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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/08/22 23:38:57 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@0f037a6d9957108decceaf0c91bd84667a077aad)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new b48fe79c97 deploying docs (apache/tvm@0f037a6d9957108decceaf0c91bd84667a077aad)
b48fe79c97 is described below
commit b48fe79c97cf9b4826f3431aefb7790b275aa160
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Thu Aug 22 23:38:48 2024 +0000
deploying docs (apache/tvm@0f037a6d9957108decceaf0c91bd84667a077aad)
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.../api/typedoc/functions/createPolyfillWASI.html | 2 +-
.../api/typedoc/functions/deleteNDArrayCache.html | 2 +-
.../api/typedoc/functions/detectGPUDevice.html | 2 +-
.../api/typedoc/functions/hasNDArrayInCache.html | 2 +-
.../api/typedoc/functions/instantiate.html | 2 +-
docs/reference/api/typedoc/functions/wasmPath.html | 2 +-
.../typedoc/interfaces/ArtifactCacheTemplate.html | 10 +-
.../api/typedoc/interfaces/Disposable.html | 4 +-
.../typedoc/interfaces/GPUDeviceDetectOutput.html | 8 +-
.../api/typedoc/interfaces/InitProgressReport.html | 8 +-
.../api/typedoc/interfaces/LibraryProvider.html | 6 +-
.../interfaces/_internal_.FunctionInfo.html | 8 +-
.../interfaces/_internal_.NDArrayCacheEntry.html | 14 +-
.../interfaces/_internal_.NDArrayShardEntry.html | 10 +-
.../api/typedoc/types/InitProgressCallback.html | 2 +-
docs/reference/api/typedoc/types/PackedFunc.html | 2 +-
.../typedoc/types/_internal_.AsyncPackedFunc.html | 2 +-
.../types/_internal_.FObjectConstructor.html | 2 +-
.../types/_internal_.FTVMWasmAllocSpace.html | 2 +-
.../types/_internal_.FTVMWasmFreeSpace.html | 2 +-
.../types/_internal_.FTVMWasmPackedCFunc.html | 2 +-
.../api/typedoc/types/_internal_.Pointer.html | 2 +-
.../typedoc/types/_internal_.TVMObjectBase.html | 2 +-
docs/searchindex.js | 2 +-
.../vta/tutorials/frontend/deploy_detection.html | 4 +-
.../vta/tutorials/frontend/sg_execution_times.html | 4 +-
.../vta/tutorials/optimize/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/sg_execution_times.html | 6 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 7 +-
docs/tutorial/autotvm_matmul_x86.html | 20 +-
docs/tutorial/autotvm_relay_x86.html | 273 ++--
docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 22 +-
docs/tutorial/tensor_expr_get_started.html | 43 +-
380 files changed, 13930 insertions(+), 13983 deletions(-)
diff --git a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
index d1533ccfd4..f012624559 100644
--- a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:00.259** total execution time for **get_started_tutorials** files:
+**00:00.258** total execution time for **get_started_tutorials** files:
+---------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_get_started_tutorials_quick_start.py` (``quick_start.py``) | 00:00.259 | 0.0 MB |
+| :ref:`sphx_glr_get_started_tutorials_quick_start.py` (``quick_start.py``) | 00:00.258 | 0.0 MB |
+---------------------------------------------------------------------------+-----------+--------+
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 8696273340..4ee8ffe3db 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 42.879 seconds)
+ **Total running time of the script:** ( 1 minutes 41.168 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 952b828b7b..f4ad2c1a14 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 889ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 942ms/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 b56382a874..9510bcb38c 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, 26.0MB/s] 21%|##1 | 8.81M/41.5M [00:00<00:01, 21.4MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:01, 24.9MB/s] 40%|#### | 16.6M/41.5M [00:00<00:01, 21.8MB/s] 58%|#####7 | 24.0M/41.5M [00:01<00:00, 25.2MB/s] 77%|#######7 | 32.0M/41.5M [00:01<00:00, 30.4MB/s] 96%|#########6| 40.0M/41.5M [00:01<00:00, 35.9MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 30.8MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:00, 57.9MB/s] 29%|##8 | 11.9M/41.5M [00:00<00:00, 48.9MB/s] 40%|###9 | 16.6M/41.5M [00:00<00:00, 27.5MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 32.3MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 42.1MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 38.9MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 38.0MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 1b4416d4f1..ce9ad916e0 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 5.065 seconds)
+ **Total running time of the script:** ( 1 minutes 7.004 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 5fff23e7c3..5a09512fb9 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 47.7MB/s] 32%|###2 | 14.3M/44.7M [00:00<00:01, 25.7MB/s] 39%|###8 | 17.2M/44.7M [00:00<00:01, 24.0MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 30.7MB/s] 72%|#######1 | 32.0M/44.7M [00:01<00:00, 35.8MB/s] 86%|########5 | 38.3M/44.7M [00:01<00:00, 36.0MB/s] 94%|#########3| 41.9M/44.7M [00:01<00:00, 31.7MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 33.6MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 74.8MB/s] 34%|###3 | 15.1M/44.7M [00:00<00:00, 65.2MB/s] 48%|####7 | 21.4M/44.7M [00:00<00:00, 54.7MB/s] 60%|#####9 | 26.7M/44.7M [00:00<00:00, 55.0MB/s] 72%|#######1 | 32.1M/44.7M [00:00<00:00, 40.6MB/s] 90%|########9 | 40.0M/44.7M [00:00<00:00, 40.4MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 50.4MB/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 45c800e967..8dde703e54 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 31.308 seconds)
+ **Total running time of the script:** ( 1 minutes 33.766 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 14f7d9c769..81a75985eb 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
=================
-**06:58.673** total execution time for **how_to_compile_models** files:
+**07:04.213** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:42.879 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:41.168 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:31.308 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:33.766 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:05.065 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:07.004 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:44.625 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:46.152 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:40.823 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:41.533 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.174 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:31.179 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.781 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.802 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.184 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.660 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.833 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.949 | 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 376a900e94..00930c481f 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)
- 4123.8338 4122.8221 4130.7468 4119.9195 3.1768
+ 4121.2842 4119.4177 4141.6465 4117.1682 6.9175
@@ -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 21.914 seconds)
+ **Total running time of the script:** ( 1 minutes 21.005 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 38e418633c..4fa04607fa 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: 1s 13598720/102967424 [==>...........................] - ETA: 1s 15024128/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 2s 19988480/102967424 [====>.........................] - ETA: 1s 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 54837248/102967424 [==============>...............] - ETA: 1s 56967168/102967424 [===============>..............] - ETA: 1s 58712064/102967424 [================>.............] -
ETA: 1s 60907520/102967424 [================>.............] - ETA: 1s 67100672/102967424 [==================>...........] - ETA: 0s 71786496/102967424 [===================>..........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424
[===========================>..] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 2s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 23412736/102967424 [=====>........................] - ETA: 1s 31260672/102967424 [========>.....................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 0s 48578560/102967424 [=============>................] - ETA: 0s 50462720/102967424 [=============>................] - ETA: 0s
65282048/102967424 [==================>...........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 100794368/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 1s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index bdbcf43513..5a7114cd06 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.2556 15.2157 15.8209 14.6006 0.4852
+ 15.4466 15.4750 15.7735 15.0960 0.1819
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 f6292c2702..4224a083fd 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<00:02, 58.9MB/s] 8%|8 | 14.3M/170M [00:00<00:03, 48.2MB/s] 11%|#1 | 19.0M/170M [00:00<00:03, 42.8MB/s] 14%|#3 | 23.0M/170M [00:00<00:03, 39.7MB/s] 16%|#5 | 26.8M/170M [00:00<00:04, 31.2MB/s] 18%|#7 | 30.3M/170M [00:00<00:05, 28.0MB/s] 19%|#9 | 33.1M/170M [00:01<00:05, 25.6MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 35.8MB/s] 27%|##7 | 46.3M/170M [00:01<00:03, 41.8MB/s] 30%|##9 | 50.6M/170M [00:01<00:03, 40.9MB/s] 34%|###3 | 57.0M/170M [00:01<00:02, 47.6MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 48.8MB/s] 42%|####2 | 72.0M/170M [00:01<00:01, 51.6MB/s] 47%|####7 | 80.0M/170M [00:01<00:01, 48.3MB/s] 51%|##### | 86.3M/170M [00:02<00:02, 41.2MB/s] 53%|#####3 | 90.5M/170M [00:02<00:02, 41.4MB/s] 56%|#####5 | 94.6M/170M [00:02<00:02, 31.0MB/
s] 58%|#####7 | 98.0M/170M [00:02<00:02, 30.7MB/s] 61%|######1 | 104M/170M [00:02<00:02, 32.3MB/s] 66%|######5 | 112M/170M [00:03<00:01, 39.5MB/s] 71%|####### | 120M/170M [00:03<00:01, 45.7MB/s] 74%|#######4 | 126M/170M [00:03<00:01, 43.8MB/s] 77%|#######6 | 131M/170M [00:03<00:01, 38.7MB/s] 79%|#######9 | 134M/170M [00:03<00:01, 29.7MB/s] 81%|########1 | 138M/170M [00:03<00:01, 27.4MB/s] 85%|########4 | 144M/170M [00:04<00:00, 31.0MB/s] 89%|########9 | 152M/170M [00:04<00:00, 33.9MB/s] 93%|#########3| 158M/170M [00:04<00:00, 28.6MB/s] 95%|#########4| 161M/170M [00:04<00:00, 28.5MB/s] 98%|#########7| 166M/170M [00:04<00:00, 29.1MB/s] 100%|#########9| 169M/170M [00:05<00:00, 22.8MB/s] 100%|##########| 170M/170M [00:05<00:00, 34.8MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 43.1MB/s] 9%|9 | 16.0M/170M [00:00<00:03, 45.2MB/s] 13%|#3 | 22.3M/170M [00:00<00:03, 46.4MB/s] 16%|#5 | 26.8M/170M [00:00<00:03, 44.9MB/s] 18%|#8 | 31.0M/170M [00:00<00:04, 34.4MB/s] 20%|## | 34.5M/170M [00:00<00:04, 34.5MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 39.6MB/s] 27%|##7 | 46.3M/170M [00:01<00:02, 45.1MB/s] 30%|##9 | 50.8M/170M [00:01<00:03, 39.5MB/s] 33%|###3 | 56.3M/170M [00:01<00:02, 43.9MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 43.7MB/s] 42%|####2 | 72.0M/170M [00:01<00:02, 47.5MB/s] 46%|####6 | 78.3M/170M [00:01<00:02, 47.6MB/s] 49%|####8 | 82.9M/170M [00:02<00:02, 44.4MB/s] 52%|#####1 | 88.0M/170M [00:02<00:02, 37.2MB/s] 56%|#####5 | 94.3M/170M [00:02<00:02, 35.2MB/s] 58%|#####7 | 97.8M/170M [00:02<00:02, 29.9MB/
s] 61%|######1 | 104M/170M [00:02<00:02, 30.9MB/s] 66%|######5 | 112M/170M [00:02<00:01, 36.9MB/s] 71%|####### | 120M/170M [00:03<00:01, 43.2MB/s] 75%|#######5 | 128M/170M [00:03<00:00, 45.8MB/s] 80%|######## | 136M/170M [00:03<00:00, 48.9MB/s] 85%|########4 | 144M/170M [00:03<00:00, 53.6MB/s] 89%|########9 | 152M/170M [00:03<00:00, 54.3MB/s] 94%|#########4| 160M/170M [00:03<00:00, 50.3MB/s] 97%|#########7| 165M/170M [00:04<00:00, 48.1MB/s] 100%|#########9| 170M/170M [00:04<00:00, 46.0MB/s] 100%|##########| 170M/170M [00:04<00:00, 43.0MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -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 42.066 seconds)
+ **Total running time of the script:** ( 3 minutes 41.810 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 b0523256cd..2565b414bf 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, 45.6MB/s] 79%|#######8 | 10.7M/13.6M [00:00<00:00, 33.2MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 20.7MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 47%|####6 | 6.30M/13.6M [00:00<00:00, 39.9MB/s] 75%|#######4 | 10.1M/13.6M [00:00<00:00, 29.6MB/s] 96%|#########5| 13.0M/13.6M [00:00<00:00, 28.3MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 31.0MB/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)
- 89.2298 89.1012 94.7746 88.5435 0.6774
+ 88.9398 88.9392 89.5695 88.4689 0.2179
@@ -448,7 +448,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.500 seconds)
+ **Total running time of the script:** ( 1 minutes 34.534 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 79d3145929..3f882000cc 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 111.2619 111.2389 113.8744 109.9355 0.5875
+ 111.3393 111.3515 115.6646 109.5698 0.6993
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 ea93e3f35b..4f165e0e37 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**10:20.347** total execution time for **how_to_deploy_models** files:
+**10:15.454** 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``) | 03:42.066 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:41.810 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:34.500 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:34.534 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.914 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.005 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:54.937 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:54.142 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:53.850 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:53.033 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:50.704 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:48.738 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.213 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.151 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:31.156 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:31.033 | 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 babbfcc9cc..55ecbf9a57 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:04.126** total execution time for **how_to_extend_tvm** files:
+**00:04.160** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.835 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.882 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.283 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.271 | 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 a0eeacc342..5221deec17 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: 25464us [25464us] (48.56%; 48.56%)
- FoldScaleAxis: 26971us [10us] (51.44%; 51.44%)
- FoldConstant: 26961us [1846us] (51.42%; 99.96%)
- InferType: 25115us [25115us] (47.90%; 93.15%)
+ InferType: 24947us [24947us] (48.61%; 48.61%)
+ FoldScaleAxis: 26376us [8us] (51.39%; 51.39%)
+ FoldConstant: 26367us [1773us] (51.38%; 99.97%)
+ InferType: 24595us [24595us] (47.92%; 93.28%)
@@ -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: 25059us [25059us] (47.69%; 47.69%)
- FoldScaleAxis: 27490us [8us] (52.31%; 52.31%)
- FoldConstant: 27481us [1809us] (52.30%; 99.97%)
- InferType: 25673us [25673us] (48.86%; 93.42%)
+ InferType: 24511us [24511us] (48.23%; 48.23%)
+ FoldScaleAxis: 26308us [8us] (51.77%; 51.77%)
+ FoldConstant: 26301us [1782us] (51.75%; 99.97%)
+ InferType: 24518us [24518us] (48.25%; 93.22%)
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 7eca8fd543..ad54c34aec 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.417888 ms
+ Convolution: 53.503105 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 8a717a097c..c8575356fc 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: 12.221440 ms
+ conv2d with tensor core: 12.223158 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 b11f1fb3e4..5f503aa49d 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: 0.017874
- Baseline: 3.333683
+ Numpy running time: 0.018762
+ Baseline: 3.340771
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.306406
+ Opt1: 0.302577
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.280615
+ Opt2: 0.285801
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.112370
+ Opt3: 0.113337
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.106259
+ Opt4: 0.106464
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111672
+ Opt5: 0.112168
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.134976
+ Opt6: 0.134311
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 b4c838c4aa..7104d7298c 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
=================
-**00:33.984** total execution time for **how_to_optimize_operators** files:
+**00:34.257** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.806 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.987 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.021 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.046 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.158 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.224 | 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 96d1e845f5..9fec9c768f 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
=================
-**03:47.679** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:47.756** 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``) | 01:39.152 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:38.724 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.054 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.920 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:20.706 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:20.653 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.492 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.387 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.174 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.970 | 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 436e7019fe..65c1ae7782 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: 0.340 ms
+ Execution time of this operator: 0.341 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 dbbe92dad4..41260f519f 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)
- 3.2170 3.2205 3.2216 3.2089 0.0058
+ 3.2307 3.2296 3.2408 3.2217 0.0078
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 14.054 seconds)
+ **Total running time of the script:** ( 1 minutes 14.920 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 c93c740abc..e8dddb9813 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)
- 751.9488 751.4659 754.2786 750.1018 1.7390
+ 758.5705 757.7586 761.0270 756.9259 1.7699
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 39.152 seconds)
+ **Total running time of the script:** ( 1 minutes 38.724 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 3bd47e1c9b..8aa73b1434 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**00:23.827** total execution time for **how_to_tune_with_autotvm** files:
+**00:24.224** 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``) | 00:23.794 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.186 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.016 | 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 |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 0a48424e40..f0b4a4be25 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: 0.037190
+ Time cost of this operator: 0.037268
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 53f3de2fe0..d02226a67e 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 304.9 98.748 (1, 2, 10, 10, 3) 2 1 [304.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.879 0.933 (1, 6, 10, 10) 1 1 [2.879]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.985 0.319 (1, 1, 10, 10, 3) 1 1 [0.985]
- Total_time - 308.765 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 305.3 98.741 (1, 2, 10, 10, 3) 2 1 [305.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.936 0.95 (1, 6, 10, 10) 1 1 [2.936]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.956 0.309 (1, 1, 10, 10, 3) 1 1 [0.956]
+ Total_time - 309.192 - - - - -
@@ -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 100.6 97.336 (1, 6, 10, 10, 1) 2 1 [100.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.782 1.724 (1, 6, 10, 10) 1 1 [1.782]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.971 0.94 (1, 1, 10, 10, 3) 1 1 [0.971]
- Total_time - 103.353 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.9 97.463 (1, 6, 10, 10, 1) 2 1 [103.9]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.75 1.642 (1, 6, 10, 10) 1 1 [1.75]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.895 (1, 1, 10, 10, 3) 1 1 [0.954]
+ Total_time - 106.605 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.680 seconds)
+ **Total running time of the script:** ( 1 minutes 26.773 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 6bc8531be1..f2feb18a5e 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, 9.20MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 14.8MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 12.8MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 20.2MB/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 32.042 seconds)
+ **Total running time of the script:** ( 1 minutes 32.361 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 1dc7734d6b..4dd7a6f7fe 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/tmpck1ulzfm/images/random'
+ '/tmp/tmp3wm58xqu/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpck1ulzfm/images/target contains 8144 images
- /tmp/tmpck1ulzfm/images/random contains 5000 images
+ /tmp/tmp3wm58xqu/images/target contains 8144 images
+ /tmp/tmp3wm58xqu/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 47s - loss: 0.2278 - accuracy: 0.9194 - val_loss: 0.1539 - val_accuracy: 0.9475 - 47s/epoch - 144ms/step
+ 328/328 - 47s - loss: 0.2076 - accuracy: 0.9249 - val_loss: 0.1151 - val_accuracy: 0.9641 - 47s/epoch - 143ms/step
Epoch 2/3
- 328/328 - 44s - loss: 0.0995 - accuracy: 0.9651 - val_loss: 0.1028 - val_accuracy: 0.9649 - 44s/epoch - 134ms/step
+ 328/328 - 44s - loss: 0.0967 - accuracy: 0.9658 - val_loss: 0.1271 - val_accuracy: 0.9539 - 44s/epoch - 134ms/step
Epoch 3/3
- 328/328 - 44s - loss: 0.0708 - accuracy: 0.9739 - val_loss: 0.1084 - val_accuracy: 0.9664 - 44s/epoch - 133ms/step
+ 328/328 - 44s - loss: 0.0651 - accuracy: 0.9755 - val_loss: 0.0966 - val_accuracy: 0.9664 - 44s/epoch - 134ms/step
- <keras.callbacks.History object at 0x7f6dedde13a0>
+ <keras.callbacks.History object at 0x7fa0887b3a30>
@@ -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:** ( 6 minutes 5.632 seconds)
+ **Total running time of the script:** ( 5 minutes 56.616 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 5410c67d7e..db3f1aa51c 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
=================
-**09:35.671** total execution time for **how_to_work_with_microtvm** files:
+**09:26.532** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 06:05.632 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:56.616 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:32.042 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:32.361 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.680 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.773 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.172 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.185 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.829 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.523 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.315 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.073 | 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 efeed8e55d..8b89a1f194 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
=================
-**00:40.963** total execution time for **how_to_work_with_relay** files:
+**00:40.666** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.144 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.980 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.837 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.781 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.977 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.899 | 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 37b564948b..a2b463aa59 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 0x7f6e14abaf70>
+ <function my_cuda_math_rule at 0x7fa18c036af0>
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 86371ab03f..495ffe2982 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:05.250** total execution time for **how_to_work_with_schedules** files:
+**00:05.489** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.489 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.423 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.064 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.381 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.707 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.697 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.698 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.691 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.126 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.130 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.070 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.065 | 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``) | 00:00.032 | 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 778495617f..8f4e3c5aca 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 28.91s!
+ yolov3-tiny inference graph built in 28.46s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.787 seconds)
+ **Total running time of the script:** ( 1 minutes 5.432 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index a12317b144..2c87b61a5d 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**01:12.787** total execution time for **topic_vta_tutorials_frontend** files:
+**01:05.432** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:12.787 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:05.432 | 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 c47d75899a..d781f116f7 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
=================
-**00:03.408** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.323** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.871 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.830 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.537 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.493 | 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 66f870245d..658c210516 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
=================
-**00:00.809** total execution time for **topic_vta_tutorials** files:
+**00:00.797** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.420 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.415 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.390 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.382 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index d36e2bd200..fb672c12bf 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,13 +207,6 @@ trials, we can load the best schedule from the log file and apply it.
