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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/07/24 07:14:24 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@89b91e2b1195b53bf7e1f6c250bc9a1247367d13)
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tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new f075686394 deploying docs (apache/tvm@89b91e2b1195b53bf7e1f6c250bc9a1247367d13)
f075686394 is described below
commit f0756863949ced4ff6305ead662bddd2b5da068e
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Jul 24 07:14:15 2024 +0000
deploying docs (apache/tvm@89b91e2b1195b53bf7e1f6c250bc9a1247367d13)
---
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462 files changed, 14821 insertions(+), 14335 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 67d3cc9b0d..c8893e98ce 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 37.542 seconds)
+ **Total running time of the script:** ( 1 minutes 40.873 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index c6c4b4a496..ef1b2f5575 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -232,7 +232,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
- 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 879ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 947ms/step
Keras top-1 id: 285, class name: Egyptian cat
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index 0b8ba167d7..eab5fa544f 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, 34.7MB/s] 23%|##3 | 9.64M/41.5M [00:00<00:01, 27.8MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 31.6MB/s] 42%|####1 | 17.4M/41.5M [00:00<00:01, 24.0MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 29.5MB/s] 77%|#######7 | 32.0M/41.5M [00:01<00:00, 34.8MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 23.6MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 27.4MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 19%|#9 | 7.99M/41.5M [00:00<00:00, 51.6MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 49.8MB/s] 46%|####5 | 19.1M/41.5M [00:00<00:00, 48.9MB/s] 57%|#####7 | 23.7M/41.5M [00:00<00:00, 45.1MB/s] 67%|######7 | 28.0M/41.5M [00:00<00:00, 38.5MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 35.6MB/s] 87%|########7 | 36.2M/41.5M [00:00<00:00, 37.4MB/s] 96%|#########6| 39.9M/41.5M [00:01<00:00, 29.1MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 35.8MB/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 28fb0651f6..d47e7aafe3 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.591 seconds)
+ **Total running time of the script:** ( 1 minutes 7.175 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 b0f1b4ac37..1c8a1571f7 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] 14%|#4 | 6.30M/44.7M [00:00<00:01, 30.2MB/s] 32%|###1 | 14.1M/44.7M [00:00<00:00, 50.8MB/s] 45%|####4 | 19.9M/44.7M [00:00<00:00, 29.2MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 25.9MB/s] 68%|######7 | 30.3M/44.7M [00:00<00:00, 33.5MB/s] 77%|#######7 | 34.5M/44.7M [00:01<00:00, 30.1MB/s] 86%|########5 | 38.3M/44.7M [00:01<00:00, 30.8MB/s] 93%|#########3| 41.7M/44.7M [00:01<00:00, 30.8MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 33.3MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 35%|###5 | 15.7M/44.7M [00:00<00:00, 165MB/s] 70%|####### | 31.4M/44.7M [00:00<00:00, 139MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 112MB/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 9f85b4fab6..66b4c2ab00 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -424,7 +424,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 29.628 seconds)
+ **Total running time of the script:** ( 1 minutes 34.931 seconds)
.. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index e46482e266..706693625e 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
=================
-**06:44.917** total execution time for **how_to_compile_models** files:
+**07:04.811** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:37.542 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:40.873 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:29.628 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:34.931 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:03.591 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:07.175 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.322 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:45.625 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:38.798 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:41.925 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.805 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.363 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:26.913 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.913 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.462 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.932 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.857 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:03.074 | 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 7a87b36bb1..38335cb0a0 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
@@ -664,7 +664,7 @@ 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)
- 3991.9818 3992.2348 3995.5816 3987.7967 2.5666
+ 4122.6380 4122.7539 4124.6818 4120.5779 1.0897
@@ -672,7 +672,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.044 seconds)
+ **Total running time of the script:** ( 1 minutes 21.536 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 14392d123c..acb942ba30 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 8380416/102967424 [=>............................] - ETA: 3s 15024128/102967424 [===>..........................] - ETA: 2s 16769024/102967424 [===>..........................] - ETA: 2s 23412736/102967424 [=====>........................] - ETA: 2s 25157632/102967424 [======>.......................] - ETA: 2s 33546240/102967424 [========>.....................] - ETA: 2s 40189952/102967424 [==========>...................] - ETA: 1s
41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 56967168/102967424 [===============>..............] - ETA: 1s 58712064/102967424 [================>.............] - ETA: 1s 65355776/102967424 [==================>...........] - ETA: 1s 67100672/102967424 [==================>...........] - ETA: 1s 69296128/102967424 [===================>..........] -
ETA: 1s 75489280/102967424 [====================>.........] - ETA: 0s 82124800/102967424 [======================>.......] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 84017152/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424
[============================>.] - ETA: 0s 102967424/102967424 [==============================] - 4s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 6635520/102967424 [>.............................] - ETA: 2s 8380416/102967424 [=>............................] - ETA: 3s 15024128/102967424 [===>..........................] - ETA: 2s 16769024/102967424 [===>..........................] - ETA: 2s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 2s 33546240/102967424 [========>.....................] - ETA: 1s
40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 56967168/102967424 [===============>..............] - ETA: 1s 58712064/102967424 [================>.............] - ETA: 1s 65355776/102967424 [==================>...........] - ETA: 1s 67100672/102967424 [==================>...........] -
ETA: 1s 69296128/102967424 [===================>..........] - ETA: 1s 75489280/102967424 [====================>.........] - ETA: 0s 82124800/102967424 [======================>.......] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424
[============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 3s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index ab6f4ce45e..870d53e6fa 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)
- 13.7711 13.7250 14.1470 13.6606 0.1338
+ 14.7929 14.7338 15.0564 14.6357 0.1494
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 0eb12d49a5..304a944f85 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<00:04, 34.7MB/s] 8%|8 | 14.3M/170M [00:00<00:03, 45.8MB/s] 11%|#1 | 19.3M/170M [00:00<00:04, 36.7MB/s] 14%|#3 | 23.1M/170M [00:00<00:05, 30.8MB/s] 16%|#5 | 26.3M/170M [00:00<00:05, 29.6MB/s] 18%|#7 | 30.3M/170M [00:00<00:05, 28.9MB/s] 20%|#9 | 33.1M/170M [00:01<00:05, 28.2MB/s] 24%|##3 | 40.0M/170M [00:01<00:04, 28.3MB/s] 27%|##7 | 46.3M/170M [00:01<00:04, 28.4MB/s] 29%|##8 | 49.0M/170M [00:01<00:05, 23.3MB/s] 33%|###2 | 56.0M/170M [00:01<00:04, 28.7MB/s] 38%|###7 | 64.0M/170M [00:02<00:03, 33.4MB/s] 42%|####2 | 72.0M/170M [00:02<00:02, 36.5MB/s] 46%|####6 | 78.3M/170M [00:02<00:02, 39.3MB/s] 48%|####8 | 82.2M/170M [00:02<00:02, 33.5MB/s] 51%|##### | 86.3M/170M [00:02<00:02, 34.5MB/s] 53%|#####2 | 89.7M/170M [00:02<00:02, 30.6MB/
s] 56%|#####5 | 94.3M/170M [00:03<00:02, 27.1MB/s] 57%|#####7 | 97.0M/170M [00:03<00:03, 23.9MB/s] 60%|###### | 102M/170M [00:03<00:02, 25.9MB/s] 62%|######1 | 105M/170M [00:03<00:02, 23.5MB/s] 66%|######5 | 112M/170M [00:03<00:02, 28.7MB/s] 71%|####### | 120M/170M [00:04<00:01, 33.6MB/s] 74%|#######4 | 126M/170M [00:04<00:01, 33.4MB/s] 76%|#######6 | 129M/170M [00:04<00:01, 27.3MB/s] 79%|#######9 | 134M/170M [00:04<00:01, 30.7MB/s] 81%|######## | 137M/170M [00:04<00:01, 28.3MB/s] 85%|########4 | 144M/170M [00:04<00:00, 29.3MB/s] 88%|########8 | 150M/170M [00:05<00:00, 28.2MB/s] 90%|######### | 153M/170M [00:05<00:00, 27.5MB/s] 94%|#########4| 160M/170M [00:05<00:00, 31.3MB/s] 98%|#########7| 166M/170M [00:05<00:00, 37.3MB/s] 100%|##########| 170M/170M [00:05<00:00, 30.2MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:02, 58.3MB/s] 8%|8 | 14.3M/170M [00:00<00:04, 38.6MB/s] 11%|# | 18.3M/170M [00:00<00:04, 34.6MB/s] 13%|#3 | 22.3M/170M [00:00<00:05, 26.2MB/s] 15%|#4 | 25.0M/170M [00:00<00:05, 25.7MB/s] 18%|#7 | 30.3M/170M [00:01<00:05, 28.4MB/s] 19%|#9 | 33.1M/170M [00:01<00:05, 26.8MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 37.0MB/s] 27%|##7 | 46.3M/170M [00:01<00:04, 26.1MB/s] 29%|##9 | 49.5M/170M [00:01<00:04, 25.3MB/s] 33%|###2 | 56.0M/170M [00:01<00:03, 33.0MB/s] 37%|###6 | 62.3M/170M [00:02<00:02, 37.8MB/s] 39%|###9 | 66.5M/170M [00:02<00:02, 38.6MB/s] 42%|####2 | 72.0M/170M [00:02<00:02, 37.0MB/s] 46%|####6 | 78.3M/170M [00:02<00:02, 32.1MB/s] 51%|##### | 86.0M/170M [00:02<00:02, 41.5MB/s] 53%|#####3 | 90.7M/170M [00:02<00:02, 33.9MB/
s] 56%|#####5 | 94.7M/170M [00:03<00:02, 29.2MB/s] 58%|#####7 | 98.0M/170M [00:03<00:03, 24.3MB/s] 60%|###### | 102M/170M [00:03<00:02, 26.9MB/s] 62%|######1 | 105M/170M [00:03<00:03, 22.1MB/s] 66%|######5 | 112M/170M [00:03<00:02, 29.0MB/s] 71%|####### | 120M/170M [00:03<00:01, 34.7MB/s] 74%|#######4 | 126M/170M [00:04<00:01, 30.2MB/s] 76%|#######6 | 130M/170M [00:04<00:01, 30.3MB/s] 79%|#######9 | 134M/170M [00:04<00:01, 28.9MB/s] 81%|######## | 137M/170M [00:04<00:01, 27.6MB/s] 85%|########4 | 144M/170M [00:04<00:00, 36.3MB/s] 88%|########8 | 150M/170M [00:05<00:00, 34.7MB/s] 91%|######### | 154M/170M [00:05<00:00, 34.3MB/s] 93%|#########3| 158M/170M [00:05<00:00, 29.6MB/s] 95%|#########5| 161M/170M [00:05<00:00, 28.3MB/s] 98%|#########7| 166M/170M [00:05<00:00, 31.1MB/s] 100%|#########9| 169M/170M [00:05<00:00, 24.6MB/s] 100%|##########| 170M/170M [00:05<00:00, 30.5MB/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).
@@ -288,7 +288,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 26.841 seconds)
+ **Total running time of the script:** ( 3 minutes 48.291 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 05865a4a4d..445155311f 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, 25.7MB/s] 65%|######4 | 8.76M/13.6M [00:00<00:00, 19.6MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 19.2MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 22.1MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 47%|####6 | 6.30M/13.6M [00:00<00:00, 19.3MB/s] 60%|###### | 8.15M/13.6M [00:00<00:00, 16.0MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 19.0MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 20.5MB/s]
@@ -400,7 +400,7 @@ 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)
- 85.9826 85.9435 88.5083 85.5817 0.3109
+ 89.3169 89.2742 90.4867 88.8068 0.2658
@@ -448,7 +448,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 31.623 seconds)
+ **Total running time of the script:** ( 1 minutes 35.397 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 f4cd89a8ab..d0a98ffa41 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)
- 108.2594 108.1872 113.9094 107.2576 0.6911
+ 110.5642 110.5480 112.7201 109.8136 0.4336
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 2e315774c5..680e2d8e86 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
=================
-**09:47.714** total execution time for **how_to_deploy_models** files:
+**10:29.395** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:26.841 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:48.291 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:31.623 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:35.397 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.044 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.536 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:51.740 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:55.128 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.435 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:53.884 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:48.739 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.918 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.707 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.781 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.578 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:31.452 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 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 5db9a01ff8..729683c3ee 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
=================
-**00:03.891** total execution time for **how_to_extend_tvm** files:
+**00:04.176** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.709 | 0.0 MB |
+| :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_infra.py` (``use_pass_infra.py``) | 00:01.175 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.274 | 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 eb55dc3365..4788eec17f 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: 25017us [25017us] (48.81%; 48.81%)
- FoldScaleAxis: 26232us [7us] (51.19%; 51.19%)
- FoldConstant: 26225us [1746us] (51.17%; 99.97%)
- InferType: 24479us [24479us] (47.76%; 93.34%)
+ InferType: 25516us [25516us] (48.50%; 48.50%)
+ FoldScaleAxis: 27096us [7us] (51.50%; 51.50%)
+ FoldConstant: 27089us [1805us] (51.49%; 99.97%)
+ InferType: 25285us [25285us] (48.06%; 93.34%)
@@ -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: 24199us [24199us] (48.62%; 48.62%)
- FoldScaleAxis: 25574us [6us] (51.38%; 51.38%)
- FoldConstant: 25568us [1623us] (51.37%; 99.98%)
- InferType: 23945us [23945us] (48.11%; 93.65%)
+ InferType: 25098us [25098us] (48.32%; 48.32%)
+ FoldScaleAxis: 26843us [7us] (51.68%; 51.68%)
+ FoldConstant: 26836us [1814us] (51.67%; 99.97%)
+ InferType: 25022us [25022us] (48.17%; 93.24%)
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 84c4163484..31b6d31dab 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: 36.699520 ms
+ Convolution: 49.599422 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 4a0b2c31ee..0b7dedc278 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: 12.212877 ms
+ conv2d with tensor core: 11.131062 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 e30552de2d..0ce76ba160 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: 0.013433
- Baseline: 3.201167
+ Numpy running time: 0.019198
+ Baseline: 3.442396
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.295324
+ Opt1: 0.297132
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.268273
+ Opt2: 0.280602
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.106015
+ Opt3: 0.117808
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.101416
+ Opt4: 0.107460
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.093673
+ Opt5: 0.112150
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.113371
+ Opt6: 0.134778
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 76af0c5106..f0c5307b56 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
=================
-**00:31.174** total execution time for **how_to_optimize_operators** files:
+**00:34.374** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.161 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.237 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.923 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.988 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.090 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.149 | 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 e189b0995b..055a4f6f16 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
=================
-**03:37.637** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:49.087** 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``) | 01:34.624 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:40.020 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:12.220 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.752 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:19.588 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:20.767 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.654 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.527 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.453 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.920 | 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..f85a2cf6db 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: 0.347 ms
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index fb82d08097..64122f8252 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)
- 3.1964 3.1963 3.1967 3.1961 0.0003
+ 3.2612 3.2638 3.2664 3.2535 0.0056
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.220 seconds)
+ **Total running time of the script:** ( 1 minutes 14.752 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 66a0fe5507..f781689b17 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)
- 716.2517 716.9909 718.6147 713.1494 2.2916
+ 756.4358 758.6245 759.2351 751.4478 3.5358
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.624 seconds)
+ **Total running time of the script:** ( 1 minutes 40.020 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 e0bc91d384..9d52dafbc6 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:22.673** total execution time for **how_to_tune_with_autotvm** files:
+**00:24.159** 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``) | 00:22.637 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.121 | 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_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 69f440e2ef..71368147e2 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: 0.037136
+ Time cost of this operator: 0.037095
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 d70e983494..7f3be97ae0 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 296.4 98.745 (1, 2, 10, 10, 3) 2 1 [296.4]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.844 0.947 (1, 6, 10, 10) 1 1 [2.844]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.924 0.308 (1, 1, 10, 10, 3) 1 1 [0.924]
- Total_time - 300.167 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.9 98.722 (1, 2, 10, 10, 3) 2 1 [304.9]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.994 0.969 (1, 6, 10, 10) 1 1 [2.994]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.952 0.308 (1, 1, 10, 10, 3) 1 1 [0.952]
+ Total_time - 308.847 - - - - -
@@ -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 97.875 97.324 (1, 6, 10, 10, 1) 2 1 [97.875]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.748 1.739 (1, 6, 10, 10) 1 1 [1.748]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.942 0.937 (1, 1, 10, 10, 3) 1 1 [0.942]
- Total_time - 100.566 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 138.2 98.158 (1, 6, 10, 10, 1) 2 1 [138.2]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.752 1.244 (1, 6, 10, 10) 1 1 [1.752]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.841 0.598 (1, 3, 10, 10, 1) 1 1 [0.841]
+ Total_time - 140.793 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.173 seconds)
+ **Total running time of the script:** ( 1 minutes 24.279 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 e0478903b1..07556bb7eb 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
- 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 17.5MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 27.4MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 13.3MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 20.9MB/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,
@@ -322,7 +322,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.738 seconds)
+ **Total running time of the script:** ( 1 minutes 32.359 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 ff6df6ce11..50f4e9a6e7 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -218,7 +218,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmp5a9eys09/images/random'
+ '/tmp/tmpd_qobwac/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmp5a9eys09/images/target contains 8144 images
- /tmp/tmp5a9eys09/images/random contains 5000 images
+ /tmp/tmpd_qobwac/images/target contains 8144 images
+ /tmp/tmpd_qobwac/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 42s - loss: 0.2079 - accuracy: 0.9267 - val_loss: 0.1345 - val_accuracy: 0.9535 - 42s/epoch - 128ms/step
+ 328/328 - 47s - loss: 0.2252 - accuracy: 0.9218 - val_loss: 0.1147 - val_accuracy: 0.9600 - 47s/epoch - 142ms/step
Epoch 2/3
- 328/328 - 40s - loss: 0.1042 - accuracy: 0.9609 - val_loss: 0.1178 - val_accuracy: 0.9626 - 40s/epoch - 121ms/step
+ 328/328 - 44s - loss: 0.1020 - accuracy: 0.9621 - val_loss: 0.1437 - val_accuracy: 0.9486 - 44s/epoch - 134ms/step
Epoch 3/3
- 328/328 - 40s - loss: 0.0651 - accuracy: 0.9757 - val_loss: 0.0963 - val_accuracy: 0.9656 - 40s/epoch - 121ms/step
+ 328/328 - 44s - loss: 0.0693 - accuracy: 0.9738 - val_loss: 0.0984 - val_accuracy: 0.9653 - 44s/epoch - 134ms/step
- <keras.callbacks.History object at 0x7fb6c54ddf40>
+ <keras.callbacks.History object at 0x7f8d1fac6670>
@@ -861,7 +861,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 5 minutes 9.842 seconds)
+ **Total running time of the script:** ( 5 minutes 12.251 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 48777cb824..7e7cbb5af9 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**08:26.305** total execution time for **how_to_work_with_microtvm** files:
+**08:40.416** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:09.842 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:12.251 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:26.738 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:32.359 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:20.173 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:24.279 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.539 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.350 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.102 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.832 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.912 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.345 | 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 c56f909604..d71419dbbc 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:37.863** total execution time for **how_to_work_with_relay** files:
+**00:40.400** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:32.709 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.523 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.315 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.877 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.834 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.994 | 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 b05d44260a..aa141490f8 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 0x7fb6ec7ecdc0>
+ <function my_cuda_math_rule at 0x7f8d61204550>
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 a40435b3ab..b8c65be700 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
=================
-**00:04.802** total execution time for **how_to_work_with_schedules** files:
+**00:05.529** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.287 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.440 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:00.930 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.412 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.659 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.704 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.647 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.682 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.117 | 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_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.068 | 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``) | 00:00.063 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.064 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 36822d82c6..b9b42485b2 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 26.69s!
