 10/25] Current/Best: 20.61/ 20.61 GFLOPS | Progress: (12/20) | 8.10 s [Task 10/25] Current/Best: 3.78/ 20.61 GFLOPS | Progress: (16/20) | 10.82 s [Task 10/25] Current/Best: 16.42/ 22.89 GFLOPS | Progress: (20/20) | 12.47 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 12.58/ 21.12 GFLOPS | Progress: (4/20) | 6.04 s [Task 11/25] Current/Best: 22.28/ 22.28 GFLOPS | Progress: (8/20) | 8.54 s [Task 11/25] Current/Best: 16.36/ 22.28 GFLOPS | Progress: (12/20) | 11.09 s [Task 11/25] Current/Best: 22.43/ 22.43 GFLOPS | Progress: (16/20) | 14.56 s [Task 11/25] Current/Best: 20.83/ 22.43 GFLOPS | Progress: (20/20) | 17.23 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 17.75/ 17.75 GFLOPS | Progress: (4/20) | 5.48 s [Task 12/25] Current/Best: 18.07/ 18.07 GFLOPS | Progress: (8/20) | 9.14 s [Task 12/25] Current/Best: 15.68/ 18.07 GFLOPS | Progress: (12/20) | 12.57 s [Task 12/25] Current/Best: 13.54/ 18.07 GFLOPS | Progress: (16/20) | 15.12 s [Task 12/25] Current/Best: 15.41/ 18.55 GFLOPS | Progress: (20/20) | 17.58 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 8.82/ 15.66 GFLOPS | Progress: (4/20) | 4.84 s [Task 13/25] Current/Best: 20.52/ 20.52 GFLOPS | Progress: (8/20) | 6.79 s [Task 13/25] Current/Best: 15.07/ 20.52 GFLOPS | Progress: (12/20) | 10.34 s [Task 13/25] Current/Best: 13.87/ 20.61 GFLOPS | Progress: (16/20) | 13.95 s [Task 13/25] Current/Best: 23.87/ 23.87 GFLOPS | Progress: (20/20) | 16.68 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 3.21/ 16.81 GFLOPS | Progress: (4/20) | 5.65 s [Task 14/25] Current/Best: 7.64/ 20.29 GFLOPS | Progress: (8/20) | 14.47 s [Task 14/25] Current/Best: 9.12/ 20.29 GFLOPS | Progress: (12/20) | 19.11 s [Task 14/25] Current/Best: 10.15/ 20.29 GFLOPS | Progress: (16/20) | 25.45 s [Task 14/25] Current/Best: 2.53/ 20.88 GFLOPS | Progress: (20/20) | 28.96 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 22.22/ 22.22 GFLOPS | Progress: (4/20) | 4.03 s [Task 15/25] Current/Best: 9.11/ 22.22 GFLOPS | Progress: (8/20) | 14.67 s [Task 15/25] Current/Best: 20.40/ 22.22 GFLOPS | Progress: (12/20) | 16.91 s [Task 15/25] Current/Best: 8.39/ 22.22 GFLOPS | Progress: (16/20) | 21.86 s [Task 15/25] Current/Best: 19.21/ 22.87 GFLOPS | Progress: (20/20) | 23.82 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 16.35/ 21.60 GFLOPS | Progress: (4/20) | 4.13 s [Task 16/25] Current/Best: 13.79/ 21.60 GFLOPS | Progress: (8/20) | 5.72 s [Task 16/25] Current/Best: 18.93/ 21.60 GFLOPS | Progress: (12/20) | 7.33 s [Task 16/25] Current/Best: 7.99/ 21.60 GFLOPS | Progress: (16/20) | 9.71 s [Task 16/25] Current/Best: 23.27/ 23.27 GFLOPS | Progress: (20/20) | 12.83 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 18.12/ 20.13 GFLOPS | Progress: (4/20) | 5.43 s [Task 17/25] Current/Best: 16.76/ 24.20 GFLOPS | Progress: (8/20) | 7.60 s [Task 17/25] Current/Best: 11.33/ 24.20 GFLOPS | Progress: (12/20) | 10.89 s [Task 17/25] Current/Best: 10.73/ 24.20 GFLOPS | Progress: (16/20) | 13.45 s [Task 17/25] Current/Best: 23.94/ 24.20 GFLOPS | Progress: (20/20) | 16.08 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 12.48/ 19.68 GFLOPS | Progress: (4/20) | 5.20 s [Task 18/25] Current/Best: 9.10/ 19.68 GFLOPS | Progress: (8/20) | 11.19 s [Task 18/25] Current/Best: 7.19/ 20.94 GFLOPS | Progress: (12/20) | 14.82 s [Task 18/25] Current/Best: 9.83/ 21.70 GFLOPS | Progress: (16/20) | 16.91 s [Task 18/25] Current/Best: 8.07/ 21.70 GFLOPS | Progress: (20/20) | 19.26 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 18.47/ 18.47 GFLOPS | Progress: (4/20) | 9.18 s [Task 19/25] Current/Best: 3.18/ 21.53 GFLOPS | Progress: (8/20) | 13.07 s [Task 19/25] Current/Best: 13.51/ 21.53 GFLOPS | Progress: (12/20) | 16.50 s [Task 19/25] Current/Best: 21.61/ 23.04 GFLOPS | Progress: (16/20) | 19.88 s [Task 19/25] Current/Best: 18.81/ 23.04 GFLOPS | Progress: (20/20) | 24.57 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 18.35/ 19.67 GFLOPS | Progress: (4/20) | 8.00 s [Task 20/25] Current/Best: 12.84/ 19.67 GFLOPS | Progress: (8/20) | 11.96 s [Task 20/25] Current/Best: 11.93/ 19.67 GFLOPS | Progress: (12/20) | 15.30 s [Task 20/25] Current/Best: 12.38/ 19.67 GFLOPS | Progress: (16/20) | 17.70 s [Task 20/25] Current/Best: 2.69/ 20.48 GFLOPS | Progress: (20/20) | 22.92 s Done.
- [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 8.25/ 17.90 GFLOPS | Progress: (4/20) | 10.17 s [Task 21/25] Current/Best: 17.58/ 18.89 GFLOPS | Progress: (8/20) | 11.96 s [Task 21/25] Current/Best: 8.39/ 18.89 GFLOPS | Progress: (12/20) | 15.35 s [Task 21/25] Current/Best: 16.79/ 20.68 GFLOPS | Progress: (16/20) | 18.95 s [Task 21/25] Current/Best: 14.07/ 22.84 GFLOPS | Progress: (20/20) | 22.53 s Done.
- [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 2.78/ 19.88 GFLOPS | Progress: (4/20) | 5.89 s [Task 22/25] Current/Best: 18.53/ 19.88 GFLOPS | Progress: (8/20) | 8.02 s [Task 22/25] Current/Best: 16.75/ 22.69 GFLOPS | Progress: (12/20) | 10.10 s [Task 22/25] Current/Best: 18.67/ 22.69 GFLOPS | Progress: (16/20) | 12.34 s [Task 22/25] Current/Best: 10.53/ 22.69 GFLOPS | Progress: (20/20) | 14.09 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 20.10/ 23.84 GFLOPS | Progress: (4/20) | 5.60 s [Task 23/25] Current/Best: 10.53/ 23.84 GFLOPS | Progress: (8/20) | 11.50 s [Task 23/25] Current/Best: 11.83/ 23.84 GFLOPS | Progress: (12/20) | 15.30 s [Task 23/25] Current/Best: 10.79/ 23.84 GFLOPS | Progress: (16/20) | 18.71 s [Task 23/25] Current/Best: 18.29/ 23.84 GFLOPS | Progress: (20/20) | 21.90 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.94/ 8.68 GFLOPS | Progress: (4/20) | 13.50 s [Task 24/25] Current/Best: 8.16/ 8.68 GFLOPS | Progress: (8/20) | 24.48 s Done.
- [Task 24/25] Current/Best: 5.79/ 9.18 GFLOPS | Progress: (12/20) | 27.06 s [Task 24/25] Current/Best: 1.19/ 9.18 GFLOPS | Progress: (16/20) | 36.24 s [Task 24/25] Current/Best: 1.34/ 10.20 GFLOPS | Progress: (20/20) | 47.25 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 1.59/ 7.19 GFLOPS | Progress: (4/20) | 7.59 s [Task 25/25] Current/Best: 5.89/ 7.91 GFLOPS | Progress: (8/20) | 9.05 s [Task 25/25] Current/Best: 6.13/ 9.56 GFLOPS | Progress: (12/20) | 13.86 s [Task 25/25] Current/Best: 10.05/ 10.05 GFLOPS | Progress: (16/20) | 18.87 s [Task 25/25] Current/Best: 5.72/ 10.05 GFLOPS | Progress: (20/20) | 21.64 s Done.