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- *E
-
-
@@ -325,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 94.691 ms
+ Execution time of this operator: 94.373 ms
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.157 seconds)
+ **Total running time of the script:** ( 1 minutes 31.046 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 708c7a2e50..d9cce6b9bc 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: 8.95/8.95 result: MeasureResult(costs=(0.0299833528,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8723728656768799, timestamp=1724362950.2361648) [('tile_y', [-1, 128]), ('tile_x', [-1, 16])],None,47
- No: 2 GFLOPS: 1.02/8.95 result: MeasureResult(costs=(0.2639377882,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.598018646240234, timestamp=1724362954.7208264) [('tile_y', [-1, 64]), ('tile_x', [-1, 2])],None,16
- No: 3 GFLOPS: 11.33/11.33 result: MeasureResult(costs=(0.0236823546,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7618095874786377, timestamp=1724362955.3602412) [('tile_y', [-1, 128]), ('tile_x', [-1, 32])],None,57
- No: 4 GFLOPS: 8.77/11.33 result: MeasureResult(costs=(0.030597176400000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8728744983673096, timestamp=1724362956.1177933) [('tile_y', [-1, 2]), ('tile_x', [-1, 16])],None,41
- No: 5 GFLOPS: 11.16/11.33 result: MeasureResult(costs=(0.0240553942,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6619982719421387, timestamp=1724362956.9152012) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
- No: 6 GFLOPS: 3.46/11.33 result: MeasureResult(costs=(0.077492431,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5147335529327393, timestamp=1724362958.4173636) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
- No: 7 GFLOPS: 3.62/11.33 result: MeasureResult(costs=(0.0740713432,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4706785678863525, timestamp=1724362959.8636284) [('tile_y', [-1, 256]), ('tile_x', [-1, 8])],None,38
- No: 8 GFLOPS: 11.48/11.48 result: MeasureResult(costs=(0.0233730344,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.670203447341919, timestamp=1724362960.5058162) [('tile_y', [-1, 64]), ('tile_x', [-1, 512])],None,96
- No: 9 GFLOPS: 12.44/12.44 result: MeasureResult(costs=(0.0215731066,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6028001308441162, timestamp=1724362961.2279918) [('tile_y', [-1, 32]), ('tile_x', [-1, 128])],None,75
- No: 10 GFLOPS: 10.39/12.44 result: MeasureResult(costs=(0.0258361542,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6784846782684326, timestamp=1724362961.9075565) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
+ No: 1 GFLOPS: 9.50/9.50 result: MeasureResult(costs=(0.0282692968,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7996618747711182, timestamp=1724366582.3184855) [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
+ No: 2 GFLOPS: 0.51/9.50 result: MeasureResult(costs=(0.5264209654,), error_no=MeasureErrorNo.NO_ERROR, all_cost=9.759294271469116, timestamp=1724366591.0012035) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
+ No: 3 GFLOPS: 14.84/14.84 result: MeasureResult(costs=(0.0180938164,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8844878673553467, timestamp=1724366591.547209) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+ No: 4 GFLOPS: 13.80/14.84 result: MeasureResult(costs=(0.0194505584,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6824333667755127, timestamp=1724366592.1202316) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
+ No: 5 GFLOPS: 4.32/14.84 result: MeasureResult(costs=(0.0620956748,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2695410251617432, timestamp=1724366593.5254917) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
+ No: 6 GFLOPS: 3.66/14.84 result: MeasureResult(costs=(0.07342722,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.469726324081421, timestamp=1724366594.9592588) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
+ No: 7 GFLOPS: 13.25/14.84 result: MeasureResult(costs=(0.0202527748,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6144826412200928, timestamp=1724366595.5412424) [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
+ No: 8 GFLOPS: 1.02/14.84 result: MeasureResult(costs=(0.2637113148,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.487766742706299, timestamp=1724366600.0192156) [('tile_y', [-1, 64]), ('tile_x', [-1, 2])],None,16
+ No: 9 GFLOPS: 1.66/14.84 result: MeasureResult(costs=(0.1621059522,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.8486368656158447, timestamp=1724366602.9771707) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
+ No: 10 GFLOPS: 10.62/14.84 result: MeasureResult(costs=(0.025269222600000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6447889804840088, timestamp=1724366603.6466482) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 0fe0794dc5..354566eb19 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': 501.38967245005915, 'median': 502.1837542501089, 'std': 3.0087177161468515}
+ {'mean': 495.45993824000215, 'median': 496.0315708000053, 'std': 1.7955089901808603}
@@ -582,31 +582,31 @@ the tuning data to.
.. code-block:: none
- [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 9.37/ 20.08 GFLOPS | Progress: (4/20) | 14.32 s [Task 1/25] Current/Best: 12.70/ 22.96 GFLOPS | Progress: (8/20) | 16.97 s [Task 1/25] Current/Best: 20.50/ 22.96 GFLOPS | Progress: (12/20) | 19.26 s [Task 1/25] Current/Best: 11.06/ 22.96 GFLOPS | Progress: (16/20) | 22.64 s [Task 1/25] Current/Best: 6.44/ 23.10 GFLOPS | Progress: (20/20) | 26.38 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 16.18/ 16.18 GFLOPS | Progress: (4/20) | 4.22 s [Task 2/25] Current/Best: 14.15/ 18.60 GFLOPS | Progress: (8/20) | 5.65 s [Task 2/25] Current/Best: 18.77/ 18.77 GFLOPS | Progress: (12/20) | 7.22 s [Task 2/25] Current/Best: 17.69/ 18.77 GFLOPS | Progress: (16/20) | 8.67 s [Task 2/25] Current/Best: 14.68/ 18.77 GFLOPS | Progress: (20/20) | 10.26 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 19.67/ 19.67 GFLOPS | Progress: (4/20) | 4.90 s [Task 3/25] Current/Best: 9.06/ 20.49 GFLOPS | Progress: (8/20) | 7.04 s [Task 3/25] Current/Best: 12.62/ 20.49 GFLOPS | Progress: (12/20) | 10.10 s [Task 3/25] Current/Best: 16.87/ 20.49 GFLOPS | Progress: (16/20) | 12.25 s [Task 3/25] Current/Best: 10.55/ 20.49 GFLOPS | Progress: (20/20) | 15.08 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 18.30/ 18.30 GFLOPS | Progress: (4/20) | 4.82 s [Task 4/25] Current/Best: 14.69/ 20.77 GFLOPS | Progress: (8/20) | 7.21 s [Task 4/25] Current/Best: 9.46/ 20.77 GFLOPS | Progress: (12/20) | 11.91 s [Task 4/25] Current/Best: 7.18/ 20.77 GFLOPS | Progress: (16/20) | 14.17 s [Task 4/25] Current/Best: 14.23/ 21.39 GFLOPS | Progress: (20/20) | 16.02 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 12.73/ 15.63 GFLOPS | Progress: (4/20) | 5.60 s [Task 5/25] Current/Best: 15.52/ 15.63 GFLOPS | Progress: (8/20) | 7.47 s [Task 5/25] Current/Best: 10.18/ 17.28 GFLOPS | Progress: (12/20) | 9.36 s [Task 5/25] Current/Best: 7.57/ 17.93 GFLOPS | Progress: (16/20) | 11.57 s [Task 5/25] Current/Best: 7.85/ 19.64 GFLOPS | Progress: (20/20) | 13.30 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 15.64/ 22.15 GFLOPS | Progress: (4/20) | 4.74 s [Task 6/25] Current/Best: 14.79/ 22.15 GFLOPS | Progress: (8/20) | 8.32 s [Task 6/25] Current/Best: 13.70/ 22.15 GFLOPS | Progress: (12/20) | 11.53 s [Task 6/25] Current/Best: 12.08/ 22.15 GFLOPS | Progress: (16/20) | 13.65 s [Task 6/25] Current/Best: 17.36/ 22.15 GFLOPS | Progress: (20/20) | 16.68 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 18.01/ 18.84 GFLOPS | Progress: (4/20) | 4.76 s [Task 7/25] Current/Best: 18.41/ 22.39 GFLOPS | Progress: (8/20) | 6.85 s [Task 7/25] Current/Best: 11.64/ 22.39 GFLOPS | Progress: (12/20) | 9.52 s [Task 7/25] Current/Best: 11.99/ 22.39 GFLOPS | Progress: (16/20) | 11.79 s [Task 7/25] Current/Best: 11.78/ 22.39 GFLOPS | Progress: (20/20) | 14.17 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 2.96/ 14.80 GFLOPS | Progress: (4/20) | 8.38 s [Task 8/25] Current/Best: 12.39/ 14.80 GFLOPS | Progress: (8/20) | 11.70 s [Task 8/25] Current/Best: 14.69/ 14.80 GFLOPS | Progress: (12/20) | 15.44 s [Task 8/25] Current/Best: 11.42/ 16.88 GFLOPS | Progress: (16/20) | 19.17 s [Task 8/25] Current/Best: 10.35/ 16.88 GFLOPS | Progress: (20/20) | 23.93 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 18.70/ 18.70 GFLOPS | Progress: (4/20) | 13.33 s [Task 9/25] Current/Best: 22.83/ 22.83 GFLOPS | Progress: (8/20) | 21.66 s [Task 9/25] Current/Best: 6.54/ 22.83 GFLOPS | Progress: (12/20) | 23.61 s [Task 9/25] Current/Best: 13.63/ 22.83 GFLOPS | Progress: (16/20) | 25.82 s [Task 9/25] Current/Best: 9.69/ 22.83 GFLOPS | Progress: (20/20) | 30.68 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 14.06/ 14.29 GFLOPS | Progress: (4/20) | 4.81 s [Task 10/25] Current/Best: 10.36/ 14.50 GFLOPS | Progress: (8/20) | 7.02 s [Task 10/25] Current/Best: 15.22/ 15.22 GFLOPS | Progress: (12/20) | 10.32 s [Task 10/25] Current/Best: 19.07/ 19.07 GFLOPS | Progress: (16/20) | 12.22 s [Task 10/25] Current/Best: 4.78/ 19.07 GFLOPS | Progress: (20/20) | 14.61 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 6.36/ 19.17 GFLOPS | Progress: (4/20) | 4.95 s [Task 11/25] Current/Best: 13.36/ 19.17 GFLOPS | Progress: (8/20) | 7.71 s [Task 11/25] Current/Best: 11.05/ 19.69 GFLOPS | Progress: (12/20) | 9.98 s [Task 11/25] Current/Best: 11.87/ 19.69 GFLOPS | Progress: (16/20) | 12.76 s [Task 11/25] Current/Best: 12.03/ 19.69 GFLOPS | Progress: (20/20) | 16.35 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 13.22/ 16.69 GFLOPS | Progress: (4/20) | 6.02 s [Task 12/25] Current/Best: 5.12/ 17.21 GFLOPS | Progress: (8/20) | 10.21 s [Task 12/25] Current/Best: 10.32/ 18.63 GFLOPS | Progress: (12/20) | 12.71 s [Task 12/25] Current/Best: 20.83/ 20.83 GFLOPS | Progress: (16/20) | 15.11 s [Task 12/25] Current/Best: 10.60/ 20.83 GFLOPS | Progress: (20/20) | 17.50 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 3.09/ 17.53 GFLOPS | Progress: (4/20) | 6.57 s [Task 13/25] Current/Best: 20.08/ 20.08 GFLOPS | Progress: (8/20) | 8.60 s [Task 13/25] Current/Best: 3.09/ 20.08 GFLOPS | Progress: (12/20) | 12.12 s [Task 13/25] Current/Best: 9.13/ 20.08 GFLOPS | Progress: (16/20) | 15.40 s [Task 13/25] Current/Best: 21.19/ 21.19 GFLOPS | Progress: (20/20) | 17.90 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 4.53/ 17.91 GFLOPS | Progress: (4/20) | 4.93 s [Task 14/25] Current/Best: 14.27/ 17.91 GFLOPS | Progress: (8/20) | 7.55 s [Task 14/25] Current/Best: 10.89/ 17.91 GFLOPS | Progress: (12/20) | 11.90 s [Task 14/25] Current/Best: 15.70/ 17.91 GFLOPS | Progress: (16/20) | 23.29 s [Task 14/25] Current/Best: 12.30/ 17.91 GFLOPS | Progress: (20/20) | 27.23 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 12.25/ 15.89 GFLOPS | Progress: (4/20) | 5.41 s [Task 15/25] Current/Best: 18.07/ 20.03 GFLOPS | Progress: (8/20) | 8.26 s [Task 15/25] Current/Best: 9.50/ 20.03 GFLOPS | Progress: (12/20) | 16.39 s [Task 15/25] Current/Best: 18.39/ 22.66 GFLOPS | Progress: (16/20) | 19.78 s [Task 15/25] Current/Best: 12.52/ 22.66 GFLOPS | Progress: (20/20
) | 25.25 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 12.01/ 13.34 GFLOPS | Progress: (4/20) | 4.99 s [Task 16/25] Current/Best: 17.00/ 17.00 GFLOPS | Progress: (8/20) | 7.34 s [Task 16/25] Current/Best: 13.90/ 20.79 GFLOPS | Progress: (12/20) | 9.48 s [Task 16/25] Current/Best: 15.40/ 20.79 GFLOPS | Progress: (16/20) | 11.11 s [Task 16/25] Current/Best: 4.63/ 21.95 GFLOPS | Progress: (20/20) | 14.08 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 13.22/ 13.22 GFLOPS | Progress: (4/20) | 5.28 s [Task 17/25] Current/Best: 6.20/ 13.22 GFLOPS | Progress: (8/20) | 9.70 s [Task 17/25] Current/Best: 20.57/ 23.10 GFLOPS | Progress: (12/20) | 11.75 s [Task 17/25] Current/Best: 5.39/ 23.10 GFLOPS | Progress: (16/20) | 16.19 s [Task 17/25] Current/Best: 22.36/ 23.10 GFLOPS | Progress: (20/20) | 18.74 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 6.52/ 14.79 GFLOPS | Progress: (4/20) | 8.44 s [Task 18/25] Current/Best: 18.74/ 21.33 GFLOPS | Progress: (8/20) | 13.99 s [Task 18/25] Current/Best: 16.50/ 21.33 GFLOPS | Progress: (12/20) | 15.92 s [Task 18/25] Current/Best: 18.49/ 21.33 GFLOPS | Progress: (16/20) | 18.56 s [Task 18/25] Current/Best: 13.83/ 21.33 GFLOPS | Progress: (20/20) | 20.58 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 7.81/ 20.96 GFLOPS | Progress: (4/20) | 5.75 s [Task 19/25] Current/Best: 11.94/ 20.96 GFLOPS | Progress: (8/20) | 9.77 s [Task 19/25] Current/Best: 9.14/ 20.96 GFLOPS | Progress: (12/20) | 13.35 s [Task 19/25] Current/Best: 14.99/ 20.96 GFLOPS | Progress: (16/20) | 16.90 s [Task 19/25] Current/Best: 20.09/ 20.96 GFLOPS | Progress: (20/20) | 19.61 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 5.55/ 12.48 GFLOPS | Progress: (4/20) | 5.94 s [Task 20/25] Current/Best: 14.49/ 20.87 GFLOPS | Progress: (8/20) | 8.31 s [Task 20/25] Current/Best: 12.23/ 20.87 GFLOPS | Progress: (12/20) | 12.21 s [Task 20/25] Current/Best: 5.79/ 20.87 GFLOPS | Progress: (16/20) | 22.80 s [Task 20/25] Current/Best: 18.61/ 23.21 GFLOPS | Progress: (20/20) | 30.65 s Done.
- [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 20.36/ 20.36 GFLOPS | Progress: (4/20) | 13.63 s [Task 21/25] Current/Best: 6.70/ 20.36 GFLOPS | Progress: (8/20) | 17.10 s [Task 21/25] Current/Best: 5.53/ 22.13 GFLOPS | Progress: (12/20) | 24.47 s [Task 21/25] Current/Best: 17.05/ 22.13 GFLOPS | Progress: (16/20) | 35.31 s [Task 21/25] Current/Best: 18.01/ 22.13 GFLOPS | Progress: (20/20) | 36.90 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 22.19/ 22.19 GFLOPS | Progress: (4/20) | 13.45 s [Task 1/25] Current/Best: 7.74/ 23.44 GFLOPS | Progress: (8/20) | 15.96 s [Task 1/25] Current/Best: 11.02/ 23.44 GFLOPS | Progress: (12/20) | 19.44 s [Task 1/25] Current/Best: 10.99/ 23.44 GFLOPS | Progress: (16/20) | 23.32 s [Task 1/25] Current/Best: 12.32/ 23.44 GFLOPS | Progress: (20/20) | 25.73 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 3.41/ 16.06 GFLOPS | Progress: (4/20) | 4.62 s [Task 2/25] Current/Best: 7.31/ 20.50 GFLOPS | Progress: (8/20) | 6.31 s [Task 2/25] Current/Best: 13.68/ 20.50 GFLOPS | Progress: (12/20) | 7.86 s [Task 2/25] Current/Best: 14.86/ 20.50 GFLOPS | Progress: (16/20) | 9.58 s [Task 2/25] Current/Best: 13.11/ 20.50 GFLOPS | Progress: (20/20) | 11.45 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 11.25/ 20.88 GFLOPS | Progress: (4/20) | 5.79 s [Task 3/25] Current/Best: 11.12/ 20.88 GFLOPS | Progress: (8/20) | 8.06 s [Task 3/25] Current/Best: 3.12/ 24.04 GFLOPS | Progress: (12/20) | 11.20 s [Task 3/25] Current/Best: 10.87/ 24.04 GFLOPS | Progress: (16/20) | 14.12 s [Task 3/25] Current/Best: 12.49/ 24.04 GFLOPS | Progress: (20/20) | 16.48 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 6.61/ 13.37 GFLOPS | Progress: (4/20) | 5.44 s [Task 4/25] Current/Best: 20.95/ 22.29 GFLOPS | Progress: (8/20) | 7.41 s [Task 4/25] Current/Best: 20.27/ 22.29 GFLOPS | Progress: (12/20) | 9.29 s [Task 4/25] Current/Best: 11.81/ 22.29 GFLOPS | Progress: (16/20) | 12.84 s [Task 4/25] Current/Best: 10.47/ 22.29 GFLOPS | Progress: (20/20) | 21.31 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 11.61/ 15.03 GFLOPS | Progress: (4/20) | 4.23 s [Task 5/25] Current/Best: 12.91/ 15.03 GFLOPS | Progress: (8/20) | 6.45 s [Task 5/25] Current/Best: 13.76/ 16.02 GFLOPS | Progress: (12/20) | 8.44 s [Task 5/25] Current/Best: 14.40/ 17.32 GFLOPS | Progress: (16/20) | 10.30 s [Task 5/25] Current/Best: 8.07/ 17.32 GFLOPS | Progress: (20/20) | 12.09 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 4.96/ 14.23 GFLOPS | Progress: (4/20) | 5.44 s [Task 6/25] Current/Best: 13.06/ 14.23 GFLOPS | Progress: (8/20) | 8.71 s [Task 6/25] Current/Best: 16.45/ 16.45 GFLOPS | Progress: (12/20) | 12.99 s [Task 6/25] Current/Best: 7.94/ 16.45 GFLOPS | Progress: (16/20) | 15.39 s [Task 6/25] Current/Best: 10.80/ 18.76 GFLOPS | Progress: (20/20) | 20.08 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 16.40/ 19.48 GFLOPS | Progress: (4/20) | 4.83 s [Task 7/25] Current/Best: 16.57/ 19.48 GFLOPS | Progress: (8/20) | 7.01 s [Task 7/25] Current/Best: 9.24/ 19.48 GFLOPS | Progress: (12/20) | 9.84 s [Task 7/25] Current/Best: 13.92/ 22.77 GFLOPS | Progress: (16/20) | 12.17 s [Task 7/25] Current/Best: 3.10/ 22.77 GFLOPS | Progress: (20/20) | 14.98 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 11.52/ 13.99 GFLOPS | Progress: (4/20) | 5.44 s [Task 8/25] Current/Best: 3.29/ 13.99 GFLOPS | Progress: (8/20) | 10.16 s [Task 8/25] Current/Best: 14.97/ 16.47 GFLOPS | Progress: (12/20) | 12.19 s [Task 8/25] Current/Best: 14.83/ 16.47 GFLOPS | Progress: (16/20) | 17.07 s [Task 8/25] Current/Best: 13.11/ 16.66 GFLOPS | Progress: (20/20) | 19.97 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 23.40/ 23.40 GFLOPS | Progress: (4/20) | 5.58 s [Task 9/25] Current/Best: 6.90/ 23.40 GFLOPS | Progress: (8/20) | 9.84 s [Task 9/25] Current/Best: 12.47/ 23.40 GFLOPS | Progress: (12/20) | 16.45 s [Task 9/25] Current/Best: 16.96/ 23.40 GFLOPS | Progress: (16/20) | 18.00 s [Task 9/25] Current/Best: 6.78/ 23.40 GFLOPS | Progress: (20/20) | 28.78 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 10.97/ 14.23 GFLOPS | Progress: (4/20) | 7.09 s [Task 10/25] Current/Best: 6.14/ 15.66 GFLOPS | Progress: (8/20) | 8.85 s [Task 10/25] Current/Best: 12.34/ 16.24 GFLOPS | Progress: (12/20) | 10.94 s [Task 10/25] Current/Best: 13.49/ 16.24 GFLOPS | Progress: (16/20) | 12.80 s [Task 10/25] Current/Best: 4.82/ 17.19 GFLOPS | Progress: (20/20) | 14.70 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 12.32/ 12.32 GFLOPS | Progress: (4/20) | 5.56 s [Task 11/25] Current/Best: 10.45/ 14.27 GFLOPS | Progress: (8/20) | 8.15 s [Task 11/25] Current/Best: 18.69/ 22.78 GFLOPS | Progress: (12/20) | 10.12 s [Task 11/25] Current/Best: 20.89/ 22.78 GFLOPS | Progress: (16/20) | 12.22 s [Task 11/25] Current/Best: 5.49/ 22.78 GFLOPS | Progress: (20/20) | 14.67 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 13.57/ 17.86 GFLOPS | Progress: (4/20) | 4.62 s [Task 12/25] Current/Best: 5.31/ 17.86 GFLOPS | Progress: (8/20) | 7.15 s [Task 12/25] Current/Best: 11.54/ 17.86 GFLOPS | Progress: (12/20) | 10.05 s [Task 12/25] Current/Best: 4.88/ 17.86 GFLOPS | Progress: (16/20) | 12.60 s [Task 12/25] Current/Best: 9.33/ 17.86 GFLOPS | Progress: (20/20) | 17.88 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 13.10/ 20.24 GFLOPS | Progress: (4/20) | 5.60 s [Task 13/25] Current/Best: 11.51/ 21.89 GFLOPS | Progress: (8/20) | 8.64 s [Task 13/25] Current/Best: 11.20/ 21.89 GFLOPS | Progress: (12/20) | 12.10 s [Task 13/25] Current/Best: 1.56/ 21.89 GFLOPS | Progress: (16/20) | 16.18 s [Task 13/25] Current/Best: 18.21/ 21.89 GFLOPS | Progress: (20/20) | 19.48 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 7.83/ 14.69 GFLOPS | Progress: (4/20) | 10.87 s [Task 14/25] Current/Best: 14.81/ 16.13 GFLOPS | Progress: (8/20) | 12.93 s [Task 14/25] Current/Best: 11.23/ 16.13 GFLOPS | Progress: (12/20) | 17.62 s [Task 14/25] Current/Best: 17.68/ 17.68 GFLOPS | Progress: (16/20) | 28.74 s [Task 14/25] Current/Best: 11.31/ 17.68 GFLOPS | Progress: (20/20) | 31.44 s Done.