+ yolov3-tiny inference graph built in 28.85s!
@@ -445,6 +445,11 @@ Download test image
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 2.434 seconds)
+
+
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
.. only:: html
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index c41a8f72cc..415432c926 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
=================
-**00:59.115** total execution time for **topic_vta_tutorials_frontend** files:
+**01:02.434** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:59.115 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:02.434 | 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 7148d5cf63..2cd344598a 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
=================
-**00:03.157** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.277** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.700 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.801 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.457 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.475 | 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 354fd0d85b..773c29b8e2 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
=================
-**00:00.726** total execution time for **topic_vta_tutorials** files:
+**00:00.761** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.376 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.393 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.351 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.368 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 3660a12207..6d218370c3 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.288 ms
+ Execution time of this operator: 94.089 ms
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 24.248 seconds)
+ **Total running time of the script:** ( 1 minutes 20.214 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 498be038e3..6d8bbb8f30 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: 10.70/10.70 result: MeasureResult(costs=(0.025077717,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6581509113311768, timestamp=1721771644.336838) [('tile_y', [-1, 512]), ('tile_x', [-1, 512])],None,99
- No: 2 GFLOPS: 2.13/10.70 result: MeasureResult(costs=(0.1257747348,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.273306131362915, timestamp=1721771646.603187) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
- No: 3 GFLOPS: 9.17/10.70 result: MeasureResult(costs=(0.0292719898,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7221262454986572, timestamp=1721771647.3277142) [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
- No: 4 GFLOPS: 15.29/15.29 result: MeasureResult(costs=(0.0175515598,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8024415969848633, timestamp=1721771647.8638089) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
- No: 5 GFLOPS: 4.20/15.29 result: MeasureResult(costs=(0.0639382428,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2860944271087646, timestamp=1721771649.3012881) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
- No: 6 GFLOPS: 5.64/15.29 result: MeasureResult(costs=(0.047573405400000005,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.0185935497283936, timestamp=1721771650.3216555) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 7 GFLOPS: 1.06/15.29 result: MeasureResult(costs=(0.25343420839999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.329624176025391, timestamp=1721771654.634418) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
- No: 8 GFLOPS: 14.32/15.29 result: MeasureResult(costs=(0.0187469228,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6022934913635254, timestamp=1721771655.1932395) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
- No: 9 GFLOPS: 3.76/15.29 result: MeasureResult(costs=(0.0713967256,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.376929759979248, timestamp=1721771656.7385616) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
- No: 10 GFLOPS: 12.31/15.29 result: MeasureResult(costs=(0.0218060292,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6451354026794434, timestamp=1721771657.3468194) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+ No: 1 GFLOPS: 0.51/0.51 result: MeasureResult(costs=(0.528855222,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.745383977890015, timestamp=1721798538.3832917) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
+ No: 2 GFLOPS: 10.00/10.00 result: MeasureResult(costs=(0.0268327668,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7016708850860596, timestamp=1721798539.0856254) [('tile_y', [-1, 512]), ('tile_x', [-1, 512])],None,99
+ No: 3 GFLOPS: 10.23/10.23 result: MeasureResult(costs=(0.0262392882,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7125883102416992, timestamp=1721798539.76902) [('tile_y', [-1, 8]), ('tile_x', [-1, 16])],None,43
+ No: 4 GFLOPS: 1.41/10.23 result: MeasureResult(costs=(0.18993410100000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.288121223449707, timestamp=1721798543.0652332) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
+ No: 5 GFLOPS: 9.30/10.23 result: MeasureResult(costs=(0.0288784738,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7362351417541504, timestamp=1721798543.92146) [('tile_y', [-1, 512]), ('tile_x', [-1, 256])],None,89
+ No: 6 GFLOPS: 11.16/11.16 result: MeasureResult(costs=(0.0240446486,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6613571643829346, timestamp=1721798544.5685666) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
+ No: 7 GFLOPS: 11.25/11.25 result: MeasureResult(costs=(0.0238530566,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6491818428039551, timestamp=1721798545.2201426) [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
+ No: 8 GFLOPS: 7.89/11.25 result: MeasureResult(costs=(0.0340259838,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8074848651885986, timestamp=1721798546.0267434) [('tile_y', [-1, 512]), ('tile_x', [-1, 32])],None,59
+ No: 9 GFLOPS: 14.76/14.76 result: MeasureResult(costs=(0.0181916222,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7924201488494873, timestamp=1721798546.9293199) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+ No: 10 GFLOPS: 10.38/14.76 result: MeasureResult(costs=(0.025859392200000005,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7074165344238281, timestamp=1721798547.6122532) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 5c47d2af94..4b6b58e122 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': 465.87660961995425, 'median': 465.88807110001653, 'std': 1.4194742272589143}
+ {'mean': 494.81117326999777, 'median': 495.2977907499985, 'std': 2.8721373969876045}
@@ -582,29 +582,30 @@ the tuning data to.
.. code-block:: none
- [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 11.38/ 15.72 GFLOPS | Progress: (4/20) | 9.85 s [Task 1/25] Current/Best: 14.27/ 22.88 GFLOPS | Progress: (8/20) | 12.12 s [Task 1/25] Current/Best: 23.56/ 23.56 GFLOPS | Progress: (12/20) | 15.47 s [Task 1/25] Current/Best: 13.95/ 23.56 GFLOPS | Progress: (16/20) | 18.72 s [Task 1/25] Current/Best: 15.72/ 23.56 GFLOPS | Progress: (20/20) | 21.24 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 19.95/ 21.02 GFLOPS | Progress: (4/20) | 3.87 s [Task 2/25] Current/Best: 7.72/ 21.02 GFLOPS | Progress: (8/20) | 5.52 s [Task 2/25] Current/Best: 8.49/ 21.02 GFLOPS | Progress: (12/20) | 6.94 s [Task 2/25] Current/Best: 14.93/ 21.02 GFLOPS | Progress: (16/20) | 8.70 s [Task 2/25] Current/Best: 7.79/ 21.02 GFLOPS | Progress: (20/20) | 10.70 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 12.89/ 15.17 GFLOPS | Progress: (4/20) | 6.12 s [Task 3/25] Current/Best: 10.41/ 23.72 GFLOPS | Progress: (8/20) | 9.36 s [Task 3/25] Current/Best: 13.86/ 23.72 GFLOPS | Progress: (12/20) | 11.80 s [Task 3/25] Current/Best: 6.44/ 23.72 GFLOPS | Progress: (16/20) | 14.69 s [Task 3/25] Current/Best: 20.90/ 23.72 GFLOPS | Progress: (20/20) | 17.07 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 11.07/ 20.91 GFLOPS | Progress: (4/20) | 4.72 s [Task 4/25] Current/Best: 13.98/ 20.91 GFLOPS | Progress: (8/20) | 10.59 s [Task 4/25] Current/Best: 6.94/ 22.52 GFLOPS | Progress: (12/20) | 14.92 s [Task 4/25] Current/Best: 13.52/ 22.52 GFLOPS | Progress: (16/20) | 17.35 s [Task 4/25] Current/Best: 13.65/ 22.52 GFLOPS | Progress: (20/20) | 19.84 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 16.70/ 16.70 GFLOPS | Progress: (4/20) | 4.31 s [Task 5/25] Current/Best: 12.32/ 21.19 GFLOPS | Progress: (8/20) | 6.49 s [Task 5/25] Current/Best: 23.15/ 23.15 GFLOPS | Progress: (12/20) | 8.25 s [Task 5/25] Current/Best: 15.62/ 23.15 GFLOPS | Progress: (16/20) | 10.05 s [Task 5/25] Current/Best: 18.38/ 23.15 GFLOPS | Progress: (20/20) | 12.02 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 14.75/ 14.85 GFLOPS | Progress: (4/20) | 5.10 s [Task 6/25] Current/Best: 4.51/ 15.56 GFLOPS | Progress: (8/20) | 8.11 s [Task 6/25] Current/Best: 5.38/ 18.80 GFLOPS | Progress: (12/20) | 11.41 s [Task 6/25] Current/Best: 12.67/ 19.80 GFLOPS | Progress: (16/20) | 14.98 s [Task 6/25] Current/Best: 9.82/ 19.80 GFLOPS | Progress: (20/20) | 18.83 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 12.44/ 20.57 GFLOPS | Progress: (4/20) | 5.63 s [Task 7/25] Current/Best: 5.73/ 20.57 GFLOPS | Progress: (8/20) | 8.21 s [Task 7/25] Current/Best: 22.63/ 22.63 GFLOPS | Progress: (12/20) | 10.10 s [Task 7/25] Current/Best: 15.93/ 22.63 GFLOPS | Progress: (16/20) | 12.66 s [Task 7/25] Current/Best: 14.65/ 22.63 GFLOPS | Progress: (20/20) | 15.00 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 11.36/ 14.36 GFLOPS | Progress: (4/20) | 7.56 s [Task 8/25] Current/Best: 7.18/ 14.36 GFLOPS | Progress: (8/20) | 10.45 s [Task 8/25] Current/Best: 14.33/ 14.36 GFLOPS | Progress: (12/20) | 19.29 s [Task 8/25] Current/Best: 14.54/ 14.54 GFLOPS | Progress: (16/20) | 22.61 s [Task 8/25] Current/Best: 9.70/ 16.45 GFLOPS | Progress: (20/20) | 30.08 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 19.96/ 19.96 GFLOPS | Progress: (4/20) | 6.69 s [Task 9/25] Current/Best: 10.90/ 19.96 GFLOPS | Progress: (8/20) | 10.55 s [Task 9/25] Current/Best: 19.45/ 19.96 GFLOPS | Progress: (12/20) | 12.28 s [Task 9/25] Current/Best: 15.43/ 19.96 GFLOPS | Progress: (16/20) | 15.83 s [Task 9/25] Current/Best: 11.52/ 19.96 GFLOPS | Progress: (20/20) | 21.02 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 6.57/ 14.66 GFLOPS | Progress: (4/20) | 4.32 s [Task 10/25] Current/Best: 3.04/ 14.67 GFLOPS | Progress: (8/20) | 6.31 s [Task 10/25] Current/Best: 5.74/ 15.06 GFLOPS | Progress: (12/20) | 8.36 s [Task 10/25] Current/Best: 14.33/ 15.06 GFLOPS | Progress: (16/20) | 10.28 s [Task 10/25] Current/Best: 15.26/ 16.76 GFLOPS | Progress: (20/20) | 11.91 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 7.32/ 21.05 GFLOPS | Progress: (4/20) | 4.70 s [Task 11/25] Current/Best: 20.35/ 21.05 GFLOPS | Progress: (8/20) | 6.85 s [Task 11/25] Current/Best: 19.80/ 22.27 GFLOPS | Progress: (12/20) | 9.18 s [Task 11/25] Current/Best: 12.22/ 22.27 GFLOPS | Progress: (16/20) | 12.17 s [Task 11/25] Current/Best: 3.20/ 22.27 GFLOPS | Progress: (20/20) | 15.59 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 13.63/ 21.19 GFLOPS | Progress: (4/20) | 5.90 s [Task 12/25] Current/Best: 12.34/ 21.19 GFLOPS | Progress: (8/20) | 9.26 s [Task 12/25] Current/Best: 4.12/ 22.40 GFLOPS | Progress: (12/20) | 11.65 s [Task 12/25] Current/Best: 6.94/ 22.40 GFLOPS | Progress: (16/20) | 14.25 s [Task 12/25] Current/Best: 7.82/ 22.40 