-
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 12.52/ 21.67 GFLOPS | Progress: (4/20) | 11.37 s [Task 1/25] Current/Best: 12.39/ 21.67 GFLOPS | Progress: (8/20) | 14.87 s [Task 1/25] Current/Best: 24.32/ 24.32 GFLOPS | Progress: (12/20) | 18.19 s [Task 1/25] Current/Best: 14.81/ 25.07 GFLOPS | Progress: (16/20) | 20.28 s [Task 1/25] Current/Best: 3.40/ 25.07 GFLOPS | Progress: (20/20) | 24.60 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 20.80/ 20.80 GFLOPS | Progress: (4/20) | 3.83 s [Task 2/25] Current/Best: 3.92/ 20.80 GFLOPS | Progress: (8/20) | 5.45 s [Task 2/25] Current/Best: 13.88/ 20.80 GFLOPS | Progress: (12/20) | 7.32 s [Task 2/25] Current/Best: 9.96/ 20.80 GFLOPS | Progress: (16/20) | 9.30 s [Task 2/25] Current/Best: 6.43/ 20.80 GFLOPS | Progress: (20/20) | 10.74 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 13.71/ 22.13 GFLOPS | Progress: (4/20) | 4.91 s [Task 3/25] Current/Best: 17.64/ 22.84 GFLOPS | Progress: (8/20) | 6.89 s [Task 3/25] Current/Best: 7.38/ 22.84 GFLOPS | Progress: (12/20) | 9.23 s [Task 3/25] Current/Best: 12.79/ 22.84 GFLOPS | Progress: (16/20) | 11.65 s [Task 3/25] Current/Best: 12.03/ 22.84 GFLOPS | Progress: (20/20) | 14.20 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 15.82/ 20.56 GFLOPS | Progress: (4/20) | 4.57 s [Task 4/25] Current/Best: 16.58/ 20.56 GFLOPS | Progress: (8/20) | 6.43 s [Task 4/25] Current/Best: 8.19/ 20.56 GFLOPS | Progress: (12/20) | 10.02 s [Task 4/25] Current/Best: 13.55/ 20.56 GFLOPS | Progress: (16/20) | 12.86 s [Task 4/25] Current/Best: 10.67/ 20.56 GFLOPS | Progress: (20/20) | 16.82 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 21.20/ 21.20 GFLOPS | Progress: (4/20) | 4.49 s [Task 5/25] Current/Best: 16.65/ 21.20 GFLOPS | Progress: (8/20) | 6.37 s [Task 5/25] Current/Best: 10.85/ 21.54 GFLOPS | Progress: (12/20) | 9.08 s [Task 5/25] Current/Best: 8.05/ 21.54 GFLOPS | Progress: (16/20) | 11.58 s [Task 5/25] Current/Best: 17.50/ 21.54 GFLOPS | Progress: (20/20) | 13.85 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 14.65/ 20.76 GFLOPS | Progress: (4/20) | 6.09 s [Task 6/25] Current/Best: 12.69/ 20.76 GFLOPS | Progress: (8/20) | 10.40 s [Task 6/25] Current/Best: 15.27/ 20.76 GFLOPS | Progress: (12/20) | 13.33 s [Task 6/25] Current/Best: 19.97/ 20.76 GFLOPS | Progress: (16/20) | 16.04 s [Task 6/25] Current/Best: 9.66/ 20.76 GFLOPS | Progress: (20/20) | 18.87 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 14.15/ 14.15 GFLOPS | Progress: (4/20) | 5.24 s [Task 7/25] Current/Best: 22.22/ 22.22 GFLOPS | Progress: (8/20) | 7.28 s [Task 7/25] Current/Best: 15.23/ 22.37 GFLOPS | Progress: (12/20) | 9.60 s [Task 7/25] Current/Best: 5.25/ 22.37 GFLOPS | Progress: (16/20) | 12.07 s [Task 7/25] Current/Best: 11.42/ 22.37 GFLOPS | Progress: (20/20) | 14.84 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 8.87/ 11.65 GFLOPS | Progress: (4/20) | 11.55 s [Task 8/25] Current/Best: 7.83/ 12.09 GFLOPS | Progress: (8/20) | 20.43 s [Task 8/25] Current/Best: 14.02/ 14.78 GFLOPS | Progress: (12/20) | 23.05 s [Task 8/25] Current/Best: 19.25/ 19.25 GFLOPS | Progress: (16/20) | 34.68 s [Task 8/25] Current/Best: 8.02/ 19.25 GFLOPS | Progress: (20/20) | 42.61 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 13.82/ 21.79 GFLOPS | Progress: (4/20) | 6.05 s [Task 9/25] Current/Best: 21.05/ 21.79 GFLOPS | Progress: (8/20) | 10.14 s [Task 9/25] Current/Best: 19.87/ 21.79 GFLOPS | Progress: (12/20) | 12.90 s [Task 9/25] Current/Best: 20.82/ 21.79 GFLOPS | Progress: (16/20) | 15.64 s [Task 9/25] Current/Best: 7.29/ 21.79 GFLOPS | Progress: (20
/20) | 17.70 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 16.50/ 16.50 GFLOPS | Progress: (4/20) | 4.57 s [Task 10/25] Current/Best: 14.72/ 16.50 GFLOPS | Progress: (8/20) | 6.81 s [Task 10/25] Current/Best: 4.81/ 16.50 GFLOPS | Progress: (12/20) | 8.92 s [Task 10/25] Current/Best: 14.67/ 16.50 GFLOPS | Progress: (16/20) | 11.15 s [Task 10/25] Current/Best: 15.82/ 16.61 GFLOPS | Progress: (20/20) | 13.02 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 20.80/ 21.19 GFLOPS | Progress: (4/20) | 4.59 s [Task 11/25] Current/Best: 21.50/ 21.50 GFLOPS | Progress: (8/20) | 6.92 s [Task 11/25] Current/Best: 20.49/ 21.50 GFLOPS | Progress: (12/20) | 9.28 s [Task 11/25] Current/Best: 19.95/ 21.50 GFLOPS | Progress: (16/20) | 11.32 s [Task 11/25] Current/Best: 15.31/ 23.13 GFLOPS | Progress: (20/20) | 13.29 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 5.12/ 15.09 GFLOPS | Progress: (4/20) | 6.88 s [Task 12/25] Current/Best: 16.63/ 17.08 GFLOPS | Progress: (8/20) | 9.01 s [Task 12/25] Current/Best: 5.35/ 17.08 GFLOPS | Progress: (12/20) | 12.66 s [Task 12/25] Current/Best: 10.50/ 17.08 GFLOPS | Progress: (16/20) | 15.85 s [Task 12/25] Current/Best: 13.57/ 19.64 GFLOPS | Progress: (20/20) | 20.03 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 20.60/ 20.60 GFLOPS | Progress: (4/20) | 6.11 s [Task 13/25] Current/Best: 12.95/ 20.60 GFLOPS | Progress: (8/20) | 8.64 s [Task 13/25] Current/Best: 10.64/ 20.60 GFLOPS | Progress: (12/20) | 11.01 s [Task 13/25] Current/Best: 15.79/ 20.60 GFLOPS | Progress: (16/20) | 14.55 s [Task 13/25] Current/Best: 16.69/ 20.60 GFLOPS | Progress: (20/20) | 18.83 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 21.50/ 21.50 GFLOPS | Progress: (4/20) | 9.36 s [Task 14/25] Current/Best: 9.72/ 21.50 GFLOPS | Progress: (8/20) | 14.00 s [Task 14/25] Current/Best: 11.51/ 21.50 GFLOPS | Progress: (12/20) | 16.89 s [Task 14/25] Current/Best: 4.92/ 21.50 GFLOPS | Progress: (16/20) | 19.58 s [Task 14/25] Current/Best: 7.72/ 21.50 GFLOPS | Progress: (20/20) | 26.48 s Done.
+ [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 7.92/ 17.15 GFLOPS | Progress: (4/20) | 12.70 s [Task 15/25] Current/Best: 20.94/ 22.30 GFLOPS | Progress: (8/20) | 23.55 s [Task 15/25] Current/Best: 12.92/ 22.30 GFLOPS | Progress: (12/20) | 26.69 s [Task 15/25] Current/Best: 12.23/ 22.30 GFLOPS | Progress: (16/20) | 31.93 s [Task 15/25] Current/Best: 7.39/ 22.30 GFLOPS | Progress: (20/20) | 38.69 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 14.10/ 20.28 GFLOPS | Progress: (4/20) | 4.30 s [Task 16/25] Current/Best: 19.02/ 20.28 GFLOPS | Progress: (8/20) | 6.39 s [Task 16/25] Current/Best: 13.83/ 20.28 GFLOPS | Progress: (12/20) | 8.31 s [Task 16/25] Current/Best: 15.02/ 20.28 GFLOPS | Progress: (16/20) | 11.75 s [Task 16/25] Current/Best: 18.34/ 20.28 GFLOPS | Progress: (20/20) | 13.57 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 10.78/ 22.99 GFLOPS | Progress: (4/20) | 4.98 s [Task 17/25] Current/Best: 19.91/ 22.99 GFLOPS | Progress: (8/20) | 7.30 s [Task 17/25] Current/Best: 23.62/ 23.62 GFLOPS | Progress: (12/20) | 10.17 s [Task 17/25] Current/Best: 21.87/ 23.62 GFLOPS | Progress: (16/20) | 12.50 s [Task 17/25] Current/Best: 18.20/ 23.62 GFLOPS | Progress: (20/20) | 15.59 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 7.25/ 17.60 GFLOPS | Progress: (4/20) | 6.28 s [Task 18/25] Current/Best: 10.57/ 17.60 GFLOPS | Progress: (8/20) | 12.48 s [Task 18/25] Current/Best: 18.70/ 18.95 GFLOPS | Progress: (12/20) | 16.62 s [Task 18/25] Current/Best: 8.71/ 18.95 GFLOPS | Progress: (16/20) | 20.67 s [Task 18/25] Current/Best: 8.74/ 18.95 GFLOPS | Progress: (20/20) | 29.94 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 6.33/ 22.27 GFLOPS | Progress: (4/20) | 6.26 s [Task 19/25] Current/Best: 21.34/ 22.27 GFLOPS | Progress: (8/20) | 9.82 s [Task 19/25] Current/Best: 11.60/ 22.27 GFLOPS | Progress: (12/20) | 12.46 s [Task 19/25] Current/Best: 3.18/ 22.27 GFLOPS | Progress: (16/20) | 16.68 s [Task 19/25] Current/Best: 18.54/ 22.27 GFLOPS | Progress: (20/20) | 22.85 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 18.22/ 19.09 GFLOPS | Progress: (4/20) | 5.35 s [Task 20/25] Current/Best: 19.44/ 19.44 GFLOPS | Progress: (8/20) | 8.35 s [Task 20/25] Current/Best: 10.53/ 22.08 GFLOPS | Progress: (12/20) | 10.77 s [Task 20/25] Current/Best: 16.39/ 22.08 GFLOPS | Progress: (16/20) | 13.89 s [Task 20/25] Current/Best: 12.99/ 22.08 GFLOPS | Progress: (20/20) | 16.97 s Done.
+ [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 7.43/ 19.86 GFLOPS | Progress: (4/20) | 7.19 s [Task 21/25] Current/Best: 17.23/ 20.79 GFLOPS | Progress: (8/20) | 10.79 s [Task 21/25] Current/Best: 19.03/ 20.79 GFLOPS | Progress: (12/20) | 21.93 s [Task 21/25] Current/Best: 13.81/ 20.79 GFLOPS | Progress: (16/20) | 24.96 s [Task 21/25] Current/Best: 13.97/ 20.79 GFLOPS | Progress: (20/20) | 26.82 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ Done.
+ [Task 22/25] Current/Best: 10.08/ 10.08 GFLOPS | Progress: (4/20) | 7.52 s [Task 22/25] Current/Best: 20.66/ 20.66 GFLOPS | Progress: (8/20) | 9.22 s [Task 22/25] Current/Best: 20.02/ 20.66 GFLOPS | Progress: (12/20) | 11.56 s [Task 22/25] Current/Best: 13.34/ 20.66 GFLOPS | Progress: (16/20) | 13.88 s [Task 22/25] Current/Best: 19.49/ 20.66 GFLOPS | Progress: (20/20) | 18.80 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 1.60/ 22.88 GFLOPS | Progress: (4/20) | 6.80 s [Task 23/25] Current/Best: 19.18/ 22.88 GFLOPS | Progress: (8/20) | 11.22 s [Task 23/25] Current/Best: 21.54/ 22.88 GFLOPS | Progress: (12/20) | 14.13 s [Task 23/25] Current/Best: 14.50/ 22.88 GFLOPS | Progress: (16/20) | 17.13 s [Task 23/25] Current/Best: 20.40/ 22.88 GFLOPS | Progress: (20/20) | 21.23 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 7.19/ 7.19 GFLOPS | Progress: (4/20) | 8.57 s [Task 24/25] Current/Best: 1.85/ 7.37 GFLOPS | Progress: (8/20) | 11.00 s [Task 24/25] Current/Best: 2.50/ 7.37 GFLOPS | Progress: (12/20) | 16.10 s [Task 24/25] Current/Best: 1.69/ 9.56 GFLOPS | Progress: (16/20) | 23.28 s [Task 24/25] Current/Best: 3.07/ 9.56 GFLOPS | Progress: (20/20) | 34.32 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 5.83/ 5.83 GFLOPS | Progress: (4/20) | 13.59 s [Task 25/25] Current/Best: 6.86/ 6.86 GFLOPS | Progress: (8/20) | 24.59 s [Task 25/25] Current/Best: 6.02/ 8.75 GFLOPS | Progress: (12/20) | 27.02 s [Task 25/25] Current/Best: 6.46/ 8.75 GFLOPS | Progress: (16/20) | 31.92 s [Task 25/25] Current/Best: 1.60/ 8.95 GFLOPS | Progress: (20
/20) | 33.69 s
@@ -675,6 +674,7 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
+ Done.