+ [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 18.09/ 18.62 GFLOPS | Progress: (4/20) | 5.07 s [Task 15/25] Current/Best: 9.10/ 19.44 GFLOPS | Progress: (8/20) | 10.09 s [Task 15/25] Current/Best: 17.08/ 19.44 GFLOPS | Progress: (12/20) | 13.05 s [Task 15/25] Current/Best: 18.83/ 19.44 GFLOPS | Progress: (16/20) | 16.64 s [Task 15/25] Current/Best: 11.53/ 19.44 GFLOPS | Progress: (20/20) | 22.09 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 21.57/ 21.57 GFLOPS | Progress: (4/20) | 4.61 s [Task 16/25] Current/Best: 10.70/ 21.57 GFLOPS | Progress: (8/20) | 8.48 s [Task 16/25] Current/Best: 15.99/ 21.57 GFLOPS | Progress: (12/20) | 10.50 s [Task 16/25] Current/Best: 9.52/ 21.57 GFLOPS | Progress: (16/20) | 13.40 s [Task 16/25] Current/Best: 17.59/ 21.57 GFLOPS | Progress: (20/20) | 16.04 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 10.85/ 19.89 GFLOPS | Progress: (4/20) | 6.86 s [Task 17/25] Current/Best: 13.32/ 21.31 GFLOPS | Progress: (8/20) | 9.07 s [Task 17/25] Current/Best: 21.78/ 21.78 GFLOPS | Progress: (12/20) | 11.09 s [Task 17/25] Current/Best: 6.19/ 21.78 GFLOPS | Progress: (16/20) | 14.02 s [Task 17/25] Current/Best: 12.29/ 22.33 GFLOPS | Progress: (20/20) | 16.44 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 5.02/ 17.16 GFLOPS | Progress: (4/20) | 6.24 s [Task 18/25] Current/Best: 4.85/ 20.59 GFLOPS | Progress: (8/20) | 8.54 s [Task 18/25] Current/Best: 14.14/ 20.59 GFLOPS | Progress: (12/20) | 11.78 s [Task 18/25] Current/Best: 18.38/ 20.59 GFLOPS | Progress: (16/20) | 14.39 s [Task 18/25] Current/Best: 16.04/ 20.59 GFLOPS | Progress: (20/20) | 18.45 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 20.75/ 20.75 GFLOPS | Progress: (4/20) | 5.28 s [Task 19/25] Current/Best: 22.44/ 22.44 GFLOPS | Progress: (8/20) | 9.06 s [Task 19/25] Current/Best: 1.55/ 22.44 GFLOPS | Progress: (12/20) | 12.71 s [Task 19/25] Current/Best: 2.69/ 22.44 GFLOPS | Progress: (16/20) | 17.27 s [Task 19/25] Current/Best: 7.18/ 22.44 GFLOPS | Progress: (20/20) | 21.39 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 16.89/ 16.89 GFLOPS | Progress: (4/20) | 14.19 s [Task 20/25] Current/Best: 18.14/ 18.59 GFLOPS | Progress: (8/20) | 17.99 s [Task 20/25] Current/Best: 12.35/ 18.59 GFLOPS | Progress: (12/20) | 30.73 s [Task 20/25] Current/Best: 19.36/ 19.36 GFLOPS | Progress: (16/20) | 42.28 s [Task 20/25] Current/Best: 6.63/ 19.36 GFLOPS | Progress: (20/20) | 53.45 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ [Task 21/25] Current/Best: 7.53/ 12.02 GFLOPS | Progress: (4/20) | 15.36 s [Task 21/25] Current/Best: 19.80/ 19.80 GFLOPS | Progress: (8/20) | 26.64 s [Task 21/25] Current/Best: 7.27/ 19.80 GFLOPS | Progress: (12/20) | 35.55 s [Task 21/25] Current/Best: 13.33/ 19.80 GFLOPS | Progress: (16/20) | 44.27 s [Task 21/25] Current/Best: 6.78/ 19.80 GFLOPS | Progress: (20/20) | 50.75 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 17.92/ 20.99 GFLOPS | Progress: (4/20) | 4.59 s [Task 22/25] Current/Best: 6.42/ 20.99 GFLOPS | Progress: (8/20) | 6.53 s [Task 22/25] Current/Best: 16.69/ 20.99 GFLOPS | Progress: (12/20) | 8.88 s [Task 22/25] Current/Best: 5.27/ 20.99 GFLOPS | Progress: (16/20) | 10.84 s [Task 22/25] Current/Best: 8.76/ 20.99 GFLOPS | Progress: (20/20) | 13.94 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 12.16/ 19.06 GFLOPS | Progress: (4/20) | 6.83 s [Task 23/25] Current/Best: 7.89/ 19.06 GFLOPS | Progress: (8/20) | 11.28 s [Task 23/25] Current/Best: 20.15/ 20.15 GFLOPS | Progress: (12/20) | 14.99 s [Task 23/25] Current/Best: 1.55/ 20.15 GFLOPS | Progress: (16/20) | 19.69 s [Task 23/25] Current/Best: 19.81/ 22.70 GFLOPS | Progress: (20/20) | 22.86 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 2.86/ 3.29 GFLOPS | Progress: (4/20) | 13.26 s [Task 24/25] Current/Best: 5.54/ 7.57 GFLOPS | Progress: (8/20) | 25.44 s [Task 24/25] Current/Best: 10.18/ 10.18 GFLOPS | Progress: (12/20) | 35.61 s [Task 24/25] Current/Best: 9.52/ 10.18 GFLOPS | Progress: (16/20) | 46.61 s [Task 24/25] Current/Best: 0.58/ 10.18 GFLOPS | Progress: (20/20) | 55.67 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 2.99/ 8.78 GFLOPS | Progress: (4/20) | 13.17 s Done.
Done.
- [Task 22/25] Current/Best: 8.46/ 19.01 GFLOPS | Progress: (4/20) | 4.43 s [Task 22/25] Current/Best: 6.83/ 19.01 GFLOPS | Progress: (8/20) | 7.24 s [Task 22/25] Current/Best: 16.62/ 20.64 GFLOPS | Progress: (12/20) | 9.35 s [Task 22/25] Current/Best: 19.80/ 20.64 GFLOPS | Progress: (16/20) | 11.24 s [Task 22/25] Current/Best: 13.74/ 20.64 GFLOPS | Progress: (20/20) | 14.17 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 7.83/ 20.70 GFLOPS | Progress: (4/20) | 6.38 s [Task 23/25] Current/Best: 19.38/ 20.70 GFLOPS | Progress: (8/20) | 9.50 s [Task 23/25] Current/Best: 21.64/ 21.64 GFLOPS | Progress: (12/20) | 12.97 s [Task 23/25] Current/Best: 22.99/ 23.63 GFLOPS | Progress: (16/20) | 16.06 s [Task 23/25] Current/Best: 18.63/ 23.63 GFLOPS | Progress: (20/20) | 19.21 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 8.04/ 8.04 GFLOPS | Progress: (4/20) | 13.53 s [Task 24/25] Current/Best: 8.39/ 8.39 GFLOPS | Progress: (8/20) | 24.54 s [Task 24/25] Current/Best: 5.70/ 8.39 GFLOPS | Progress: (12/20) | 33.06 s [Task 24/25] Current/Best: 1.88/ 9.77 GFLOPS | Progress: (16/20) | 44.12 s [Task 24/25] Current/Best: 1.78/ 9.77 GFLOPS | Progress: (20/20) | 54.45 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 1.54/ 5.78 GFLOPS | Progress: (4/20) | 5.40 s [Task 25/25] Current/Best: 1.55/ 9.18 GFLOPS | Progress: (8/20) | 7.02 s [Task 25/25] Current/Best: 1.54/ 9.18 GFLOPS | Progress: (12/20) | 13.26 s [Task 25/25] Current/Best: 5.57/ 9.84 GFLOPS | Progress: (16/20) | 17.04 s [Task 25/25] Current/Best: 4.48/ 9.84 GFLOPS | Progress: (20/
20) | 22.73 s Done.
-
+ [Task 25/25] Current/Best: 3.36/ 8.82 GFLOPS | Progress: (8/20) | 17.36 s [Task 25/25] Current/Best: 2.85/ 8.97 GFLOPS | Progress: (12/20) | 28.34 s [Task 25/25] Current/Best: 5.49/ 8.97 GFLOPS | Progress: (16/20) | 31.61 s [Task 25/25] Current/Best: 5.68/ 8.97 GFLOPS | Progress: (20/20) | 34.45 s
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621102
- class='n02123159 tiger cat' with probability=0.356380
+ class='n02123045 tabby, tabby cat' with probability=0.621104
+ class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 415.46755112995015, 'median': 415.50322764996963, 'std': 0.7467951614569145}
- unoptimized: {'mean': 501.38967245005915, 'median': 502.1837542501089, 'std': 3.0087177161468515}
+ optimized: {'mean': 413.2245501200009, 'median': 413.0197849000069, 'std': 2.1648056371522664}
+ unoptimized: {'mean': 495.45993824000215, 'median': 496.0315708000053, 'std': 1.7955089901808603}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 24.758 seconds)
+ **Total running time of the script:** ( 14 minutes 18.061 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 2c7c46f61a..af84ce412d 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
- 1.195e-07 secs/op
+ 1.211e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index be49ccc99f..e54cd93c8d 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, 0x253b15f0)), stage(b, placeholder(b, 0x25242770)), 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, 0xe928a30)), stage(b, placeholder(b, 0x13a1b540)), 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 e53784644f..47b0a736ea 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**17:06.160** total execution time for **tutorial** files:
+**18:06.234** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:24.758 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 14:18.061 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:36.157 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:31.046 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.178 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.504 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:44.508 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:44.556 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:19.544 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:29.540 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.940 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.433 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.892 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.895 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.182 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.198 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
@@ -30,7 +30,7 @@ Computation times
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 646f047fd4..c481246b48 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000008
+ parallel: 0.000006
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000040
+ vector: 0.000042
# 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 6.892199999128934e-06 1.0
- naive 7.8771e-06 1.1429006704674185
- parallel 8.3054e-06 1.2050433825265767
- vector 4.0157399999999996e-05 5.826499521934253
+ numpy 6.708480000270356e-06 1.0
+ naive 7.8841e-06 1.1752438703972086
+ parallel 6.0857e-06 0.907165259455904
+ vector 4.18149e-05 6.23314074101955
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018174
+ Numpy running time: 0.019269
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.340492
+ none: 3.440321
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.297197
+ blocking: 0.288157
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.273404
+ vectorization: 0.276178
# 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: 0.113457
+ loop permutation: 0.115147
# 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: 0.105362
+ array packing: 0.106477
# 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: 0.111152
+ block caching: 0.111107
# 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: 0.133797
+ parallelization: 0.133402
# 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 3.3404915206999997 1.0
- blocking 0.297196816 0.08896798993751746
- vectorization 0.2734035837 0.08184531587815805
- loop permutation 0.1134568544 0.0339641198598897
- array packing 0.1053621017 0.03154089781312224
- block caching 0.1111523263 0.03327424291042896
- parallelization 0.133797377 0.04005320060562907
+ none 3.4403213378000004 1.0
+ blocking 0.2881571788 0.0837587976547183
+ vectorization 0.27617769280000004 0.08027671420269386
+ loop permutation 0.1151468936 0.03346980769931031
+ array packing 0.106477015 0.030949729558718893
+ block caching 0.11110655790000001 0.03229540121128623
+ parallelization 0.1334016305 0.0387759216077493
@@ -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 0.504 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index 022c886ed5..895925bca0 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-20289e8502dd27c91f3945418c864ad7233aec89
+0f037a6d9957108decceaf0c91bd84667a077aad
diff --git a/docs/get_started/tutorials/sg_execution_times.html b/docs/get_started/tutorials/sg_execution_times.html
index 03ce80ab6f..ecd50afd39 100644
--- a/docs/get_started/tutorials/sg_execution_times.html
+++ b/docs/get_started/tutorials/sg_execution_times.html
@@ -371,7 +371,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-get-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.259</strong> total execution time for <strong>get_started_tutorials</strong> files:</p>
+<p><strong>00:00.258</strong> total execution time for <strong>get_started_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 80%" />
@@ -380,7 +380,7 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="quick_start.html#sphx-glr-get-started-tutorials-quick-start-py"><span class="std std-ref">Quick Start</span></a> (<code class="docutils literal notranslate"><span class="pre">quick_start.py</span></code>)</p></td>
-<td><p>00:00.259</p></td>
+<td><p>00:00.258</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index a874623524..0741a198cb 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -615,7 +615,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.879 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 41.168 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 002c33eb6a..b3dc6fc62b 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -536,7 +536,7 @@ 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 889ms/step
+1/1 [==============================] - 1s 942ms/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 108af3f33f..7e0ee38cc6 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -479,14 +479,13 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 15%|#5 | 6.33M/41.5M [00:00<00:01, 26.0MB/s]
- 21%|##1 | 8.81M/41.5M [00:00<00:01, 21.4MB/s]
- 35%|###4 | 14.3M/41.5M [00:00<00:01, 24.9MB/s]
- 40%|#### | 16.6M/41.5M [00:00<00:01, 21.8MB/s]
- 58%|#####7 | 24.0M/41.5M [00:01<00:00, 25.2MB/s]
- 77%|#######7 | 32.0M/41.5M [00:01<00:00, 30.4MB/s]
- 96%|#########6| 40.0M/41.5M [00:01<00:00, 35.9MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 30.8MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 57.9MB/s]
+ 29%|##8 | 11.9M/41.5M [00:00<00:00, 48.9MB/s]
+ 40%|###9 | 16.6M/41.5M [00:00<00:00, 27.5MB/s]
+ 58%|#####7 | 24.0M/41.5M [00:00<00:00, 32.3MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 42.1MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 38.9MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 38.0MB/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 dd3d1d1096..c5f3eb2e87 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -514,7 +514,7 @@ 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 5.065 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.004 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 86f2b2068f..bb605b041a 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -462,14 +462,13 @@ 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]
- 18%|#7 | 7.99M/44.7M [00:00<00:00, 47.7MB/s]
- 32%|###2 | 14.3M/44.7M [00:00<00:01, 25.7MB/s]
- 39%|###8 | 17.2M/44.7M [00:00<00:01, 24.0MB/s]
- 54%|#####3 | 24.0M/44.7M [00:00<00:00, 30.7MB/s]
- 72%|#######1 | 32.0M/44.7M [00:01<00:00, 35.8MB/s]
- 86%|########5 | 38.3M/44.7M [00:01<00:00, 36.0MB/s]
- 94%|#########3| 41.9M/44.7M [00:01<00:00, 31.7MB/s]
-100%|##########| 44.7M/44.7M [00:01<00:00, 33.6MB/s]
+ 18%|#7 | 7.99M/44.7M [00:00<00:00, 74.8MB/s]
+ 34%|###3 | 15.1M/44.7M [00:00<00:00, 65.2MB/s]
+ 48%|####7 | 21.4M/44.7M [00:00<00:00, 54.7MB/s]
+ 60%|#####9 | 26.7M/44.7M [00:00<00:00, 55.0MB/s]
+ 72%|#######1 | 32.1M/44.7M [00:00<00:00, 40.6MB/s]
+ 90%|########9 | 40.0M/44.7M [00:00<00:00, 40.4MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 50.4MB/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 f78dbc61ad..d321ec9509 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -679,7 +679,7 @@ 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 31.308 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 33.766 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 a5103f51f7..77133131dc 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>06:58.673</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:04.213</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -380,39 +380,39 @@
</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>01:42.879</p></td>
+<td><p>01:41.168</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>01:31.308</p></td>
+<td><p>01:33.766</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>01:05.065</p></td>
+<td><p>01:07.004</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>00:44.625</p></td>
+<td><p>00:46.152</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>00:40.823</p></td>
+<td><p>00:41.533</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>00:30.174</p></td>
+<td><p>00:31.179</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:27.781</p></td>
+<td><p>00:27.802</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>00:13.184</p></td>
+<td><p>00:12.660</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>00:02.833</p></td>
+<td><p>00:02.949</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 2abf9692e2..62009ec804 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -855,10 +855,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4123.8338 4122.8221 4130.7468 4119.9195 3.1768
+ 4121.2842 4119.4177 4141.6465 4117.1682 6.9175
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.914 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.005 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 83de0b1756..713f3b275d 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
@@ -470,31 +470,19 @@ to run this tutorial with a real device over rpc.</p>
8192/102967424 [..............................] - ETA: 0s
8380416/102967424 [=>............................] - ETA: 1s
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-102967424/102967424 [==============================] - 2s 0us/step
+100794368/102967424 [============================>.] - ETA: 0s
+102967424/102967424 [==============================] - 1s 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 e3d847fd29..460d88ed2e 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -693,7 +693,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.2556 15.2157 15.8209 14.6006 0.4852
+ 15.4466 15.4750 15.7735 15.0960 0.1819
</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 2abf6b33ee..7ce56d1263 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -485,38 +485,34 @@ 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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+ 5%|4 | 7.99M/170M [00:00<00:03, 43.1MB/s]
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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 +599,7 @@ 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 42.066 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 41.810 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 983709441c..98bec74ccb 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -526,9 +526,10 @@ 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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-100%|##########| 13.6M/13.6M [00:00<00:00, 20.7MB/s]
+ 47%|####6 | 6.30M/13.6M [00:00<00:00, 39.9MB/s]
+ 75%|#######4 | 10.1M/13.6M [00:00<00:00, 29.6MB/s]
+ 96%|#########5| 13.0M/13.6M [00:00<00:00, 28.3MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 31.0MB/s]
</pre></div>
</div>
</div>
@@ -613,7 +614,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.2298 89.1012 94.7746 88.5435 0.6774
+ 88.9398 88.9392 89.5695 88.4689 0.2179
</pre></div>
</div>
<div class="admonition note">
@@ -652,7 +653,7 @@ 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 34.500 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.534 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 09305d5bb4..e48160a4b8 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -611,7 +611,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 111.2619 111.2389 113.8744 109.9355 0.5875
+ 111.3393 111.3515 115.6646 109.5698 0.6993
</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 1664ea2b8d..bd69739c94 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -371,7 +371,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:20.347</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:15.454</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -380,35 +380,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:42.066</p></td>
+<td><p>03:41.810</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>01:34.500</p></td>
+<td><p>01:34.534</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>01:21.914</p></td>
+<td><p>01:21.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:54.937</p></td>
+<td><p>00:54.142</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:53.850</p></td>
+<td><p>00:53.033</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:50.704</p></td>
+<td><p>00:48.738</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>00:31.213</p></td>
+<td><p>00:31.151</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>00:31.156</p></td>
+<td><p>00:31.033</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 2f377d4ddd..304f063d68 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -371,7 +371,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:04.126</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.160</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -380,11 +380,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:02.835</p></td>
+<td><p>00:02.882</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>00:01.283</p></td>
+<td><p>00:01.271</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 61942f51b5..ab25b38e10 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -556,10 +556,10 @@ 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: 25464us [25464us] (48.56%; 48.56%)
-FoldScaleAxis: 26971us [10us] (51.44%; 51.44%)
- FoldConstant: 26961us [1846us] (51.42%; 99.96%)
- InferType: 25115us [25115us] (47.90%; 93.15%)
+InferType: 24947us [24947us] (48.61%; 48.61%)
+FoldScaleAxis: 26376us [8us] (51.39%; 51.39%)
+ FoldConstant: 26367us [1773us] (51.38%; 99.97%)
+ InferType: 24595us [24595us] (47.92%; 93.28%)
</pre></div>
</div>
</div>
@@ -581,10 +581,10 @@ 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: 25059us [25059us] (47.69%; 47.69%)
-FoldScaleAxis: 27490us [8us] (52.31%; 52.31%)
- FoldConstant: 27481us [1809us] (52.30%; 99.97%)
- InferType: 25673us [25673us] (48.86%; 93.42%)
+InferType: 24511us [24511us] (48.23%; 48.23%)
+FoldScaleAxis: 26308us [8us] (51.77%; 51.77%)
+ FoldConstant: 26301us [1782us] (51.75%; 99.97%)
+ InferType: 24518us [24518us] (48.25%; 93.22%)
</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 57b7e4fae4..c89ac95765 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -606,7 +606,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.417888 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.503105 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 74469ac6ff..05d601631c 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -888,7 +888,7 @@ 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: 12.221440 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.223158 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 a481faa640..4c549f48c8 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -503,8 +503,8 @@ 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: 0.017874
-Baseline: 3.333683
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018762
+Baseline: 3.340771
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -563,7 +563,7 @@ 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: 0.306406
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.302577
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -620,7 +620,7 @@ 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: 0.280615
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.285801
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -675,7 +675,7 @@ 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: 0.112370
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.113337
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -752,7 +752,7 @@ 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: 0.106259
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.106464
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -830,7 +830,7 @@ 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: 0.111672
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112168
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -910,7 +910,7 @@ 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: 0.134976
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.134311
</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 2b3ed8a186..fa89502944 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>00:33.984</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.257</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -380,15 +380,15 @@
</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>00:30.806</p></td>
+<td><p>00:30.987</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>00:02.021</p></td>
+<td><p>00:02.046</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>00:01.158</p></td>