GFLOPS | Progress: (20/20) | 17.30 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 13.58/ 19.81 GFLOPS | Progress: (4/20) | 4.67 s [Task 13/25] Current/Best: 11.13/ 21.32 GFLOPS | Progress: (8/20) | 7.38 s [Task 13/25] Current/Best: 12.34/ 21.32 GFLOPS | Progress: (12/20) | 10.41 s [Task 13/25] Current/Best: 7.53/ 21.32 GFLOPS | Progress: (16/20) | 13.94 s [Task 13/25] Current/Best: 6.41/ 21.32 GFLOPS | Progress: (20/20) | 17.42 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 12.70/ 12.70 GFLOPS | Progress: (4/20) | 4.95 s [Task 14/25] Current/Best: 11.85/ 12.70 GFLOPS | Progress: (8/20) | 13.53 s [Task 14/25] Current/Best: 10.15/ 22.24 GFLOPS | Progress: (12/20) | 15.63 s [Task 14/25] Current/Best: 14.78/ 22.24 GFLOPS | Progress: (16/20) | 17.85 s [Task 14/25] Current/Best: 11.67/ 22.24 GFLOPS | Progress: (20/20) | 22.17 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 15.53/ 15.53 GFLOPS | Progress: (4/20) | 4.40 s [Task 15/25] Current/Best: 13.95/ 22.94 GFLOPS | Progress: (8/20) | 6.72 s [Task 15/25] Current/Best: 13.88/ 22.94 GFLOPS | Progress: (12/20) | 9.94 s [Task 15/25] Current/Best: 13.69/ 22.94 GFLOPS | Progress: (16/20) | 15.21 s [Task 15/25] Current/Best: 21.70/ 22.94 GFLOPS | Progress: (20/20) | 17.11 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 16.10/ 16.10 GFLOPS | Progress: (4/20) | 4.27 s [Task 16/25] Current/Best: 14.18/ 18.10 GFLOPS | Progress: (8/20) | 7.17 s [Task 16/25] Current/Best: 11.67/ 18.10 GFLOPS | Progress: (12/20) | 9.88 s [Task 16/25] Current/Best: 10.95/ 18.10 GFLOPS | Progress: (16/20) | 13.89 s [Task 16/25] Current/Best: 12.63/ 21.49 GFLOPS | Progress: (20/20) | 16.27 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 22.56/ 22.56 GFLOPS | Progress: (4/20) | 5.74 s [Task 17/25] Current/Best: 12.99/ 23.77 GFLOPS | Progress: (8/20) | 8.15 s [Task 17/25] Current/Best: 24.48/ 24.48 GFLOPS | Progress: (12/20) | 10.78 s [Task 17/25] Current/Best: 7.46/ 24.48 GFLOPS | Progress: (16/20) | 13.01 s [Task 17/25] Current/Best: 22.87/ 24.48 GFLOPS | Progress: (20/20) | 15.87 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 3.08/ 16.16 GFLOPS | Progress: (4/20) | 5.66 s [Task 18/25] Current/Best: 7.97/ 16.16 GFLOPS | Progress: (8/20) | 12.46 s [Task 18/25] Current/Best: 16.04/ 16.16 GFLOPS | Progress: (12/20) | 14.85 s [Task 18/25] Current/Best: 16.30/ 16.99 GFLOPS | Progress: (16/20) | 19.88 s [Task 18/25] Current/Best: 15.98/ 19.08 GFLOPS | Progress: (20/20) | 25.11 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 19.15/ 19.15 GFLOPS | Progress: (4/20) | 4.81 s [Task 19/25] Current/Best: 8.06/ 19.15 GFLOPS | Progress: (8/20) | 7.92 s [Task 19/25] Current/Best: 6.25/ 19.39 GFLOPS | Progress: (12/20) | 11.68 s [Task 19/25] Current/Best: 9.29/ 19.58 GFLOPS | Progress: (16/20) | 14.31 s [Task 19/25] Current/Best: 5.55/ 19.97 GFLOPS | Progress: (20/20) | 17.24 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 12.33/ 14.85 GFLOPS | Progress: (4/20) | 13.99 s [Task 20/25] Current/Best: 8.05/ 17.55 GFLOPS | Progress: (8/20) | 25.57 s [Task 20/25] Current/Best: 5.65/ 20.47 GFLOPS | Progress: (12/20) | 38.28 s [Task 20/25] Current/Best: 2.73/ 20.47 GFLOPS | Progress: (16/20) | 47.22 s [Task 20/25] Current/Best: 19.35/ 20.47 GFLOPS | Progress: (20/20) | 53.76 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- [Task 21/25] Current/Best: 20.75/ 22.45 GFLOPS | Progress: (4/20) | 4.26 s [Task 21/25] Current/Best: 10.78/ 22.45 GFLOPS | Progress: (8/20) | 6.40 s [Task 21/25] Current/Best: 2.81/ 22.45 GFLOPS | Progress: (12/20) | 8.43 s [Task 21/25] Current/Best: 7.99/ 22.86 GFLOPS | Progress: (16/20) | 11.88 s [Task 21/25] Current/Best: 19.20/ 22.86 GFLOPS | Progress: (20/20) | 22.97 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 10.76/ 10.76 GFLOPS | Progress: (4/20) | 6.08 s [Task 22/25] Current/Best: 13.48/ 21.01 GFLOPS | Progress: (8/20) | 8.15 s [Task 22/25] Current/Best: 10.16/ 21.01 GFLOPS | Progress: (12/20) | 11.27 s [Task 22/25] Current/Best: 17.01/ 21.01 GFLOPS | Progress: (16/20) | 13.35 s [Task 22/25] Current/Best: 8.82/ 22.49 GFLOPS | Progress: (20/20) | 17.81 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 19.60/ 24.45 GFLOPS | Progress: (4/20) | 5.28 s [Task 23/25] Current/Best: 19.46/ 24.45 GFLOPS | Progress: (8/20) | 7.43 s [Task 23/25] Current/Best: 12.92/ 24.45 GFLOPS | Progress: (12/20) | 10.50 s [Task 23/25] Current/Best: 9.41/ 24.45 GFLOPS | Progress: (16/20) | 13.79 s [Task 23/25] Current/Best: 24.73/ 24.73 GFLOPS | Progress: (20/20) | 16.66 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 1.20/ 7.67 GFLOPS | Progress: (4/20) | 11.43 s [Task 24/25] Current/Best: 2.29/ 7.67 GFLOPS | Progress: (8/20) | 14.39 s [Task 24/25] Current/Best: 4.42/ 7.67 GFLOPS | Progress: (12/20) | 25.34 s [Task 24/25] Current/Best: 3.66/ 7.80 GFLOPS | Progress: (16/20) | 27.78 s [Task 24/25] Current/Best: 1.81/ 10.35 GFLOPS | Progress: (20/20) | 33.35 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 4.37/ 8.89 GFLOPS | Progress: (4/20) | 13.35 s [Task 25/25] Current/Best: 9.07/ 9.36 GFLOPS | Progress: (8/20) | 16.27 s [Task 25/25] Current/Best: 6.02/ 9.55 GFLOPS | Progress: (12/20) | 18.93 s [Task 25/25] Current/Best: 8.65/ 9.55 GFLOPS | Progress: (16/20) | 20.90 s [Task 25/25] Current/Best: 2.83/ 9.55 GFLOPS | Progress: (2
0/20) | 31.90 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 3.44/ 17.97 GFLOPS | Progress: (4/20) | 9.60 s [Task 1/25] Current/Best: 11.97/ 19.97 GFLOPS | Progress: (8/20) | 14.56 s [Task 1/25] Current/Best: 12.89/ 19.97 GFLOPS | Progress: (12/20) | 17.75 s [Task 1/25] Current/Best: 15.60/ 24.00 GFLOPS | Progress: (16/20) | 21.23 s [Task 1/25] Current/Best: 9.12/ 24.00 GFLOPS | Progress: (20/20) | 24.47 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 18.58/ 18.58 GFLOPS | Progress: (4/20) | 4.39 s [Task 2/25] Current/Best: 3.99/ 18.58 GFLOPS | Progress: (8/20) | 5.83 s [Task 2/25] Current/Best: 10.45/ 18.58 GFLOPS | Progress: (12/20) | 7.57 s [Task 2/25] Current/Best: 13.03/ 18.58 GFLOPS | Progress: (16/20) | 9.65 s [Task 2/25] Current/Best: 7.78/ 18.58 GFLOPS | Progress: (20/20) | 11.27 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 13.08/ 19.04 GFLOPS | Progress: (4/20) | 6.21 s [Task 3/25] Current/Best: 10.21/ 19.04 GFLOPS | Progress: (8/20) | 8.60 s [Task 3/25] Current/Best: 13.10/ 21.23 GFLOPS | Progress: (12/20) | 11.12 s [Task 3/25] Current/Best: 21.90/ 21.90 GFLOPS | Progress: (16/20) | 13.63 s [Task 3/25] Current/Best: 22.31/ 22.31 GFLOPS | Progress: (20/20) | 15.75 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 16.36/ 16.36 GFLOPS | Progress: (4/20) | 4.32 s [Task 4/25] Current/Best: 6.23/ 18.96 GFLOPS | Progress: (8/20) | 7.52 s [Task 4/25] Current/Best: 17.30/ 18.96 GFLOPS | Progress: (12/20) | 9.33 s [Task 4/25] Current/Best: 18.38/ 18.96 GFLOPS | Progress: (16/20) | 11.44 s [Task 4/25] Current/Best: 20.34/ 20.34 GFLOPS | Progress: (20/20) | 13.63 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 2.60/ 17.35 GFLOPS | Progress: (4/20) | 4.96 s [Task 5/25] Current/Best: 2.85/ 17.35 GFLOPS | Progress: (8/20) | 7.41 s [Task 5/25] Current/Best: 5.72/ 17.35 GFLOPS | Progress: (12/20) | 9.63 s [Task 5/25] Current/Best: 14.04/ 20.45 GFLOPS | Progress: (16/20) | 11.89 s [Task 5/25] Current/Best: 19.11/ 20.45 GFLOPS | Progress: (20/20) | 14.20 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 16.16/ 16.16 GFLOPS | Progress: (4/20) | 5.81 s [Task 6/25] Current/Best: 9.07/ 16.87 GFLOPS | Progress: (8/20) | 10.58 s [Task 6/25] Current/Best: 7.86/ 16.87 GFLOPS | Progress: (12/20) | 13.90 s [Task 6/25] Current/Best: 15.20/ 17.47 GFLOPS | Progress: (16/20) | 17.16 s [Task 6/25] Current/Best: 5.62/ 17.47 GFLOPS | Progress: (20/20) | 19.86 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 3.06/ 18.76 GFLOPS | Progress: (4/20) | 5.54 s [Task 7/25] Current/Best: 8.36/ 18.76 GFLOPS | Progress: (8/20) | 7.84 s [Task 7/25] Current/Best: 12.09/ 19.95 GFLOPS | Progress: (12/20) | 10.09 s [Task 7/25] Current/Best: 2.78/ 19.95 GFLOPS | Progress: (16/20) | 13.11 s [Task 7/25] Current/Best: 5.21/ 19.95 GFLOPS | Progress: (20/20) | 16.14 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 10.47/ 13.93 GFLOPS | Progress: (4/20) | 5.66 s [Task 8/25] Current/Best: 18.26/ 18.26 GFLOPS | Progress: (8/20) | 17.17 s [Task 8/25] Current/Best: 10.39/ 18.83 GFLOPS | Progress: (12/20) | 20.55 s [Task 8/25] Current/Best: 10.76/ 18.83 GFLOPS | Progress: (16/20) | 26.38 s [Task 8/25] Current/Best: 12.07/ 18.83 GFLOPS | Progress: (20/20) | 28.49 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 11.11/ 17.32 GFLOPS | Progress: (4/20) | 4.78 s [Task 9/25] Current/Best: 6.23/ 17.32 GFLOPS | Progress: (8/20) | 10.56 s [Task 9/25] Current/Best: 18.79/ 18.79 GFLOPS | Progress: (12/20) | 16.76 s [Task 9/25] Current/Best: 16.47/ 18.79 GFLOPS | Progress: (16/20) | 18.73 s [Task 9/25] Current/Best: 6.28/ 18.79 GFLOPS | Progress: (20/
20) | 22.28 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 11.55/ 22.19 GFLOPS | Progress: (4/20) | 5.01 s [Task 10/25] Current/Best: 14.37/ 22.19 GFLOPS | Progress: (8/20) | 7.21 s [Task 10/25] Current/Best: 17.77/ 22.19 GFLOPS | Progress: (12/20) | 8.99 s [Task 10/25] Current/Best: 15.02/ 22.19 GFLOPS | Progress: (16/20) | 11.31 s [Task 10/25] Current/Best: 15.26/ 22.19 GFLOPS | Progress: (20/20) | 13.10 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 8.55/ 19.84 GFLOPS | Progress: (4/20) | 4.72 s [Task 11/25] Current/Best: 17.43/ 19.84 GFLOPS | Progress: (8/20) | 6.99 s [Task 11/25] Current/Best: 6.16/ 19.84 GFLOPS | Progress: (12/20) | 9.94 s [Task 11/25] Current/Best: 11.40/ 22.53 GFLOPS | Progress: (16/20) | 12.99 s [Task 11/25] Current/Best: 20.21/ 22.53 GFLOPS | Progress: (20/20) | 15.97 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 19.91/ 19.91 GFLOPS | Progress: (4/20) | 5.26 s [Task 12/25] Current/Best: 15.85/ 19.91 GFLOPS | Progress: (8/20) | 8.20 s [Task 12/25] Current/Best: 12.18/ 19.91 GFLOPS | Progress: (12/20) | 14.04 s [Task 12/25] Current/Best: 14.78/ 19.91 GFLOPS | Progress: (16/20) | 17.11 s [Task 12/25] Current/Best: 18.23/ 19.91 GFLOPS | Progress: (20/20) | 20.51 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 8.98/ 19.55 GFLOPS | Progress: (4/20) | 5.08 s [Task 13/25] Current/Best: 12.24/ 19.55 GFLOPS | Progress: (8/20) | 8.55 s [Task 13/25] Current/Best: 13.22/ 20.70 GFLOPS | Progress: (12/20) | 11.33 s [Task 13/25] Current/Best: 21.34/ 21.55 GFLOPS | Progress: (16/20) | 13.80 s [Task 13/25] Current/Best: 4.70/ 21.55 GFLOPS | Progress: (20/20) | 16.23 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 13.74/ 13.74 GFLOPS | Progress: (4/20) | 12.22 s [Task 14/25] Current/Best: 5.51/ 18.29 GFLOPS | Progress: (8/20) | 15.56 s [Task 14/25] Current/Best: 14.92/ 18.29 GFLOPS | Progress: (12/20) | 27.37 s [Task 14/25] Current/Best: 13.13/ 18.29 GFLOPS | Progress: (16/20) | 31.13 s [Task 14/25] Current/Best: 13.48/ 18.29 GFLOPS | Progress: (20/20) | 34.13 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 14.92/ 17.15 GFLOPS | Progress: (4/20) | 9.76 s [Task 15/25] Current/Best: 8.38/ 17.15 GFLOPS | Progress: (8/20) | 17.06 s [Task 15/25] Current/Best: 9.04/ 17.15 GFLOPS | Progress: (12/20) | 28.62 s [Task 15/25] Current/Best: 11.14/ 20.29 GFLOPS | Progress: (16/20) | 30.70 s [Task 15/25] Current/Best: 10.95/ 20.29 GFLOPS | Progress: (20
/20) | 37.14 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 17.90/ 17.90 GFLOPS | Progress: (4/20) | 4.46 s [Task 16/25] Current/Best: 11.00/ 17.90 GFLOPS | Progress: (8/20) | 7.60 s Done.
+ Done.
