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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/02/28 06:15:56 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@563ef9587cfa913cf96f9ec061cdab43ce744b70)
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tqchen pushed a commit to branch asf-site
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The following commit(s) were added to refs/heads/asf-site by this push:
new 9f530f35d2 deploying docs (apache/tvm@563ef9587cfa913cf96f9ec061cdab43ce744b70)
9f530f35d2 is described below
commit 9f530f35d212c66a4e198a7e939c6288bc08598d
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Feb 28 06:15:41 2024 +0000
deploying docs (apache/tvm@563ef9587cfa913cf96f9ec061cdab43ce744b70)
---
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.../typedoc/classes/_internal_.RuntimeContext.html | 54 +-
.../api/typedoc/classes/_internal_.TVMString.html | 14 +-
.../typedoc/classes/_internal_.WebGPUContext.html | 28 +-
.../typedoc/enums/_internal_.RPCServerState.html | 14 +-
docs/reference/api/typedoc/functions/assert.html | 2 +-
.../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 +-
.../api/typedoc/interfaces/Disposable.html | 4 +-
.../typedoc/interfaces/GPUDeviceDetectOutput.html | 8 +-
.../api/typedoc/interfaces/InitProgressReport.html | 10 +-
.../api/typedoc/interfaces/LibraryProvider.html | 6 +-
.../_internal_.ArtifactCacheTemplate.html | 8 +-
.../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 +-
.../types/_internal_.FObjectConstructor.html | 2 +-
.../types/_internal_.FTVMWasmAllocSpace.html | 2 +-
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.../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 | 2 +-
.../vta/tutorials/frontend/sg_execution_times.html | 4 +-
.../vta/tutorials/optimize/sg_execution_times.html | 4 +-
docs/topic/vta/tutorials/sg_execution_times.html | 6 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 8 +-
docs/tutorial/autotvm_matmul_x86.html | 20 +-
docs/tutorial/autotvm_relay_x86.html | 269 +-
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 | 44 +-
662 files changed, 258757 insertions(+), 257139 deletions(-)
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index ba8489f71c..3c995b9890 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 27.877 seconds)
+ **Total running time of the script:** ( 1 minutes 28.056 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index ee2f2b2b76..e5715bd721 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<00:00, 46.9MB/s] 26%|##6 | 10.8M/41.5M [00:00<00:00, 34.8MB/s] 39%|###8 | 16.0M/41.5M [00:00<00:00, 33.7MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 36.5MB/s] 62%|######2 | 25.8M/41.5M [00:00<00:00, 35.4MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.0MB/s] 86%|########6 | 35.7M/41.5M [00:00<00:00, 38.9MB/s] 95%|#########5| 39.4M/41.5M [00:01<00:00, 34.4MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 35.7MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:00, 51.7MB/s] 27%|##7 | 11.3M/41.5M [00:00<00:00, 41.6MB/s] 37%|###6 | 15.3M/41.5M [00:00<00:00, 33.5MB/s] 45%|####4 | 18.6M/41.5M [00:00<00:00, 25.9MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 30.5MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.5MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 40.3MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 37.3MB/s]
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 10bdb343ab..c540b2a713 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] 17%|#6 | 7.48M/44.7M [00:00<00:00, 78.4MB/s] 34%|###3 | 15.0M/44.7M [00:00<00:00, 78.8MB/s] 50%|##### | 22.5M/44.7M [00:00<00:00, 72.7MB/s] 66%|######6 | 29.5M/44.7M [00:00<00:00, 61.4MB/s] 80%|#######9 | 35.6M/44.7M [00:00<00:00, 42.7MB/s] 90%|######### | 40.4M/44.7M [00:00<00:00, 43.4MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 54.9MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 55.0MB/s] 36%|###5 | 16.0M/44.7M [00:00<00:00, 55.6MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 60.4MB/s] 72%|#######1 | 32.0M/44.7M [00:00<00:00, 56.1MB/s] 90%|########9 | 40.0M/44.7M [00:00<00:00, 52.5MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 59.1MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index f5143661d8..f38cdd6f6d 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 24.944 seconds)
+ **Total running time of the script:** ( 1 minutes 26.358 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 7a0c70ae99..d9604ae72c 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
=================
-**06:12.389** total execution time for **how_to_compile_models** files:
+**06:16.312** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:27.877 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:28.056 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:24.944 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:26.358 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:57.743 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:58.621 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:40.253 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:40.973 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:35.435 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:35.792 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:26.421 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:26.880 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.687 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.706 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.376 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.252 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.652 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.673 | 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 59ec527932..1af81eb8c6 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -673,7 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3869.3919 3868.9551 3874.8382 3866.4956 2.4531
+ 3931.6974 3931.9528 3938.9781 3927.5907 3.1503
@@ -681,7 +681,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 14.507 seconds)
+ **Total running time of the script:** ( 1 minutes 15.441 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 2e23d14253..56519fb9d0 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