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621103
- class='n02123159 tiger cat' with probability=0.356379
+ class='n02123045 tabby, tabby cat' with probability=0.621104
+ class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 382.82310683993273, 'median': 382.03536674955103, 'std': 2.5488279921898767}
- unoptimized: {'mean': 467.1754210700601, 'median': 467.11238465031784, 'std': 0.5777047017991859}
+ optimized: {'mean': 406.2914800499493, 'median': 405.8106577500439, 'std': 2.0954542420005913}
+ unoptimized: {'mean': 469.0988353099965, 'median': 469.264257750001, 'std': 1.1990900522260681}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 12 minutes 34.506 seconds)
+ **Total running time of the script:** ( 13 minutes 9.075 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 db825a9f21..628dcfa5c7 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
- 1.178e-07 secs/op
+ 1.137e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 44ffc64caa..3fff74fdbc 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, 0x1bb4e290)), stage(b, placeholder(b, 0x25318280)), 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, 0x12c61940)), stage(b, placeholder(b, 0x28edd410)), 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 4ca51fca6a..c34182d581 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,32 +5,32 @@
Computation times
=================
-**16:12.687** total execution time for **tutorial** files:
+**17:00.448** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:34.506 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:09.075 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:26.646 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:34.301 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.388 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.342 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.077 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.259 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:28.120 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:32.071 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.882 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.323 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.864 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.885 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.204 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.193 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 1947a76363..0a15a25b8d 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000005
- naive: 0.000006
+ Numpy running time: 0.000007
+ naive: 0.000008
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000006
+ parallel: 0.000007
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000038
+ 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 4.889330011792481e-06 1.0
- naive 5.6749000000000005e-06 1.1606702730870728
- parallel 5.8917e-06 1.2050117267171416
- vector 3.8426999999999996e-05 7.859359034329581
+ numpy 7.4482800118858e-06 1.0
+ naive 7.519199999999999e-06 1.0095216597658825
+ parallel 6.815599999999999e-06 0.9150568975822359
+ vector 3.91541e-05 5.256797534131203
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.013921
+ Numpy running time: 0.017550
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.401851
+ none: 3.351915
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.280760
+ blocking: 0.295257
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.257020
+ vectorization: 0.280451
# 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: 0.110472
+ loop permutation: 0.112447
# 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: 0.103486
+ array packing: 0.103539
# 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: 0.094344
+ block caching: 0.107929
# 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: 0.111926
+ parallelization: 0.128364
# 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 3.4018513300000004 1.0
- blocking 0.2807601478 0.08253157488807719
- vectorization 0.2570200521 0.07555299369887512
- loop permutation 0.11047245450000001 0.032474215885266215
- array packing 0.1034855109 0.030420350821151253
- block caching 0.0943444393 0.027733263493322616
- parallelization 0.11192621200000001 0.03290155892850261
+ none 3.3519146379999993 1.0
+ blocking 0.2952568378 0.0880860253577854
+ vectorization 0.2804505039 0.0836687488161505
+ loop permutation 0.1124470104 0.03354709846283383
+ array packing 0.1035391454 0.030889553160512204
+ block caching 0.10792898290000001 0.03219920390466699
+ parallelization 0.12836405369999998 0.03829574066259381
diff --git a/docs/commit_hash b/docs/commit_hash
index 761e5b04f1..3834f27ac4 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-0f38ef2d6e6ecb7d1b8e164582f417b15b8f4e9a
+e257fb8a41159a2558dc1fccb5e3dd3c45001820
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 592f7d0500..e05410c931 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 35.903 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.662 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
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<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 11e2e3fa82..eb8ec25102 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -468,16 +468,15 @@ 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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+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 29.0MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 30.5MB/s]
</pre></div>
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</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 601d097dc0..b4bc503e48 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 2.458 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 5.459 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 4e77b5c6ca..1666a1aeca 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -451,14 +451,15 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 0f12a78c3a..a9a6378a16 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -671,7 +671,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.569 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 32.692 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 ff96658a7d..f4f9e6b8e3 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:43.241</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:55.944</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 @@
</colgroup>
<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>
-<td><p>01:35.903</p></td>
+<td><p>01:39.662</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
-<td><p>01:30.569</p></td>
+<td><p>01:32.692</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:02.458</p></td>
+<td><p>01:05.459</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:43.905</p></td>
+<td><p>00:44.893</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:38.966</p></td>
+<td><p>00:40.265</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:28.705</p></td>
+<td><p>00:29.097</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:27.883</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>
-<td><p>00:12.016</p></td>
+<td><p>00:13.041</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>00:02.836</p></td>
+<td><p>00:02.897</p></td>
<td><p>0.0 MB</p></td>
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diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 717d430436..d9ef0ad096 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)
- 3993.5226 3992.8541 3998.6957 3990.8793 2.1687
+ 4125.8090 4125.9218 4127.8100 4123.8300 1.3218
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.439 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.899 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
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<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 754e9bdb84..262b9a3830 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
@@ -458,27 +458,33 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index e9ec8cbdad..589c84114e 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -682,7 +682,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.5436 14.4490 15.2599 14.3580 0.2558
+ 15.8191 15.7680 16.2768 15.4724 0.2436
</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 fea302ce96..c48639f207 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -474,37 +474,40 @@ 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).
@@ -595,7 +598,7 @@ 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 34.091 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 43.156 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index 8532738063..34f281acee 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -515,9 +515,10 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
</div>
</div>
@@ -608,7 +609,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 86.2873 86.2275 89.9730 85.8618 0.4318
+ 87.8449 87.8215 89.4329 87.4240 0.2779
</pre></div>
</div>
<div class="admonition note">
@@ -647,7 +648,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.511 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.090 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 d1976827a9..f22c68247c 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)
- 107.3905 107.2979 112.5537 104.6349 0.8696
+ 108.7687 108.7454 113.4321 107.9973 0.5371
</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 917e757dab..9b54679080 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:52.762</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:12.292</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -369,35 +369,35 @@
</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>03:34.091</p></td>
+<td><p>03:43.156</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>01:29.511</p></td>
+<td><p>01:30.090</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>01:18.439</p></td>
+<td><p>01:20.899</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:52.061</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:54.155</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:51.466</p></td>
+<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>00:52.204</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>00:47.271</p></td>
+<td><p>00:49.981</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:29.963</p></td>
+<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>00:31.161</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:29.951</p></td>
+<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>00:30.638</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>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 4001c72e99..270ce88814 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>00:04.133</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.123</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>00:02.894</p></td>
+<td><p>00:02.864</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>00:01.231</p></td>
+<td><p>00:01.251</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>00:00.007</p></td>
+<td><p>00:00.008</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 12392f3eeb..3d51e70f55 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: 24285us [24285us] (48.42%; 48.42%)
-FoldScaleAxis: 25872us [10us] (51.58%; 51.58%)
- FoldConstant: 25862us [1796us] (51.56%; 99.96%)
- InferType: 24066us [24066us] (47.98%; 93.06%)
+InferType: 25156us [25156us] (48.16%; 48.16%)
+FoldScaleAxis: 27073us [8us] (51.84%; 51.84%)
+ FoldConstant: 27065us [1885us] (51.82%; 99.97%)
+ InferType: 25181us [25181us] (48.21%; 93.04%)
</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: 24615us [24615us] (48.18%; 48.18%)
-FoldScaleAxis: 26474us [10us] (51.82%; 51.82%)
- FoldConstant: 26464us [1903us] (51.80%; 99.96%)
- InferType: 24561us [24561us] (48.07%; 92.81%)
+InferType: 25260us [25260us] (48.30%; 48.30%)
+FoldScaleAxis: 27043us [10us] (51.70%; 51.70%)
+ FoldConstant: 27033us [1943us] (51.69%; 99.96%)
+ InferType: 25090us [25090us] (47.97%; 92.81%)
</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 101c9f688c..2c23dbd28e 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -595,7 +595,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.518337 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 34.566398 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 d09231fe7c..188669f701 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: 12.264816 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.273059 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 34d9c4c66a..a90c495f09 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: 0.014200
-Baseline: 3.352059
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.017773
+Baseline: 3.445178
</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: 0.295102
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.289836
</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: 0.278885
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.303563
</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: 0.113360
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.115953
</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: 0.105984
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.140538
</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: 0.095547
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110639
</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: 0.112764
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.130842
</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 af7869c108..2a1da94f5a 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:32.190</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.405</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,15 +369,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:29.014</p></td>
+<td><p>00:31.451</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
-<td><p>00:01.916</p></td>
+<td><p>00:01.806</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.260</p></td>
+<td><p>00:01.148</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 0b7b4ead2b..77fa3ee774 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:36.976</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:43.512</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -369,27 +369,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:34.578</p></td>
+<td><p>01:37.782</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:12.734</p></td>
+<td><p>01:14.249</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:18.122</p></td>
+<td><p>00:18.367</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:16.402</p></td>
+<td><p>00:16.894</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:15.040</p></td>
+<td><p>00:16.119</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.098</p></td>
+<td><p>00:00.102</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index cdfe2a0dcb..c521dbf338 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1032,7 +1032,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.341 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.334 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index a61ca4895e..9a9f2750bf 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -923,7 +923,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.2540 3.2515 3.2657 3.2447 0.0088
+ 3.2194 3.2182 3.2239 3.2160 0.0033
</pre></div>
</div>
</div>
@@ -945,7 +945,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.734 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.249 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 6d1701cf0a..793a6f8e62 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -945,7 +945,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 709.7304 709.8460 710.9923 708.3529 1.0806
+ 771.4275 771.7011 772.5295 770.0519 1.0298
</pre></div>
</div>
</div>
@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
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<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
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@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:22.645</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.884</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -369,15 +369,15 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
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Best config:
,None
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@@ -664,10 +664,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
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+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.927 0.953 (1, 6, 10, 10) 1 1 [2.927]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.977 0.318 (1, 1, 10, 10, 3) 1 1 [0.977]
+Total_time - 307.204 - - - - -
</pre></div>
</div>
</div>
@@ -719,13 +719,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 97.375 97.325 (1, 6, 10, 10, 1) 2 1 [97.375]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.737 1.736 (1, 6, 10, 10) 1 1 [1.737]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.939 0.939 (1, 1, 10, 10, 3) 1 1 [0.939]
-Total_time - 100.051 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 135.6 98.1 (1, 6, 10, 10, 1) 2 1 [135.6]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.281 (1, 6, 10, 10) 1 1 [1.77]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.855 0.619 (1, 3, 10, 10, 1) 1 1 [0.855]
+Total_time - 138.226 - - - - -
</pre></div>
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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 +475,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 86.9MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 17.9MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 28.0MB/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.