+<td><p>00:01.224</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 5456135639..517f9cd18c 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>03:47.679</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:47.756</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -380,23 +380,23 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:39.152</p></td>
+<td><p>01:38.724</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>01:14.054</p></td>
+<td><p>01:14.920</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>00:20.706</p></td>
+<td><p>00:20.653</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>00:17.492</p></td>
+<td><p>00:17.387</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>00:16.174</p></td>
+<td><p>00:15.970</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index 89b4c67747..73c21adab8 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
@@ -1043,7 +1043,7 @@ 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: 0.340 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.341 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 52dc34d408..fb9fd3bfb5 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -934,7 +934,7 @@ 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)
- 3.2170 3.2205 3.2216 3.2089 0.0058
+ 3.2307 3.2296 3.2408 3.2217 0.0078
</pre></div>
</div>
</div>
@@ -956,7 +956,7 @@ 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 14.054 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.920 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 3db9a202fa..4c88a70801 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -956,7 +956,7 @@ 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)
- 751.9488 751.4659 754.2786 750.1018 1.7390
+ 758.5705 757.7586 761.0270 756.9259 1.7699
</pre></div>
</div>
</div>
@@ -978,7 +978,7 @@ 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 39.152 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.724 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 5d5bed6c0e..729353db2d 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>00:23.827</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:24.224</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -380,11 +380,11 @@
</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>00:23.794</p></td>
+<td><p>00:24.186</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>00:00.016</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>
@@ -396,7 +396,7 @@
<td><p>0.0 MB</p></td>
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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 01af48815a..8f021e43f7 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -641,7 +641,7 @@ and measure running time.</p>
Best config:
,None
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</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 2629b87abe..9d4c212b3a 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -675,10 +675,10 @@ 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 304.9 98.748 (1, 2, 10, 10, 3) 2 1 [304.9]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.879 0.933 (1, 6, 10, 10) 1 1 [2.879]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.985 0.319 (1, 1, 10, 10, 3) 1 1 [0.985]
-Total_time - 308.765 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 305.3 98.741 (1, 2, 10, 10, 3) 2 1 [305.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.936 0.95 (1, 6, 10, 10) 1 1 [2.936]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.956 0.309 (1, 1, 10, 10, 3) 1 1 [0.956]
+Total_time - 309.192 - - - - -
</pre></div>
</div>
</div>
@@ -730,13 +730,13 @@ 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 100.6 97.336 (1, 6, 10, 10, 1) 2 1 [100.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.782 1.724 (1, 6, 10, 10) 1 1 [1.782]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.971 0.94 (1, 1, 10, 10, 3) 1 1 [0.971]
-Total_time - 103.353 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 103.9 97.463 (1, 6, 10, 10, 1) 2 1 [103.9]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.75 1.642 (1, 6, 10, 10) 1 1 [1.75]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.895 (1, 1, 10, 10, 3) 1 1 [0.954]
+Total_time - 106.605 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.680 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.773 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 e49345cc5d..41fd0f7e92 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -486,8 +486,8 @@ 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, 9.20MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 14.8MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 12.8MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 20.2MB/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>
@@ -611,7 +611,7 @@ 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 32.042 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 32.361 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 676fa561a3..2371440773 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -555,7 +555,7 @@ 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/tmpck1ulzfm/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp3wm58xqu/images/random'
</pre></div>
</div>
</div>
@@ -615,8 +615,8 @@ 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/tmpck1ulzfm/images/target contains 8144 images
-/tmp/tmpck1ulzfm/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/tmp3wm58xqu/images/target contains 8144 images
+/tmp/tmp3wm58xqu/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -728,13 +728,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 47s - loss: 0.2278 - accuracy: 0.9194 - val_loss: 0.1539 - val_accuracy: 0.9475 - 47s/epoch - 144ms/step
+328/328 - 47s - loss: 0.2076 - accuracy: 0.9249 - val_loss: 0.1151 - val_accuracy: 0.9641 - 47s/epoch - 143ms/step
Epoch 2/3
-328/328 - 44s - loss: 0.0995 - accuracy: 0.9651 - val_loss: 0.1028 - val_accuracy: 0.9649 - 44s/epoch - 134ms/step
+328/328 - 44s - loss: 0.0967 - accuracy: 0.9658 - val_loss: 0.1271 - val_accuracy: 0.9539 - 44s/epoch - 134ms/step
Epoch 3/3
-328/328 - 44s - loss: 0.0708 - accuracy: 0.9739 - val_loss: 0.1084 - val_accuracy: 0.9664 - 44s/epoch - 133ms/step
+328/328 - 44s - loss: 0.0651 - accuracy: 0.9755 - val_loss: 0.0966 - val_accuracy: 0.9664 - 44s/epoch - 134ms/step
-<keras.callbacks.History object at 0x7f6dedde13a0>
+<keras.callbacks.History object at 0x7fa0887b3a30>
</pre></div>
</div>
</div>
@@ -998,7 +998,7 @@ 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> ( 6 minutes 5.632 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 56.616 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">
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<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 00bd3ca11f..0cbd03deff 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>09:35.671</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
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<table class="docutils align-default">
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@@ -380,27 +380,27 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
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<tr class="row-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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<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>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index ef129d0a12..f62687e21e 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -371,7 +371,7 @@
<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>00:40.963</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:40.666</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -380,15 +380,15 @@
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<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>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index f09ca9ceed..77e49da0f2 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -583,7 +583,7 @@ 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 0x7f6e14abaf70>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fa18c036af0>
</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 b5a66abbe5..26f068c32d 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -371,7 +371,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:05.250</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -380,23 +380,23 @@
</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>00:02.489</p></td>
+<td><p>00:02.423</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
-<td><p>00:01.064</p></td>
+<td><p>00:01.381</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>00:00.707</p></td>
+<td><p>00:00.697</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>00:00.698</p></td>
+<td><p>00:00.691</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>00:00.126</p></td>
+<td><p>00:00.130</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>
@@ -404,7 +404,7 @@
<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>00:00.065</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>
diff --git a/docs/reference/api/doxygen/aot__executor_8h_source.html b/docs/reference/api/doxygen/aot__executor_8h_source.html
index e2c6cd5b52..f5ccd2d9c7 100644
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+++ b/docs/reference/api/doxygen/aot__executor_8h_source.html
@@ -137,7 +137,7 @@ $(function() {
<div class="ttc" id="aaot__executor_8h_html_aa576382ee317ceef3e1858d0a953df95"><div class="ttname"><a href="aot__executor_8h.html#aa576382ee317ceef3e1858d0a953df95">TVMAotExecutor_Run</a></div><div class="ttdeci">int TVMAotExecutor_Run(TVMAotExecutor *executor)</div><div class="ttdoc">Run the generated program.</div></div>
<div class="ttc" id="aaot__executor_8h_html_ab663a086fc2c4f6df166625a9ac83ee1"><div class="ttname"><a href="aot__executor_8h.html#ab663a086fc2c4f6df166625a9ac83ee1">TVMAotExecutor_Create</a></div><div class="ttdeci">int TVMAotExecutor_Create(TVMModuleHandle module_handle, const DLDevice device, TVMAotExecutor **executor, const char *module_name)</div><div class="ttdoc">Allocate a new AotExecutor with TVMPlatformMemoryAllocate and initialize it.</div></div>
<div class="ttc" id="aaot__executor_8h_html_ad3b4f9b4d0b73d9c1b7e42d77076ffdb"><div class="ttname"><a href="aot__executor_8h.html#ad3b4f9b4d0b73d9c1b7e42d77076ffdb">TVMAotExecutor_GetInputIndex</a></div><div class="ttdeci">int TVMAotExecutor_GetInputIndex(TVMAotExecutor *executor, const char *name)</div><div class="ttdoc">Return the input index of the specified input name.</div></div>
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+<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:229</div></div>
<div class="ttc" id="ametadata__types_8h_html"><div class="ttname"><a href="metadata__types_8h.html">metadata_types.h</a></div><div class="ttdoc">Defines types which can be used in metadata here which are also shared between C and C++ code bases.</div></div>
<div class="ttc" id="astructTVMAotExecutor_html"><div class="ttname"><a href="structTVMAotExecutor.html">TVMAotExecutor</a></div><div class="ttdef"><b>Definition:</b> aot_executor.h:37</div></div>
<div class="ttc" id="astructTVMAotExecutor_html_a0d4158663d39f79d88d2bc0355c9f1eb"><div class="ttname"><a href="structTVMAotExecutor.html#a0d4158663d39f79d88d2bc0355c9f1eb">TVMAotExecutor::module_handle</a></div><div class="ttdeci">TVMModuleHandle module_handle</div><div class="ttdoc">The code module that contains the compiled model.</div><div class="ttdef"><b>Definition:</b> aot_executor.h:41</div></div>
diff --git a/docs/reference/api/doxygen/c__backend__api_8h_source.html b/docs/reference/api/doxygen/c__backend__api_8h_source.html
index 0a122997fb..5f35ef72fd 100644
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+++ b/docs/reference/api/doxygen/c__backend__api_8h_source.html
@@ -140,7 +140,7 @@ $(function() {
<div class="ttc" id="ac__backend__api_8h_html_ac1b48128210969500e27aad08a7fe51b"><div class="ttname"><a href="c__backend__api_8h.html#ac1b48128210969500e27aad08a7fe51b">FTVMParallelLambda</a></div><div class="ttdeci">int(* FTVMParallelLambda)(int task_id, TVMParallelGroupEnv *penv, void *cdata)</div><div class="ttdoc">The callback function to execute a parallel lambda.</div><div class="ttdef"><b>Definition:</b> c_backend_api.h:134</div></div>
<div class="ttc" id="ac__backend__api_8h_html_ae70bd3ee026eb55b438ada05b08f0ce8"><div class="ttname"><a href="c__backend__api_8h.html#ae70bd3ee026eb55b438ada05b08f0ce8">TVMBackendRunOnce</a></div><div class="ttdeci">int TVMBackendRunOnce(void **handle, int(*f)(void *), void *cdata, int nbytes)</div><div class="ttdoc">Simple static initialization function. Run f once and set handle to be not null. This function is mai...</div></div>
<div class="ttc" id="ac__runtime__api_8h_html"><div class="ttname"><a href="c__runtime__api_8h.html">c_runtime_api.h</a></div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_acf57d257a6e0841d84ebbd2a339d183e"><div class="ttname"><a href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a></div><div class="ttdeci">void * TVMFunctionHandle</div><div class="ttdoc">Handle to packed function handle.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:232</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_acf57d257a6e0841d84ebbd2a339d183e"><div class="ttname"><a href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a></div><div class="ttdeci">void * TVMFunctionHandle</div><div class="ttdoc">Handle to packed function handle.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:231</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1attr_html_a61b1ef1047fb722a4e5ec2167c9963d7"><div class="ttname"><a href="namespacetvm_1_1tir_1_1attr.html#a61b1ef1047fb722a4e5ec2167c9963d7">tvm::tir::attr::device_id</a></div><div class="ttdeci">constexpr const char * device_id</div><div class="ttdoc">The allocation device for global malloc in host.</div><div class="ttdef"><b>Definition:</b> stmt.h:1420</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1attr_html_a7e4e7cd47471a9089022214d63d24206"><div class="ttname"><a href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">tvm::tir::attr::device_type</a></div><div class="ttdeci">constexpr const char * device_type</div><div class="ttdoc">The device type.</div><div class="ttdef"><b>Definition:</b> stmt.h:1422</div></div>
<div class="ttc" id="astructTVMParallelGroupEnv_html"><div class="ttname"><a href="structTVMParallelGroupEnv.html">TVMParallelGroupEnv</a></div><div class="ttdoc">Environment for TVM parallel task.</div><div class="ttdef"><b>Definition:</b> c_backend_api.h:119</div></div>
diff --git a/docs/reference/api/doxygen/c__runtime__api_8h_source.html b/docs/reference/api/doxygen/c__runtime__api_8h_source.html
index fc4122b61f..1572045ac0 100644
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<div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="unionTVMValue.html"> 210</a></span> <span class="keyword">typedef</span> <span class="keyword">union </span>{</div>
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-<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  </div>
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-<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  </div>
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-<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  </div>
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-<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  </div>
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-<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  </div>
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-<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  </div>
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-<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  </div>
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-<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a>* out);</div>
-<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  </div>
-<div class="line"><a name="l00359"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac16764609b45ac911a5c9971e195e377"> 359</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ac16764609b45ac911a5c9971e195e377">TVMModFree</a>(<a class="code" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a> <a class="code" href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">mod< [...]
-<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  </div>
-<div class="line"><a name="l00366"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#af420f0d74004615c03bb40270ad5d489"> 366</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#af420f0d74004615c03bb40270ad5d489">TVMFuncFree</a>(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> func);</div>
-<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  </div>
-<div class="line"><a name="l00388"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a8a01e20ffd5784465df0759c950154b5"> 388</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a8a01e20ffd5784465df0759c950154b5">TVMFuncCall</a>(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> func, <a class="code" href="unionTVMValue.html">TVMValue</a>* arg_values, <span clas [...]
-<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="unionTVMValue.html">TVMValue</a>* ret_val, <span class="keywordtype">int</span>* ret_type_code);</div>
-<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  </div>
-<div class="line"><a name="l00402"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad0c98ff469bd844546a71f61d2e164f3"> 402</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad0c98ff469bd844546a71f61d2e164f3">TVMCFuncSetReturn</a>(<a class="code" href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4">TVMRetValueHandle</a> <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ae7816fdebd5d56f2145 [...]
-<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  </div>
-<div class="line"><a name="l00414"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a6fee438ab81fbf4235375d9f17f36d6f"> 414</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a6fee438ab81fbf4235375d9f17f36d6f">TVMCbArgToReturn</a>(<a class="code" href="unionTVMValue.html">TVMValue</a>* value, <span class="keywordtype">int</span>* code);</div>
-<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  </div>
-<div class="line"><a name="l00427"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3"> 427</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a>)(<a class="code" href="unionTVMValue.html">TVMValue</a>* args, <span class="keywordtype">int</span>* type_codes, <span class="keywordtype">int</span> num_args, <a class="code" href="c__r [...]
-<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordtype">void</span>* resource_handle);</div>
-<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  </div>
-<div class="line"><a name="l00434"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742"> 434</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a>)(<span class="keywordtype">void</span>* resource_handle);</div>
-<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  </div>
-<div class="line"><a name="l00445"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d"> 445</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d">TVMExtensionFuncDeclarer</a>)(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> register_func_handle);</div>
-<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  </div>
-<div class="line"><a name="l00458"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#adfd8d1f9e7229625d4acd7da3b10287c"> 458</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#adfd8d1f9e7229625d4acd7da3b10287c">TVMFuncCreateFromCFunc</a>(<a class="code" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a> func, <span class="keywordtype">void</span>* resource_handle,</div>
-<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a> fin, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a>* out);</div>
-<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  </div>
-<div class="line"><a name="l00470"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a9642167d9a76bfe7e81174e8776d504b"> 470</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a9642167d9a76bfe7e81174e8776d504b">TVMFuncRegisterGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandl [...]
-<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  </div>
-<div class="line"><a name="l00481"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad3bd42da244a0e32ac82d7428e01a010"> 481</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad3bd42da244a0e32ac82d7428e01a010">TVMFuncGetGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> [...]
-<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  </div>
-<div class="line"><a name="l00489"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a392740d028710fcd356b2b63ab90fd01"> 489</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a392740d028710fcd356b2b63ab90fd01">TVMFuncListGlobalNames</a>(<span class="keywordtype">int</span>* out_size, <span class="keyword">const</span> <span class="keywordtype">char</span>*** out_array);</div>
-<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  </div>
-<div class="line"><a name="l00495"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab98d6b66089da8b33b662ccdb243b26b"> 495</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ab98d6b66089da8b33b662ccdb243b26b">TVMFuncRemoveGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name);</div>
-<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  </div>
-<div class="line"><a name="l00497"></a><span class="lineno"> 497</span> <span class="comment">// Array related apis for quick proptyping</span></div>
-<div class="line"><a name="l00512"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a014b89c1a3b17473d82cb3973eeedba7"> 512</a></span> <span class="comment"></span>TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a014b89c1a3b17473d82cb3973eeedba7">TVMArrayAlloc</a>(<span class="keyword">const</span> <a class="code" href="c__runtime__api_8h.html#ace8007daffd9f2c6d954c24d870bfcc4">tvm_index_t</a>* <a class="code" href="n [...]
-<div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordtype">int</span> dtype_lanes, <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a61b1ef1047fb722a4e5ec2167c9963d7">device_id</a>, <a class="code" href="c__runtime__api_8h.html#a73ca5 [...]
-<div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  </div>
-<div class="line"><a name="l00520"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a591e48e52098965e235657dab56bc096"> 520</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a591e48e52098965e235657dab56bc096">TVMArrayFree</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle);</div>
-<div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  </div>
-<div class="line"><a name="l00529"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a50e9b3dfdc1f4228bf52a26da14456a1"> 529</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a50e9b3dfdc1f4228bf52a26da14456a1">TVMArrayCopyFromBytes</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle, <span class="keywordtype">void</span>* data, <span class="keywordtype [...]
-<div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  </div>
-<div class="line"><a name="l00538"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae246eaa00342c042f3f194605ad9bc7a"> 538</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae246eaa00342c042f3f194605ad9bc7a">TVMArrayCopyToBytes</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle, <span class="keywordtype">void</span>* data, <span class="keywordtype"> [...]
-<div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  </div>
-<div class="line"><a name="l00547"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a120628b5226af6deee4010c5a512f618"> 547</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a120628b5226af6deee4010c5a512f618">TVMArrayCopyFromTo</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> from, <a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e [...]