+ [Task 16/25] Current/Best: 11.33/ 17.90 GFLOPS | Progress: (12/20) | 9.42 s [Task 16/25] Current/Best: 15.82/ 17.90 GFLOPS | Progress: (16/20) | 11.60 s [Task 16/25] Current/Best: 11.08/ 17.90 GFLOPS | Progress: (20/20) | 14.86 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 12.26/ 22.26 GFLOPS | Progress: (4/20) | 5.26 s [Task 17/25] Current/Best: 20.35/ 22.26 GFLOPS | Progress: (8/20) | 7.61 s [Task 17/25] Current/Best: 3.02/ 22.26 GFLOPS | Progress: (12/20) | 10.56 s [Task 17/25] Current/Best: 3.11/ 22.91 GFLOPS | Progress: (16/20) | 13.42 s [Task 17/25] Current/Best: 16.56/ 22.91 GFLOPS | Progress: (20/20) | 16.01 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 7.87/ 17.13 GFLOPS | Progress: (4/20) | 4.91 s [Task 18/25] Current/Best: 1.57/ 20.28 GFLOPS | Progress: (8/20) | 8.07 s [Task 18/25] Current/Best: 12.72/ 20.28 GFLOPS | Progress: (12/20) | 11.32 s [Task 18/25] Current/Best: 11.63/ 20.28 GFLOPS | Progress: (16/20) | 13.90 s [Task 18/25] Current/Best: 16.11/ 20.28 GFLOPS | Progress: (20/20) | 17.65 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 11.99/ 11.99 GFLOPS | Progress: (4/20) | 6.07 s [Task 19/25] Current/Best: 15.10/ 21.17 GFLOPS | Progress: (8/20) | 9.27 s [Task 19/25] Current/Best: 18.93/ 21.17 GFLOPS | Progress: (12/20) | 12.94 s [Task 19/25] Current/Best: 22.14/ 22.14 GFLOPS | Progress: (16/20) | 16.92 s [Task 19/25] Current/Best: 20.97/ 22.14 GFLOPS | Progress: (20/20) | 19.53 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 6.27/ 14.25 GFLOPS | Progress: (4/20) | 9.34 s [Task 20/25] Current/Best: 7.42/ 14.25 GFLOPS | Progress: (8/20) | 21.11 s [Task 20/25] Current/Best: 16.95/ 20.15 GFLOPS | Progress: (12/20) | 24.27 s [Task 20/25] Current/Best: 18.47/ 20.15 GFLOPS | Progress: (16/20) | 28.00 s [Task 20/25] Current/Best: 15.28/ 20.15 GFLOPS | Progress: (20/20) | 39.74 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 2.74/ 16.96 GFLOPS | Progress: (4/20) | 14.84 s [Task 21/25] Current/Best: 20.72/ 20.72 GFLOPS | Progress: (8/20) | 17.81 s [Task 21/25] Current/Best: 4.81/ 20.72 GFLOPS | Progress: (12/20) | 22.56 s [Task 21/25] Current/Best: 6.44/ 20.72 GFLOPS | Progress: (16/20) | 29.58 s [Task 21/25] Current/Best: 12.21/ 20.72 GFLOPS | Progress: (20
/20) | 35.27 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 6.38/ 19.42 GFLOPS | Progress: (4/20) | 4.29 s [Task 22/25] Current/Best: 11.37/ 19.42 GFLOPS | Progress: (8/20) | 7.12 s [Task 22/25] Current/Best: 4.31/ 19.42 GFLOPS | Progress: (12/20) | 10.25 s [Task 22/25] Current/Best: 5.18/ 19.42 GFLOPS | Progress: (16/20) | 13.68 s [Task 22/25] Current/Best: 16.20/ 19.42 GFLOPS | Progress: (20/20) | 15.94 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 10.80/ 19.55 GFLOPS | Progress: (4/20) | 5.22 s [Task 23/25] Current/Best: 3.09/ 19.55 GFLOPS | Progress: (8/20) | 11.09 s [Task 23/25] Current/Best: 7.92/ 20.45 GFLOPS | Progress: (12/20) | 14.42 s [Task 23/25] Current/Best: 19.54/ 21.08 GFLOPS | Progress: (16/20) | 17.12 s [Task 23/25] Current/Best: 3.08/ 22.60 GFLOPS | Progress: (20/20) | 19.85 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.18/ 3.18 GFLOPS | Progress: (4/20) | 13.28 s [Task 24/25] Current/Best: 0.99/ 7.71 GFLOPS | Progress: (8/20) | 24.37 s Done.
+ Done.
+ [Task 24/25] Current/Best: 2.31/ 7.71 GFLOPS | Progress: (12/20) | 35.10 s [Task 24/25] Current/Best: 8.20/ 8.20 GFLOPS | Progress: (16/20) | 46.53 s [Task 24/25] Current/Best: 5.50/ 8.20 GFLOPS | Progress: (20/20) | 48.39 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) | 13.50 s [Task 25/25] Current/Best: 6.33/ 9.29 GFLOPS | Progress: (8/20) | 24.49 s [Task 25/25] Current/Best: 5.74/ 9.29 GFLOPS | Progress: (12/20) | 28.00 s [Task 25/25] Current/Best: 7.95/ 9.29 GFLOPS | Progress: (16/20) | 29.46 s [Task 25/25] Current/Best: 7.59/ 9.29 GFLOPS | Progress: (20/20) | 32.67 s
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
Done.
Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 379.46695129005093, 'median': 378.68288884983485, 'std': 2.0495101343514097}
- unoptimized: {'mean': 465.87660961995425, 'median': 465.88807110001653, 'std': 1.4194742272589143}
+ optimized: {'mean': 417.05075393000016, 'median': 417.0194615499895, 'std': 1.4058979234997393}
+ unoptimized: {'mean': 494.81117326999777, 'median': 495.2977907499985, 'std': 2.8721373969876045}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 12 minutes 46.882 seconds)
+ **Total running time of the script:** ( 13 minutes 47.683 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 9fa588ecb7..ac3af0bf83 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
- 1.185e-07 secs/op
+ 1.312e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 6b12e6ea48..efb9a7aacf 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, 0x2319da40)), stage(b, placeholder(b, 0xdedb250)), 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, 0x17d8a7c0)), stage(b, placeholder(b, 0x23a975f0)), 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 77e251a32d..6f98eea4e9 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,31 +5,31 @@
Computation times
=================
-**16:12.490** total execution time for **tutorial** files:
+**17:20.528** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:46.882 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:47.683 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:24.248 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:20.214 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:56.025 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.813 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.568 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:45.069 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:19.882 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:24.179 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.863 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.496 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.847 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.886 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.175 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.189 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.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_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_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 37147c9c75..5f9e16ece2 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000006
- naive: 0.000006
+ Numpy running time: 0.000007
+ naive: 0.000010
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000039
+ vector: 0.000040
# 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 5.581860023085028e-06 1.0
- naive 5.6329e-06 1.0091439012629992
- parallel 7.0923e-06 1.2705979674639296
- vector 3.85478e-05 6.905905888104855
+ numpy 7.101240000793041e-06 1.0
+ naive 1.01851e-05 1.4342706342642362
+ parallel 7.0793e-06 0.9969103986359298
+ vector 4.00157e-05 5.635029937803988
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.013829
+ Numpy running time: 0.019395
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.199052
+ none: 3.450763
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.289568
+ blocking: 0.293644
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.265936
+ vectorization: 0.280361
# 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: 0.105106
+ loop permutation: 0.114746
# 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: 0.101167
+ array packing: 0.105374
# 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: 0.093956
+ block caching: 0.111285
# 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: 0.112138
+ parallelization: 0.133137
# 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 3.1990518697 1.0
- blocking 0.2895681939 0.09051687990515611
- vectorization 0.2659355806 0.08312949943663742
- loop permutation 0.1051064304 0.03285549427801452
- array packing 0.10116672069999999 0.0316239701075829
- block caching 0.0939560612 0.02936997117486907
- parallelization 0.11213826739999999 0.035053594617243916
+ none 3.4507631827 1.0
+ blocking 0.2936438873 0.0850953460881203
+ vectorization 0.2803613654 0.0812461912209911
+ loop permutation 0.1147457042 0.03325226859242739
+ array packing 0.10537357520000001 0.030536310265589416
+ block caching 0.11128536879999999 0.03224949465031859
+ parallelization 0.13313682980000002 0.03858185066638767
@@ -1594,6 +1594,11 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 0.813 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index bcfea9974a..357e5de852 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-7c9969bbdfc7f032f270f9f75eeb53bf6e78ff7b
+89b91e2b1195b53bf7e1f6c250bc9a1247367d13
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7bcd2e9828..74061edf02 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -614,7 +614,7 @@ 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 37.542 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.873 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
index db31229224..046db23a30 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -535,7 +535,7 @@ Tensorflow is also required since it’s used as the default backend of keras.</
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 879ms/step
+1/1 [==============================] - 1s 947ms/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 8f6f246876..f782fdce17 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -478,14 +478,15 @@ 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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- 15%|#5 | 6.33M/41.5M [00:00<00:01, 34.7MB/s]
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- 35%|###4 | 14.3M/41.5M [00:00<00:00, 31.6MB/s]
- 42%|####1 | 17.4M/41.5M [00:00<00:01, 24.0MB/s]
- 58%|#####7 | 24.0M/41.5M [00:00<00:00, 29.5MB/s]
- 77%|#######7 | 32.0M/41.5M [00:01<00:00, 34.8MB/s]
- 92%|#########2| 38.3M/41.5M [00:01<00:00, 23.6MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 27.4MB/s]
+ 19%|#9 | 7.99M/41.5M [00:00<00:00, 51.6MB/s]
+ 35%|###4 | 14.3M/41.5M [00:00<00:00, 49.8MB/s]
+ 46%|####5 | 19.1M/41.5M [00:00<00:00, 48.9MB/s]
+ 57%|#####7 | 23.7M/41.5M [00:00<00:00, 45.1MB/s]
+ 67%|######7 | 28.0M/41.5M [00:00<00:00, 38.5MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 35.6MB/s]
+ 87%|########7 | 36.2M/41.5M [00:00<00:00, 37.4MB/s]
+ 96%|#########6| 39.9M/41.5M [00:01<00:00, 29.1MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 35.8MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index e63bf7fc3f..d060542b97 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -513,7 +513,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.591 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.175 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 d5b30512e7..76481030aa 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -461,15 +461,9 @@ be unstable.</p>
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]
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- 45%|####4 | 19.9M/44.7M [00:00<00:00, 29.2MB/s]
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- 68%|######7 | 30.3M/44.7M [00:00<00:00, 33.5MB/s]
- 77%|#######7 | 34.5M/44.7M [00:01<00:00, 30.1MB/s]
- 86%|########5 | 38.3M/44.7M [00:01<00:00, 30.8MB/s]
- 93%|#########3| 41.7M/44.7M [00:01<00:00, 30.8MB/s]
-100%|##########| 44.7M/44.7M [00:01<00:00, 33.3MB/s]
+ 35%|###5 | 15.7M/44.7M [00:00<00:00, 165MB/s]
+ 70%|####### | 31.4M/44.7M [00:00<00:00, 139MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 112MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index c6a1a32ee4..a40d786048 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -678,7 +678,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.628 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.931 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index b55bf111aa..d8185d20c8 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>06:44.917</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:04.811</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -379,39 +379,39 @@
</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>01:37.542</p></td>
+<td><p>01:40.873</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>01:29.628</p></td>
+<td><p>01:34.931</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:03.591</p></td>
+<td><p>01:07.175</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>00:43.322</p></td>
+<td><p>00:45.625</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>00:38.798</p></td>
+<td><p>00:41.925</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>00:29.805</p></td>
+<td><p>00:30.363</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>00:26.913</p></td>
+<td><p>00:27.913</p></td>
<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>00:12.462</p></td>
+<td><p>00:12.932</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>00:02.857</p></td>
+<td><p>00:03.074</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 9a155c81d0..956baac414 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -854,10 +854,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3991.9818 3992.2348 3995.5816 3987.7967 2.5666
+ 4122.6380 4122.7539 4124.6818 4120.5779 1.0897
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.044 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.536 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 9d6a78bdfd..9841d20388 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
@@ -468,12 +468,13 @@ 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
8192/102967424 [..............................] - ETA: 0s
+ 6635520/102967424 [>.............................] - ETA: 2s
8380416/102967424 [=>............................] - ETA: 3s
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+ 23412736/102967424 [=====>........................] - ETA: 1s
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+ 33546240/102967424 [========>.....................] - ETA: 1s
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@@ -486,12 +487,12 @@ to run this tutorial with a real device over rpc.</p>
75489280/102967424 [====================>.........] - ETA: 0s
82124800/102967424 [======================>.......] - ETA: 0s
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- 84017152/102967424 [=======================>......] - ETA: 0s
90521600/102967424 [=========================>....] - ETA: 0s
92266496/102967424 [=========================>....] - ETA: 0s
98910208/102967424 [===========================>..] - ETA: 0s
100646912/102967424 [============================>.] - ETA: 0s
-102967424/102967424 [==============================] - 4s 0us/step
+102850560/102967424 [============================>.] - ETA: 0s
+102967424/102967424 [==============================] - 3s 0us/step
</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 249817acc6..f166eae6d6 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -692,7 +692,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 13.7711 13.7250 14.1470 13.6606 0.1338
+ 14.7929 14.7338 15.0564 14.6357 0.1494
</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 86e0610160..0373b2dc7b 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -484,39 +484,41 @@ 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).