@@ -603,7 +604,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
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<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 273ed6d50c..bc33d92c33 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -543,7 +543,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
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</pre></div>
</div>
</div>
@@ -603,8 +603,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
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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/tmpzu9nmts7/images/target contains 8144 images
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</pre></div>
</div>
</div>
@@ -716,13 +716,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
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+328/328 - 39s - loss: 0.2065 - accuracy: 0.9288 - val_loss: 0.1103 - val_accuracy: 0.9588 - 39s/epoch - 119ms/step
Epoch 2/3
-328/328 - 34s - loss: 0.0962 - accuracy: 0.9647 - val_loss: 0.0976 - val_accuracy: 0.9630 - 34s/epoch - 105ms/step
+328/328 - 35s - loss: 0.0963 - accuracy: 0.9652 - val_loss: 0.1108 - val_accuracy: 0.9577 - 35s/epoch - 108ms/step
Epoch 3/3
-328/328 - 34s - loss: 0.0623 - accuracy: 0.9753 - val_loss: 0.1254 - val_accuracy: 0.9532 - 34s/epoch - 105ms/step
+328/328 - 35s - loss: 0.0656 - accuracy: 0.9763 - val_loss: 0.0951 - val_accuracy: 0.9679 - 35s/epoch - 108ms/step
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</pre></div>
</div>
</div>
@@ -986,7 +986,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
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@@ -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>
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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_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
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@@ -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>
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<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>00:01.811</p></td>
+<td><p>00:02.103</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>
</tr>
</tbody>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 980bd7f63c..8ed0acb1c4 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 0x7fc2c61f8dc0>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f6bae34f310>
</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 2cf4c28228..e365305e6a 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>00:05.369</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.119</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 @@
</colgroup>
<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>00:02.556</p></td>
+<td><p>00:02.407</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>00:01.079</p></td>
+<td><p>00:01.053</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>00:00.724</p></td>
+<td><p>00:00.689</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>
-<td><p>00:00.718</p></td>
+<td><p>00:00.678</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>00:00.123</p></td>
+<td><p>00:00.121</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>
-<td><p>00:00.070</p></td>
+<td><p>00:00.072</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
-<td><p>00:00.067</p></td>
+<td><p>00:00.065</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
-<td><p>00:00.033</p></td>
+<td><p>00:00.034</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/reference/api/doxygen/annotated.html b/docs/reference/api/doxygen/annotated.html
index e6a7a947c5..55da091d6c 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -787,151 +787,154 @@ $(function() {
<tr id="row_1_6_250_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Runtime.html" target="_self">Runtime</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html" title="Runtime information.">RuntimeNode</a> </td></tr>
<tr id="row_1_6_251_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RuntimeRegEntry.html" target="_self">RuntimeRegEntry</a></td><td class="desc">Helper structure to register Runtimes </td></tr>
<tr id="row_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_7_" class="arrow" onclick="toggleFolder('1_7_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime.html" target="_self">runtime</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_0_" class="arrow" onclick="toggleFolder('1_7_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1memory.html" target="_self">memory</a></td><td class="desc"></td></tr>
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-<tr id="row_1_7_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html" target="_self">StorageObj</a></td><td class="desc">An object representing a storage allocation </td></tr>
-<tr id="row_1_7_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Storage.html" target="_self">Storage</a></td><td class="desc">Reference to storage </td></tr>
-<tr id="row_1_7_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_1_" class="arrow" onclick="toggleFolder('1_7_1_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1metadata.html" target="_self">metadata</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataNode.html" target="_self">MetadataNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html" target="_self">Metadata</a></td><td class="desc"></td></tr>
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-<tr id="row_1_7_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBaseNode.html" target="_self">MetadataBaseNode</a></td><td class="desc">Common base class for all <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html">Metadata</a> </td></tr>
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-<tr id="row_1_7_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode.html" target="_self">DeviceWrapperNode</a></td><td class="desc">Wrapper for <code>Device</code> because <code>Device</code> is not passable across the <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed fun [...]
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-<tr id="row_1_7_3_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1ReportNode.html" target="_self">ReportNode</a></td><td class="desc">Data collected from a profiling run. Includes per-call metrics and per-device metrics </td></tr>
-<tr id="row_1_7_3_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">Report</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" target="_self">MetricCollectorNode</a></td><td class="desc">Interface for user defined profiling metric collection </td></tr>
-<tr id="row_1_7_3_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling metric collection.">Metri [...]
-<tr id="row_1_7_3_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1profiling_1_1CallFrame.html" target="_self">CallFrame</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Profiler.html" target="_self">Profiler</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DurationNode.html" target="_self">DurationNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html" target="_self">PercentNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1CountNode.html" target="_self">CountNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_3_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1RatioNode.html" target="_self">RatioNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_4_" class="arrow" onclick="toggleFolder('1_7_4_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1relax__vm.html" target="_self">relax_vm</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_7_4_0_" class="arrow" onclick="toggleFolder('1_7_4_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1Instruction.html" target="_self">Instruction</a></td><td class="desc">A single virtual machine instruction </td></tr>
-<tr id="row_1_7_4_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1Instruction_1_1Arg.html" target="_self">Arg</a></td><td class="desc">The auxiliary data structure for instruction argument </td></tr>
-<tr id="row_1_7_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1VMFuncInfo.html" target="_self">VMFuncInfo</a></td><td class="desc">Information entry in executable function table </td></tr>
-<tr id="row_1_7_4_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1Executable.html" target="_self">Executable</a></td><td class="desc">The executable emitted by the VM compiler </td></tr>
-<tr id="row_1_7_4_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_7_4_3_" class="arrow" onclick="toggleFolder('1_7_4_3_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata.html" target="_self">NDArrayCacheMetadata</a></td><td class="desc">Metadata for <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" t [...]
-<tr id="row_1_7_4_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span id="arr_1_7_4_3_0_" class="arrow" onclick="toggleFolder('1_7_4_3_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata_1_1FileRecord.html" target="_self">FileRecord</a></td><td class="desc">Each shard of <a class="el" href="classtvm_1_1runtime_1_1NDAr [...]
-<tr id="row_1_7_4_3_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata_1_1FileRecord_1_1ParamRecord.html" target="_self">ParamRecord</a></td><td class="desc">Metadata of each parameter </td></tr>
-<tr id="row_1_7_4_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VMClosureObj.html" target="_self">VMClosureObj</a></td><td class="desc">An object representing a vm closure </td></tr>
-<tr id="row_1_7_4_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VMClosure.html" target="_self">VMClosure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_1_7_4_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VirtualMachine.html" target="_self">VirtualMachine</a></td><td class="desc">The virtual machine </td></tr>
-<tr id="row_1_7_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_5_" class="arrow" onclick="toggleFolder('1_7_5_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1threading.html" target="_self">threading</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1threading_1_1ThreadGroup.html" target="_self">ThreadGroup</a></td><td class="desc">A platform-agnostic abstraction for managing a collection of thread pool threads </td></tr>
-<tr id="row_1_7_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_6_" class="arrow" onclick="toggleFolder('1_7_6_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1vm.html" target="_self">vm</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_6_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html" target="_self">Instruction</a></td><td class="desc">A single virtual machine instruction </td></tr>
-<tr id="row_1_7_6_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html" target="_self">Executable</a></td><td class="desc">The executable emitted by the VM compiler </td></tr>
-<tr id="row_1_7_6_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosureObj.html" target="_self">VMClosureObj</a></td><td class="desc">An object representing a vm closure </td></tr>
-<tr id="row_1_7_6_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">VMClosure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_1_7_6_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
-<tr id="row_1_7_6_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html" target="_self">VMFrame</a></td><td class="desc">A representation of a stack frame </td></tr>
-<tr id="row_1_7_6_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html" target="_self">VirtualMachine</a></td><td class="desc">The virtual machine </td></tr>
-<tr id="row_1_7_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html" target="_self">PackedFuncValueConverter< PrimExpr ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html" target="_self">PackedFuncValueConverter< tvm::Integer ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Bool_01_4.html" target="_self">PackedFuncValueConverter< tvm::Bool ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html" target="_self">ADTObj</a></td><td class="desc">An object representing a structure or enumeration </td></tr>
-<tr id="row_1_7_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
-<tr id="row_1_7_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html" target="_self">ArrayNode</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs.">Array</a> node content in array </td></tr>
-<tr id="row_1_7_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1is__valid__iterator.html" target="_self">is_valid_iterator</a></td><td class="desc">Helper struct for type-checking </td></tr>
-<tr id="row_1_7_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1is__valid__iterator_3_01Optional_3_01T_01_4_00_01IterType_01_4.html" target="_self">is_valid_iterator< Optional< T >, IterType ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_15_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_15_" class="arrow" onclick="toggleFolder('1_7_15_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">Array</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of O [...]
-<tr id="row_1_7_15_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">ValueConverter</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html" target="_self">ObjectHash</a></td><td class="desc">String-aware <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> equal functor </td></tr>
-<tr id="row_1_7_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html" target="_self">ObjectEqual</a></td><td class="desc">String-aware <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> hash functor </td></tr>
-<tr id="row_1_7_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_self">InplaceArrayBase</a></td><td class="desc">Base template for classes with array like memory layout </td></tr>
-<tr id="row_1_7_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html" target="_self">IterAdapter</a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
-<tr id="row_1_7_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html" target="_self">ReverseIterAdapter</a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
-<tr id="row_1_7_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ClosureObj.html" target="_self">ClosureObj</a></td><td class="desc">An object representing a closure. This object is used by both the Relay VM and interpreter </td></tr>
-<tr id="row_1_7_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">Closure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_1_7_23_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_23_" class="arrow" onclick="toggleFolder('1_7_23_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1MapNode.html" target="_self">MapNode</a></td><td class="desc">Shared content of all specializations of hash map </td></tr>
-<tr id="row_1_7_23_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html" target="_self">iterator</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html" target="_self">SmallMapNode</a></td><td class="desc">A specialization of small-sized hash map </td></tr>
-<tr id="row_1_7_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html" target="_self">DenseMapNode</a></td><td class="desc">A specialization of hash map that implements the idea of array-based hash map. Another reference implementation can be found [1] </td></tr>
-<tr id="row_1_7_26_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_26_" class="arrow" onclick="toggleFolder('1_7_26_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">Map</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map impleme [...]
-<tr id="row_1_7_26_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html" target="_self">iterator</a></td><td class="desc">Iterator of the hash map </td></tr>
-<tr id="row_1_7_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html" target="_self">NullOptType</a></td><td class="desc">Helper to represent nullptr for optional </td></tr>
-<tr id="row_1_7_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">Optional</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T.">Optional</a> container that to represent to a Nullable variant of [...]