-<div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  </div>
-<div class="line"><a name="l00556"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad67b8f8766fce6b62c82a458e3c9b053"> 556</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad67b8f8766fce6b62c82a458e3c9b053">TVMArrayFromDLPack</a>(DLManagedTensor* from, <a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a>* out);</div>
-<div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  </div>
-<div class="line"><a name="l00565"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae690840d1af9c7b0fe5b9b457456f60d"> 565</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae690840d1af9c7b0fe5b9b457456f60d">TVMArrayToDLPack</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> from, DLManagedTensor** out);</div>
-<div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  </div>
-<div class="line"><a name="l00571"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a6483791fc218022e690b6e0d81a095b0"> 571</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#a6483791fc218022e690b6e0d81a095b0">TVMDLManagedTensorCallDeleter</a>(DLManagedTensor* dltensor);</div>
-<div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  </div>
-<div class="line"><a name="l00581"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a71d0c47cca9068b17cf0a1f4c58d8ca9"> 581</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a71d0c47cca9068b17cf0a1f4c58d8ca9">TVMStreamCreate</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a [...]
-<div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  </div>
-<div class="line"><a name="l00591"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a9dd0545318bfee4e2f4ccce7d7008e01"> 591</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a9dd0545318bfee4e2f4ccce7d7008e01">TVMStreamFree</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a c [...]
-<div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  </div>
-<div class="line"><a name="l00604"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac414ed248ddb1bfb561685bba3de5e89"> 604</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ac414ed248ddb1bfb561685bba3de5e89">TVMSetStream</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a cl [...]
-<div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  </div>
-<div class="line"><a name="l00614"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a386d7efd946bc750af8bf109f93f6ce2"> 614</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a386d7efd946bc750af8bf109f93f6ce2">TVMSynchronize</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a [...]
-<div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  </div>
-<div class="line"><a name="l00625"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa91f776ed41a36790409f78725f81419"> 625</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa91f776ed41a36790409f78725f81419">TVMStreamStreamSynchronize</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">in [...]
-<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a> dst);</div>
-<div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  </div>
-<div class="line"><a name="l00635"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab9ddee6cf53918337cd4ca68d8bfc8b1"> 635</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ab9ddee6cf53918337cd4ca68d8bfc8b1">TVMObjectGetTypeIndex</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj, <span class="keywordtype">unsigned</span>* out_tindex);</div>
-<div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  </div>
-<div class="line"><a name="l00643"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a77d84581b6d0d78c77d88139c6adbf81"> 643</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a77d84581b6d0d78c77d88139c6adbf81">TVMObjectTypeKey2Index</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* type_key, <span class="keywordtype">unsigned</span>* out_tindex);</div>
-<div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  </div>
-<div class="line"><a name="l00651"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae867a91b746fbe91e289b0d2ab0446aa"> 651</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae867a91b746fbe91e289b0d2ab0446aa">TVMObjectTypeIndex2Key</a>(<span class="keywordtype">unsigned</span> tindex, <span class="keywordtype">char</span>** out_type_key);</div>
-<div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  </div>
-<div class="line"><a name="l00660"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae899a6a211b7913e92420a01b804db64"> 660</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae899a6a211b7913e92420a01b804db64">TVMObjectRetain</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj);</div>
-<div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  </div>
-<div class="line"><a name="l00670"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a660b709e0d509be7909fc31e3690d3f9"> 670</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a660b709e0d509be7909fc31e3690d3f9">TVMObjectFree</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj);</div>
-<div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  </div>
-<div class="line"><a name="l00677"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa9f2237f2fdcb96a75e7f3c6fa4209aa"> 677</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa9f2237f2fdcb96a75e7f3c6fa4209aa">TVMByteArrayFree</a>(<a class="code" href="structTVMByteArray.html">TVMByteArray</a>* arr);</div>
-<div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  </div>
-<div class="line"><a name="l00689"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a06455d20ce57f3d3d13776ff6bdf0b61"> 689</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a06455d20ce57f3d3d13776ff6bdf0b61">TVMDeviceAllocDataSpace</a>(DLDevice dev, <span class="keywordtype">size_t</span> nbytes, <span class="keywordtype">size_t</span> alignment,</div>
-<div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  DLDataType type_hint, <span class="keywordtype">void</span>** out_data);</div>
-<div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  </div>
-<div class="line"><a name="l00705"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a54212f093a2fc24a5fd7c3fecf29813a"> 705</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a54212f093a2fc24a5fd7c3fecf29813a">TVMDeviceAllocDataSpaceWithScope</a>(DLDevice dev, <span class="keywordtype">int</span> ndim, <span class="keyword">const</span> int64_t* <a class="code" href="namespacetvm_1_1topi.html#af30c02f3a3f37c79 [...]
-<div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  DLDataType dtype, <span class="keyword">const</span> <span class="keywordtype">char</span>* mem_scope,</div>
-<div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keywordtype">void</span>** out_data);</div>
-<div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  </div>
-<div class="line"><a name="l00715"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a05c7535a79c99bddaf9e3aebde6013e6"> 715</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a05c7535a79c99bddaf9e3aebde6013e6">TVMDeviceFreeDataSpace</a>(DLDevice dev, <span class="keywordtype">void</span>* ptr);</div>
-<div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  </div>
-<div class="line"><a name="l00726"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa588ee4bed71a4e7cb1f08ae8c46568f"> 726</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa588ee4bed71a4e7cb1f08ae8c46568f">TVMDeviceCopyDataFromTo</a>(DLTensor* from, DLTensor* to, <a class="code" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a> stream);</div>
-<div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  </div>
-<div class="line"><a name="l00735"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a1d193b0c247af874dad4b31afc8ee7d8"> 735</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a1d193b0c247af874dad4b31afc8ee7d8">TVMObjectDerivedFrom</a>(uint32_t child_type_index, uint32_t parent_type_index,</div>
-<div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordtype">int</span>* is_derived);</div>
-<div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  </div>
-<div class="line"><a name="l00738"></a><span class="lineno"> 738</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
-<div class="line"><a name="l00739"></a><span class="lineno"> 739</span> } <span class="comment">// TVM_EXTERN_C</span></div>
-<div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="preprocessor">#endif</span></div>
-<div class="line"><a name="l00741"></a><span class="lineno"> 741</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_C_RUNTIME_API_H_</span></div>
+<div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="unionTVMValue.html#abefae93ebafe8818c0060df2bf31e6e5"> 212</a></span>  <span class="keywordtype">double</span> <a class="code" href="unionTVMValue.html#abefae93ebafe8818c0060df2bf31e6e5">v_float64</a>;</div>
+<div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="unionTVMValue.html#a3b172297f9f7bf2f5391f7d87309cd44"> 213</a></span>  <span class="keywordtype">void</span>* <a class="code" href="unionTVMValue.html#a3b172297f9f7bf2f5391f7d87309cd44">v_handle</a>;</div>
+<div class="line"><a name="l00214"></a><span class="lineno"><a class="line" href="unionTVMValue.html#ab0e4dea6ca370fe7a7d22f4fc23a4f47"> 214</a></span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="unionTVMValue.html#ab0e4dea6ca370fe7a7d22f4fc23a4f47">v_str</a>;</div>
+<div class="line"><a name="l00215"></a><span class="lineno"><a class="line" href="unionTVMValue.html#a75af73d1e39bc5de7073a008d44b0d16"> 215</a></span>  DLDataType <a class="code" href="unionTVMValue.html#a75af73d1e39bc5de7073a008d44b0d16">v_type</a>;</div>
+<div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="unionTVMValue.html#a2296018ee2f5953b9cfb1a36475c303f"> 216</a></span>  DLDevice <a class="code" href="unionTVMValue.html#a2296018ee2f5953b9cfb1a36475c303f">v_device</a>;</div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> } <a class="code" href="unionTVMValue.html">TVMValue</a>;</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  </div>
+<div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="structTVMByteArray.html"> 223</a></span> <span class="keyword">typedef</span> <span class="keyword">struct </span>{</div>
+<div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1"> 224</a></span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1">data</a>;</div>
+<div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476"> 225</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476">size</a>;</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> } <a class="code" href="structTVMByteArray.html">TVMByteArray</a>;</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  </div>
+<div class="line"><a name="l00229"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce"> 229</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a>;</div>
+<div class="line"><a name="l00231"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e"> 231</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a>;</div>
+<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4"> 233</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4">TVMRetValueHandle</a>;</div>
+<div class="line"><a name="l00238"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757"> 238</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a>;</div>
+<div class="line"><a name="l00240"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0"> 240</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a>;</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  </div>
+<div class="line"><a name="l00247"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a477111f9accd70633dc5f2e7139b6cf4"> 247</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#a477111f9accd70633dc5f2e7139b6cf4">TVMAPISetLastError</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* msg);</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  </div>
+<div class="line"><a name="l00254"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a5c4a7d6f88c0eaa5d67ca70df0991244"> 254</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#a5c4a7d6f88c0eaa5d67ca70df0991244">TVMAPISetLastPythonError</a>(<span class="keywordtype">void</span>* py_object);</div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  </div>
+<div class="line"><a name="l00266"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab305492ac6ef86d73ff0553269e0796f"> 266</a></span> TVM_DLL <span class="keywordtype">void</span>* <a class="code" href="c__runtime__api_8h.html#ab305492ac6ef86d73ff0553269e0796f">TVMGetLastPythonError</a>();</div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  </div>
+<div class="line"><a name="l00277"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a775383bcd8c0237e36bdf0c9654d62c3"> 277</a></span> TVM_DLL <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="c__runtime__api_8h.html#a775383bcd8c0237e36bdf0c9654d62c3">TVMGetLastError</a>(<span class="keywordtype">void</span>);</div>
+<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  </div>
+<div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a209fb326e0d22de1949c8812dba2d0d5"> 288</a></span> TVM_DLL <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="c__runtime__api_8h.html#a209fb326e0d22de1949c8812dba2d0d5">TVMGetLastBacktrace</a>();</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  </div>
+<div class="line"><a name="l00302"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab87d040544cbb69b0eac36cd4453d0e6"> 302</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#ab87d040544cbb69b0eac36cd4453d0e6">TVMDropLastPythonError</a>();</div>
+<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  </div>
+<div class="line"><a name="l00312"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#acb905f16569e0700f23b9a2c4588ee4c"> 312</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#acb905f16569e0700f23b9a2c4588ee4c">TVMThrowLastError</a>();</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  </div>
+<div class="line"><a name="l00324"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a47f79bb2004573a8e36a175d1b73fc64"> 324</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a47f79bb2004573a8e36a175d1b73fc64">TVMModLoadFromFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* file_name, <span class="keyword">const</span> <span class="keywordtype">char</span>* format, <a class="cod [...]
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  </div>
+<div class="line"><a name="l00334"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aaeb0d67f6d7e90e671dd5a53b1d6b81a"> 334</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aaeb0d67f6d7e90e671dd5a53b1d6b81a">TVMModImport</a>(<a class="code" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a> <a class="code" href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">mo [...]
+<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  </div>
+<div class="line"><a name="l00344"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a5a19a18084c94396a673efa309405a6c"> 344</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a5a19a18084c94396a673efa309405a6c">TVMModGetFunction</a>(<a class="code" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a> <a class="code" href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a [...]
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a>* out);</div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  </div>
+<div class="line"><a name="l00358"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac16764609b45ac911a5c9971e195e377"> 358</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ac16764609b45ac911a5c9971e195e377">TVMModFree</a>(<a class="code" href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a> <a class="code" href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">mod< [...]
+<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  </div>
+<div class="line"><a name="l00365"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#af420f0d74004615c03bb40270ad5d489"> 365</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#af420f0d74004615c03bb40270ad5d489">TVMFuncFree</a>(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> func);</div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  </div>
+<div class="line"><a name="l00387"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a8a01e20ffd5784465df0759c950154b5"> 387</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a8a01e20ffd5784465df0759c950154b5">TVMFuncCall</a>(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> func, <a class="code" href="unionTVMValue.html">TVMValue</a>* arg_values, <span clas [...]
+<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="unionTVMValue.html">TVMValue</a>* ret_val, <span class="keywordtype">int</span>* ret_type_code);</div>
+<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  </div>
+<div class="line"><a name="l00401"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad0c98ff469bd844546a71f61d2e164f3"> 401</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad0c98ff469bd844546a71f61d2e164f3">TVMCFuncSetReturn</a>(<a class="code" href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4">TVMRetValueHandle</a> <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ae7816fdebd5d56f2145 [...]
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  </div>
+<div class="line"><a name="l00413"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a6fee438ab81fbf4235375d9f17f36d6f"> 413</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a6fee438ab81fbf4235375d9f17f36d6f">TVMCbArgToReturn</a>(<a class="code" href="unionTVMValue.html">TVMValue</a>* value, <span class="keywordtype">int</span>* code);</div>
+<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  </div>
+<div class="line"><a name="l00426"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3"> 426</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a>)(<a class="code" href="unionTVMValue.html">TVMValue</a>* args, <span class="keywordtype">int</span>* type_codes, <span class="keywordtype">int</span> num_args, <a class="code" href="c__r [...]
+<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordtype">void</span>* resource_handle);</div>
+<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  </div>
+<div class="line"><a name="l00433"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742"> 433</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a>)(<span class="keywordtype">void</span>* resource_handle);</div>
+<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  </div>
+<div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d"> 444</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d">TVMExtensionFuncDeclarer</a>)(<a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> register_func_handle);</div>
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  </div>
+<div class="line"><a name="l00457"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#adfd8d1f9e7229625d4acd7da3b10287c"> 457</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#adfd8d1f9e7229625d4acd7da3b10287c">TVMFuncCreateFromCFunc</a>(<a class="code" href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a> func, <span class="keywordtype">void</span>* resource_handle,</div>
+<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a> fin, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a>* out);</div>
+<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  </div>
+<div class="line"><a name="l00469"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a9642167d9a76bfe7e81174e8776d504b"> 469</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a9642167d9a76bfe7e81174e8776d504b">TVMFuncRegisterGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandl [...]
+<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  </div>
+<div class="line"><a name="l00480"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad3bd42da244a0e32ac82d7428e01a010"> 480</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad3bd42da244a0e32ac82d7428e01a010">TVMFuncGetGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name, <a class="code" href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a> [...]
+<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  </div>
+<div class="line"><a name="l00488"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a392740d028710fcd356b2b63ab90fd01"> 488</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a392740d028710fcd356b2b63ab90fd01">TVMFuncListGlobalNames</a>(<span class="keywordtype">int</span>* out_size, <span class="keyword">const</span> <span class="keywordtype">char</span>*** out_array);</div>
+<div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  </div>
+<div class="line"><a name="l00494"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab98d6b66089da8b33b662ccdb243b26b"> 494</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ab98d6b66089da8b33b662ccdb243b26b">TVMFuncRemoveGlobal</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name);</div>
+<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  </div>
+<div class="line"><a name="l00496"></a><span class="lineno"> 496</span> <span class="comment">// Array related apis for quick proptyping</span></div>
+<div class="line"><a name="l00511"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a014b89c1a3b17473d82cb3973eeedba7"> 511</a></span> <span class="comment"></span>TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a014b89c1a3b17473d82cb3973eeedba7">TVMArrayAlloc</a>(<span class="keyword">const</span> <a class="code" href="c__runtime__api_8h.html#ace8007daffd9f2c6d954c24d870bfcc4">tvm_index_t</a>* <a class="code" href="n [...]
+<div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keywordtype">int</span> dtype_lanes, <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a61b1ef1047fb722a4e5ec2167c9963d7">device_id</a>, <a class="code" href="c__runtime__api_8h.html#a73ca5 [...]
+<div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  </div>
+<div class="line"><a name="l00519"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a591e48e52098965e235657dab56bc096"> 519</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a591e48e52098965e235657dab56bc096">TVMArrayFree</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle);</div>
+<div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  </div>
+<div class="line"><a name="l00528"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a50e9b3dfdc1f4228bf52a26da14456a1"> 528</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a50e9b3dfdc1f4228bf52a26da14456a1">TVMArrayCopyFromBytes</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle, <span class="keywordtype">void</span>* data, <span class="keywordtype [...]
+<div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  </div>
+<div class="line"><a name="l00537"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae246eaa00342c042f3f194605ad9bc7a"> 537</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae246eaa00342c042f3f194605ad9bc7a">TVMArrayCopyToBytes</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> handle, <span class="keywordtype">void</span>* data, <span class="keywordtype"> [...]
+<div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  </div>
+<div class="line"><a name="l00546"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a120628b5226af6deee4010c5a512f618"> 546</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a120628b5226af6deee4010c5a512f618">TVMArrayCopyFromTo</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> from, <a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e [...]
+<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  </div>
+<div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ad67b8f8766fce6b62c82a458e3c9b053"> 555</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ad67b8f8766fce6b62c82a458e3c9b053">TVMArrayFromDLPack</a>(DLManagedTensor* from, <a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a>* out);</div>
+<div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  </div>
+<div class="line"><a name="l00564"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae690840d1af9c7b0fe5b9b457456f60d"> 564</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae690840d1af9c7b0fe5b9b457456f60d">TVMArrayToDLPack</a>(<a class="code" href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a> from, DLManagedTensor** out);</div>
+<div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  </div>
+<div class="line"><a name="l00570"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a6483791fc218022e690b6e0d81a095b0"> 570</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="c__runtime__api_8h.html#a6483791fc218022e690b6e0d81a095b0">TVMDLManagedTensorCallDeleter</a>(DLManagedTensor* dltensor);</div>
+<div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  </div>
+<div class="line"><a name="l00580"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a71d0c47cca9068b17cf0a1f4c58d8ca9"> 580</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a71d0c47cca9068b17cf0a1f4c58d8ca9">TVMStreamCreate</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a [...]
+<div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  </div>
+<div class="line"><a name="l00590"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a9dd0545318bfee4e2f4ccce7d7008e01"> 590</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a9dd0545318bfee4e2f4ccce7d7008e01">TVMStreamFree</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a c [...]
+<div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  </div>
+<div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ac414ed248ddb1bfb561685bba3de5e89"> 603</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ac414ed248ddb1bfb561685bba3de5e89">TVMSetStream</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a cl [...]
+<div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  </div>
+<div class="line"><a name="l00613"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a386d7efd946bc750af8bf109f93f6ce2"> 613</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a386d7efd946bc750af8bf109f93f6ce2">TVMSynchronize</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">int</span> <a [...]
+<div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  </div>
+<div class="line"><a name="l00624"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa91f776ed41a36790409f78725f81419"> 624</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa91f776ed41a36790409f78725f81419">TVMStreamStreamSynchronize</a>(<span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">device_type</a>, <span class="keywordtype">in [...]
+<div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a> dst);</div>
+<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  </div>
+<div class="line"><a name="l00634"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ab9ddee6cf53918337cd4ca68d8bfc8b1"> 634</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ab9ddee6cf53918337cd4ca68d8bfc8b1">TVMObjectGetTypeIndex</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj, <span class="keywordtype">unsigned</span>* out_tindex);</div>
+<div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  </div>
+<div class="line"><a name="l00642"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a77d84581b6d0d78c77d88139c6adbf81"> 642</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a77d84581b6d0d78c77d88139c6adbf81">TVMObjectTypeKey2Index</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* type_key, <span class="keywordtype">unsigned</span>* out_tindex);</div>
+<div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  </div>
+<div class="line"><a name="l00650"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae867a91b746fbe91e289b0d2ab0446aa"> 650</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae867a91b746fbe91e289b0d2ab0446aa">TVMObjectTypeIndex2Key</a>(<span class="keywordtype">unsigned</span> tindex, <span class="keywordtype">char</span>** out_type_key);</div>
+<div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  </div>
+<div class="line"><a name="l00659"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#ae899a6a211b7913e92420a01b804db64"> 659</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#ae899a6a211b7913e92420a01b804db64">TVMObjectRetain</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj);</div>
+<div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  </div>
+<div class="line"><a name="l00669"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a660b709e0d509be7909fc31e3690d3f9"> 669</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a660b709e0d509be7909fc31e3690d3f9">TVMObjectFree</a>(<a class="code" href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a> obj);</div>
+<div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  </div>
+<div class="line"><a name="l00676"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa9f2237f2fdcb96a75e7f3c6fa4209aa"> 676</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa9f2237f2fdcb96a75e7f3c6fa4209aa">TVMByteArrayFree</a>(<a class="code" href="structTVMByteArray.html">TVMByteArray</a>* arr);</div>
+<div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  </div>
+<div class="line"><a name="l00688"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a06455d20ce57f3d3d13776ff6bdf0b61"> 688</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a06455d20ce57f3d3d13776ff6bdf0b61">TVMDeviceAllocDataSpace</a>(DLDevice dev, <span class="keywordtype">size_t</span> nbytes, <span class="keywordtype">size_t</span> alignment,</div>
+<div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  DLDataType type_hint, <span class="keywordtype">void</span>** out_data);</div>
+<div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  </div>
+<div class="line"><a name="l00704"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a54212f093a2fc24a5fd7c3fecf29813a"> 704</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a54212f093a2fc24a5fd7c3fecf29813a">TVMDeviceAllocDataSpaceWithScope</a>(DLDevice dev, <span class="keywordtype">int</span> ndim, <span class="keyword">const</span> int64_t* <a class="code" href="namespacetvm_1_1topi.html#af30c02f3a3f37c79 [...]