@@ -603,7 +605,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 26.841 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 48.291 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 f801636fd2..3ae5d150ee 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -525,10 +525,10 @@ 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>
@@ -613,7 +613,7 @@ 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)
- 85.9826 85.9435 88.5083 85.5817 0.3109
+ 89.3169 89.2742 90.4867 88.8068 0.2658
</pre></div>
</div>
<div class="admonition note">
@@ -652,7 +652,7 @@ 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 31.623 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 35.397 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 df87c42709..d8e43be862 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -610,7 +610,7 @@ 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)
- 108.2594 108.1872 113.9094 107.2576 0.6911
+ 110.5642 110.5480 112.7201 109.8136 0.4336
</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 5382c41291..4306006439 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>09:47.714</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:29.395</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -379,39 +379,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:26.841</p></td>
+<td><p>03:48.291</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>01:31.623</p></td>
+<td><p>01:35.397</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>01:18.044</p></td>
+<td><p>01:21.536</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>00:51.740</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>00:55.128</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>00:51.435</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>00:53.884</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>00:48.739</p></td>
+<td><p>00:51.918</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:29.707</p></td>
+<td><p>00:31.781</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:29.578</p></td>
+<td><p>00:31.452</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.007</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/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index b7c261da27..a2fd5085c1 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>00:03.891</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.176</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,15 +379,15 @@
</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>00:02.709</p></td>
+<td><p>00:02.894</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>00:01.175</p></td>
+<td><p>00:01.274</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 a9112a32ac..f1bcc1d0c8 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -555,10 +555,10 @@ 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: 25017us [25017us] (48.81%; 48.81%)
-FoldScaleAxis: 26232us [7us] (51.19%; 51.19%)
- FoldConstant: 26225us [1746us] (51.17%; 99.97%)
- InferType: 24479us [24479us] (47.76%; 93.34%)
+InferType: 25516us [25516us] (48.50%; 48.50%)
+FoldScaleAxis: 27096us [7us] (51.50%; 51.50%)
+ FoldConstant: 27089us [1805us] (51.49%; 99.97%)
+ InferType: 25285us [25285us] (48.06%; 93.34%)
</pre></div>
</div>
</div>
@@ -580,10 +580,10 @@ 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: 24199us [24199us] (48.62%; 48.62%)
-FoldScaleAxis: 25574us [6us] (51.38%; 51.38%)
- FoldConstant: 25568us [1623us] (51.37%; 99.98%)
- InferType: 23945us [23945us] (48.11%; 93.65%)
+InferType: 25098us [25098us] (48.32%; 48.32%)
+FoldScaleAxis: 26843us [7us] (51.68%; 51.68%)
+ FoldConstant: 26836us [1814us] (51.67%; 99.97%)
+ InferType: 25022us [25022us] (48.17%; 93.24%)
</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 5301a4ad7f..1bc455e06b 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -605,7 +605,7 @@ 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: 36.699520 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 49.599422 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 b317492549..869f596c74 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -887,7 +887,7 @@ 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: 12.212877 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.131062 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 93de8a5bd3..fafcd757f6 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -502,8 +502,8 @@ 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: 0.013433
-Baseline: 3.201167
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019198
+Baseline: 3.442396
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -562,7 +562,7 @@ 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: 0.295324
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.297132
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -619,7 +619,7 @@ 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: 0.268273
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.280602
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -674,7 +674,7 @@ 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: 0.106015
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117808
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -751,7 +751,7 @@ 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: 0.101416
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107460
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -829,7 +829,7 @@ 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: 0.093673
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112150
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -909,7 +909,7 @@ 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: 0.113371
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.134778
</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 bb24a5a1e4..b517dca58d 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>00:31.174</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.374</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,15 +379,15 @@
</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>00:28.161</p></td>
+<td><p>00:31.237</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>00:01.923</p></td>
+<td><p>00:01.988</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>00:01.090</p></td>
+<td><p>00:01.149</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 37379e4517..13f5403863 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>03:37.637</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:49.087</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -379,27 +379,27 @@
</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>01:34.624</p></td>
+<td><p>01:40.020</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>01:12.220</p></td>
+<td><p>01:14.752</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>00:19.588</p></td>
+<td><p>00:20.767</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>00:16.654</p></td>
+<td><p>00:17.527</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:14.453</p></td>
+<td><p>00:15.920</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 16c1374f49..de22ce299e 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
@@ -1042,7 +1042,7 @@ 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: 0.347 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index e9a9962b6c..9c98e1f294 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -933,7 +933,7 @@ 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)
- 3.1964 3.1963 3.1967 3.1961 0.0003
+ 3.2612 3.2638 3.2664 3.2535 0.0056
</pre></div>
</div>
</div>
@@ -955,7 +955,7 @@ 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 12.220 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.752 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 9365ce1ddb..d02f09f5d5 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -955,7 +955,7 @@ 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)
- 716.2517 716.9909 718.6147 713.1494 2.2916
+ 756.4358 758.6245 759.2351 751.4478 3.5358
</pre></div>
</div>
</div>
@@ -977,7 +977,7 @@ 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 34.624 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.020 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 0f6c0cfd78..343d95836a 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>00:22.673</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:24.159</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -379,11 +379,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:22.637</p></td>
+<td><p>00:24.121</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.020</p></td>
+<td><p>00:00.021</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index a0e215c168..b9015ee322 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -640,7 +640,7 @@ and measure running time.</p>
Best config:
,None
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+Time cost of this operator: 0.037095
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 41cc1dd334..0e1e94a3a0 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -674,10 +674,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.4 98.745 (1, 2, 10, 10, 3) 2 1 [296.4]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.844 0.947 (1, 6, 10, 10) 1 1 [2.844]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.924 0.308 (1, 1, 10, 10, 3) 1 1 [0.924]
-Total_time - 300.167 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.9 98.722 (1, 2, 10, 10, 3) 2 1 [304.9]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.994 0.969 (1, 6, 10, 10) 1 1 [2.994]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.952 0.308 (1, 1, 10, 10, 3) 1 1 [0.952]
+Total_time - 308.847 - - - - -
</pre></div>
</div>
</div>
@@ -729,13 +729,13 @@ 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 97.875 97.324 (1, 6, 10, 10, 1) 2 1 [97.875]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.748 1.739 (1, 6, 10, 10) 1 1 [1.748]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.942 0.937 (1, 1, 10, 10, 3) 1 1 [0.942]
-Total_time - 100.566 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 138.2 98.158 (1, 6, 10, 10, 1) 2 1 [138.2]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.752 1.244 (1, 6, 10, 10) 1 1 [1.752]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.841 0.598 (1, 3, 10, 10, 1) 1 1 [0.841]
+Total_time - 140.793 - - - - -
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 24.279 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 3b9aca149c..6d41e7b37e 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -485,8 +485,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 17.5MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 27.4MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 13.3MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 20.9MB/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,
</pre></div>
@@ -610,7 +610,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.738 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 32.359 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 51686f8945..45b8b0f2cb 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -554,7 +554,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp5a9eys09/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpd_qobwac/images/random'
</pre></div>
</div>
</div>
@@ -614,8 +614,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmp5a9eys09/images/target contains 8144 images
-/tmp/tmp5a9eys09/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpd_qobwac/images/target contains 8144 images
+/tmp/tmpd_qobwac/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -727,13 +727,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 42s - loss: 0.2079 - accuracy: 0.9267 - val_loss: 0.1345 - val_accuracy: 0.9535 - 42s/epoch - 128ms/step
+328/328 - 47s - loss: 0.2252 - accuracy: 0.9218 - val_loss: 0.1147 - val_accuracy: 0.9600 - 47s/epoch - 142ms/step
Epoch 2/3
-328/328 - 40s - loss: 0.1042 - accuracy: 0.9609 - val_loss: 0.1178 - val_accuracy: 0.9626 - 40s/epoch - 121ms/step
+328/328 - 44s - loss: 0.1020 - accuracy: 0.9621 - val_loss: 0.1437 - val_accuracy: 0.9486 - 44s/epoch - 134ms/step
Epoch 3/3
-328/328 - 40s - loss: 0.0651 - accuracy: 0.9757 - val_loss: 0.0963 - val_accuracy: 0.9656 - 40s/epoch - 121ms/step
+328/328 - 44s - loss: 0.0693 - accuracy: 0.9738 - val_loss: 0.0984 - val_accuracy: 0.9653 - 44s/epoch - 134ms/step
-<keras.callbacks.History object at 0x7fb6c54ddf40>
+<keras.callbacks.History object at 0x7f8d1fac6670>
</pre></div>
</div>
</div>
@@ -997,7 +997,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 9.842 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 12.251 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index fe11ee5e1c..f9a00de766 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:26.305</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>08:40.416</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -379,27 +379,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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+<td><p>05:12.251</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_pytorch.html#sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">4. microTVM PyTorch Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_pytorch.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:20.173</p></td>
+<td><p>01:24.279</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:07.912</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index d8fbdb884a..83b6fcfa2d 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:37.863</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:40.400</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -379,15 +379,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:32.709</p></td>
+<td><p>00:34.523</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
-<td><p>00:03.315</p></td>
+<td><p>00:03.877</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
-<td><p>00:01.834</p></td>
+<td><p>00:01.994</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>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index a6b469262c..2fcfddfd2c 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -582,7 +582,7 @@ 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 0x7fb6ec7ecdc0>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f8d61204550>
</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 70a3c4c2e2..8677463aeb 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -370,7 +370,7 @@
<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>00:04.802</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.529</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,35 +379,35 @@
</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>00:02.287</p></td>
+<td><p>00:02.440</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>00:00.930</p></td>
+<td><p>00:01.412</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>00:00.659</p></td>
+<td><p>00:00.704</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>00:00.647</p></td>
+<td><p>00:00.682</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>00:00.117</p></td>
+<td><p>00:00.123</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>00:00.068</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>00:00.063</p></td>
+<td><p>00:00.064</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>00:00.031</p></td>
+<td><p>00:00.032</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 0d1e0645b6..7ab911fc6d 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -891,54 +891,55 @@ $(function() {
<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_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="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html" target="_self">ThreadLocalDiscoWorker</a></td><td class="desc">A threadlocal wrapper of <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" title="A worker in Disco. It takes a channel to communication with the controler. [...]
+<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_1DRefObj.html" target="_self">DRefObj</a></td><td class="desc">An object that exists on all workers </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_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_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_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_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_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_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_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_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_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_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_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_46_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_46_" class="arrow" onclick="toggleFolder('1_7_46_')">►</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_46_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_46_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_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_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_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_1ModuleNode.html" target="_self">ModuleNode</a></td><td class="desc">Base container of module </td></tr>
+<tr id="row_1_7_49_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_49_" class="arrow" onclick="toggleFolder('1_7_49_')">►</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_49_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_49_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_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_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_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="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_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_1Object.html" target="_self">Object</a></td><td class="desc">Base class of all object containers </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_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_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="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_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_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_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_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_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="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_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="structtvm_1_1runtime_1_1SignaturePrinter.html" target="_self">SignaturePrinter</a></td><td class="desc"></td></tr>
+<tr id="row_1_7_59_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_7_59_" class="arrow" onclick="toggleFolder('1_7_59_')">►</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_59_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_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_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_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_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_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_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_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="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_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.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_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_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_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="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_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_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_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_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_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_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_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_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_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_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_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_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_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="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">TVMArgsSetter</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_1TVMArgsSetterApply.html" target="_self">TVMArgsSetterApply</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_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_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_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_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="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_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_1TimerNode.html" target="_self">TimerNode</a></td><td class="desc">Base class for all implementations </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_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_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_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_81_" class="even" style="display:none;"><td class="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>
diff --git a/docs/reference/api/doxygen/classes.html b/docs/reference/api/doxygen/classes.html
index 86bd930706..c2ac66be12 100644
--- a/docs/reference/api/doxygen/classes.html
+++ b/docs/reference/api/doxygen/classes.html
@@ -123,7 +123,7 @@ $(function() {
<dd><a class="el" href="classtvm_1_1relax_1_1SameShapeConstraint.html">SameShapeConstraint</a> (<a class="el" href="namespacetvm_1_1relax.html">tvm::relax</a>)</dd><dd><a class="el" href="classtvm_1_1relax_1_1SameShapeConstraintNode.html">SameShapeConstraintNode</a> (<a class="el" href="namespacetvm_1_1relax.html">tvm::relax</a>)</dd><dd><a class="el" href="classtvm_1_1te_1_1ScanOp.html">ScanOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)</dd><dd><a class="el" href="str [...]
<dl class="classindex even">
<dt class="alphachar"><a name="letter_T">T</a></dt>
-<dd><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html">TakeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)</dd><dd><a class="el" href="structtvm_1_1relax_1_1TakeAttrs.html">TakeAttrs</a> (<a class="el" href="namespacetvm_1_1relax.html">tvm::relax</a>)</dd><dd><a class="el" href="classtvm_1_1Target.html">Target</a> (<a class="el" href="namespacetvm.html">tvm</a>)</dd><dd><a class="el" href="classtvm_1_1TargetKind.html">TargetKind</a> (<a class="el" href=" [...]
+<dd><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html">TakeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)</dd><dd><a class="el" href="structtvm_1_1relax_1_1TakeAttrs.html">TakeAttrs</a> (<a class="el" href="namespacetvm_1_1relax.html">tvm::relax</a>)</dd><dd><a class="el" href="classtvm_1_1Target.html">Target</a> (<a class="el" href="namespacetvm.html">tvm</a>)</dd><dd><a class="el" href="classtvm_1_1TargetKind.html">TargetKind</a> (<a class="el" href=" [...]
<dl class="classindex odd">
<dt class="alphachar"><a name="letter_U">U</a></dt>
<dd><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html">Unframer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)</dd><dd><a class="el" href="structtvm_1_1relay_1_1UniformAttrs.html">UniformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)</dd><dd><a class="el" href="structtvm_1_1relay_1_1UniqueAttrs.html">UniqueAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)</dd><dd>< [...]
diff --git a/docs/reference/api/doxygen/disco__worker_8h.html b/docs/reference/api/doxygen/disco__worker_8h.html
index 10f2bc7cc6..f1f70808d5 100644
--- a/docs/reference/api/doxygen/disco__worker_8h.html
+++ b/docs/reference/api/doxygen/disco__worker_8h.html
@@ -92,6 +92,9 @@ Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html">tvm::runtime::DiscoWorker</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">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 in-between. <a href="classtvm_1_1runtime_1_1DiscoWorker.html#details">More...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">tvm::runtime::ThreadLocalDiscoWorker</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">A threadlocal wrapper of <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" title="A worker in Disco. It takes a channel to communication with the controler. The worker can be run in a...">DiscoWorker</a>. <a href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