-<tr id="row_1_7_29_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_29_" class="arrow" onclick="toggleFolder('1_7_29_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj.html" target="_self">ShapeTupleObj</a></td><td class="desc">An object representing a shape tuple </td></tr>
-<tr id="row_1_7_29_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj_1_1FromStd.html" target="_self">FromStd</a></td><td class="desc">An object representing shape tuple moved from std::vector </td></tr>
-<tr id="row_1_7_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
-<tr id="row_1_7_31_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_31_" class="arrow" onclick="toggleFolder('1_7_31_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1StringObj.html" target="_self">StringObj</a></td><td class="desc">An object representing string. It's POD type </td></tr>
-<tr id="row_1_7_31_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html" target="_self">FromStd</a></td><td class="desc">An object representing string moved from std::string </td></tr>
-<tr id="row_1_7_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">String</a></td><td class="desc">Reference to string objects </td></tr>
-<tr id="row_1_7_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Variant.html" target="_self">Variant</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DataType.html" target="_self">DataType</a></td><td class="desc">Runtime primitive data type </td></tr>
-<tr id="row_1_7_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html" target="_self">DeviceAPI</a></td><td class="desc">TVM Runtime Device API, abstracts the device specific interface for memory management </td></tr>
-<tr id="row_1_7_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" target="_self">DiscoWorker</a></td><td class="desc">A worker in Disco. It takes a channel to communication with the controler. The worker can be run in a separate thread or process as long as the channel supports bi-directional communication i [...]
-<tr id="row_1_7_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DRefObj.html" target="_self">DRefObj</a></td><td class="desc">An object that exists on all workers </td></tr>
-<tr id="row_1_7_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DRef.html" target="_self">DRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1runtime_1_1DRefObj.html" title="An object that exists on all workers.">DRefObj</a> </td></tr>
-<tr id="row_1_7_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SessionObj.html" target="_self">SessionObj</a></td><td class="desc">A Disco interactive session. It allows users to interact with the Disco command queue with various <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-e [...]
-<tr id="row_1_7_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Session.html" target="_self">Session</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1runtime_1_1SessionObj.html" title="A Disco interactive session. It allows users to interact with the Disco command queue with various Pa...">Sessi [...]
-<tr id="row_1_7_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DiscoChannel.html" target="_self">DiscoChannel</a></td><td class="desc">A bi-directional channel for controler-worker communication. This channel is primarily used to transfer control messages but not data </td></tr>
-<tr id="row_1_7_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WorkerZeroData.html" target="_self">WorkerZeroData</a></td><td class="desc">A special communication channel between controler and worker-0, assuming they are always collocated in the same process </td></tr>
-<tr id="row_1_7_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html" target="_self">ObjAllocatorBase</a></td><td class="desc">Base class of object allocators that implements make. Use curiously recurring template pattern </td></tr>
-<tr id="row_1_7_44_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_44_" class="arrow" onclick="toggleFolder('1_7_44_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator.html" target="_self">SimpleObjAllocator</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_44_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html" target="_self">ArrayHandler</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_44_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html" target="_self">Handler</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM.">Module</a> container of TVM </td></tr>
-<tr id="row_1_7_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html" target="_self">ModuleNode</a></td><td class="desc">Base container of module </td></tr>
-<tr id="row_1_7_47_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_47_" class="arrow" onclick="toggleFolder('1_7_47_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by re [...]
-<tr id="row_1_7_47_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html" target="_self">Container</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Object</a> container class that backs <a class="el" href="classtvm_1_1runtime_1_1 [...]
-<tr id="row_1_7_47_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html" target="_self">ContainerBase</a></td><td class="desc">The container base structure contains all the fields except for the <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Objec [...]
-<tr id="row_1_7_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NVTXScopedRange.html" target="_self">NVTXScopedRange</a></td><td class="desc">A class to create a NVTX range. No-op if TVM is not built against NVTX </td></tr>
-<tr id="row_1_7_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
-<tr id="row_1_7_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Object.html" target="_self">Object</a></td><td class="desc">Base class of all object containers </td></tr>
-<tr id="row_1_7_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_self">ObjectPtr</a></td><td class="desc">A custom smart pointer for <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Object</a> </td></tr>
-<tr id="row_1_7_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" target="_self">ObjectRef</a></td><td class="desc">Base class of all object reference </td></tr>
-<tr id="row_1_7_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html" target="_self">ObjectPtrHash</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> hash functor </td></tr>
-<tr id="row_1_7_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html" target="_self">ObjectPtrEqual</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> equal functor </td></tr>
-<tr id="row_1_7_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">TypedPackedFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#TypedPackedFuncAnchor">TypedPackedFunc<R(Args..)></a> </td></tr>
-<tr id="row_1_7_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1SignaturePrinter.html" target="_self">SignaturePrinter</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_57_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_57_" class="arrow" onclick="toggleFolder('1_7_57_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html" target="_self">PackedFuncObj</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">O [...]
-<tr id="row_1_7_57_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncObj_1_1Extractor.html" target="_self">Extractor</a></td><td class="desc">Internal struct for extracting the callable method from callable type </td></tr>
-<tr id="row_1_7_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFuncSubObj.html" target="_self">PackedFuncSubObj</a></td><td class="desc">Derived object class for constructing <a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html" title="Object container class that backs PackedFunc.">PackedFuncObj</a> </td></tr>
-<tr id="row_1_7_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
-<tr id="row_1_7_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedPackedFunc< R(Args...)></a></td><td class="desc">A <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by [...]
-<tr id="row_1_7_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
-<tr id="row_1_7_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html" target="_self">ObjectTypeChecker</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> traits for runtime type check during FFI conversion </td></tr>
-<tr id="row_1_7_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html" target="_self">ObjectTypeChecker< Array< T > ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html" target="_self">ObjectTypeChecker< Map< K, V > ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
-<tr id="row_1_7_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">TVMArgValue</a></td><td class="desc">A single argument value to <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed format.">PackedFunc [...]
-<tr id="row_1_7_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">TVMMovableArgValue_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>.">TypedPackedFunc</a> t [...]
-<tr id="row_1_7_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">TVMMovableArgValueWithContext_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>." [...]
-<tr id="row_1_7_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">TVMRetValue</a></td><td class="desc">Return Value container, Unlike <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" title="A single argument value to PackedFunc. Containing both type_code and TVMValue.">TVMArgValue< [...]
-<tr id="row_1_7_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html" target="_self">PackedFuncValueConverter</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> trait to specify special value conversion rules from <a class="el" href=" [...]
-<tr id="row_1_7_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">TVMArgsSetter</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TVMArgsSetterApply.html" target="_self">TVMArgsSetterApply</a></td><td class="desc"></td></tr>
-<tr id="row_1_7_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html" target="_self">PackedFuncValueConverter<::tvm::runtime::String ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html" target="_self">PackedFuncValueConverter< Optional< T > ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Variant_3_01VariantTypes_8_8_8_01_4_01_4.html" target="_self">PackedFuncValueConverter< Variant< VariantTypes... > ></a></td><td class="desc"></td></tr>
-<tr id="row_1_7_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TimerNode.html" target="_self">TimerNode</a></td><td class="desc">Base class for all implementations </td></tr>
-<tr id="row_1_7_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device.">Timer</a> for a specific device </td></tr>
-<tr id="row_1_7_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WrappedPythonObject.html" target="_self">WrappedPythonObject</a></td><td class="desc">A class that wraps a Python object and preserves its ownership </td></tr>
-<tr id="row_1_7_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function.">Registry</a> for global function </td></tr>
+<tr id="row_1_7_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_0_" class="arrow" onclick="toggleFolder('1_7_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1cuda__ipc.html" target="_self">cuda_ipc</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1cuda__ipc_1_1CUDAIPCMemoryObj.html" target="_self">CUDAIPCMemoryObj</a></td><td class="desc">The CUDA IPC (interprocess communication) memory object, which internally contains data pointers to CUDA IPC memory. It is be useful for efficient all-reduce implement [...]
+<tr id="row_1_7_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1cuda__ipc_1_1CUDAIPCMemory.html" target="_self">CUDAIPCMemory</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1runtime_1_1cuda__ipc_1_1CUDAIPCMemoryObj.html" title="The CUDA IPC (interprocess communication) memory object, which int [...]
+<tr id="row_1_7_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_1_" class="arrow" onclick="toggleFolder('1_7_1_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1memory.html" target="_self">memory</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html" target="_self">Buffer</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html" target="_self">Allocator</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html" target="_self">MemoryManager</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html" target="_self">StorageObj</a></td><td class="desc">An object representing a storage allocation </td></tr>
+<tr id="row_1_7_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Storage.html" target="_self">Storage</a></td><td class="desc">Reference to storage </td></tr>
+<tr id="row_1_7_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_2_" class="arrow" onclick="toggleFolder('1_7_2_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1metadata.html" target="_self">metadata</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataNode.html" target="_self">MetadataNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html" target="_self">Metadata</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1TensorInfoNode.html" target="_self">TensorInfoNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1TensorInfo.html" target="_self">TensorInfo</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadataNode.html" target="_self">ConstantInfoMetadataNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadata.html" target="_self">ConstantInfoMetadata</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_2_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBaseNode.html" target="_self">MetadataBaseNode</a></td><td class="desc">Common base class for all <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html">Metadata</a> </td></tr>
+<tr id="row_1_7_2_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html" target="_self">MetadataBase</a></td><td class="desc">Reference class for the common <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBaseNode.html" title="Common base class for all Metadata.">MetadataBaseNode</a> cl [...]
+<tr id="row_1_7_2_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html" target="_self">ArrayAccessor</a></td><td class="desc">A span-like class which permits access to <a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs.">Ar [...]
+<tr id="row_1_7_2_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayIterator.html" target="_self">ArrayIterator</a></td><td class="desc">An iterator implementation that lazily instantiates the C++ wrapping <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html">Metadata</a> class </td></tr>
+<tr id="row_1_7_2_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html" target="_self">ArrayAccessor< const char *, ::tvm::runtime::String ></a></td><td class="desc">A specialization of <a class="el" href="classtvm_1_1runtime_1_1met [...]
+<tr id="row_1_7_2_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArrayNode.html" target="_self">MetadataArrayNode</a></td><td class="desc">Container for arrays in the metadata </td></tr>
+<tr id="row_1_7_2_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" target="_self">MetadataArray</a></td><td class="desc">Reference class for <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" title="Reference class for MetadataArray.">MetadataArray</a> </td></tr>
+<tr id="row_1_7_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_3_" class="arrow" onclick="toggleFolder('1_7_3_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html" target="_self">micro_rpc</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html" target="_self">FrameBuffer</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1PacketFieldSizeBytes.html" target="_self">PacketFieldSizeBytes</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html" target="_self">Unframer</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Framer.html" target="_self">Framer</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1micro__rpc_1_1SessionHeader.html" target="_self">SessionHeader</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_3_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Session.html" target="_self">Session</a></td><td class="desc">CRT communication session management class. Assumes the following properties provided by the underlying transport: </td></tr>
+<tr id="row_1_7_3_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1WriteStream.html" target="_self">WriteStream</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_4_" class="arrow" onclick="toggleFolder('1_7_4_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1profiling.html" target="_self">profiling</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode.html" target="_self">DeviceWrapperNode</a></td><td class="desc">Wrapper for <code>Device</code> because <code>Device</code> is not passable across the <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed fun [...]