+<div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  DLDataType dtype, <span class="keyword">const</span> <span class="keywordtype">char</span>* mem_scope,</div>
+<div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordtype">void</span>** out_data);</div>
+<div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  </div>
+<div class="line"><a name="l00714"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a05c7535a79c99bddaf9e3aebde6013e6"> 714</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a05c7535a79c99bddaf9e3aebde6013e6">TVMDeviceFreeDataSpace</a>(DLDevice dev, <span class="keywordtype">void</span>* ptr);</div>
+<div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  </div>
+<div class="line"><a name="l00725"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#aa588ee4bed71a4e7cb1f08ae8c46568f"> 725</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#aa588ee4bed71a4e7cb1f08ae8c46568f">TVMDeviceCopyDataFromTo</a>(DLTensor* from, DLTensor* to, <a class="code" href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a> stream);</div>
+<div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  </div>
+<div class="line"><a name="l00734"></a><span class="lineno"><a class="line" href="c__runtime__api_8h.html#a1d193b0c247af874dad4b31afc8ee7d8"> 734</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="c__runtime__api_8h.html#a1d193b0c247af874dad4b31afc8ee7d8">TVMObjectDerivedFrom</a>(uint32_t child_type_index, uint32_t parent_type_index,</div>
+<div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordtype">int</span>* is_derived);</div>
+<div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  </div>
+<div class="line"><a name="l00737"></a><span class="lineno"> 737</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l00738"></a><span class="lineno"> 738</span> } <span class="comment">// TVM_EXTERN_C</span></div>
+<div class="line"><a name="l00739"></a><span class="lineno"> 739</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_C_RUNTIME_API_H_</span></div>
<div class="ttc" id="ac__runtime__api_8h_html_a014b89c1a3b17473d82cb3973eeedba7"><div class="ttname"><a href="c__runtime__api_8h.html#a014b89c1a3b17473d82cb3973eeedba7">TVMArrayAlloc</a></div><div class="ttdeci">int TVMArrayAlloc(const tvm_index_t *shape, int ndim, int dtype_code, int dtype_bits, int dtype_lanes, int device_type, int device_id, TVMArrayHandle *out)</div><div class="ttdoc">Allocate a nd-array's memory, including space of shape, of given spec.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a05c7535a79c99bddaf9e3aebde6013e6"><div class="ttname"><a href="c__runtime__api_8h.html#a05c7535a79c99bddaf9e3aebde6013e6">TVMDeviceFreeDataSpace</a></div><div class="ttdeci">int TVMDeviceFreeDataSpace(DLDevice dev, void *ptr)</div><div class="ttdoc">Free a data space on device.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a06455d20ce57f3d3d13776ff6bdf0b61"><div class="ttname"><a href="c__runtime__api_8h.html#a06455d20ce57f3d3d13776ff6bdf0b61">TVMDeviceAllocDataSpace</a></div><div class="ttdeci">int TVMDeviceAllocDataSpace(DLDevice dev, size_t nbytes, size_t alignment, DLDataType type_hint, void **out_data)</div><div class="ttdoc">Allocate a data space on device.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_a07954203342f2787acf988c4c351d9c3"><div class="ttname"><a href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a></div><div class="ttdeci">int(* TVMPackedCFunc)(TVMValue *args, int *type_codes, int num_args, TVMRetValueHandle ret, void *resource_handle)</div><div class="ttdoc">C type of packed function.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:427</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_a07954203342f2787acf988c4c351d9c3"><div class="ttname"><a href="c__runtime__api_8h.html#a07954203342f2787acf988c4c351d9c3">TVMPackedCFunc</a></div><div class="ttdeci">int(* TVMPackedCFunc)(TVMValue *args, int *type_codes, int num_args, TVMRetValueHandle ret, void *resource_handle)</div><div class="ttdoc">C type of packed function.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:426</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a120628b5226af6deee4010c5a512f618"><div class="ttname"><a href="c__runtime__api_8h.html#a120628b5226af6deee4010c5a512f618">TVMArrayCopyFromTo</a></div><div class="ttdeci">int TVMArrayCopyFromTo(TVMArrayHandle from, TVMArrayHandle to, TVMStreamHandle stream)</div><div class="ttdoc">Copy the array, both from and to must be valid during the copy.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a190e81769e805cca153514137a66e793"><div class="ttname"><a href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793">TVMArgTypeCode</a></div><div class="ttdeci">TVMArgTypeCode</div><div class="ttdoc">The type code in used and only used in TVM FFI for argument passing.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:175</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a190e81769e805cca153514137a66e793a2a80307d3075829e12d14ba6ec52a6ee"><div class="ttname"><a href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a2a80307d3075829e12d14ba6ec52a6ee">kTVMExtReserveEnd</a></div><div class="ttdeci">@ kTVMExtReserveEnd</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:199</div></div>
@@ -393,7 +392,7 @@ $(function() {
<div class="ttc" id="ac__runtime__api_8h_html_a209fb326e0d22de1949c8812dba2d0d5"><div class="ttname"><a href="c__runtime__api_8h.html#a209fb326e0d22de1949c8812dba2d0d5">TVMGetLastBacktrace</a></div><div class="ttdeci">const char * TVMGetLastBacktrace()</div><div class="ttdoc">Return the backtrace of the most recent error.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a386d7efd946bc750af8bf109f93f6ce2"><div class="ttname"><a href="c__runtime__api_8h.html#a386d7efd946bc750af8bf109f93f6ce2">TVMSynchronize</a></div><div class="ttdeci">int TVMSynchronize(int device_type, int device_id, TVMStreamHandle stream)</div><div class="ttdoc">Wait until all computations on stream completes.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a392740d028710fcd356b2b63ab90fd01"><div class="ttname"><a href="c__runtime__api_8h.html#a392740d028710fcd356b2b63ab90fd01">TVMFuncListGlobalNames</a></div><div class="ttdeci">int TVMFuncListGlobalNames(int *out_size, const char ***out_array)</div><div class="ttdoc">List all the globally registered function name.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_a3980ab80477e57fc6045c12ea8b9e48d"><div class="ttname"><a href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d">TVMExtensionFuncDeclarer</a></div><div class="ttdeci">int(* TVMExtensionFuncDeclarer)(TVMFunctionHandle register_func_handle)</div><div class="ttdoc">Signature for extension function declarer.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:445</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_a3980ab80477e57fc6045c12ea8b9e48d"><div class="ttname"><a href="c__runtime__api_8h.html#a3980ab80477e57fc6045c12ea8b9e48d">TVMExtensionFuncDeclarer</a></div><div class="ttdeci">int(* TVMExtensionFuncDeclarer)(TVMFunctionHandle register_func_handle)</div><div class="ttdoc">Signature for extension function declarer.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:444</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a477111f9accd70633dc5f2e7139b6cf4"><div class="ttname"><a href="c__runtime__api_8h.html#a477111f9accd70633dc5f2e7139b6cf4">TVMAPISetLastError</a></div><div class="ttdeci">void TVMAPISetLastError(const char *msg)</div><div class="ttdoc">Used for implementing C API function. Set last error message before return.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a47f79bb2004573a8e36a175d1b73fc64"><div class="ttname"><a href="c__runtime__api_8h.html#a47f79bb2004573a8e36a175d1b73fc64">TVMModLoadFromFile</a></div><div class="ttdeci">int TVMModLoadFromFile(const char *file_name, const char *format, TVMModuleHandle *out)</div><div class="ttdoc">Load module from file.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a50e9b3dfdc1f4228bf52a26da14456a1"><div class="ttname"><a href="c__runtime__api_8h.html#a50e9b3dfdc1f4228bf52a26da14456a1">TVMArrayCopyFromBytes</a></div><div class="ttdeci">int TVMArrayCopyFromBytes(TVMArrayHandle handle, void *data, size_t nbytes)</div><div class="ttdoc">Copy array data from CPU byte array.</div></div>
@@ -409,7 +408,7 @@ $(function() {
<div class="ttc" id="ac__runtime__api_8h_html_a5c4a7d6f88c0eaa5d67ca70df0991244"><div class="ttname"><a href="c__runtime__api_8h.html#a5c4a7d6f88c0eaa5d67ca70df0991244">TVMAPISetLastPythonError</a></div><div class="ttdeci">void TVMAPISetLastPythonError(void *py_object)</div><div class="ttdoc">Used for implementing C API function. Set last exception before return.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a6483791fc218022e690b6e0d81a095b0"><div class="ttname"><a href="c__runtime__api_8h.html#a6483791fc218022e690b6e0d81a095b0">TVMDLManagedTensorCallDeleter</a></div><div class="ttdeci">void TVMDLManagedTensorCallDeleter(DLManagedTensor *dltensor)</div><div class="ttdoc">Delete (free) a DLManagedTensor's data.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a660b709e0d509be7909fc31e3690d3f9"><div class="ttname"><a href="c__runtime__api_8h.html#a660b709e0d509be7909fc31e3690d3f9">TVMObjectFree</a></div><div class="ttdeci">int TVMObjectFree(TVMObjectHandle obj)</div><div class="ttdoc">Free the object.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_a6cd1076476117e74454f67931c2da1d4"><div class="ttname"><a href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4">TVMRetValueHandle</a></div><div class="ttdeci">void * TVMRetValueHandle</div><div class="ttdoc">Handle to hold return value.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:234</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_a6cd1076476117e74454f67931c2da1d4"><div class="ttname"><a href="c__runtime__api_8h.html#a6cd1076476117e74454f67931c2da1d4">TVMRetValueHandle</a></div><div class="ttdeci">void * TVMRetValueHandle</div><div class="ttdoc">Handle to hold return value.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:233</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a6fee438ab81fbf4235375d9f17f36d6f"><div class="ttname"><a href="c__runtime__api_8h.html#a6fee438ab81fbf4235375d9f17f36d6f">TVMCbArgToReturn</a></div><div class="ttdeci">int TVMCbArgToReturn(TVMValue *value, int *code)</div><div class="ttdoc">Inplace translate callback argument value to return value. This is only needed for non-POD arguments.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a71d0c47cca9068b17cf0a1f4c58d8ca9"><div class="ttname"><a href="c__runtime__api_8h.html#a71d0c47cca9068b17cf0a1f4c58d8ca9">TVMStreamCreate</a></div><div class="ttdeci">int TVMStreamCreate(int device_type, int device_id, TVMStreamHandle *out)</div><div class="ttdoc">Create a new runtime stream.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a73ca58cb32f4a4adf71d274dc1e27be4"><div class="ttname"><a href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a></div><div class="ttdeci">DLTensor * TVMArrayHandle</div><div class="ttdoc">the array handle</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:204</div></div>
@@ -422,18 +421,18 @@ $(function() {
<div class="ttc" id="ac__runtime__api_8h_html_aa91f776ed41a36790409f78725f81419"><div class="ttname"><a href="c__runtime__api_8h.html#aa91f776ed41a36790409f78725f81419">TVMStreamStreamSynchronize</a></div><div class="ttdeci">int TVMStreamStreamSynchronize(int device_type, int device_id, TVMStreamHandle src, TVMStreamHandle dst)</div><div class="ttdoc">Synchronize two streams of execution.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_aa9f2237f2fdcb96a75e7f3c6fa4209aa"><div class="ttname"><a href="c__runtime__api_8h.html#aa9f2237f2fdcb96a75e7f3c6fa4209aa">TVMByteArrayFree</a></div><div class="ttdeci">int TVMByteArrayFree(TVMByteArray *arr)</div><div class="ttdoc">Free a TVMByteArray returned from TVMFuncCall, and associated memory.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_aaeb0d67f6d7e90e671dd5a53b1d6b81a"><div class="ttname"><a href="c__runtime__api_8h.html#aaeb0d67f6d7e90e671dd5a53b1d6b81a">TVMModImport</a></div><div class="ttdeci">int TVMModImport(TVMModuleHandle mod, TVMModuleHandle dep)</div><div class="ttdoc">Add dep to mod's dependency. This allows functions in this module to use modules.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:239</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:238</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ab305492ac6ef86d73ff0553269e0796f"><div class="ttname"><a href="c__runtime__api_8h.html#ab305492ac6ef86d73ff0553269e0796f">TVMGetLastPythonError</a></div><div class="ttdeci">void * TVMGetLastPythonError()</div><div class="ttdoc">Return the previous python error, if any.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ab87d040544cbb69b0eac36cd4453d0e6"><div class="ttname"><a href="c__runtime__api_8h.html#ab87d040544cbb69b0eac36cd4453d0e6">TVMDropLastPythonError</a></div><div class="ttdeci">void TVMDropLastPythonError()</div><div class="ttdoc">Remove the propagated python error, if any.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ab98d6b66089da8b33b662ccdb243b26b"><div class="ttname"><a href="c__runtime__api_8h.html#ab98d6b66089da8b33b662ccdb243b26b">TVMFuncRemoveGlobal</a></div><div class="ttdeci">int TVMFuncRemoveGlobal(const char *name)</div><div class="ttdoc">Remove a global function.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ab9ddee6cf53918337cd4ca68d8bfc8b1"><div class="ttname"><a href="c__runtime__api_8h.html#ab9ddee6cf53918337cd4ca68d8bfc8b1">TVMObjectGetTypeIndex</a></div><div class="ttdeci">int TVMObjectGetTypeIndex(TVMObjectHandle obj, unsigned *out_tindex)</div><div class="ttdoc">Get the type_index from an object.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ac035a1354f2e5d7f8613af73cc735742"><div class="ttname"><a href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a></div><div class="ttdeci">void(* TVMPackedCFuncFinalizer)(void *resource_handle)</div><div class="ttdoc">C callback to free the resource handle in C packed function.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:434</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ac035a1354f2e5d7f8613af73cc735742"><div class="ttname"><a href="c__runtime__api_8h.html#ac035a1354f2e5d7f8613af73cc735742">TVMPackedCFuncFinalizer</a></div><div class="ttdeci">void(* TVMPackedCFuncFinalizer)(void *resource_handle)</div><div class="ttdoc">C callback to free the resource handle in C packed function.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:433</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ac16764609b45ac911a5c9971e195e377"><div class="ttname"><a href="c__runtime__api_8h.html#ac16764609b45ac911a5c9971e195e377">TVMModFree</a></div><div class="ttdeci">int TVMModFree(TVMModuleHandle mod)</div><div class="ttdoc">Free the Module.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ac414ed248ddb1bfb561685bba3de5e89"><div class="ttname"><a href="c__runtime__api_8h.html#ac414ed248ddb1bfb561685bba3de5e89">TVMSetStream</a></div><div class="ttdeci">int TVMSetStream(int device_type, int device_id, TVMStreamHandle handle)</div><div class="ttdoc">Set the runtime stream of current thread to be stream. The subsequent calls to the same device_type w...</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:230</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:229</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_acb905f16569e0700f23b9a2c4588ee4c"><div class="ttname"><a href="c__runtime__api_8h.html#acb905f16569e0700f23b9a2c4588ee4c">TVMThrowLastError</a></div><div class="ttdeci">void TVMThrowLastError()</div><div class="ttdoc">Re-throw the most recent error.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ace8007daffd9f2c6d954c24d870bfcc4"><div class="ttname"><a href="c__runtime__api_8h.html#ace8007daffd9f2c6d954c24d870bfcc4">tvm_index_t</a></div><div class="ttdeci">int64_t tvm_index_t</div><div class="ttdoc">type of array index.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:89</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_acf57d257a6e0841d84ebbd2a339d183e"><div class="ttname"><a href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a></div><div class="ttdeci">void * TVMFunctionHandle</div><div class="ttdoc">Handle to packed function handle.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:232</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_acf57d257a6e0841d84ebbd2a339d183e"><div class="ttname"><a href="c__runtime__api_8h.html#acf57d257a6e0841d84ebbd2a339d183e">TVMFunctionHandle</a></div><div class="ttdeci">void * TVMFunctionHandle</div><div class="ttdoc">Handle to packed function handle.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:231</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ad0c98ff469bd844546a71f61d2e164f3"><div class="ttname"><a href="c__runtime__api_8h.html#ad0c98ff469bd844546a71f61d2e164f3">TVMCFuncSetReturn</a></div><div class="ttdeci">int TVMCFuncSetReturn(TVMRetValueHandle ret, TVMValue *value, int *type_code, int num_ret)</div><div class="ttdoc">Set the return value of TVMPackedCFunc.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ad3bd42da244a0e32ac82d7428e01a010"><div class="ttname"><a href="c__runtime__api_8h.html#ad3bd42da244a0e32ac82d7428e01a010">TVMFuncGetGlobal</a></div><div class="ttdeci">int TVMFuncGetGlobal(const char *name, TVMFunctionHandle *out)</div><div class="ttdoc">Get a global function.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ad67b8f8766fce6b62c82a458e3c9b053"><div class="ttname"><a href="c__runtime__api_8h.html#ad67b8f8766fce6b62c82a458e3c9b053">TVMArrayFromDLPack</a></div><div class="ttdeci">int TVMArrayFromDLPack(DLManagedTensor *from, TVMArrayHandle *out)</div><div class="ttdoc">Produce an array from the DLManagedTensor that shares data memory with the DLManagedTensor.</div></div>
@@ -443,23 +442,22 @@ $(function() {
<div class="ttc" id="ac__runtime__api_8h_html_ae867a91b746fbe91e289b0d2ab0446aa"><div class="ttname"><a href="c__runtime__api_8h.html#ae867a91b746fbe91e289b0d2ab0446aa">TVMObjectTypeIndex2Key</a></div><div class="ttdeci">int TVMObjectTypeIndex2Key(unsigned tindex, char **out_type_key)</div><div class="ttdoc">Convert type index to type key.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ae899a6a211b7913e92420a01b804db64"><div class="ttname"><a href="c__runtime__api_8h.html#ae899a6a211b7913e92420a01b804db64">TVMObjectRetain</a></div><div class="ttdeci">int TVMObjectRetain(TVMObjectHandle obj)</div><div class="ttdoc">Increase the reference count of an object.</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_af420f0d74004615c03bb40270ad5d489"><div class="ttname"><a href="c__runtime__api_8h.html#af420f0d74004615c03bb40270ad5d489">TVMFuncFree</a></div><div class="ttdeci">int TVMFuncFree(TVMFunctionHandle func)</div><div class="ttdoc">Free the function when it is no longer needed.</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_aff795d001e3761c75b2b8f422465d5a0"><div class="ttname"><a href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a></div><div class="ttdeci">void * TVMObjectHandle</div><div class="ttdoc">Handle to Object.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:241</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_aff795d001e3761c75b2b8f422465d5a0"><div class="ttname"><a href="c__runtime__api_8h.html#aff795d001e3761c75b2b8f422465d5a0">TVMObjectHandle</a></div><div class="ttdeci">void * TVMObjectHandle</div><div class="ttdoc">Handle to Object.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:240</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1attr_html_a61b1ef1047fb722a4e5ec2167c9963d7"><div class="ttname"><a href="namespacetvm_1_1tir_1_1attr.html#a61b1ef1047fb722a4e5ec2167c9963d7">tvm::tir::attr::device_id</a></div><div class="ttdeci">constexpr const char * device_id</div><div class="ttdoc">The allocation device for global malloc in host.</div><div class="ttdef"><b>Definition:</b> stmt.h:1420</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1attr_html_a7e4e7cd47471a9089022214d63d24206"><div class="ttname"><a href="namespacetvm_1_1tir_1_1attr.html#a7e4e7cd47471a9089022214d63d24206">tvm::tir::attr::device_type</a></div><div class="ttdeci">constexpr const char * device_type</div><div class="ttdoc">The device type.</div><div class="ttdef"><b>Definition:</b> stmt.h:1422</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ae7816fdebd5d56f2145cdf371b756eb4"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae7816fdebd5d56f2145cdf371b756eb4">tvm::tir::builtin::ret</a></div><div class="ttdeci">const Op & ret()</div><div class="ttdoc">Return value.</div></div>
<div class="ttc" id="anamespacetvm_1_1topi_html_aaa95d3ad68932ab206efbe0a326db6a2"><div class="ttname"><a href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">tvm::topi::mod</a></div><div class="ttdeci">tvm::PrimExpr mod(const tvm::PrimExpr &a, const tvm::PrimExpr &b)</div><div class="ttdef"><b>Definition:</b> broadcast.h:290</div></div>