diff --git a/docs/reference/api/doxygen/disco__worker_8h_source.html b/docs/reference/api/doxygen/disco__worker_8h_source.html
index b4b0d17e3d..086f62d429 100644
--- a/docs/reference/api/doxygen/disco__worker_8h_source.html
+++ b/docs/reference/api/doxygen/disco__worker_8h_source.html
@@ -126,10 +126,18 @@ $(function() {
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">struct </span>Impl;</div>
<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1DiscoWorker.html#a379c6dd5a183ed1df75515f7f21a3f49"> 94</a></span>  <span class="keyword">friend</span> <span class="keyword">struct </span><a class="code" href="classtvm_1_1runtime_1_1DiscoWorker.html#a379c6dd5a183ed1df75515f7f21a3f49">DiscoWorker::Impl</a>;</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> };</div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  </div>
-<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> } <span class="comment">// namespace runtime</span></div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_DISCO_DISCO_WORKER_H_</span></div>
+<div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html"> 99</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">ThreadLocalDiscoWorker</a> {</div>
+<div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d"> 101</a></span>  <a class="code" href="classtvm_1_1runtime_1_1DiscoWorker.html">DiscoWorker</a>* <a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">worker</a>;</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  </div>
+<div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1"> 106</a></span>  <span class="keyword">static</span> <a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">ThreadLocalDiscoWorker</a>* <a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">Get</a>() {</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  thread_local <span class="keyword">static</span> <a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">ThreadLocalDiscoWorker</a> <a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">worker</a>;</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">return</span> &<a class="code" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">worker</a>;</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span> };</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  </div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> } <span class="comment">// namespace runtime</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_DISCO_DISCO_WORKER_H_</span></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DiscoChannel_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1DiscoChannel.html">tvm::runtime::DiscoChannel</a></div><div class="ttdoc">A bi-directional channel for controler-worker communication. This channel is primarily used to transf...</div><div class="ttdef"><b>Definition:</b> session.h:291</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DiscoWorker_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1DiscoWorker.html">tvm::runtime::DiscoWorker</a></div><div class="ttdoc">A worker in Disco. It takes a channel to communication with the controler. The worker can be run in a...</div><div class="ttdef"><b>Definition:</b> disco_worker.h:41</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DiscoWorker_html_a082098b2f37942e9285116d4f2e9f733"><div class="ttname"><a href="classtvm_1_1runtime_1_1DiscoWorker.html#a082098b2f37942e9285116d4f2e9f733">tvm::runtime::DiscoWorker::ccl</a></div><div class="ttdeci">String ccl</div><div class="ttdoc">The name of the underlying collective communication library.</div><div class="ttdef"><b>Definition:</b> disco_worker.h:78</div></div>
@@ -152,6 +160,9 @@ $(function() {
<div class="ttc" id="anamespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript.</div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="anamespacetvm_html_a7c2095aed90b2129ba631b90103313a2"><div class="ttname"><a href="namespacetvm.html#a7c2095aed90b2129ba631b90103313a2">tvm::Device</a></div><div class="ttdeci">DLDevice Device</div><div class="ttdef"><b>Definition:</b> ndarray.h:43</div></div>
<div class="ttc" id="apacked__func_8h_html"><div class="ttname"><a href="packed__func_8h.html">packed_func.h</a></div><div class="ttdoc">Type-erased function used across TVM API.</div></div>
+<div class="ttc" id="astructtvm_1_1runtime_1_1ThreadLocalDiscoWorker_html"><div class="ttname"><a href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">tvm::runtime::ThreadLocalDiscoWorker</a></div><div class="ttdoc">A threadlocal wrapper of DiscoWorker.</div><div class="ttdef"><b>Definition:</b> disco_worker.h:99</div></div>
+<div class="ttc" id="astructtvm_1_1runtime_1_1ThreadLocalDiscoWorker_html_a382185eacea869e97da1c49ca46a1f9d"><div class="ttname"><a href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">tvm::runtime::ThreadLocalDiscoWorker::worker</a></div><div class="ttdeci">DiscoWorker * worker</div><div class="ttdoc">The Disco worker.</div><div class="ttdef"><b>Definition:</b> disco_worker.h:101</div></div>
+<div class="ttc" id="astructtvm_1_1runtime_1_1ThreadLocalDiscoWorker_html_a999c9c506f5019a424ed360ea2aa2ec1"><div class="ttname"><a href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">tvm::runtime::ThreadLocalDiscoWorker::Get</a></div><div class="ttdeci">static ThreadLocalDiscoWorker * Get()</div><div class="ttdoc">Get the threadlocal Disco worker.</div><div class="ttdef"><b>Definition:</b> disco_worker.h:106</div></div>
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index 755a61bdda..74e74bb565 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -551,9 +551,9 @@ $(function() {
</li>
<li>at()
: <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a410953f5ac23f6ca5caa257def8b08bd">tvm::runtime::ArrayNode</a>
-, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a6071908cdeb00617d3b28a70d05ac649">tvm::runtime::DenseMapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a773d2e9099e34ab3bcf3b1870d0aee28">tvm::runtime::DenseMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map< K, V, typename, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a29503ec61af7a5bb9b030a00cfdff01a">tvm::runtime::MapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a07d50937020663f46ce9b1f31f066a7a">tvm::runtime::ShapeTuple</a>
, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#a0593c84ceb05afb1a3f87045a3dc3a59">tvm::runtime::SmallMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaeda6a88310d41a22ce884fb1570b0d2">tvm::runtime::String</a>
@@ -599,7 +599,7 @@ $(function() {
: <a class="el" href="classtvm_1_1AttributeAccessPathNode.html#a8f1bf5db4a1495e454b196856fd5cf21">tvm::AttributeAccessPathNode</a>
</li>
<li>AttrInitEntry()
-: <a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html#af07c4a3a8f4663ac03ae238ab7b9d791">tvm::detail::AttrInitEntry< T ></a>
+: <a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html#ad68ac350b0d49e97caab8443cc8fb08b">tvm::detail::AttrInitEntry< T ></a>
</li>
<li>AttrInitVisitor()
: <a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html#ac3c800c9249fee195db2a5fa473fe960">tvm::detail::AttrInitVisitor< FFind ></a>
diff --git a/docs/reference/api/doxygen/functions_func_g.html b/docs/reference/api/doxygen/functions_func_g.html
index 40ea1879e9..312fdf7121 100644
--- a/docs/reference/api/doxygen/functions_func_g.html
+++ b/docs/reference/api/doxygen/functions_func_g.html
@@ -115,6 +115,7 @@ $(function() {
</li>
<li>Get()
: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#ac0d725d400aaae58f525eedfb92c135b">tvm::runtime::Registry</a>
+, <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">tvm::runtime::ThreadLocalDiscoWorker</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html#a84baa43243371ac9aa0c67aa1b5dd570">tvm::script::ir_builder::IRBuilderNode</a>
, <a class="el" href="classtvm_1_1SourceName.html#a3a5d8c66e575b4c5a0a8d6ca6fab7e12">tvm::SourceName</a>
, <a class="el" href="classtvm_1_1TargetKind.html#a11c894b5e17dd940287648261d5efc0d">tvm::TargetKind</a>
@@ -174,7 +175,7 @@ $(function() {
, <a class="el" href="classtvm_1_1DictAttrs.html#a4bff303c0d4e87ed01ffe4cdb36b05d0">tvm::DictAttrs</a>
, <a class="el" href="classtvm_1_1IRModuleNode.html#a3fc3b5d49de0e20a5e7a28d5b462232a">tvm::IRModuleNode</a>
, <a class="el" href="classtvm_1_1ReflectionVTable.html#a800ed4d832ef6cb9f1cbf8d5ef01fc78">tvm::ReflectionVTable</a>
-, <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html#af376babb39ea146d407de2a009b5995b">tvm::relay::ExecutorNode</a>
+, <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html#a7f123ca7dfec3ad6bb0dfd2aecaf468f">tvm::relay::ExecutorNode</a>
, <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html#a272b4a534f93f1aa7304bc20b8f124a1">tvm::relay::RuntimeNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html#abc5161577ee6c41a6dfe269a9ab1db61">tvm::runtime::DeviceAPI</a>
, <a class="el" href="classtvm_1_1TargetNode.html#a008fae4839d63a3a7a9bc7e0f0e40480">tvm::TargetNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_s.html b/docs/reference/api/doxygen/functions_func_s.html
index 46093f5628..a39313bb87 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
@@ -798,7 +798,7 @@ $(function() {
</li>
<li>Span()
: <a class="el" href="classtvm_1_1Span.html#a5216631b639e8c802263d87d3fe9e5f6">tvm::Span</a>
-, <a class="el" href="classtvm_1_1support_1_1Span.html#a3c22dd06856e7029e7107adf38eb72f5">tvm::support::Span< T, W ></a>
+, <a class="el" href="classtvm_1_1support_1_1Span.html#a77653730a2542edf93b7c4413a72f3ec">tvm::support::Span< T, W ></a>
</li>
<li>SpecializedCondition()
: <a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html#a48d119ee1c6033929a5592cfc2592e60">tvm::te::SpecializedCondition</a>
@@ -820,7 +820,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#aadcac01989e684f676cfdc74b6e6f415">tvm::te::Stage</a>
</li>
<li>SplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a184575a8029d77f7a3bee23d81141df5">tvm::auto_scheduler::SplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a64ed86582a56a2645b3e4eb44ecb31af">tvm::auto_scheduler::SplitStep</a>
</li>
<li>StableHashBytes()
: <a class="el" href="classtvm_1_1runtime_1_1String.html#a88aa19338db83ae838183e7478f09e60">tvm::runtime::String</a>
@@ -891,7 +891,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a93d1d23f24d903db844f75f51fe09a36">tvm::tir::ScheduleNode</a>
</li>
<li>StorageAlignStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#af50b7c2f020f8e0a80f5bcc8e559b394">tvm::auto_scheduler::StorageAlignStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#a99dbb8c55d9e7d78268b6d43fd348bc7">tvm::auto_scheduler::StorageAlignStep</a>
</li>
<li>Str()
: <a class="el" href="classtvm_1_1script_1_1printer_1_1LiteralDoc.html#a8423a0ede5a29b24f6db5d9f6574a200">tvm::script::printer::LiteralDoc</a>
diff --git a/docs/reference/api/doxygen/functions_func_t.html b/docs/reference/api/doxygen/functions_func_t.html
index e6743d2e9b..63ead4ca5b 100644
--- a/docs/reference/api/doxygen/functions_func_t.html
+++ b/docs/reference/api/doxygen/functions_func_t.html
@@ -122,7 +122,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#ab5fe485e1d730c36b096c060b8d2ef9d">tvm::te::Stage</a>
</li>
<li>Tensorize()
-: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#afa67abdb51145a49d42cd1464429d928">tvm::tir::ScheduleNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a66983e2dde6aeb18b443616398fff8bf">tvm::tir::ScheduleNode</a>
</li>
<li>TensorNode()
: <a class="el" href="classtvm_1_1te_1_1TensorNode.html#a153569448cb1bf9d2924d35639c3b8b8">tvm::te::TensorNode</a>
diff --git a/docs/reference/api/doxygen/functions_g.html b/docs/reference/api/doxygen/functions_g.html
index 970e145d2b..0e3f7f8f9c 100644
--- a/docs/reference/api/doxygen/functions_g.html
+++ b/docs/reference/api/doxygen/functions_g.html
@@ -118,6 +118,7 @@ $(function() {
</li>
<li>Get()
: <a class="el" href="classtvm_1_1runtime_1_1Registry.html#ac0d725d400aaae58f525eedfb92c135b">tvm::runtime::Registry</a>
+, <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">tvm::runtime::ThreadLocalDiscoWorker</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html#a84baa43243371ac9aa0c67aa1b5dd570">tvm::script::ir_builder::IRBuilderNode</a>
, <a class="el" href="classtvm_1_1SourceName.html#a3a5d8c66e575b4c5a0a8d6ca6fab7e12">tvm::SourceName</a>
, <a class="el" href="classtvm_1_1TargetKind.html#a11c894b5e17dd940287648261d5efc0d">tvm::TargetKind</a>
@@ -176,8 +177,8 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#a6fd69a9b1f984b3a0fa50c11d28c9655">tvm::te::Stage</a>
</li>
<li>GetAttr()
-: <a class="el" href="classtvm_1_1BaseFuncNode.html#a4acde3748b71b19ee409b3cc5564dd51">tvm::BaseFuncNode</a>
-, <a class="el" href="classtvm_1_1DictAttrs.html#a4bff303c0d4e87ed01ffe4cdb36b05d0">tvm::DictAttrs</a>
+: <a class="el" href="classtvm_1_1BaseFuncNode.html#a289921be4c78cc70bd34615edce7a820">tvm::BaseFuncNode</a>
+, <a class="el" href="classtvm_1_1DictAttrs.html#af021c0a1d934693d21cb93d4bfd60aca">tvm::DictAttrs</a>
, <a class="el" href="classtvm_1_1IRModuleNode.html#a3fc3b5d49de0e20a5e7a28d5b462232a">tvm::IRModuleNode</a>
, <a class="el" href="classtvm_1_1ReflectionVTable.html#a800ed4d832ef6cb9f1cbf8d5ef01fc78">tvm::ReflectionVTable</a>
, <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html#a7f123ca7dfec3ad6bb0dfd2aecaf468f">tvm::relay::ExecutorNode</a>
@@ -218,7 +219,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1relax__vm_1_1VirtualMachine.html#af7956e4a6ffd61d0e92643bd34b5acf5">tvm::runtime::relax_vm::VirtualMachine</a>
</li>
<li>GetConfig()
-: <a class="el" href="classtvm_1_1transform_1_1PassContextNode.html#a34609e3ea0d3eb42c6a571536ed667a7">tvm::transform::PassContextNode</a>
+: <a class="el" href="classtvm_1_1transform_1_1PassContextNode.html#abc4e20b2148514b0e205095883c6268c">tvm::transform::PassContextNode</a>
</li>
<li>GetConstants()
: <a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html#af89ade803e9ef425bad26ff5888d033d">tvm::runtime::vm::Executable</a>
diff --git a/docs/reference/api/doxygen/functions_i.html b/docs/reference/api/doxygen/functions_i.html
index 64e2e3f899..8d25c15d10 100644
--- a/docs/reference/api/doxygen/functions_i.html
+++ b/docs/reference/api/doxygen/functions_i.html
@@ -207,7 +207,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1Layout.html#a772258f23824fe6488bc3a8ce8ff1b80">tvm::tir::Layout</a>
</li>
<li>IndexWithNegativeIndices()
-: <a class="el" href="classtvm_1_1te_1_1Tensor.html#a592a866646d16ea7d8909e6750835455">tvm::te::Tensor</a>
+: <a class="el" href="classtvm_1_1te_1_1Tensor.html#a473c010f36297e4091bc476a1a88cd51">tvm::te::Tensor</a>
</li>
<li>indices
: <a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html#a39c0f8724ab7b6692e006b72ce1f258e">tvm::arith::IterMapResultNode</a>
@@ -389,7 +389,7 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html#a1c5ea6cf05289065ee8967cbfb897181">tvm::arith::IntConstraintsTransform</a>
</li>
<li>Integer()
-: <a class="el" href="classtvm_1_1Integer.html#a2d3969d98441b5b2ee5d8a986a56c410">tvm::Integer</a>
+: <a class="el" href="classtvm_1_1Integer.html#afc4e496d3fdfd643d04e9d75c0977021">tvm::Integer</a>
</li>
<li>Internal
: <a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html#a8bde6eb35df6b3a9f53810e0bc79fdfd">tvm::te::SpecializedCondition</a>
diff --git a/docs/reference/api/doxygen/functions_l.html b/docs/reference/api/doxygen/functions_l.html
index ff64eb448b..28e078e45f 100644
--- a/docs/reference/api/doxygen/functions_l.html
+++ b/docs/reference/api/doxygen/functions_l.html
@@ -136,7 +136,7 @@ $(function() {
, <a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html#aa7186c5dcdc4dc32d6ba5a4086ddde4a">tvm::relay::UpSamplingAttrs</a>
</li>
<li>Layout()
-: <a class="el" href="classtvm_1_1tir_1_1Layout.html#acd20f31024c5a15e8109c64109582028">tvm::tir::Layout</a>
+: <a class="el" href="classtvm_1_1tir_1_1Layout.html#ace4d6c608f7148bb2c6aefe33b4e4ab5">tvm::tir::Layout</a>
</li>
<li>layout_free_placeholders_key
: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html#a173f178286d543c71feb0f8a06d0cb7b">tvm::auto_scheduler::ComputeDAG</a>
diff --git a/docs/reference/api/doxygen/functions_o.html b/docs/reference/api/doxygen/functions_o.html
index 6d5bbb2184..71f8f64003 100644
--- a/docs/reference/api/doxygen/functions_o.html
+++ b/docs/reference/api/doxygen/functions_o.html
@@ -102,7 +102,7 @@ $(function() {
</li>
<li>ObjectPtr
: <a class="el" href="classtvm_1_1runtime_1_1Object.html#a4193bb155125953e77ded93c0fb46965">tvm::runtime::Object</a>
-, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a4e8fc75dab845ba616f4d4dc31c5164f">tvm::runtime::ObjectPtr< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a29eed1f62407d1ea7b28725ea620f0e4">tvm::runtime::ObjectPtr< T ></a>
</li>
<li>ObjectPtrHash
: <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#ab1db3e3b77985946e4379d31829030a5">tvm::runtime::ObjectPtr< T ></a>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index 9628e07b73..8bdedce08a 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -84,7 +84,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a46879dbe84105fb621a6167f8d73b223">tvm::runtime::vm::Instruction</a>
</li>
<li>Target()
-: <a class="el" href="classtvm_1_1Target.html#a1abb29217d8e394f0b88e28178c70bb0">tvm::Target</a>
+: <a class="el" href="classtvm_1_1Target.html#a77f3d7cc97d8cfd7172af58b4e784d89">tvm::Target</a>
</li>
<li>target
: <a class="el" href="classtvm_1_1VDeviceNode.html#a05a30c026e6448638a34092b3b85d11f">tvm::VDeviceNode</a>
@@ -227,7 +227,7 @@ $(function() {
, <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html#a92b543750ea55b9cfd6852139e2ddbd6">tvm::te::TensorIntrinCallNode</a>
</li>
<li>TensorStructInfo()
-: <a class="el" href="classtvm_1_1relax_1_1TensorStructInfo.html#a8c65789299732f6eb67e080615bba7f6">tvm::relax::TensorStructInfo</a>
+: <a class="el" href="classtvm_1_1relax_1_1TensorStructInfo.html#a5e3f3e23649da6d895a35da581d3ad24">tvm::relax::TensorStructInfo</a>
</li>
<li>TensorType()
: <a class="el" href="classtvm_1_1TensorType.html#ade4460e9b02b42757a83808dec478b87">tvm::TensorType</a>
diff --git a/docs/reference/api/doxygen/functions_v.html b/docs/reference/api/doxygen/functions_v.html
index 9326b847df..82efad2acb 100644
--- a/docs/reference/api/doxygen/functions_v.html
+++ b/docs/reference/api/doxygen/functions_v.html
@@ -195,7 +195,7 @@ $(function() {
, <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html#ae701bd89e0cafb77ae5f9eec127d1fe8">tvm::tir::LetStmtNode</a>
</li>
<li>Var()
-: <a class="el" href="classtvm_1_1tir_1_1Var.html#a8d4803f34057c3a0460cf2147239e17a">tvm::tir::Var</a>
+: <a class="el" href="classtvm_1_1tir_1_1Var.html#a21ba7568a83bfc2a5896f9e0ff181129">tvm::tir::Var</a>
</li>
<li>var_name
: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1relax_1_1IfFrameNode.html#acb127acb4c84e6ccb55564812d6d8f01">tvm::script::ir_builder::relax::IfFrameNode</a>
diff --git a/docs/reference/api/doxygen/functions_vars_w.html b/docs/reference/api/doxygen/functions_vars_w.html
index b9f2fe99b6..3e968c4505 100644
--- a/docs/reference/api/doxygen/functions_vars_w.html
+++ b/docs/reference/api/doxygen/functions_vars_w.html
@@ -86,6 +86,9 @@ $(function() {
<li>window_size
: <a class="el" href="structtvm_1_1relax_1_1AttentionAttrs.html#ae1c9674bc11f2bec3e94e634a039bd97">tvm::relax::AttentionAttrs</a>
</li>
+<li>worker
+: <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">tvm::runtime::ThreadLocalDiscoWorker</a>
+</li>
<li>worker_id
: <a class="el" href="classtvm_1_1runtime_1_1cuda__ipc_1_1CUDAIPCMemoryObj.html#aee159316a5612aa8dbe42b9941e788b3">tvm::runtime::cuda_ipc::CUDAIPCMemoryObj</a>
, <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html#a5a6414e30855435fa0e6c0e226867edd">tvm::runtime::DiscoWorker</a>
diff --git a/docs/reference/api/doxygen/functions_w.html b/docs/reference/api/doxygen/functions_w.html
index ff2a15e149..afaae8de00 100644
--- a/docs/reference/api/doxygen/functions_w.html
+++ b/docs/reference/api/doxygen/functions_w.html
@@ -143,6 +143,9 @@ $(function() {
<li>WithStructInfo()
: <a class="el" href="classtvm_1_1relax_1_1ExprMutator.html#a3985949433f04660d1824d026466f37a">tvm::relax::ExprMutator</a>
</li>
+<li>worker
+: <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a382185eacea869e97da1c49ca46a1f9d">tvm::runtime::ThreadLocalDiscoWorker</a>
+</li>
<li>worker_id
: <a class="el" href="classtvm_1_1runtime_1_1cuda__ipc_1_1CUDAIPCMemoryObj.html#aee159316a5612aa8dbe42b9941e788b3">tvm::runtime::cuda_ipc::CUDAIPCMemoryObj</a>
, <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html#a5a6414e30855435fa0e6c0e226867edd">tvm::runtime::DiscoWorker</a>
diff --git a/docs/reference/api/doxygen/hierarchy.html b/docs/reference/api/doxygen/hierarchy.html
index 041b1a3967..7008dbb7ec 100644
--- a/docs/reference/api/doxygen/hierarchy.html
+++ b/docs/reference/api/doxygen/hierarchy.html
@@ -1776,100 +1776,101 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_184_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagRegEntry.html" target="_self">tvm::TargetTagRegEntry</a></td><td class="desc"></td></tr>
<tr id="row_185_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1te_1_1TensorDom.html" target="_self">tvm::te::TensorDom</a></td><td class="desc">Temporary data structure to store union of bounds of each axis of <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result.">Tensor</a> </td></tr>
<tr id="row_186_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1threading_1_1ThreadGroup.html" target="_self">tvm::runtime::threading::ThreadGroup</a></td><td class="desc">A platform-agnostic abstraction for managing a collection of thread pool threads </td></tr>
-<tr id="row_187_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html" target="_self">tvm::arith::TransitiveComparisonAnalyzer</a></td><td class="desc">Using previously specified knowns, compare the expressions provided </td></tr>
-<tr id="row_188_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_188_" class="arrow" onclick="toggleFolder('188_')">►</span><span class="icona"><span class="icon">C</span></span><b>std::true_type</b></td><td class="desc"></td></tr>
-<tr id="row_188_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html" target="_self">tvm::detail::is_specialized< Container< Args... >, Container ></a></td><td class="desc"></td></tr>
-<tr id="row_189_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
-<tr id="row_190_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMAotExecutor.html" target="_self">TVMAotExecutor</a></td><td class="desc"></td></tr>
-<tr id="row_191_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
-<tr id="row_192_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
-<tr id="row_193_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">tvm::runtime::TVMArgsSetter</a></td><td class="desc"></td></tr>
-<tr id="row_194_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TVMArgsSetterApply.html" target="_self">tvm::runtime::TVMArgsSetterApply< i, T ></a></td><td class="desc"></td></tr>
-<tr id="row_195_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
-<tr id="row_196_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMConstantInfo.html" target="_self">TVMConstantInfo</a></td><td class="desc">Describes one constant argument to <code>run_model</code> </td></tr>
-<tr id="row_197_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
-<tr id="row_198_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphExecutorGraphAttr.html" target="_self">TVMGraphExecutorGraphAttr</a></td><td class="desc"></td></tr>
-<tr id="row_199_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMetadata.html" target="_self">TVMMetadata</a></td><td class="desc">Top-level metadata structure. Holds all other metadata types </td></tr>
-<tr id="row_200_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
-<tr id="row_201_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">tvm::runtime::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..)>.">TypedPackedFunc</a> to [...]