+<tr id="row_1_7_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html" target="_self">DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </td></tr>
+<tr id="row_1_7_4_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1ReportNode.html" target="_self">ReportNode</a></td><td class="desc">Data collected from a profiling run. Includes per-call metrics and per-device metrics </td></tr>
+<tr id="row_1_7_4_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">Report</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" target="_self">MetricCollectorNode</a></td><td class="desc">Interface for user defined profiling metric collection </td></tr>
+<tr id="row_1_7_4_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling metric collection.">Metri [...]
+<tr id="row_1_7_4_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1profiling_1_1CallFrame.html" target="_self">CallFrame</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Profiler.html" target="_self">Profiler</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DurationNode.html" target="_self">DurationNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html" target="_self">PercentNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1CountNode.html" target="_self">CountNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_4_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1RatioNode.html" target="_self">RatioNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_5_" class="arrow" onclick="toggleFolder('1_7_5_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1relax__vm.html" target="_self">relax_vm</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_7_5_0_" class="arrow" onclick="toggleFolder('1_7_5_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1Instruction.html" target="_self">Instruction</a></td><td class="desc">A single virtual machine instruction </td></tr>
+<tr id="row_1_7_5_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1Instruction_1_1Arg.html" target="_self">Arg</a></td><td class="desc">The auxiliary data structure for instruction argument </td></tr>
+<tr id="row_1_7_5_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1VMFuncInfo.html" target="_self">VMFuncInfo</a></td><td class="desc">Information entry in executable function table </td></tr>
+<tr id="row_1_7_5_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1Executable.html" target="_self">Executable</a></td><td class="desc">The executable emitted by the VM compiler </td></tr>
+<tr id="row_1_7_5_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_7_5_3_" class="arrow" onclick="toggleFolder('1_7_5_3_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata.html" target="_self">NDArrayCacheMetadata</a></td><td class="desc">Metadata for <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" t [...]
+<tr id="row_1_7_5_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span id="arr_1_7_5_3_0_" class="arrow" onclick="toggleFolder('1_7_5_3_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata_1_1FileRecord.html" target="_self">FileRecord</a></td><td class="desc">Each shard of <a class="el" href="classtvm_1_1runtime_1_1NDAr [...]
+<tr id="row_1_7_5_3_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1NDArrayCacheMetadata_1_1FileRecord_1_1ParamRecord.html" target="_self">ParamRecord</a></td><td class="desc">Metadata of each parameter </td></tr>
+<tr id="row_1_7_5_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VMClosureObj.html" target="_self">VMClosureObj</a></td><td class="desc">An object representing a vm closure </td></tr>
+<tr id="row_1_7_5_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VMClosure.html" target="_self">VMClosure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_1_7_5_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VirtualMachine.html" target="_self">VirtualMachine</a></td><td class="desc">The virtual machine </td></tr>
+<tr id="row_1_7_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_6_" class="arrow" onclick="toggleFolder('1_7_6_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1threading.html" target="_self">threading</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_6_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1threading_1_1ThreadGroup.html" target="_self">ThreadGroup</a></td><td class="desc">A platform-agnostic abstraction for managing a collection of thread pool threads </td></tr>
+<tr id="row_1_7_7_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_7_" class="arrow" onclick="toggleFolder('1_7_7_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1vm.html" target="_self">vm</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_7_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html" target="_self">Instruction</a></td><td class="desc">A single virtual machine instruction </td></tr>
+<tr id="row_1_7_7_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html" target="_self">Executable</a></td><td class="desc">The executable emitted by the VM compiler </td></tr>
+<tr id="row_1_7_7_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosureObj.html" target="_self">VMClosureObj</a></td><td class="desc">An object representing a vm closure </td></tr>
+<tr id="row_1_7_7_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">VMClosure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_1_7_7_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
+<tr id="row_1_7_7_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html" target="_self">VMFrame</a></td><td class="desc">A representation of a stack frame </td></tr>
+<tr id="row_1_7_7_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html" target="_self">VirtualMachine</a></td><td class="desc">The virtual machine </td></tr>
+<tr id="row_1_7_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html" target="_self">PackedFuncValueConverter< PrimExpr ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html" target="_self">PackedFuncValueConverter< tvm::Integer ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Bool_01_4.html" target="_self">PackedFuncValueConverter< tvm::Bool ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html" target="_self">ADTObj</a></td><td class="desc">An object representing a structure or enumeration </td></tr>
+<tr id="row_1_7_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
+<tr id="row_1_7_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html" target="_self">ArrayNode</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs.">Array</a> node content in array </td></tr>
+<tr id="row_1_7_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1is__valid__iterator.html" target="_self">is_valid_iterator</a></td><td class="desc">Helper struct for type-checking </td></tr>
+<tr id="row_1_7_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1is__valid__iterator_3_01Optional_3_01T_01_4_00_01IterType_01_4.html" target="_self">is_valid_iterator< Optional< T >, IterType ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_16_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_16_" class="arrow" onclick="toggleFolder('1_7_16_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">Array</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of O [...]
+<tr id="row_1_7_16_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">ValueConverter</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html" target="_self">ObjectHash</a></td><td class="desc">String-aware <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> equal functor </td></tr>
+<tr id="row_1_7_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html" target="_self">ObjectEqual</a></td><td class="desc">String-aware <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> hash functor </td></tr>
+<tr id="row_1_7_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_self">InplaceArrayBase</a></td><td class="desc">Base template for classes with array like memory layout </td></tr>
+<tr id="row_1_7_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html" target="_self">IterAdapter</a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
+<tr id="row_1_7_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html" target="_self">ReverseIterAdapter</a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
+<tr id="row_1_7_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ClosureObj.html" target="_self">ClosureObj</a></td><td class="desc">An object representing a closure. This object is used by both the Relay VM and interpreter </td></tr>
+<tr id="row_1_7_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">Closure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_1_7_24_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_24_" class="arrow" onclick="toggleFolder('1_7_24_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1MapNode.html" target="_self">MapNode</a></td><td class="desc">Shared content of all specializations of hash map </td></tr>
+<tr id="row_1_7_24_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html" target="_self">iterator</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html" target="_self">SmallMapNode</a></td><td class="desc">A specialization of small-sized hash map </td></tr>
+<tr id="row_1_7_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html" target="_self">DenseMapNode</a></td><td class="desc">A specialization of hash map that implements the idea of array-based hash map. Another reference implementation can be found [1] </td></tr>
+<tr id="row_1_7_27_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_27_" class="arrow" onclick="toggleFolder('1_7_27_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">Map</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map impleme [...]
+<tr id="row_1_7_27_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html" target="_self">iterator</a></td><td class="desc">Iterator of the hash map </td></tr>
+<tr id="row_1_7_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html" target="_self">NullOptType</a></td><td class="desc">Helper to represent nullptr for optional </td></tr>
+<tr id="row_1_7_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">Optional</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T.">Optional</a> container that to represent to a Nullable variant of [...]
+<tr id="row_1_7_30_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_30_" class="arrow" onclick="toggleFolder('1_7_30_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj.html" target="_self">ShapeTupleObj</a></td><td class="desc">An object representing a shape tuple </td></tr>
+<tr id="row_1_7_30_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj_1_1FromStd.html" target="_self">FromStd</a></td><td class="desc">An object representing shape tuple moved from std::vector </td></tr>
+<tr id="row_1_7_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
+<tr id="row_1_7_32_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_32_" class="arrow" onclick="toggleFolder('1_7_32_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1StringObj.html" target="_self">StringObj</a></td><td class="desc">An object representing string. It's POD type </td></tr>
+<tr id="row_1_7_32_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html" target="_self">FromStd</a></td><td class="desc">An object representing string moved from std::string </td></tr>
+<tr id="row_1_7_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">String</a></td><td class="desc">Reference to string objects </td></tr>
+<tr id="row_1_7_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Variant.html" target="_self">Variant</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DataType.html" target="_self">DataType</a></td><td class="desc">Runtime primitive data type </td></tr>
+<tr id="row_1_7_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html" target="_self">DeviceAPI</a></td><td class="desc">TVM Runtime Device API, abstracts the device specific interface for memory management </td></tr>
+<tr id="row_1_7_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" target="_self">DiscoWorker</a></td><td class="desc">A worker in Disco. It takes a channel to communication with the controler. The worker can be run in a separate thread or process as long as the channel supports bi-directional communication i [...]
+<tr id="row_1_7_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DRefObj.html" target="_self">DRefObj</a></td><td class="desc">An object that exists on all workers </td></tr>
+<tr id="row_1_7_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DRef.html" target="_self">DRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1runtime_1_1DRefObj.html" title="An object that exists on all workers.">DRefObj</a> </td></tr>
+<tr id="row_1_7_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SessionObj.html" target="_self">SessionObj</a></td><td class="desc">A Disco interactive session. It allows users to interact with the Disco command queue with various <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-e [...]
+<tr id="row_1_7_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Session.html" target="_self">Session</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1runtime_1_1SessionObj.html" title="A Disco interactive session. It allows users to interact with the Disco command queue with various Pa...">Sessi [...]
+<tr id="row_1_7_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DiscoChannel.html" target="_self">DiscoChannel</a></td><td class="desc">A bi-directional channel for controler-worker communication. This channel is primarily used to transfer control messages but not data </td></tr>
+<tr id="row_1_7_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WorkerZeroData.html" target="_self">WorkerZeroData</a></td><td class="desc">A special communication channel between controler and worker-0, assuming they are always collocated in the same process </td></tr>
+<tr id="row_1_7_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html" target="_self">ObjAllocatorBase</a></td><td class="desc">Base class of object allocators that implements make. Use curiously recurring template pattern </td></tr>
+<tr id="row_1_7_45_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_45_" class="arrow" onclick="toggleFolder('1_7_45_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator.html" target="_self">SimpleObjAllocator</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_45_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html" target="_self">ArrayHandler</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_45_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html" target="_self">Handler</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM.">Module</a> container of TVM </td></tr>
+<tr id="row_1_7_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html" target="_self">ModuleNode</a></td><td class="desc">Base container of module </td></tr>
+<tr id="row_1_7_48_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_48_" class="arrow" onclick="toggleFolder('1_7_48_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by re [...]
+<tr id="row_1_7_48_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html" target="_self">Container</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Object</a> container class that backs <a class="el" href="classtvm_1_1runtime_1_1 [...]
+<tr id="row_1_7_48_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html" target="_self">ContainerBase</a></td><td class="desc">The container base structure contains all the fields except for the <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Objec [...]
+<tr id="row_1_7_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NVTXScopedRange.html" target="_self">NVTXScopedRange</a></td><td class="desc">A class to create a NVTX range. No-op if TVM is not built against NVTX </td></tr>
+<tr id="row_1_7_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
+<tr id="row_1_7_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Object.html" target="_self">Object</a></td><td class="desc">Base class of all object containers </td></tr>
+<tr id="row_1_7_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_self">ObjectPtr</a></td><td class="desc">A custom smart pointer for <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">Object</a> </td></tr>
+<tr id="row_1_7_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" target="_self">ObjectRef</a></td><td class="desc">Base class of all object reference </td></tr>
+<tr id="row_1_7_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html" target="_self">ObjectPtrHash</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> hash functor </td></tr>
+<tr id="row_1_7_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html" target="_self">ObjectPtrEqual</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference.">ObjectRef</a> equal functor </td></tr>
+<tr id="row_1_7_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">TypedPackedFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#TypedPackedFuncAnchor">TypedPackedFunc<R(Args..)></a> </td></tr>
+<tr id="row_1_7_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1SignaturePrinter.html" target="_self">SignaturePrinter</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_58_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_58_" class="arrow" onclick="toggleFolder('1_7_58_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html" target="_self">PackedFuncObj</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers.">O [...]