<div class="ttc" id="anamespacetvm_1_1topi_html_af30c02f3a3f37c7963b3af60fb9c72a1"><div class="ttname"><a href="namespacetvm_1_1topi.html#af30c02f3a3f37c7963b3af60fb9c72a1">tvm::topi::shape</a></div><div class="ttdeci">Tensor shape(const Tensor &src, DataType dtype, const std::string name="T_shape", const std::string tag=kInjective)</div><div class="ttdoc">Get the shape of input tensor.</div><div class="ttdef"><b>Definition:</b> transform.h:1853</div></div>
-<div class="ttc" id="astructTVMByteArray_html"><div class="ttname"><a href="structTVMByteArray.html">TVMByteArray</a></div><div class="ttdoc">Byte array type used to pass in byte array When kTVMBytes is used as data type.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:224</div></div>
-<div class="ttc" id="astructTVMByteArray_html_a86d8e8341ce407b7d9374d887143e476"><div class="ttname"><a href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476">TVMByteArray::size</a></div><div class="ttdeci">size_t size</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:226</div></div>
-<div class="ttc" id="astructTVMByteArray_html_ab124e3227a75e0e4d55452f675f4fde1"><div class="ttname"><a href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1">TVMByteArray::data</a></div><div class="ttdeci">const char * data</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:225</div></div>
+<div class="ttc" id="astructTVMByteArray_html"><div class="ttname"><a href="structTVMByteArray.html">TVMByteArray</a></div><div class="ttdoc">Byte array type used to pass in byte array When kTVMBytes is used as data type.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:223</div></div>
+<div class="ttc" id="astructTVMByteArray_html_a86d8e8341ce407b7d9374d887143e476"><div class="ttname"><a href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476">TVMByteArray::size</a></div><div class="ttdeci">size_t size</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:225</div></div>
+<div class="ttc" id="astructTVMByteArray_html_ab124e3227a75e0e4d55452f675f4fde1"><div class="ttname"><a href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1">TVMByteArray::data</a></div><div class="ttdeci">const char * data</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:224</div></div>
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+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1TVMRetValue.html
@@ -87,7 +87,7 @@ Inheritance diagram for tvm::runtime::TVMRetValue:</div>
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Collaboration diagram for tvm::runtime::TVMRetValue:</div>
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<div class="ttc" id="ac__runtime__api_8h_html_a190e81769e805cca153514137a66e793a41343a05e42d08053d9fa1da85f70af6"><div class="ttname"><a href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a41343a05e42d08053d9fa1da85f70af6">kTVMModuleHandle</a></div><div class="ttdeci">@ kTVMModuleHandle</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:184</div></div>
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+<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:229</div></div>
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<div class="ttc" id="acrt_2packed__func_8h_html_a1b379b0d66cecf7035d7d9efa4d87e5e"><div class="ttname"><a href="crt_2packed__func_8h.html#a1b379b0d66cecf7035d7d9efa4d87e5e">String2DLDataType</a></div><div class="ttdeci">DLDataType String2DLDataType(const char *s)</div></div>
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<div class="ttc" id="astructTVMPackedFunc_html_addd284bc33730c6830aa6c14acc4632c"><div class="ttname"><a href="structTVMPackedFunc.html#addd284bc33730c6830aa6c14acc4632c">TVMPackedFunc::args</a></div><div class="ttdeci">TVMArgs args</div><div class="ttdef"><b>Definition:</b> packed_func.h:49</div></div>
<div class="ttc" id="aunionTVMValue_html"><div class="ttname"><a href="unionTVMValue.html">TVMValue</a></div><div class="ttdoc">Union type of values being passed through API and function calls.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:210</div></div>
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</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/device__api_8h_source.html b/docs/reference/api/doxygen/device__api_8h_source.html
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<div class="ttc" id="ac__runtime__api_8h_html_a57cbccb14c35a0e62dbc1b911188fcefa474961a2b52fee7b33e529015162e884"><div class="ttname"><a href="c__runtime__api_8h.html#a57cbccb14c35a0e62dbc1b911188fcefa474961a2b52fee7b33e529015162e884">TVMDeviceExtType_End</a></div><div class="ttdeci">@ TVMDeviceExtType_End</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:126</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:239</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:238</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DeviceAPI_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1DeviceAPI.html">tvm::runtime::DeviceAPI</a></div><div class="ttdoc">TVM Runtime Device API, abstracts the device specific interface for memory management.</div><div class="ttdef"><b>Definition:</b> device_api.h:82</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DeviceAPI_html_a04a11f5fc72ad905880f639e5a7a4bd5"><div class="ttname"><a href="classtvm_1_1runtime_1_1DeviceAPI.html#a04a11f5fc72ad905880f639e5a7a4bd5">tvm::runtime::DeviceAPI::NeedSetDevice</a></div><div class="ttdeci">static bool NeedSetDevice(int device_type)</div><div class="ttdoc">Whether a certian device type requires set device device before launching the kernel function.</div><div class="ttdef"><b>Definition:</b> device_api.h:239</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DeviceAPI_html_a1dfa1eda8018e8e52af5fd08f7ae2e5c"><div class="ttname"><a href="classtvm_1_1runtime_1_1DeviceAPI.html#a1dfa1eda8018e8e52af5fd08f7ae2e5c">tvm::runtime::DeviceAPI::Get</a></div><div class="ttdeci">static DeviceAPI * Get(Device dev, bool allow_missing=false)</div><div class="ttdoc">Get device API based on device.</div></div>
diff --git a/docs/reference/api/doxygen/functions_b.html b/docs/reference/api/doxygen/functions_b.html
index 63b2d61ae1..7fc25bab68 100644
--- a/docs/reference/api/doxygen/functions_b.html
+++ b/docs/reference/api/doxygen/functions_b.html
@@ -221,7 +221,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BlockDependenceInfo.html#a51aa1c7985e83c71f57a5a03aa64d423">tvm::tir::BlockDependenceInfo</a>
</li>
<li>BlockInfo()
-: <a class="el" href="structtvm_1_1tir_1_1BlockInfo.html#a2cfc1a31fe21b77d5b14907d6e645212">tvm::tir::BlockInfo</a>
+: <a class="el" href="structtvm_1_1tir_1_1BlockInfo.html#a41dd8c062eb43ab169ed59d27c37741e">tvm::tir::BlockInfo</a>
</li>
<li>Blockize()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#ad519f290c705afd441eaf683c4e24693">tvm::tir::ScheduleNode</a>
diff --git a/docs/reference/api/doxygen/functions_c.html b/docs/reference/api/doxygen/functions_c.html
index 500904739f..f4e3de7f5f 100644
--- a/docs/reference/api/doxygen/functions_c.html
+++ b/docs/reference/api/doxygen/functions_c.html
@@ -97,7 +97,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html#ad2c24762f35f7f9ebe85d7c03cba1c8e">tvm::auto_scheduler::CacheWriteStep</a>
</li>
<li>CalcCapacityImpl()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a9c7976c159330719285dbc5f0de07bd9">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#af82bc60b2c258ad05586bea49de7dd42">tvm::runtime::Array< T, typename ></a>
</li>
<li>Call()
: <a class="el" href="classtvm_1_1relax_1_1Call.html#ad290bc43f966baed02ef4e1fa11ca4ea">tvm::relax::Call</a>
diff --git a/docs/reference/api/doxygen/functions_d.html b/docs/reference/api/doxygen/functions_d.html
index 2c6bc81d66..c2bfeb872f 100644
--- a/docs/reference/api/doxygen/functions_d.html
+++ b/docs/reference/api/doxygen/functions_d.html
@@ -273,7 +273,7 @@ $(function() {
: <a class="el" href="classtvm_1_1SHashReducer.html#a74260485bd50d1bfa52ded457a6a7777">tvm::SHashReducer</a>
</li>
<li>Define()
-: <a class="el" href="classtvm_1_1script_1_1printer_1_1IRDocsifierNode.html#a7ce8b7c1199f958d6dc16b24db426b9d">tvm::script::printer::IRDocsifierNode</a>
+: <a class="el" href="classtvm_1_1script_1_1printer_1_1IRDocsifierNode.html#a05f2f73b4ed424594bcb3bd14837e922">tvm::script::printer::IRDocsifierNode</a>
</li>
<li>defined()
: <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a17d8d5ad92691f9e18e3e0ae8ef69e4f">tvm::runtime::ObjectRef</a>
diff --git a/docs/reference/api/doxygen/functions_f.html b/docs/reference/api/doxygen/functions_f.html
index 8aa93b4d27..1026355c05 100644
--- a/docs/reference/api/doxygen/functions_f.html
+++ b/docs/reference/api/doxygen/functions_f.html
@@ -567,9 +567,9 @@ $(function() {
, <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html#a55e31fd35b2925bb92d5a37c28c77ff7">tvm::meta_schedule::RunnerFutureNode</a>
</li>
<li>From()
-: <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html#a591376391761bec8e7487e2f3e6491b5">tvm::runtime::PackedFuncValueConverter< TObjectRef ></a>
-, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Array_3_01T_01_4_01_4.html#aa5bf5cc195cfc6d34bbb48bbeef1dfd4">tvm::runtime::PackedFuncValueConverter< Array< T > ></a>
-, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Map_3_01T_00_01U_01_4_01_4.html#af80360b6dba6c5a8d5e6a399fd1843c6">tvm::runtime::PackedFuncValueConverter< Map< T, U > ></a>
+: <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html#a450c3f2c6181da37f3bc04a9fa422a98">tvm::runtime::PackedFuncValueConverter< TObjectRef ></a>
+, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Array_3_01T_01_4_01_4.html#a7c9260f57fdcd440cef31669a1a7d487">tvm::runtime::PackedFuncValueConverter< Array< T > ></a>
+, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Map_3_01T_00_01U_01_4_01_4.html#a5948ef59d31fca3da8882e47fe968d0f">tvm::runtime::PackedFuncValueConverter< Map< T, U > ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html#a621ed59fef92109e666150923621379c">tvm::runtime::PackedFuncValueConverter< Optional< T > ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html#ae5f3235a16b845e4ef73835a351956c7">tvm::runtime::PackedFuncValueConverter< PrimExpr ></a>
, <a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01runtime_1_1Int_01_4.html#a40c7bda176e3c1f4e5492d9e1b8f6e05">tvm::runtime::PackedFuncValueConverter< runtime::Int ></a>
@@ -764,7 +764,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Fuse.html#a10b77eec10eb7dbc536b0c8d65163f9c">tvm::te::Fuse</a>
</li>
<li>fuse()
-: <a class="el" href="classtvm_1_1te_1_1Stage.html#a07b721494aa3c0c79e8a8654c433708f">tvm::te::Stage</a>
+: <a class="el" href="classtvm_1_1te_1_1Stage.html#a5658065d9cbbee620bbd107d30c4ae72">tvm::te::Stage</a>
</li>
<li>Fuse()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a5b9a41d190be0f8d817b4936732bd0ef">tvm::tir::ScheduleNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_n.html b/docs/reference/api/doxygen/functions_func_n.html
index a9519ec73c..eee33d0745 100644
--- a/docs/reference/api/doxygen/functions_func_n.html
+++ b/docs/reference/api/doxygen/functions_func_n.html
@@ -86,7 +86,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relax_1_1BlockBuilderNode.html#a0d90afaaadeab9dedac9a61311c910f5">tvm::relax::BlockBuilderNode</a>
</li>
<li>NameSupply()
-: <a class="el" href="classtvm_1_1NameSupply.html#ad901ec248469c98ade75839887823700">tvm::NameSupply</a>
+: <a class="el" href="classtvm_1_1NameSupply.html#a4ad1b3236ebe88ea083148702549fdc6">tvm::NameSupply</a>
</li>
<li>NameSupplyNode()
: <a class="el" href="classtvm_1_1NameSupplyNode.html#aba5bb3284648f9f8740eb76500499a12">tvm::NameSupplyNode</a>
diff --git a/docs/reference/api/doxygen/functions_i.html b/docs/reference/api/doxygen/functions_i.html
index 521268eb7a..c6ef4ee620 100644
--- a/docs/reference/api/doxygen/functions_i.html
+++ b/docs/reference/api/doxygen/functions_i.html
@@ -767,7 +767,7 @@ $(function() {
<li>iterator
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a98e5ad633b8195d954c98067213ae29f">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a75e3f2657cdb7cc613bf922429983165">tvm::runtime::MapNode::iterator</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ad605c9f9aaed23e669c2a3c595d08ba4">tvm::runtime::MapNode::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html#a79703afd52032715cc2d9d4c6830147f">tvm::runtime::metadata::ArrayAccessor< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html#a3216d91ef7fb0771463e76c9a0afcd1c">tvm::runtime::metadata::ArrayAccessor< const char *, ::tvm::runtime::String ></a>
, <a class="el" href="classtvm_1_1support_1_1Span.html#af6fbb6a54a3ecf8673146acae0c8228b">tvm::support::Span< T, W ></a>
diff --git a/docs/reference/api/doxygen/functions_l.html b/docs/reference/api/doxygen/functions_l.html
index a48eb9492b..57d1e3b651 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#ace4d6c608f7148bb2c6aefe33b4e4ab5">tvm::tir::Layout</a>
+: <a class="el" href="classtvm_1_1tir_1_1Layout.html#acd20f31024c5a15e8109c64109582028">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_p.html b/docs/reference/api/doxygen/functions_p.html
index ff5406a5bf..1847f432a4 100644
--- a/docs/reference/api/doxygen/functions_p.html
+++ b/docs/reference/api/doxygen/functions_p.html
@@ -206,7 +206,7 @@ $(function() {
: <a class="el" href="classtvm_1_1transform_1_1SequentialNode.html#ae78f131a5e8a1d3143ff4671c45af2ac">tvm::transform::SequentialNode</a>
</li>
<li>PassContext()
-: <a class="el" href="classtvm_1_1transform_1_1PassContext.html#a8f5f511c2e29991eceff7b1f4883313e">tvm::transform::PassContext</a>
+: <a class="el" href="classtvm_1_1transform_1_1PassContext.html#a906dadb55586afece28d71d3436fdf1d">tvm::transform::PassContext</a>
</li>
<li>PassContextNode()
: <a class="el" href="classtvm_1_1transform_1_1PassContextNode.html#aa91df8069b6573e72305ab57e9bc620f">tvm::transform::PassContextNode</a>
@@ -266,7 +266,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html#a96adb22c131d7bac1e54a7b646116143">tvm::relay::PatternConstructor</a>
</li>
<li>PatternContext()
-: <a class="el" href="classtvm_1_1relax_1_1PatternContext.html#ae7208ad6046d56d0c7098ca78d85f0b4">tvm::relax::PatternContext</a>
+: <a class="el" href="classtvm_1_1relax_1_1PatternContext.html#ad06ccd048982b7ac3b8230b9a34a75d9">tvm::relax::PatternContext</a>
</li>
<li>patterns
: <a class="el" href="classtvm_1_1relax_1_1PatternSeqNode.html#a1cca092a78997a85dc98359bb95ca1a5">tvm::relax::PatternSeqNode</a>
@@ -283,7 +283,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relay_1_1PatternVar.html#a54c56ad693111a8375e55961bd9b49fa">tvm::relay::PatternVar</a>
</li>
<li>PatternWildcard()
-: <a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html#aab7d1690088beab9987f97cdebd64c0c">tvm::relay::PatternWildcard</a>
+: <a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html#aea56c9cc7113d61aee41cd6569aef9d5">tvm::relay::PatternWildcard</a>
</li>
<li>pc
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html#af798dec5a7e9049d6673093d2f3e7dea">tvm::runtime::vm::VMFrame</a>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index 2456c8050b..017bff5739 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#a77f3d7cc97d8cfd7172af58b4e784d89">tvm::Target</a>
+: <a class="el" href="classtvm_1_1Target.html#a1abb29217d8e394f0b88e28178c70bb0">tvm::Target</a>
</li>
<li>target
: <a class="el" href="classtvm_1_1VDeviceNode.html#a05a30c026e6448638a34092b3b85d11f">tvm::VDeviceNode</a>
diff --git a/docs/reference/api/doxygen/functions_v.html b/docs/reference/api/doxygen/functions_v.html
index 396222024d..21403babe2 100644
--- a/docs/reference/api/doxygen/functions_v.html
+++ b/docs/reference/api/doxygen/functions_v.html
@@ -64,9 +64,6 @@ $(function() {
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
<h3><a id="index_v"></a>- v -</h3><ul>
-<li>v_bool
-: <a class="el" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">TVMValue</a>
-</li>
<li>v_device
: <a class="el" href="unionTVMValue.html#a2296018ee2f5953b9cfb1a36475c303f">TVMValue</a>
</li>
@@ -77,7 +74,7 @@ $(function() {
: <a class="el" href="unionTVMValue.html#a3b172297f9f7bf2f5391f7d87309cd44">TVMValue</a>
</li>
<li>v_info()
-: <a class="el" href="structtvm_1_1relay_1_1v__info.html#aaf1979af7915eb9f241285a92fc06173">tvm::relay::v_info</a>
+: <a class="el" href="structtvm_1_1relay_1_1v__info.html#a00cc81b690617d77cbc6cfb500fde1ee">tvm::relay::v_info</a>
</li>
<li>v_int64
: <a class="el" href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">TVMValue</a>
@@ -181,7 +178,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relax_1_1BindingNode.html#a31222a9c83a8532274cd8a56f991157b">tvm::relax::BindingNode</a>
</li>
<li>Var()
-: <a class="el" href="classtvm_1_1relax_1_1Var.html#aae5444abb7bba2fc448629e488c9e8db">tvm::relax::Var</a>
+: <a class="el" href="classtvm_1_1relax_1_1Var.html#a90de8238055e87900a6ee518d9fb655d">tvm::relax::Var</a>
</li>
<li>var
: <a class="el" href="classtvm_1_1relay_1_1LetNode.html#a3a841faeeb71a7e59f99feffb182a7c1">tvm::relay::LetNode</a>
@@ -224,7 +221,7 @@ $(function() {
: <a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html#a77bebc24e0c21245de2620593c2c2272">tvm::relay::MultiBoxTransformLocAttrs</a>
</li>
<li>Variant()
-: <a class="el" href="classtvm_1_1runtime_1_1Variant.html#a260befe44d25be3a2b197c0073add1cf">tvm::runtime::Variant< V ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Variant.html#aefba483b7ff13025691c4bab5999b627">tvm::runtime::Variant< V ></a>
</li>
<li>VariantNames()
: <a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Variant_3_01FirstVariant_00_01RemainingVariants_8_8_8_01_4_01_4.html#a0a78c0eaedd92c2c64f25f6f857d6881">tvm::runtime::ObjectTypeChecker< Variant< FirstVariant, RemainingVariants... > ></a>
@@ -686,8 +683,8 @@ $(function() {
, <a class="el" href="classtvm_1_1relax_1_1ExprVisitor.html#af0defc6110a893f66c129c3cfc33f772">tvm::relax::ExprVisitor</a>
</li>
<li>VisitBinding_()
-: <a class="el" href="classtvm_1_1relax_1_1ExprMutator.html#a38ba10298ddf8d46e3452271da6b1c1b">tvm::relax::ExprMutator</a>
-, <a class="el" href="classtvm_1_1relax_1_1ExprVisitor.html#a7c2af851a80bbcae0f97db19a6ed06f6">tvm::relax::ExprVisitor</a>
+: <a class="el" href="classtvm_1_1relax_1_1ExprMutator.html#af6af1c92b8dd9d7664c030b4bdeb175e">tvm::relax::ExprMutator</a>
+, <a class="el" href="classtvm_1_1relax_1_1ExprVisitor.html#aec225234a9f91df3ecc76d5a719ddbbb">tvm::relax::ExprVisitor</a>
</li>
<li>VisitBindingBlock()
: <a class="el" href="classtvm_1_1relax_1_1ExprMutator.html#a75158a490df0c60cdaae00615a441e84">tvm::relax::ExprMutator</a>
@@ -696,7 +693,7 @@ $(function() {
</li>
<li>VisitBindingBlock_()
: <a class="el" href="classtvm_1_1relax_1_1ExprMutator.html#a1799c60b009222e901d64059460f3475">tvm::relax::ExprMutator</a>
-, <a class="el" href="classtvm_1_1relax_1_1ExprVisitor.html#af7efd2e79dccd972628355a60d8fd87a">tvm::relax::ExprVisitor</a>
+, <a class="el" href="classtvm_1_1relax_1_1ExprVisitor.html#a4f07b9a37f76c7fe7b094529a1d8c2f9">tvm::relax::ExprVisitor</a>
</li>
<li>VisitBlockAnnotations()
: <a class="el" href="classtvm_1_1tir_1_1IndexDataTypeRewriter.html#ac3cbdd10c2660208ba65dab805968c79">tvm::tir::IndexDataTypeRewriter</a>
@@ -722,7 +719,7 @@ $(function() {
, <a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html#ae7e67d3a1709b0a180572417698ffaa8">tvm::relay::DFPatternVisitor</a>
</li>
<li>VisitDFPattern_()
-: <a class="el" href="classtvm_1_1relax_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html#a6ad337783c709c6dab0397adb902fffd">tvm::relax::DFPatternFunctor< R(const DFPattern &n, Args...)></a>
+: <a class="el" href="classtvm_1_1relax_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html#a4240b8ac3cce25625080937709589f0a">tvm::relax::DFPatternFunctor< R(const DFPattern &n, Args...)></a>
, <a class="el" href="classtvm_1_1relax_1_1DFPatternVisitor.html#a76f9eced0aed3ad3f557a9b2e7784a0a">tvm::relax::DFPatternVisitor</a>
, <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html#a3508d4f2f172303005aa3563c3f31646">tvm::relay::DFPatternFunctor< R(const DFPattern &n, Args...)></a>
, <a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html#a77888582580ddea4c743715b9579d83b">tvm::relay::DFPatternVisitor</a>
diff --git a/docs/reference/api/doxygen/functions_vars_v.html b/docs/reference/api/doxygen/functions_vars_v.html
index f5cc36b95f..be8b2e89b4 100644
--- a/docs/reference/api/doxygen/functions_vars_v.html
+++ b/docs/reference/api/doxygen/functions_vars_v.html
@@ -64,9 +64,6 @@ $(function() {
 
<h3><a id="index_v"></a>- v -</h3><ul>
-<li>v_bool
-: <a class="el" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">TVMValue</a>
-</li>
<li>v_device
: <a class="el" href="unionTVMValue.html#a2296018ee2f5953b9cfb1a36475c303f">TVMValue</a>
</li>
diff --git a/docs/reference/api/doxygen/graph__executor_8h_source.html b/docs/reference/api/doxygen/graph__executor_8h_source.html
index 982398beab..82ce8b42d9 100644
--- a/docs/reference/api/doxygen/graph__executor_8h_source.html
+++ b/docs/reference/api/doxygen/graph__executor_8h_source.html
@@ -149,7 +149,7 @@ $(function() {
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  </div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_CRT_GRAPH_EXECUTOR_H_</span></div>