-<tr id="row_202_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name.">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
-<tr id="row_203_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
-<tr id="row_204_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
-<tr id="row_205_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
-<tr id="row_206_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_206_" class="arrow" onclick="toggleFolder('206_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">tvm::runtime::TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
-<tr id="row_206_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">tvm::runtime::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_206_1_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">tvm::runtime::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_206_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">tvm::runtime::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_207_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TVMScriptPrinter.html" target="_self">tvm::TVMScriptPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
-<tr id="row_208_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMTensorInfo.html" target="_self">TVMTensorInfo</a></td><td class="desc">Describes one tensor argument to <code>run_model</code>. NOTE: while TIR allows for other types of arguments, such as scalars, the AOT run_model function does not currently accept these. Therefore it's not possible to express tho [...]
-<tr id="row_209_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
-<tr id="row_210_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
-<tr id="row_211_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< bool(const Array< Type > &args, int num_inputs, const Attrs &attrs, const TypeReporter &reporter)></a></td><td class="desc"></td></tr>
-<tr id="row_212_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< FType ></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_213_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(ObjectRef json_attrs)></a></td><td class="desc"></td></tr>
-<tr id="row_214_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(Schedule sch, const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision)></a></td><td class="desc"></td></tr>
-<tr id="row_215_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< RunnerResult >(int)></a></td><td class="desc"></td></tr>
-<tr id="row_216_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< te::Tensor >(const Attrs &attrs, const Array< te::Tensor > &inputs, const Type &out_type)></a></td><td class="desc"></td></tr>
-<tr id="row_217_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >()></a></td><td class="desc"></td></tr>
-<tr id="row_218_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >(const Workload &, int)></a></td><td class="desc"></td></tr>
-<tr id="row_219_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tvm::runtime::NDArray >(const TuneContext &context, const Array< MeasureCandidate > &candidates)></a></td><td class="desc"></td></tr>
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-<tr id="row_221_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool(const IRModule &)></a></td><td class="desc"></td></tr>
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-<tr id="row_223_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< int64_t()></a></td><td class="desc"></td></tr>
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-<tr id="row_225_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< IRModule >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
-<tr id="row_226_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< tir::Schedule >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
-<tr id="row_227_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< TuningRecord >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
-<tr id="row_228_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::runtime::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 packed form [...]
-<tr id="row_229_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< RunnerResult()></a></td><td class="desc"></td></tr>
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-<tr id="row_231_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String(const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision, const Array< String > &outputs)></a></td><td class="desc"></td></tr>
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-<tr id="row_233_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< te::Schedule(const Attrs &attrs, const Array< te::Tensor > &outs, const Target &target)></a></td><td class="desc"></td></tr>
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-<tr id="row_235_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void()></a></td><td class="desc"></td></tr>
-<tr id="row_236_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(Array< TuneContext > tasks, Array< FloatImm > task_weights, int max_trials_global, int max_trials_per_task, int num_trials_per_iter, Builder builder, Runner runner, Array< MeasureCallback > me [...]
-<tr id="row_237_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TaskScheduler &task_scheduler, int task_id, const Array< MeasureCandidate > &measure_candidates, const Array< BuilderResult > &builds, const Array< RunnerResult > &results)></a></t [...]
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-<tr id="row_243_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(tvm::DiagnosticContext ctx)></a></td><td class="desc"></td></tr>
-<tr id="row_244_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Workload(const IRModule &)></a></td><td class="desc"></td></tr>
-<tr id="row_245_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< FType ></a></td><td class="desc"></td></tr>
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-<tr id="row_247_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_247_" class="arrow" onclick="toggleFolder('247_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< Type(const Type &n)></a></td><td class="desc"></td></tr>
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-<tr id="row_248_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">tvm::TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
-<tr id="row_249_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">tvm::runtime::TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
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-<tr id="row_255_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int64__t_01_4.html" target="_self">tvm::detail::TypeName< int64_t ></a></td><td class="desc"></td></tr>
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-<tr id="row_259_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1v__info.html" target="_self">tvm::relay::v_info</a></td><td class="desc">A struct to keep info of traversed expr in ExpandDataflow function </td></tr>
-<tr id="row_260_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">tvm::runtime::Array< T, typename >::ValueConverter</a></td><td class="desc"></td></tr>
-<tr id="row_261_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1ValueTypeInfoMaker.html" target="_self">tvm::detail::ValueTypeInfoMaker< ValueType, IsArray, IsMap ></a></td><td class="desc"></td></tr>
-<tr id="row_262_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1script_1_1printer_1_1IRDocsifierNode_1_1VariableInfo.html" target="_self">tvm::script::printer::IRDocsifierNode::VariableInfo</a></td><td class="desc">Information about a variable, including its optional name and its doc creator </td></tr>
-<tr id="row_263_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relax_1_1VarUsageInfo.html" target="_self">tvm::relax::VarUsageInfo</a></td><td class="desc">A utility struct returned by CollectVarUsage </td></tr>
-<tr id="row_264_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDeviceCache.html" target="_self">tvm::VirtualDeviceCache</a></td><td class="desc">A cache of <code>VirtualDevices</code>. This can be used: </td></tr>
-<tr id="row_265_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html" target="_self">tvm::runtime::vm::VMFrame</a></td><td class="desc">A representation of a stack frame </td></tr>
-<tr id="row_266_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1VMFuncInfo.html" target="_self">tvm::runtime::relax_vm::VMFuncInfo</a></td><td class="desc">Information entry in executable function table </td></tr>
-<tr id="row_267_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">tvm::runtime::vm::VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
-<tr id="row_268_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">tvm::With< ContextType ></a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
-<tr id="row_269_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WorkerZeroData.html" target="_self">tvm::runtime::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_270_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html" target="_self">tvm::meta_schedule::WorkloadEqual</a></td><td class="desc">The equality check for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode.">Workload</a> </td></tr>
-<tr id="row_271_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relax_1_1WorkloadEqual.html" target="_self">tvm::relax::WorkloadEqual</a></td><td class="desc">The equality check for Workload </td></tr>
-<tr id="row_272_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadHash.html" target="_self">tvm::meta_schedule::WorkloadHash</a></td><td class="desc">The hash method for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode.">Workload</a> </td></tr>
-<tr id="row_273_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WrappedPythonObject.html" target="_self">tvm::runtime::WrappedPythonObject</a></td><td class="desc">A class that wraps a Python object and preserves its ownership </td></tr>
-<tr id="row_274_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1WriteStream.html" target="_self">tvm::runtime::micro_rpc::WriteStream</a></td><td class="desc"></td></tr>
+<tr id="row_187_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html" target="_self">tvm::runtime::ThreadLocalDiscoWorker</a></td><td class="desc">A threadlocal wrapper of <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" title="A worker in Disco. It takes a channel to communication with the controler. The worker can be run in [...]
+<tr id="row_188_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html" target="_self">tvm::arith::TransitiveComparisonAnalyzer</a></td><td class="desc">Using previously specified knowns, compare the expressions provided </td></tr>
+<tr id="row_189_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_189_" class="arrow" onclick="toggleFolder('189_')">►</span><span class="icona"><span class="icon">C</span></span><b>std::true_type</b></td><td class="desc"></td></tr>
+<tr id="row_189_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html" target="_self">tvm::detail::is_specialized< Container< Args... >, Container ></a></td><td class="desc"></td></tr>
+<tr id="row_190_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
+<tr id="row_191_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMAotExecutor.html" target="_self">TVMAotExecutor</a></td><td class="desc"></td></tr>
+<tr id="row_192_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
+<tr id="row_193_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_194_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">tvm::runtime::TVMArgsSetter</a></td><td class="desc"></td></tr>
+<tr id="row_195_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TVMArgsSetterApply.html" target="_self">tvm::runtime::TVMArgsSetterApply< i, T ></a></td><td class="desc"></td></tr>
+<tr id="row_196_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
+<tr id="row_197_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMConstantInfo.html" target="_self">TVMConstantInfo</a></td><td class="desc">Describes one constant argument to <code>run_model</code> </td></tr>
+<tr id="row_198_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
+<tr id="row_199_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphExecutorGraphAttr.html" target="_self">TVMGraphExecutorGraphAttr</a></td><td class="desc"></td></tr>
+<tr id="row_200_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMetadata.html" target="_self">TVMMetadata</a></td><td class="desc">Top-level metadata structure. Holds all other metadata types </td></tr>
+<tr id="row_201_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
+<tr id="row_202_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">tvm::runtime::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..)>.">TypedPacke [...]
+<tr id="row_203_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name.">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
+<tr id="row_204_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
+<tr id="row_205_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
+<tr id="row_206_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
+<tr id="row_207_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_207_" class="arrow" onclick="toggleFolder('207_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">tvm::runtime::TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
+<tr id="row_207_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">tvm::runtime::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 [...]
+<tr id="row_207_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">tvm::runtime::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..)>.">TypedPac [...]
+<tr id="row_207_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">tvm::runtime::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." [...]
+<tr id="row_208_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TVMScriptPrinter.html" target="_self">tvm::TVMScriptPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
+<tr id="row_209_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMTensorInfo.html" target="_self">TVMTensorInfo</a></td><td class="desc">Describes one tensor argument to <code>run_model</code>. NOTE: while TIR allows for other types of arguments, such as scalars, the AOT run_model function does not currently accept these. Therefore it's not possible to express those in this me [...]
+<tr id="row_210_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
+<tr id="row_211_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
+<tr id="row_212_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< bool(const Array< Type > &args, int num_inputs, const Attrs &attrs, const TypeReporter &reporter)></a></td><td class="desc"></td></tr>
+<tr id="row_213_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< FType ></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_214_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(ObjectRef json_attrs)></a></td><td class="desc"></td></tr>
+<tr id="row_215_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(Schedule sch, const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision)></a></td><td class="desc"></td></tr>
+<tr id="row_216_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< RunnerResult >(int)></a></td><td class="desc"></td></tr>
+<tr id="row_217_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< te::Tensor >(const Attrs &attrs, const Array< te::Tensor > &inputs, const Type &out_type)></a></td><td class="desc"></td></tr>
+<tr id="row_218_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >()></a></td><td class="desc"></td></tr>
+<tr id="row_219_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >(const Workload &, int)></a></td><td class="desc"></td></tr>
+<tr id="row_220_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tvm::runtime::NDArray >(const TuneContext &context, const Array< MeasureCandidate > &candidates)></a></td><td class="desc"></td></tr>
+<tr id="row_221_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool()></a></td><td class="desc"></td></tr>
+<tr id="row_222_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool(const IRModule &)></a></td><td class="desc"></td></tr>
+<tr id="row_223_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< int()></a></td><td class="desc"></td></tr>
+<tr id="row_224_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< int64_t()></a></td><td class="desc"></td></tr>
+<tr id="row_225_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< ObjectRef(Array< ObjectRef > attrs)></a></td><td class="desc"></td></tr>
+<tr id="row_226_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< IRModule >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
+<tr id="row_227_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< tir::Schedule >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
+<tr id="row_228_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< TuningRecord >(const IRModule &, const Target &, const String &)></a></td><td class="desc"></td></tr>
+<tr id="row_229_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::runtime::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 packed format.">PackedFu [...]
+<tr id="row_230_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< RunnerResult()></a></td><td class="desc"></td></tr>
+<tr id="row_231_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String()></a></td><td class="desc"></td></tr>
+<tr id="row_232_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String(const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision, const Array< String > &outputs)></a></td><td class="desc">< [...]
+<tr id="row_233_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< TargetJSON(TargetJSON)></a></td><td class="desc"></td></tr>
+<tr id="row_234_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< te::Schedule(const Attrs &attrs, const Array< te::Tensor > &outs, const Target &target)></a></td><td class="desc"></td></tr>
+<tr id="row_235_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< tvm::tir::Stmt(Array< tvm::tir::Var > loop_vars, Array< Range > loop_extents, tvm::tir::Stmt loop_body)></a></td><td class="desc"></td></tr>
+<tr id="row_236_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void()></a></td><td class="desc"></td></tr>
+<tr id="row_237_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(Array< TuneContext > tasks, Array< FloatImm > task_weights, int max_trials_global, int max_trials_per_task, int num_trials_per_iter, Builder builder, Runner runner, Array< MeasureCallback > measure_callbac [...]