+<tr id="row_1_7_58_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncObj_1_1Extractor.html" target="_self">Extractor</a></td><td class="desc">Internal struct for extracting the callable method from callable type </td></tr>
+<tr id="row_1_7_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFuncSubObj.html" target="_self">PackedFuncSubObj</a></td><td class="desc">Derived object class for constructing <a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html" title="Object container class that backs PackedFunc.">PackedFuncObj</a> </td></tr>
+<tr id="row_1_7_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
+<tr id="row_1_7_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedPackedFunc< R(Args...)></a></td><td class="desc">A <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by [...]
+<tr id="row_1_7_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
+<tr id="row_1_7_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html" target="_self">ObjectTypeChecker</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> traits for runtime type check during FFI conversion </td></tr>
+<tr id="row_1_7_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html" target="_self">ObjectTypeChecker< Array< T > ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html" target="_self">ObjectTypeChecker< Map< K, V > ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
+<tr id="row_1_7_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">TVMArgValue</a></td><td class="desc">A single argument value to <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed format.">PackedFunc [...]
+<tr id="row_1_7_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">TVMMovableArgValue_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>.">TypedPackedFunc</a> t [...]
+<tr id="row_1_7_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">TVMMovableArgValueWithContext_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>." [...]
+<tr id="row_1_7_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">TVMRetValue</a></td><td class="desc">Return Value container, Unlike <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" title="A single argument value to PackedFunc. Containing both type_code and TVMValue.">TVMArgValue< [...]
+<tr id="row_1_7_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html" target="_self">PackedFuncValueConverter</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> trait to specify special value conversion rules from <a class="el" href=" [...]
+<tr id="row_1_7_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">TVMArgsSetter</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TVMArgsSetterApply.html" target="_self">TVMArgsSetterApply</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1tvm_1_1runtime_1_1String_01_4.html" target="_self">PackedFuncValueConverter<::tvm::runtime::String ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html" target="_self">PackedFuncValueConverter< Optional< T > ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Variant_3_01VariantTypes_8_8_8_01_4_01_4.html" target="_self">PackedFuncValueConverter< Variant< VariantTypes... > ></a></td><td class="desc"></td></tr>
+<tr id="row_1_7_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TimerNode.html" target="_self">TimerNode</a></td><td class="desc">Base class for all implementations </td></tr>
+<tr id="row_1_7_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device.">Timer</a> for a specific device </td></tr>
+<tr id="row_1_7_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WrappedPythonObject.html" target="_self">WrappedPythonObject</a></td><td class="desc">A class that wraps a Python object and preserves its ownership </td></tr>
+<tr id="row_1_7_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function.">Registry</a> for global function </td></tr>
<tr id="row_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_8_" class="arrow" onclick="toggleFolder('1_8_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1script.html" target="_self">script</a></td><td class="desc"></td></tr>
<tr id="row_1_8_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_8_0_" class="arrow" onclick="toggleFolder('1_8_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1script_1_1ir__builder.html" target="_self">ir_builder</a></td><td class="desc"></td></tr>
<tr id="row_1_8_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_8_0_0_" class="arrow" onclick="toggleFolder('1_8_0_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1details.html" target="_self">details</a></td><td class="desc"></td></tr>
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-<polygon fill="midnightblue" stroke="midnightblue" points="13584.53,-326.57 13578.66,-335.4 13588.75,-332.16 13584.53,-326.57"/>
+<path fill="none" stroke="midnightblue" d="M1759.53,-327.16C1752.97,-317.68 1745.52,-306.92 1739.75,-298.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1756.67,-329.17 1765.24,-335.4 1762.42,-325.18 1756.67,-329.17"/>
</g>
<!-- Node116->Node117 -->
<g id="edge186" class="edge">
<title>Node116->Node117</title>
-<path fill="none" stroke="midnightblue" d="M13893.82,-328.02C13902.76,-318.34 13913.07,-307.17 13921.01,-298.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13891.23,-325.67 13887.02,-335.4 13896.37,-330.42 13891.23,-325.67"/>
+<path fill="none" stroke="midnightblue" d="M2401.18,-332.23C2369.64,-321.71 2329.85,-308.45 2300.23,-298.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2400.09,-335.55 2410.69,-335.4 2402.31,-328.91 2400.09,-335.55"/>
</g>
<!-- Node118->Node76 -->
<g id="edge188" class="edge">
<title>Node118->Node76</title>
-<path fill="none" stroke="midnightblue" d="M12718.7,-392.63C12725.5,-374.98 12730.51,-351.27 12718,-335 12647.36,-243.13 12580.18,-287.84 12466,-268 12271.73,-234.24 12037.88,-222.8 11929.14,-219.12"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12715.31,-391.65 12714.61,-402.22 12721.75,-394.4 12715.31,-391.65"/>
+<path fill="none" stroke="midnightblue" d="M1068.88,-396.8C1057.85,-388.81 1047.05,-378.48 1041,-366 1034.99,-353.6 1034.24,-347 1041,-335 1071.93,-280.11 1139.98,-247.93 1186.66,-231.52"/>
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</g>
<!-- Node118->Node77 -->
<g id="edge190" class="edge">
<title>Node118->Node77</title>
-<path fill="none" stroke="midnightblue" d="M12724.91,-394.02C12736.25,-376.88 12746.74,-353.2 12735,-335 12695.86,-274.32 12656.3,-288.26 12587,-268 12314.76,-188.42 11974.76,-161.55 11837.12,-153.54"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12721.96,-392.14 12719.02,-402.32 12727.66,-396.19 12721.96,-392.14"/>
+<path fill="none" stroke="midnightblue" d="M1063.61,-397.16C1051.52,-389.3 1039.75,-378.97 1033,-366 1026.64,-353.78 1029.79,-348.4 1033,-335 1053.66,-248.62 1151.92,-189.99 1203.39,-164.66"/>
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</g>
<!-- Node118->Node112 -->
<g id="edge189" class="edge">
<title>Node118->Node112</title>
-<path fill="none" stroke="midnightblue" d="M12758.82,-399.23C12790.36,-388.71 12830.15,-375.45 12859.77,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12757.69,-395.91 12749.31,-402.4 12759.91,-402.55 12757.69,-395.91"/>
+<path fill="none" stroke="midnightblue" d="M1178.5,-401.28C1233.07,-389.99 1306.61,-374.76 1357.95,-364.14"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1177.63,-397.89 1168.54,-403.35 1179.04,-404.75 1177.63,-397.89"/>
</g>
<!-- Node118->Node113 -->
<g id="edge191" class="edge">
<title>Node118->Node113</title>
-<path fill="none" stroke="midnightblue" d="M12739.95,-396.95C12755.06,-387.8 12773.11,-376.6 12789,-366 12808.6,-352.93 12810.09,-343.67 12832,-335 12890.02,-312.04 13064.3,-296.07 13158.67,-288.85"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12737.73,-394.21 12730.97,-402.36 12741.34,-400.2 12737.73,-394.21"/>
+<path fill="none" stroke="midnightblue" d="M1075.74,-395.98C1055.05,-378.99 1033.67,-354.54 1050,-335 1077.2,-302.45 1193.93,-290.85 1267.82,-286.73"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1073.82,-398.92 1083.85,-402.33 1078.13,-393.41 1073.82,-398.92"/>
</g>
<!-- Node119->Node110 -->
<g id="edge196" class="edge">
<title>Node119->Node110</title>
-<path fill="none" stroke="midnightblue" d="M13341.45,-410.18C13307.01,-407.38 13265.43,-404.22 13228,-402 12839.47,-378.95 12740.76,-399.7 12353,-366 12337.4,-364.64 12320.62,-362.67 12305,-360.61"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13341.17,-413.67 13351.42,-411 13341.74,-406.69 13341.17,-413.67"/>
+<path fill="none" stroke="midnightblue" d="M1931,-404.71C1852.9,-392.27 1731.23,-372.88 1658.3,-361.27"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1930.82,-408.23 1941.25,-406.34 1931.93,-401.31 1930.82,-408.23"/>
</g>
<!-- Node119->Node111 -->
<g id="edge206" class="edge">
<title>Node119->Node111</title>
-<path fill="none" stroke="midnightblue" d="M13341.22,-402.45C13267.08,-388.26 13163.13,-368.12 13159,-366 13141.1,-356.81 13144.55,-342.8 13126,-335 13036.91,-297.56 12353.44,-305.26 12257,-299 12223.34,-296.82 12185.87,-293.56 12155.25,-290.69"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13341,-405.97 13351.47,-404.41 13342.31,-399.1 13341,-405.97"/>
+<path fill="none" stroke="midnightblue" d="M2031.13,-395.33C2049.64,-377.84 2068.47,-353.05 2052,-335 2022.44,-302.61 1702.63,-303.38 1659,-299 1635.89,-296.68 1610.54,-293.96 1588.23,-291.51"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2028.43,-393.05 2023.33,-402.34 2033.11,-398.26 2028.43,-393.05"/>
</g>
<!-- Node119->Node112 -->
<g id="edge197" class="edge">
<title>Node119->Node112</title>
-<path fill="none" stroke="midnightblue" d="M13341.13,-407.1C13327.14,-405.37 13312.62,-403.6 13299,-402 13154.23,-384.96 13117.01,-388.61 12973,-366 12969.77,-365.49 12966.46,-364.94 12963.13,-364.35"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13340.85,-410.6 13351.2,-408.36 13341.71,-403.65 13340.85,-410.6"/>
+<path fill="none" stroke="midnightblue" d="M1931.17,-409.35C1837.2,-400.1 1669.9,-383.14 1527,-366 1511.79,-364.18 1495.39,-362.05 1480.08,-360.01"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1930.85,-412.84 1941.15,-410.33 1931.54,-405.87 1930.85,-412.84"/>
</g>
<!-- Node119->Node113 -->
<g id="edge207" class="edge">
<title>Node119->Node113</title>