<div class="ttc" id="ac__runtime__api_8h_html"><div class="ttname"><a href="c__runtime__api_8h.html">c_runtime_api.h</a></div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:230</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:229</div></div>
<div class="ttc" id="acrt_2packed__func_8h_html"><div class="ttname"><a href="crt_2packed__func_8h.html">packed_func.h</a></div><div class="ttdoc">Type-erased function used across TVM API.</div></div>
<div class="ttc" id="agraph__executor_8h_html_a0017621bbbb01622a3544117e8437395"><div class="ttname"><a href="graph__executor_8h.html#a0017621bbbb01622a3544117e8437395">TVMGraphExecutorGraphAttr</a></div><div class="ttdeci">struct TVMGraphExecutorGraphAttr TVMGraphExecutorGraphAttr</div></div>
<div class="ttc" id="agraph__executor_8h_html_a4ffb31d1bae06cc87a488955bc27f56d"><div class="ttname"><a href="graph__executor_8h.html#a4ffb31d1bae06cc87a488955bc27f56d">TVMGraphExecutor_LoadParams</a></div><div class="ttdeci">int TVMGraphExecutor_LoadParams(TVMGraphExecutor *executor, const char *param_blob, const uint32_t param_size)</div><div class="ttdoc">Load parameters from parameter blob.</div></div>
diff --git a/docs/reference/api/doxygen/ndarray_8h_source.html b/docs/reference/api/doxygen/ndarray_8h_source.html
index 22c6d9338a..42a6a0e181 100644
--- a/docs/reference/api/doxygen/ndarray_8h_source.html
+++ b/docs/reference/api/doxygen/ndarray_8h_source.html
@@ -428,7 +428,7 @@ $(function() {
<div class="ttc" id="ac__runtime__api_8h_html"><div class="ttname"><a href="c__runtime__api_8h.html">c_runtime_api.h</a></div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a73ca58cb32f4a4adf71d274dc1e27be4"><div class="ttname"><a href="c__runtime__api_8h.html#a73ca58cb32f4a4adf71d274dc1e27be4">TVMArrayHandle</a></div><div class="ttdeci">DLTensor * TVMArrayHandle</div><div class="ttdoc">the array handle</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:204</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_a775383bcd8c0237e36bdf0c9654d62c3"><div class="ttname"><a href="c__runtime__api_8h.html#a775383bcd8c0237e36bdf0c9654d62c3">TVMGetLastError</a></div><div class="ttdeci">const char * TVMGetLastError(void)</div><div class="ttdoc">return str message of the last error all function in this file will return 0 when success and nonzero...</div></div>
-<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:239</div></div>
+<div class="ttc" id="ac__runtime__api_8h_html_ab1d5f6b7945e1410602a8a057fda5757"><div class="ttname"><a href="c__runtime__api_8h.html#ab1d5f6b7945e1410602a8a057fda5757">TVMStreamHandle</a></div><div class="ttdeci">void * TVMStreamHandle</div><div class="ttdoc">The stream that is specific to device can be NULL, which indicates the default one.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:238</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ace8007daffd9f2c6d954c24d870bfcc4"><div class="ttname"><a href="c__runtime__api_8h.html#ace8007daffd9f2c6d954c24d870bfcc4">tvm_index_t</a></div><div class="ttdeci">int64_t tvm_index_t</div><div class="ttdoc">type of array index.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:89</div></div>
<div class="ttc" id="ac__runtime__api_8h_html_ae246eaa00342c042f3f194605ad9bc7a"><div class="ttname"><a href="c__runtime__api_8h.html#ae246eaa00342c042f3f194605ad9bc7a">TVMArrayCopyToBytes</a></div><div class="ttdeci">int TVMArrayCopyToBytes(TVMArrayHandle handle, void *data, size_t nbytes)</div><div class="ttdoc">Copy array data to CPU byte array.</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html_a18a8a0fef87b74b18fc254f49465cc56"><div class="ttname"><a href="classtvm_1_1PrimExpr.html#a18a8a0fef87b74b18fc254f49465cc56">tvm::PrimExpr::dtype</a></div><div class="ttdeci">DataType dtype() const</div><div class="ttdef"><b>Definition:</b> expr.h:129</div></div>
diff --git a/docs/reference/api/doxygen/packed__func_8h_source.html b/docs/reference/api/doxygen/packed__func_8h_source.html
index 3cdd0fcfef..ba89e42bef 100644
--- a/docs/reference/api/doxygen/packed__func_8h_source.html
+++ b/docs/reference/api/doxygen/packed__func_8h_source.html
@@ -466,7 +466,7 @@ $(function() {
<div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="comment">// conversions. This is publicly exposed, as it can be useful in</span></div>
<div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="comment">// specializations of PackedFuncValueConverter.</span></div>
<div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">if</span> (<a class="code" href="classtvm_1_1runtime_1_1TVMPODValue__.html#aed3e983e990c5c4ed3ac09b95055297e">type_code_</a> == <a class="code" href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a6f249d7a46d0457ca41dff1d921580e3">kTVMArgBool</a>) {</div>
-<div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordflow">return</span> <a class="code" href="classtvm_1_1runtime_1_1TVMPODValue__.html#a8ffdfcc7099faf19ee07a5c03ce06af8">value_</a>.<a class="code" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">v_bool</a>;</div>
+<div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">bool</span><span class="keyword">></span>(<a class="code" href="classtvm_1_1runtime_1_1TVMPODValue__.html#a8ffdfcc7099faf19ee07a5c03ce06af8">value_</a>.<a class="code" href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">v_int64</a>);</div>
<div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordflow">return</span> std::nullopt;</div>
<div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  }</div>
@@ -754,7 +754,7 @@ $(function() {
<div class="line"><a name="l01041"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1TVMRetValue.html#addc4ffa96d47397893755d5e7e016084"> 1041</a></span>  <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html">TVMRetValue</a>& <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#addc4ffa96d47397893755d5e7e016084">operator=</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1DataType.html">DataType</a>& oth [...]
<div class="line"><a name="l01042"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1TVMRetValue.html#a510ec87e6718c0e6197e7321ca8774f2"> 1042</a></span>  <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html">TVMRetValue</a>& <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a510ec87e6718c0e6197e7321ca8774f2">operator=</a>(<span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a080afe25d822583 [...]
<div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  this->SwitchToPOD(<a class="code" href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a6f249d7a46d0457ca41dff1d921580e3">kTVMArgBool</a>);</div>
-<div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <a class="code" href="classtvm_1_1runtime_1_1TVMPODValue__.html#a8ffdfcc7099faf19ee07a5c03ce06af8">value_</a>.<a class="code" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">v_bool</a> = <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a080afe25d8225839bccb0ada3272c60c">value</a>;</div>
+<div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <a class="code" href="classtvm_1_1runtime_1_1TVMPODValue__.html#a8ffdfcc7099faf19ee07a5c03ce06af8">value_</a>.<a class="code" href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">v_int64</a> = <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a080afe25d8225839bccb0ada3272c60c">value</a>;</div>
<div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  }</div>
<div class="line"><a name="l01047"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1TVMRetValue.html#a952f62623ec147f366363b664d749566"> 1047</a></span>  <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html">TVMRetValue</a>& <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a952f62623ec147f366363b664d749566">operator=</a>(std::string <a class="code" href="classtvm_1_1runtime_1_1TVMRetValue.html#a080afe25d8225839bccb0ada3272c60c">value</ [...]
@@ -1433,7 +1433,7 @@ $(function() {
<div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  type_codes_[i] = kDLInt;</div>
<div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  }</div>
<div class="line"><a name="l01833"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a34f1cf011c85b72cd54273982257983e"> 1833</a></span>  TVM_ALWAYS_INLINE <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a34f1cf011c85b72cd54273982257983e">operator()</a>(<span class="keywordtype">size_t</span> i, <span class="keywordtype">bool</span> value)<span class="keyword"> const </span>{</div>
-<div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  values_[i].<a class="code" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">v_bool</a> = value;</div>
+<div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  values_[i].<a class="code" href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">v_int64</a> = value;</div>
<div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  type_codes_[i] = <a class="code" href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a6f249d7a46d0457ca41dff1d921580e3">kTVMArgBool</a>;</div>
<div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  }</div>
<div class="line"><a name="l01837"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a2e484d891ed2be751d514bb58e48faa5"> 1837</a></span>  TVM_ALWAYS_INLINE <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a2e484d891ed2be751d514bb58e48faa5">operator()</a>(<span class="keywordtype">size_t</span> i, uint64_t value)<span class="keyword"> const </span>{</div>
@@ -1742,7 +1742,7 @@ $(function() {
<div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  std::is_base_of_v<ContainerType, Bool::ContainerType>) {</div>
<div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <span class="keywordflow">if</span> (std::is_base_of_v<Bool::ContainerType, ContainerType> ||</div>
<div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  ptr-><a class="code" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a><<a class="code" href="classtvm_1_1runtime_1_1Object.html">Bool::ContainerType</a>>()) {</div>
-<div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  values_[i].<a class="code" href="unionTVMValue.html#a5a3bf395d4f2c92f618f261d24740d36">v_bool</a> = <span class="keyword">static_cast<</span><a class="code" href="classtvm_1_1runtime_1_1Object.html">Bool::ContainerType</a>*<span class="keyword">></span>(ptr)->value;</div>
+<div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  values_[i].<a class="code" href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">v_int64</a> = <span class="keyword">static_cast<</span><a class="code" href="classtvm_1_1runtime_1_1Object.html">Bool::ContainerType</a>*<span class="keyword">></span>(ptr)->value;</div>
<div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  type_codes_[i] = <a class="code" href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a6f249d7a46d0457ca41dff1d921580e3">kTVMArgBool</a>;</div>
<div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  }</div>
@@ -1927,7 +1927,7 @@ $(function() {
<div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>  </div>
<div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <span class="keywordflow">if</span> constexpr (std::is_base_of_v<TObjectRef, Bool>) {</div>
<div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keywordflow">if</span> (type_code_ == <a class="code" href="c__runtime__api_8h.html#a190e81769e805cca153514137a66e793a6f249d7a46d0457ca41dff1d921580e3">kTVMArgBool</a>) {</div>
-<div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1runtime.html#a11a4e38a2cd536f650c81f35ab4fc652">Bool</a>(value_.v_bool);</div>
+<div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1runtime.html#a11a4e38a2cd536f650c81f35ab4fc652">Bool</a>(value_.v_int64);</div>
<div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  }</div>
<div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  }</div>
<div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  </div>
@@ -2497,9 +2497,9 @@ $(function() {
<div class="ttc" id="apacked__func_8h_html_ad1e150bb815d5cb964e15d4fa7b5fd93"><div class="ttname"><a href="packed__func_8h.html#ad1e150bb815d5cb964e15d4fa7b5fd93">TVM_CHECK_TYPE_CODE</a></div><div class="ttdeci">#define TVM_CHECK_TYPE_CODE(CODE, T)</div><div class="ttdef"><b>Definition:</b> packed_func.h:438</div></div>
<div class="ttc" id="aruntime_2module_8h_html"><div class="ttname"><a href="runtime_2module_8h.html">module.h</a></div><div class="ttdoc">Runtime container of the functions generated by TVM, This is used to support dynamically link,...</div></div>
<div class="ttc" id="astructTVMArgs_html"><div class="ttname"><a href="structTVMArgs.html">TVMArgs</a></div><div class="ttdef"><b>Definition:</b> packed_func.h:38</div></div>
-<div class="ttc" id="astructTVMByteArray_html"><div class="ttname"><a href="structTVMByteArray.html">TVMByteArray</a></div><div class="ttdoc">Byte array type used to pass in byte array When kTVMBytes is used as data type.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:224</div></div>
-<div class="ttc" id="astructTVMByteArray_html_a86d8e8341ce407b7d9374d887143e476"><div class="ttname"><a href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476">TVMByteArray::size</a></div><div class="ttdeci">size_t size</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:226</div></div>
-<div class="ttc" id="astructTVMByteArray_html_ab124e3227a75e0e4d55452f675f4fde1"><div class="ttname"><a href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1">TVMByteArray::data</a></div><div class="ttdeci">const char * data</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:225</div></div>
+<div class="ttc" id="astructTVMByteArray_html"><div class="ttname"><a href="structTVMByteArray.html">TVMByteArray</a></div><div class="ttdoc">Byte array type used to pass in byte array When kTVMBytes is used as data type.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:223</div></div>
+<div class="ttc" id="astructTVMByteArray_html_a86d8e8341ce407b7d9374d887143e476"><div class="ttname"><a href="structTVMByteArray.html#a86d8e8341ce407b7d9374d887143e476">TVMByteArray::size</a></div><div class="ttdeci">size_t size</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:225</div></div>
+<div class="ttc" id="astructTVMByteArray_html_ab124e3227a75e0e4d55452f675f4fde1"><div class="ttname"><a href="structTVMByteArray.html#ab124e3227a75e0e4d55452f675f4fde1">TVMByteArray::data</a></div><div class="ttdeci">const char * data</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:224</div></div>
<div class="ttc" id="astructtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4_html_aab22b555cfe16d040c204527c73a3287"><div class="ttname"><a href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html#aab22b555cfe16d040c204527c73a3287">tvm::runtime::ObjectTypeChecker< Array< T > >::TypeName</a></div><div class="ttdeci">static std::string TypeName()</div><div class="ttdef"><b>Definition:</b> packed_func.h:522</div></div>
<div class="ttc" id="astructtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4_html_add60b83f5e77c1d9016b65898bcbfe69"><div class="ttname"><a href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html#add60b83f5e77c1d9016b65898bcbfe69">tvm::runtime::ObjectTypeChecker< Array< T > >::Check</a></div><div class="ttdeci">static bool Check(const Object *ptr)</div><div class="ttdef"><b>Definition:</b> packed_func.h:509</div></div>
<div class="ttc" id="astructtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4_html_aecb7f71a675e74979a0ed08202cf09cc"><div class="ttname"><a href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html#aecb7f71a675e74979a0ed08202cf09cc">tvm::runtime::ObjectTypeChecker< Array< T > >::CheckAndGetMismatch</a></div><div class="ttdeci">static Optional< String > CheckAndGetMismatch(const Object *ptr)</div><div class="ttdef"><b>Definition:</b> pac [...]
@@ -2538,13 +2538,12 @@ $(function() {
<div class="ttc" id="astructtvm_1_1runtime_1_1TVMArgsSetterApply_html_a77729fcdf7b931f39ed9f2771bfded75"><div class="ttname"><a href="structtvm_1_1runtime_1_1TVMArgsSetterApply.html#a77729fcdf7b931f39ed9f2771bfded75">tvm::runtime::TVMArgsSetterApply::F</a></div><div class="ttdeci">static TVM_ALWAYS_INLINE void F(TVMArgsSetter *setter, T &&value)</div><div class="ttdef"><b>Definition:</b> packed_func.h:1938</div></div>
<div class="ttc" id="astructtvm_1_1runtime_1_1TypeIndex_html_aed93c7318efc8052201d4c404b21a40da23eb6ecfca76a965bf4727a4e931584b"><div class="ttname"><a href="structtvm_1_1runtime_1_1TypeIndex.html#aed93c7318efc8052201d4c404b21a40da23eb6ecfca76a965bf4727a4e931584b">tvm::runtime::TypeIndex::kRuntimePackedFunc</a></div><div class="ttdeci">@ kRuntimePackedFunc</div><div class="ttdoc">runtime::PackedFunc.</div><div class="ttdef"><b>Definition:</b> object.h:74</div></div>
<div class="ttc" id="aunionTVMValue_html"><div class="ttname"><a href="unionTVMValue.html">TVMValue</a></div><div class="ttdoc">Union type of values being passed through API and function calls.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:210</div></div>
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+<div class="ttc" id="aunionTVMValue_html_a2296018ee2f5953b9cfb1a36475c303f"><div class="ttname"><a href="unionTVMValue.html#a2296018ee2f5953b9cfb1a36475c303f">TVMValue::v_device</a></div><div class="ttdeci">DLDevice v_device</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:216</div></div>
+<div class="ttc" id="aunionTVMValue_html_a3b172297f9f7bf2f5391f7d87309cd44"><div class="ttname"><a href="unionTVMValue.html#a3b172297f9f7bf2f5391f7d87309cd44">TVMValue::v_handle</a></div><div class="ttdeci">void * v_handle</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:213</div></div>
+<div class="ttc" id="aunionTVMValue_html_a75af73d1e39bc5de7073a008d44b0d16"><div class="ttname"><a href="unionTVMValue.html#a75af73d1e39bc5de7073a008d44b0d16">TVMValue::v_type</a></div><div class="ttdeci">DLDataType v_type</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:215</div></div>
<div class="ttc" id="aunionTVMValue_html_aa1c40fa9e74fbf97541fd9735062c4cc"><div class="ttname"><a href="unionTVMValue.html#aa1c40fa9e74fbf97541fd9735062c4cc">TVMValue::v_int64</a></div><div class="ttdeci">int64_t v_int64</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:211</div></div>
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+<div class="ttc" id="aunionTVMValue_html_ab0e4dea6ca370fe7a7d22f4fc23a4f47"><div class="ttname"><a href="unionTVMValue.html#ab0e4dea6ca370fe7a7d22f4fc23a4f47">TVMValue::v_str</a></div><div class="ttdeci">const char * v_str</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:214</div></div>
+<div class="ttc" id="aunionTVMValue_html_abefae93ebafe8818c0060df2bf31e6e5"><div class="ttname"><a href="unionTVMValue.html#abefae93ebafe8818c0060df2bf31e6e5">TVMValue::v_float64</a></div><div class="ttdeci">double v_float64</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:212</div></div>
<div class="ttc" id="avariant_8h_html"><div class="ttname"><a href="variant_8h.html">variant.h</a></div><div class="ttdoc">Runtime Variant container types.</div></div>
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
diff --git a/docs/reference/api/doxygen/runtime_2crt_2module_8h_source.html b/docs/reference/api/doxygen/runtime_2crt_2module_8h_source.html
index 8cb0b49d03..78653e198c 100644
--- a/docs/reference/api/doxygen/runtime_2crt_2module_8h_source.html
+++ b/docs/reference/api/doxygen/runtime_2crt_2module_8h_source.html
@@ -111,7 +111,7 @@ $(function() {
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_CRT_MODULE_H_</span></div>
<div class="ttc" id="ac__backend__api_8h_html"><div class="ttname"><a href="c__backend__api_8h.html">c_backend_api.h</a></div><div class="ttdoc">TVM runtime backend API.</div></div>
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+<div class="ttc" id="ac__runtime__api_8h_html_ac4b0321c489287dcf4637e15328298ce"><div class="ttname"><a href="c__runtime__api_8h.html#ac4b0321c489287dcf4637e15328298ce">TVMModuleHandle</a></div><div class="ttdeci">void * TVMModuleHandle</div><div class="ttdoc">Handle to TVM runtime modules.</div><div class="ttdef"><b>Definition:</b> c_runtime_api.h:229</div></div>
<div class="ttc" id="afunc__registry_8h_html"><div class="ttname"><a href="func__registry_8h.html">func_registry.h</a></div><div class="ttdoc">Defines generic string-based function lookup structs.</div></div>
<div class="ttc" id="anamespacetvm_1_1topi_html_aaa95d3ad68932ab206efbe0a326db6a2"><div class="ttname"><a href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">tvm::topi::mod</a></div><div class="ttdeci">tvm::PrimExpr mod(const tvm::PrimExpr &a, const tvm::PrimExpr &b)</div><div class="ttdef"><b>Definition:</b> broadcast.h:290</div></div>
<div class="ttc" id="aruntime_2crt_2module_8h_html_a32fdb5a1df93075a184a36d2549833fa"><div class="ttname"><a href="runtime_2crt_2module_8h.html#a32fdb5a1df93075a184a36d2549833fa">TVMSystemLibEntryPoint</a></div><div class="ttdeci">const TVMModule * TVMSystemLibEntryPoint(void)</div><div class="ttdoc">Entry point for the system lib module.</div></div>
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