+<tr id="row_238_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TaskScheduler &task_scheduler, int task_id, const Array< MeasureCandidate > &measure_candidates, const Array< BuilderResult > &builds, const Array< RunnerResult > &result [...]
+<tr id="row_239_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &, const Array< MeasureCandidate > &, const Array< RunnerResult > &)></a></td><td class="desc"></td></tr>
+<tr id="row_240_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &, const Array< MeasureCandidate > &, void *p_addr)></a></td><td class="desc"></td></tr>
+<tr id="row_241_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuningRecord &)></a></td><td class="desc"></td></tr>
+<tr id="row_242_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(size_t, void *)></a></td><td class="desc"></td></tr>
+<tr id="row_243_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(String)></a></td><td class="desc"></td></tr>
+<tr id="row_244_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(tvm::DiagnosticContext ctx)></a></td><td class="desc"></td></tr>
+<tr id="row_245_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Workload(const IRModule &)></a></td><td class="desc"></td></tr>
+<tr id="row_246_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< FType ></a></td><td class="desc"></td></tr>
+<tr id="row_247_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">tvm::TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_248_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_248_" class="arrow" onclick="toggleFolder('248_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< Type(const Type &n)></a></td><td class="desc"></td></tr>
+<tr id="row_248_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">tvm::TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions.">TypeMutator</a> that mutates expressions </td></tr>
+<tr id="row_249_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_249_" class="arrow" onclick="toggleFolder('249_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< void(const Type &n)></a></td><td class="desc"></td></tr>
+<tr id="row_249_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">tvm::TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
+<tr id="row_250_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">tvm::runtime::TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
+<tr id="row_251_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName.html" target="_self">tvm::detail::TypeName< T ></a></td><td class="desc">Helper struct to get the type name known to tvm </td></tr>
+<tr id="row_252_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01bool_01_4.html" target="_self">tvm::detail::TypeName< bool ></a></td><td class="desc"></td></tr>
+<tr id="row_253_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01DataType_01_4.html" target="_self">tvm::detail::TypeName< DataType ></a></td><td class="desc"></td></tr>
+<tr id="row_254_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01double_01_4.html" target="_self">tvm::detail::TypeName< double ></a></td><td class="desc"></td></tr>
+<tr id="row_255_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int_01_4.html" target="_self">tvm::detail::TypeName< int ></a></td><td class="desc"></td></tr>
+<tr id="row_256_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int64__t_01_4.html" target="_self">tvm::detail::TypeName< int64_t ></a></td><td class="desc"></td></tr>
+<tr id="row_257_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01uint64__t_01_4.html" target="_self">tvm::detail::TypeName< uint64_t ></a></td><td class="desc"></td></tr>
+<tr id="row_258_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01void_01_5_01_4.html" target="_self">tvm::detail::TypeName< void * ></a></td><td class="desc"></td></tr>
+<tr id="row_259_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html" target="_self">tvm::runtime::micro_rpc::Unframer</a></td><td class="desc"></td></tr>
+<tr id="row_260_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1v__info.html" target="_self">tvm::relay::v_info</a></td><td class="desc">A struct to keep info of traversed expr in ExpandDataflow function </td></tr>
+<tr id="row_261_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">tvm::runtime::Array< T, typename >::ValueConverter</a></td><td class="desc"></td></tr>
+<tr id="row_262_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1ValueTypeInfoMaker.html" target="_self">tvm::detail::ValueTypeInfoMaker< ValueType, IsArray, IsMap ></a></td><td class="desc"></td></tr>
+<tr id="row_263_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1script_1_1printer_1_1IRDocsifierNode_1_1VariableInfo.html" target="_self">tvm::script::printer::IRDocsifierNode::VariableInfo</a></td><td class="desc">Information about a variable, including its optional name and its doc creator </td></tr>
+<tr id="row_264_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relax_1_1VarUsageInfo.html" target="_self">tvm::relax::VarUsageInfo</a></td><td class="desc">A utility struct returned by CollectVarUsage </td></tr>
+<tr id="row_265_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDeviceCache.html" target="_self">tvm::VirtualDeviceCache</a></td><td class="desc">A cache of <code>VirtualDevices</code>. This can be used: </td></tr>
+<tr id="row_266_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html" target="_self">tvm::runtime::vm::VMFrame</a></td><td class="desc">A representation of a stack frame </td></tr>
+<tr id="row_267_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1relax__vm_1_1VMFuncInfo.html" target="_self">tvm::runtime::relax_vm::VMFuncInfo</a></td><td class="desc">Information entry in executable function table </td></tr>
+<tr id="row_268_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">tvm::runtime::vm::VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
+<tr id="row_269_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">tvm::With< ContextType ></a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
+<tr id="row_270_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WorkerZeroData.html" target="_self">tvm::runtime::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_271_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html" target="_self">tvm::meta_schedule::WorkloadEqual</a></td><td class="desc">The equality check for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode.">Workload</a> </td></tr>
+<tr id="row_272_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relax_1_1WorkloadEqual.html" target="_self">tvm::relax::WorkloadEqual</a></td><td class="desc">The equality check for Workload </td></tr>
+<tr id="row_273_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadHash.html" target="_self">tvm::meta_schedule::WorkloadHash</a></td><td class="desc">The hash method for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode.">Workload</a> </td></tr>
+<tr id="row_274_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1WrappedPythonObject.html" target="_self">tvm::runtime::WrappedPythonObject</a></td><td class="desc">A class that wraps a Python object and preserves its ownership </td></tr>
+<tr id="row_275_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1WriteStream.html" target="_self">tvm::runtime::micro_rpc::WriteStream</a></td><td class="desc"></td></tr>
</table>
</div><!-- directory -->
</div><!-- contents -->
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<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_183.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_184.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_185.svg" width="210" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_185.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_186.svg" width="210" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_186.svg" width="210" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_187.svg" width="230" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_187.svg" width="210" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_188.svg" width="235" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_188.svg" width="230" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_189.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_189.svg" width="235" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_190.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_191.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_192.svg" width="236" height="126"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_192.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_193.svg" width="243" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_193.svg" width="236" height="126"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_194.svg" width="211" height="96"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_194.svg" width="243" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_195.svg" width="235" height="96"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_195.svg" width="211" height="96"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_196.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_196.svg" width="235" height="96"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_197.svg" width="354" height="155"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_197.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_198.svg" width="286" height="126"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_198.svg" width="354" height="155"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_199.svg" width="291" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_199.svg" width="286" height="126"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_200.svg" width="266" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_200.svg" width="291" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_201.svg" width="267" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_201.svg" width="266" height="82"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_202.svg" width="212" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_202.svg" width="267" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_203.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_203.svg" width="212" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_204.svg" width="306" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_204.svg" width="210" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_205.svg" width="274" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_205.svg" width="306" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_206.svg" width="192" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_206.svg" width="274" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_207.svg" width="224" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_207.svg" width="192" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_208.svg" width="218" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_208.svg" width="224" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_209.svg" width="231" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_209.svg" width="218" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_210.svg" width="232" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_210.svg" width="231" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_211.svg" width="230" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_211.svg" width="232" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_212.svg" width="172" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_212.svg" width="230" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_213.svg" width="159" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_213.svg" width="172" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_214.svg" width="231" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_214.svg" width="159" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_215.svg" width="164" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_215.svg" width="231" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_216.svg" width="411" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_216.svg" width="164" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_217.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_217.svg" width="411" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_218.svg" width="414" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_218.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_219.svg" width="126" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_219.svg" width="414" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_220.svg" width="247" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_220.svg" width="126" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_221.svg" width="156" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_221.svg" width="247" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_222.svg" width="238" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_222.svg" width="156" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_223.svg" width="195" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_223.svg" width="238" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_224.svg" width="188" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_224.svg" width="195" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_225.svg" width="198" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_225.svg" width="188" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_226.svg" width="170" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_226.svg" width="198" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_227.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_227.svg" width="170" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_228.svg" width="150" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_228.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_229.svg" width="190" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_229.svg" width="150" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_230.svg" width="188" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_230.svg" width="190" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_231.svg" width="166" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_231.svg" width="188" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_232.svg" width="1506" height="118"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_232.svg" width="166" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_233.svg" width="186" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_233.svg" width="1506" height="118"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_234.svg" width="871" height="118"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_234.svg" width="186" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_235.svg" width="187" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_235.svg" width="871" height="118"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_236.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_236.svg" width="187" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_237.svg" width="194" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_237.svg" width="162" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_238.svg" width="152" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_238.svg" width="194" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_239.svg" width="168" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_239.svg" width="152" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_240.svg" width="203" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_240.svg" width="168" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_241.svg" width="162" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_241.svg" width="203" height="67"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_242.svg" width="206" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_242.svg" width="162" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</td></tr>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_244.svg" width="176" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_245.svg" width="206" height="96"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_246.svg" width="170" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_247.svg" width="212" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_248.svg" width="194" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_249.svg" width="375" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_250.svg" width="195" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_250.svg" width="364" height="52"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_251.svg" width="208" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_251.svg" width="195" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_252.svg" width="148" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_252.svg" width="208" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_253.svg" width="140" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_253.svg" width="148" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_254.svg" width="140" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_255.svg" width="127" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_255.svg" width="91" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_256.svg" width="127" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_258.svg" width="220" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_258.svg" width="146" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_259.svg" width="220" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_260.svg" width="124" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_261.svg" width="199" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_261.svg" width="110" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_262.svg" width="199" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_263.svg" width="138" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_263.svg" width="122" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_264.svg" width="174" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_264.svg" width="138" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_265.svg" width="130" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_265.svg" width="174" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_266.svg" width="98" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_266.svg" width="130" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</td></tr>
+<tr><td><iframe scrolling="no" frameborder="0" src="inherit_graph_267.svg" width="98" height="38"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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</table>
</div><!-- contents -->
diff --git a/docs/reference/api/doxygen/namespaces.html b/docs/reference/api/doxygen/namespaces.html
index 287c10a8c5..97e422a44d 100644
--- a/docs/reference/api/doxygen/namespaces.html
+++ b/docs/reference/api/doxygen/namespaces.html
@@ -901,54 +901,55 @@ $(function() {
<tr id="row_1_9_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_1DataType.html" target="_self">DataType</a></td><td class="desc">Runtime primitive data type </td></tr>
<tr id="row_1_9_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_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_9_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_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_9_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_1DRefObj.html" target="_self">DRefObj</a></td><td class="desc">An object that exists on all workers </td></tr>
-<tr id="row_1_9_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_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_9_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_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_9_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_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_9_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_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_9_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_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_9_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_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_9_47_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_47_" class="arrow" onclick="toggleFolder('1_9_47_')">►</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_9_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_1SimpleObjAllocator_1_1ArrayHandler.html" target="_self">ArrayHandler</a></td><td class="desc"></td></tr>
-<tr id="row_1_9_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_1SimpleObjAllocator_1_1Handler.html" target="_self">Handler</a></td><td class="desc"></td></tr>
-<tr id="row_1_9_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_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_9_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_1ModuleNode.html" target="_self">ModuleNode</a></td><td class="desc">Base container of module </td></tr>
-<tr id="row_1_9_50_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_50_" class="arrow" onclick="toggleFolder('1_9_50_')">►</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_9_50_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_9_50_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_9_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_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_9_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="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_9_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_1Object.html" target="_self">Object</a></td><td class="desc">Base class of all object containers </td></tr>
-<tr id="row_1_9_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="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_9_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_1ObjectRef.html" target="_self">ObjectRef</a></td><td class="desc">Base class of all object reference </td></tr>
-<tr id="row_1_9_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_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_9_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_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_9_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_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_9_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="structtvm_1_1runtime_1_1SignaturePrinter.html" target="_self">SignaturePrinter</a></td><td class="desc"></td></tr>
-<tr id="row_1_9_60_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_60_" class="arrow" onclick="toggleFolder('1_9_60_')">►</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_9_60_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_9_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_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_9_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_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_9_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="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_9_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="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
-<tr id="row_1_9_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.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_9_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="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_9_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="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_9_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_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_9_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_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_9_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_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_9_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_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_9_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_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_9_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.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_9_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="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">TVMArgsSetter</a></td><td class="desc"></td></tr>
-<tr id="row_1_9_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_1TVMArgsSetterApply.html" target="_self">TVMArgsSetterApply</a></td><td class="desc"></td></tr>
-<tr id="row_1_9_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_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_9_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="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_9_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="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_9_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_1TimerNode.html" target="_self">TimerNode</a></td><td class="desc">Base class for all implementations </td></tr>
-<tr id="row_1_9_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_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_9_81_" class="even" style="display:none;"><td class="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_9_82_" class="even" style="display:none;"><td class="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_9_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="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html" target="_self">ThreadLocalDiscoWorker</a></td><td class="desc">A threadlocal wrapper of <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" title="A worker in Disco. It takes a channel to communication with the controler. [...]
+<tr id="row_1_9_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_1DRefObj.html" target="_self">DRefObj</a></td><td class="desc">An object that exists on all workers </td></tr>
+<tr id="row_1_9_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_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_9_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_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_9_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_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_9_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_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_9_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_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_9_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_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_9_48_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_48_" class="arrow" onclick="toggleFolder('1_9_48_')">►</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_9_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_1SimpleObjAllocator_1_1ArrayHandler.html" target="_self">ArrayHandler</a></td><td class="desc"></td></tr>
+<tr id="row_1_9_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_1SimpleObjAllocator_1_1Handler.html" target="_self">Handler</a></td><td class="desc"></td></tr>
+<tr id="row_1_9_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_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_9_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_1ModuleNode.html" target="_self">ModuleNode</a></td><td class="desc">Base container of module </td></tr>
+<tr id="row_1_9_51_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_51_" class="arrow" onclick="toggleFolder('1_9_51_')">►</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_9_51_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_9_51_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_9_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_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_9_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_1TypeIndex.html" target="_self">TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
+<tr id="row_1_9_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="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_9_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_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_9_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_1ObjectRef.html" target="_self">ObjectRef</a></td><td class="desc">Base class of all object reference </td></tr>
+<tr id="row_1_9_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_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_9_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="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_9_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_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_9_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="structtvm_1_1runtime_1_1SignaturePrinter.html" target="_self">SignaturePrinter</a></td><td class="desc"></td></tr>
+<tr id="row_1_9_61_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_9_61_" class="arrow" onclick="toggleFolder('1_9_61_')">►</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_9_61_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_9_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_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_9_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="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_9_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="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_9_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_1TVMArgs.html" target="_self">TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
+<tr id="row_1_9_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="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_9_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="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_9_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="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_9_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_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_9_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_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_9_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_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_9_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_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_9_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="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_9_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.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_9_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="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">TVMArgsSetter</a></td><td class="desc"></td></tr>
+<tr id="row_1_9_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_1TVMArgsSetterApply.html" target="_self">TVMArgsSetterApply</a></td><td class="desc"></td></tr>
+<tr id="row_1_9_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="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_9_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="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_9_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="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_9_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_1TimerNode.html" target="_self">TimerNode</a></td><td class="desc">Base class for all implementations </td></tr>
+<tr id="row_1_9_81_" class="even" style="display:none;"><td class="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_9_82_" class="even" style="display:none;"><td class="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_9_83_" class="even" style="display:none;"><td class="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_10_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_10_" class="arrow" onclick="toggleFolder('1_10_')">►</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_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_10_0_" class="arrow" onclick="toggleFolder('1_10_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_10_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_10_0_0_" class="arrow" onclick="toggleFolder('1_10_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>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1runtime.html b/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
index 7c18be4b17..6ce7393cf1 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
@@ -188,6 +188,9 @@ Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html">DiscoWorker</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">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 in-between. <a href="classtvm_1_1runtime_1_1DiscoWorker.html#details">More...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html">ThreadLocalDiscoWorker</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">A threadlocal wrapper of <a class="el" href="classtvm_1_1runtime_1_1DiscoWorker.html" title="A worker in Disco. It takes a channel to communication with the controler. The worker can be run in a...">DiscoWorker</a>. <a href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DRefObj.html">DRefObj</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">An object that exists on all workers. <a href="classtvm_1_1runtime_1_1DRefObj.html#details">More...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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