-<path fill="none" stroke="midnightblue" d="M13341.41,-401.94C13302.12,-392.78 13259.03,-380.07 13245,-366 13226.87,-347.83 13222.3,-316.71 13221.23,-298.64"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13340.77,-405.38 13351.29,-404.19 13342.32,-398.56 13340.77,-405.38"/>
+<path fill="none" stroke="midnightblue" d="M2018.34,-393.06C2026.7,-374.92 2033.42,-350.37 2019,-335 1976.04,-289.21 1517.54,-304.62 1455,-299 1434.55,-297.16 1412.26,-294.72 1392.24,-292.36"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2015.02,-391.87 2013.65,-402.38 2021.27,-395.02 2015.02,-391.87"/>
</g>
<!-- Node119->Node114 -->
<g id="edge198" class="edge">
<title>Node119->Node114</title>
-<path fill="none" stroke="midnightblue" d="M13455.67,-398.27C13478.76,-387.92 13507.24,-375.15 13528.6,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13454.16,-395.11 13446.46,-402.4 13457.02,-401.5 13454.16,-395.11"/>
+<path fill="none" stroke="midnightblue" d="M1945.61,-399.71C1908.18,-389.14 1860.4,-375.63 1824.93,-365.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1944.8,-403.12 1955.38,-402.48 1946.71,-396.39 1944.8,-403.12"/>
</g>
<!-- Node119->Node115 -->
<g id="edge208" class="edge">
<title>Node119->Node115</title>
-<path fill="none" stroke="midnightblue" d="M13344.92,-399.59C13301.3,-385.79 13257.88,-363.86 13283,-335 13321.28,-291.03 13490.76,-284.13 13583.99,-283.76"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13343.91,-402.94 13354.49,-402.5 13345.94,-396.25 13343.91,-402.94"/>
+<path fill="none" stroke="midnightblue" d="M2047.56,-397.76C2077.88,-381.85 2110,-358 2090,-335 2052.16,-291.48 1884.89,-284.36 1792.26,-283.85"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2045.63,-394.81 2038.27,-402.43 2048.78,-401.07 2045.63,-394.81"/>
</g>
<!-- Node119->Node116 -->
<g id="edge199" class="edge">
<title>Node119->Node116</title>
-<path fill="none" stroke="midnightblue" d="M13488.95,-406.03C13578.93,-393.28 13729.19,-372.01 13812.91,-360.15"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13488.23,-402.59 13478.82,-407.46 13489.21,-409.53 13488.23,-402.59"/>
+<path fill="none" stroke="midnightblue" d="M2078.82,-405.79C2166.21,-393.11 2310.13,-372.23 2391.67,-360.4"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2078.11,-402.36 2068.71,-407.26 2079.11,-409.28 2078.11,-402.36"/>
</g>
<!-- Node119->Node117 -->
<g id="edge209" class="edge">
<title>Node119->Node117</title>
-<path fill="none" stroke="midnightblue" d="M13366.36,-398.45C13331.61,-382.93 13294.63,-359.27 13317,-335 13321.68,-329.92 13716.43,-300.46 13871.99,-289.03"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13365.01,-401.68 13375.58,-402.41 13367.77,-395.25 13365.01,-401.68"/>
+<path fill="none" stroke="midnightblue" d="M2053.87,-398.82C2076.82,-389.92 2104.28,-378.39 2128,-366 2150.51,-354.24 2154.52,-348.55 2176,-335 2195.8,-322.51 2218.47,-308.58 2234.94,-298.52"/>
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</g>
<!-- Node119->Node120 -->
<g id="edge200" class="edge">
<title>Node119->Node120</title>
-<path fill="none" stroke="midnightblue" d="M13340.92,-402.71C13339.6,-402.47 13338.3,-402.23 13337,-402 13245.53,-385.51 13222.54,-382.09 13131,-366 13126.3,-365.17 13121.45,-364.32 13116.57,-363.46"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13340.69,-406.23 13351.16,-404.57 13341.95,-399.34 13340.69,-406.23"/>
+<path fill="none" stroke="midnightblue" d="M2078.75,-412.89C2310.17,-401.57 3007.33,-367.4 3019,-366 3021.67,-365.68 3024.38,-365.32 3027.12,-364.93"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2078.56,-409.4 2068.74,-413.38 2078.9,-416.39 2078.56,-409.4"/>
</g>
<!-- Node119->Node121 -->
<g id="edge201" class="edge">
<title>Node119->Node121</title>
-<path fill="none" stroke="midnightblue" d="M13341.38,-400.43C13305.48,-391.75 13261.97,-379.93 13224,-366 13193.95,-354.97 13189.99,-343.01 13159,-335 13016.25,-298.09 12571.44,-287.9 12400.68,-285.32"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13340.75,-403.88 13351.29,-402.8 13342.37,-397.07 13340.75,-403.88"/>
+<path fill="none" stroke="midnightblue" d="M2074.49,-399.8C2103.56,-391.52 2137.21,-380.22 2166,-366 2187.45,-355.4 2187.62,-343.45 2210,-335 2281.19,-308.14 2497.02,-293.57 2608.2,-287.67"/>
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</g>
<!-- Node119->Node122 -->
<g id="edge204" class="edge">
<title>Node119->Node122</title>
-<path fill="none" stroke="midnightblue" d="M13341.43,-410.43C13306.99,-407.68 13265.41,-404.49 13228,-402 12941.52,-382.9 12869.31,-387.46 12583,-366 12554.75,-363.88 12523.63,-361.05 12496.8,-358.45"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13341.16,-413.92 13351.41,-411.23 13341.72,-406.94 13341.16,-413.92"/>
+<path fill="none" stroke="midnightblue" d="M2078.61,-409.75C2216.8,-397.08 2513.08,-369.92 2646.47,-357.69"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2078.18,-406.28 2068.54,-410.67 2078.82,-413.25 2078.18,-406.28"/>
</g>
<!-- Node119->Node123 -->
<g id="edge210" class="edge">
<title>Node119->Node123</title>
-<path fill="none" stroke="midnightblue" d="M13403.93,-393.28C13399.51,-384.02 13394.57,-373.66 13390.71,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13400.81,-394.88 13408.27,-402.4 13407.13,-391.86 13400.81,-394.88"/>
+<path fill="none" stroke="midnightblue" d="M1986.68,-394.45C1978.75,-384.9 1969.7,-374 1962.69,-365.58"/>
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</g>
<!-- Node119->Node124 -->
<g id="edge211" class="edge">
<title>Node119->Node124</title>
-<path fill="none" stroke="midnightblue" d="M13341.28,-410.24C13189.77,-397.36 12846.06,-368.16 12708.51,-356.47"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13341.16,-413.74 13351.42,-411.1 13341.75,-406.76 13341.16,-413.74"/>
+<path fill="none" stroke="midnightblue" d="M2078.99,-412.8C2139.92,-409.79 2228.55,-405.48 2306,-402 2690.83,-384.73 2789.9,-414.92 3172,-366 3174.72,-365.65 3177.48,-365.25 3180.27,-364.81"/>
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</g>
<!-- Node119->Node125 -->
<g id="edge212" class="edge">
<title>Node119->Node125</title>
-<path fill="none" stroke="midnightblue" d="M13485.94,-400.21C13533.32,-389.32 13594.89,-375.18 13639.47,-364.94"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="13485.02,-396.83 13476.06,-402.48 13486.59,-403.65 13485.02,-396.83"/>
+<path fill="none" stroke="midnightblue" d="M2073.58,-400.11C2118.25,-389.44 2175.9,-375.66 2218.42,-365.5"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2072.6,-396.75 2063.69,-402.48 2074.22,-403.56 2072.6,-396.75"/>
</g>
<!-- Node121->Node76 -->
<g id="edge202" class="edge">
<title>Node121->Node76</title>
-<path fill="none" stroke="midnightblue" d="M12255.32,-271.64C12163.56,-258.82 12012.83,-237.74 11929.02,-226.03"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12255,-275.13 12265.39,-273.05 12255.97,-268.2 12255,-275.13"/>
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</g>
<!-- Node121->Node77 -->
<g id="edge203" class="edge">
<title>Node121->Node77</title>
-<path fill="none" stroke="midnightblue" d="M12263.76,-266.12C12153.16,-239.96 11938.49,-189.17 11834.42,-164.56"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12263.28,-269.6 12273.81,-268.5 12264.89,-262.79 12263.28,-269.6"/>
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</g>
<!-- Node122->Node121 -->
<g id="edge205" class="edge">
<title>Node122->Node121</title>
-<path fill="none" stroke="midnightblue" d="M12399.81,-329.73C12384.95,-319.68 12367.24,-307.68 12353.79,-298.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="12397.93,-332.69 12408.17,-335.4 12401.85,-326.89 12397.93,-332.69"/>
+<path fill="none" stroke="midnightblue" d="M2700.6,-326.58C2695.14,-317.24 2689.01,-306.75 2684.23,-298.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2697.69,-328.53 2705.75,-335.4 2703.73,-325 2697.69,-328.53"/>
</g>
<!-- Node126->Node57 -->
<g id="edge214" class="edge">
<title>Node126->Node57</title>
-<path fill="none" stroke="midnightblue" d="M14568.52,-397.88C14608.82,-377.56 14671.57,-342.09 14716,-299 14767.5,-249.05 14785.09,-233.63 14806,-165 14821.67,-113.55 14784.87,-87.16 14735,-67 14632.64,-25.62 11018.31,-17.51 10524.64,-16.6"/>
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+<path fill="none" stroke="midnightblue" d="M4813.47,-400.19C4869.68,-387.41 4938.36,-370.97 4943,-366 5034.7,-267.82 5015.18,-83.41 5007.37,-30.83"/>
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</g>
<!-- Node127->Node56 -->
<g id="edge217" class="edge">
<title>Node127->Node56</title>
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</g>
<!-- Node127->Node63 -->
<g id="edge218" class="edge">
<title>Node127->Node63</title>
-<path fill="none" stroke="midnightblue" d="M11428.43,-395.4C11407.85,-375.81 11374.26,-347.82 11339,-335 11087.94,-243.7 10990.65,-388.66 10739,-299 10716.81,-291.09 10717.43,-277.79 10696,-268 10648.65,-246.36 10590.97,-233.42 10547.59,-226.06"/>
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+<path fill="none" stroke="midnightblue" d="M3581.4,-394.37C3597.29,-374.86 3623.29,-347.64 3653,-335 3781.84,-280.21 4153.36,-354.25 4282,-299 4300.49,-291.06 4298.14,-278.99 4315,-268 4344.14,-249 4381.62,-234.5 4406.65,-226.01"/>
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</g>
<!-- Node127->Node66 -->
<g id="edge216" class="edge">
<title>Node127->Node66</title>
-<path fill="none" stroke="midnightblue" d="M11394.38,-399.43C11353.81,-386.75 11300.2,-370 11268.29,-360.03"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="11393.53,-402.83 11404.12,-402.48 11395.62,-396.15 11393.53,-402.83"/>
+<path fill="none" stroke="midnightblue" d="M3613.61,-398.95C3648.57,-386.32 3694.18,-369.85 3721.44,-360.01"/>
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</g>
<!-- Node128->Node63 -->
<g id="edge221" class="edge">
<title>Node128->Node63</title>
-<path fill="none" stroke="midnightblue" d="M11318.73,-393.62C11328.38,-375.46 11336.73,-350.62 11322,-335 11274.96,-285.13 10771.17,-320.3 10706,-299 10681.86,-291.11 10681.11,-278.52 10658,-268 10614.54,-248.22 10561.47,-234.2 10525.24,-226.03"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="11315.62,-392 11313.68,-402.42 11321.69,-395.48 11315.62,-392"/>
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<!-- Node128->Node66 -->
<g id="edge220" class="edge">
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