- 8192/102967424 [..............................] - ETA: 0s 6635520/102967424 [>.............................] - ETA: 1s 8380416/102967424 [=>............................] - ETA: 2s 15024128/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 2s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s
50323456/102967424 [=============>................] - ETA: 1s 58712064/102967424 [================>.............] - ETA: 0s 65355776/102967424 [==================>...........] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 69296128/102967424 [===================>..........] - ETA: 0s 73850880/102967424 [====================>.........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 82124800/102967424 [======================>.......] -
ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 2s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 630784/102967424 [..............................] - ETA: 8s 5971968/102967424 [>.............................] - ETA: 1s 8380416/102967424 [=>............................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 37928960/102967424 [==========>...................] - ETA: 1s
41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 0s 50323456/102967424 [=============>................] - ETA: 0s 58712064/102967424 [================>.............] - ETA: 0s 63856640/102967424 [=================>............] - ETA: 0s 65355776/102967424 [==================>...........] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 70213632/102967424 [===================>..........] -
ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 82124800/102967424 [======================>.......] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 91979776/102967424 [=========================>....] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424
[============================>.] - ETA: 0s 102967424/102967424 [==============================] - 2s 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 c8144be64f..0708d1ecf9 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 13.8329 13.7768 14.2839 13.3256 0.3437
+ 13.6361 13.6103 13.8469 13.5288 0.1074
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 f965708211..7bf4a18c68 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 49.9MB/s] 9%|9 | 15.8M/170M [00:00<00:02, 64.3MB/s] 13%|#3 | 22.3M/170M [00:00<00:02, 57.1MB/s] 16%|#6 | 28.0M/170M [00:00<00:02, 55.1MB/s] 20%|#9 | 33.3M/170M [00:00<00:04, 32.3MB/s] 24%|##3 | 40.0M/170M [00:00<00:03, 37.8MB/s] 28%|##8 | 48.0M/170M [00:01<00:03, 37.2MB/s] 33%|###2 | 56.0M/170M [00:01<00:02, 40.2MB/s] 37%|###6 | 62.3M/170M [00:01<00:02, 43.5MB/s] 39%|###9 | 66.9M/170M [00:01<00:02, 40.1MB/s] 42%|####2 | 72.0M/170M [00:01<00:02, 37.2MB/s] 47%|####7 | 80.0M/170M [00:01<00:02, 42.3MB/s] 52%|#####1 | 88.0M/170M [00:02<00:01, 49.4MB/s] 57%|#####6 | 96.0M/170M [00:02<00:01, 49.2MB/s] 61%|######1 | 104M/170M [00:02<00:01, 46.7MB/s] 66%|######5 | 112M/170M [00:02<00:01, 52.4MB/s] 71%|####### | 120M/170M [00:02<00:01, 51.4MB/s]
75%|#######5 | 128M/170M [00:02<00:00, 50.1MB/s] 78%|#######8 | 133M/170M [00:03<00:00, 48.9MB/s] 81%|########1 | 138M/170M [00:03<00:00, 44.8MB/s] 85%|########4 | 144M/170M [00:03<00:00, 49.8MB/s] 89%|########8 | 151M/170M [00:03<00:00, 55.2MB/s] 92%|#########2| 156M/170M [00:03<00:00, 51.9MB/s] 95%|#########5| 162M/170M [00:03<00:00, 41.4MB/s] 98%|#########7| 166M/170M [00:03<00:00, 37.0MB/s] 100%|##########| 170M/170M [00:04<00:00, 44.3MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:02, 57.3MB/s] 9%|9 | 16.0M/170M [00:00<00:03, 52.8MB/s] 14%|#4 | 24.0M/170M [00:00<00:03, 43.6MB/s] 18%|#7 | 30.3M/170M [00:00<00:03, 42.1MB/s] 23%|##3 | 39.4M/170M [00:00<00:02, 54.7MB/s] 28%|##8 | 48.0M/170M [00:01<00:02, 51.7MB/s] 37%|###6 | 62.1M/170M [00:01<00:01, 73.5MB/s] 41%|####1 | 70.3M/170M [00:01<00:01, 63.1MB/s] 46%|####5 | 77.4M/170M [00:01<00:01, 65.2MB/s] 50%|####9 | 84.4M/170M [00:01<00:01, 56.0MB/s] 53%|#####3 | 90.4M/170M [00:01<00:01, 57.4MB/s] 57%|#####6 | 96.4M/170M [00:01<00:01, 46.8MB/s] 61%|######1 | 104M/170M [00:02<00:01, 50.6MB/s] 66%|######5 | 112M/170M [00:02<00:01, 52.8MB/s] 71%|####### | 120M/170M [00:02<00:00, 58.7MB/s] 75%|#######5 | 128M/170M [00:02<00:00, 52.5MB/s] 80%|######## | 136M/170M [00:02<00:00, 49.5MB/s]
85%|########4 | 144M/170M [00:02<00:00, 54.8MB/s] 89%|########9 | 152M/170M [00:02<00:00, 57.6MB/s] 94%|#########4| 160M/170M [00:03<00:00, 56.9MB/s] 98%|#########7| 166M/170M [00:03<00:00, 44.3MB/s] 100%|##########| 170M/170M [00:03<00:00, 52.8MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 15.032 seconds)
+ **Total running time of the script:** ( 3 minutes 15.004 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 219d2439ce..5fcbd25478 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, 52.0MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 44.2MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 49.6MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 59%|#####8 | 7.99M/13.6M [00:00<00:00, 62.1MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 91.7MB/s]
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 83.2266 83.1432 83.8235 82.8631 0.2519
+ 84.5716 84.3256 99.2646 84.1383 1.6463
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 22.623 seconds)
+ **Total running time of the script:** ( 1 minutes 23.280 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 2aefe4e741..81dbf49297 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)
- 99.9112 99.8940 101.5831 99.2885 0.2883
+ 102.0743 102.0062 104.1448 101.7435 0.2825
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 c4a12d59e8..090524ac73 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**09:07.286** total execution time for **how_to_deploy_models** files:
+**09:10.662** 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``) | 03:15.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:15.004 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:22.623 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:23.280 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:14.507 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:15.441 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:47.941 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:47.806 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:47.354 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:47.766 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.310 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.777 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:27.858 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:28.326 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:27.653 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.254 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.009 | 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 021322299f..994acf3c2c 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:03.692** total execution time for **how_to_extend_tvm** files:
+**00:03.754** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.562 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.612 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.122 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.134 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 775d9d1825..3307f28c64 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: 23998us [23998us] (49.03%; 49.03%)
- FoldScaleAxis: 24945us [6us] (50.97%; 50.97%)
- FoldConstant: 24939us [1705us] (50.95%; 99.97%)
- InferType: 23233us [23233us] (47.47%; 93.16%)
+ InferType: 24150us [24150us] (48.86%; 48.86%)
+ FoldScaleAxis: 25279us [6us] (51.14%; 51.14%)
+ FoldConstant: 25273us [1713us] (51.13%; 99.98%)
+ InferType: 23560us [23560us] (47.66%; 93.22%)
@@ -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: 22993us [22993us] (48.45%; 48.45%)
- FoldScaleAxis: 24465us [5us] (51.55%; 51.55%)
- FoldConstant: 24459us [1567us] (51.54%; 99.98%)
- InferType: 22893us [22893us] (48.24%; 93.59%)
+ InferType: 23261us [23261us] (48.55%; 48.55%)
+ FoldScaleAxis: 24655us [5us] (51.45%; 51.45%)
+ FoldConstant: 24650us [1588us] (51.44%; 99.98%)
+ InferType: 23061us [23061us] (48.13%; 93.56%)
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 3a88fd2bec..ca990e4882 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: 33.933055 ms
+ Convolution: 53.526657 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 5723596697..e60305701d 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: 12.276394 ms
+ conv2d with tensor core: 12.126416 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 3ed6b48501..7292fcd098 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: 0.013185
- Baseline: 3.228810
+ Numpy running time: 0.013319
+ Baseline: 3.238822
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.282098
+ Opt1: 0.275729
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.253881
+ Opt2: 0.261426
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.102643
+ Opt3: 0.106849
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.098629
+ Opt4: 0.099563
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.093493
+ Opt5: 0.092628
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.112774
+ Opt6: 0.109652
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 cd44748d34..83ac3f2486 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
=================
-**00:30.686** total execution time for **how_to_optimize_operators** files:
+**00:30.540** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:27.754 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:27.726 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.757 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.710 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.175 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.104 | 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 bf920d20ce..7c49f6a04d 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
=================
-**03:22.935** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:24.651** 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``) | 01:28.289 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:28.915 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:08.050 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:08.743 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.721 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.859 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.404 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.555 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.375 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.483 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.096 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.095 | 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 d08fb77416..e4320ab4cc 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: 0.349 ms
+ Execution time of this operator: 0.337 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 84f6566a9f..90c6ec1506 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)
- 3.2591 3.2615 3.2626 3.2531 0.0042
+ 3.2117 3.2120 3.2124 3.2106 0.0008
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 8.050 seconds)
+ **Total running time of the script:** ( 1 minutes 8.743 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 190c68da85..36a780f8ab 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)
- 689.4338 690.9611 691.3769 685.9634 2.4598
+ 694.1912 692.5876 698.4321 691.5539 3.0283
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.289 seconds)
+ **Total running time of the script:** ( 1 minutes 28.915 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 d571d0812f..92a42b95d9 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:22.505** total execution time for **how_to_tune_with_autotvm** files:
+**00:22.701** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.469 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.667 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.019 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 0ae09b9f58..cc952c652c 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: 0.037084
+ Time cost of this operator: 0.037354
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 c4bb97bda0..a421a310cb 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 286.6 98.733 (1, 2, 10, 10, 3) 2 1 [286.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.782 0.958 (1, 6, 10, 10) 1 1 [2.782]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.896 0.309 (1, 1, 10, 10, 3) 1 1 [0.896]
- Total_time - 290.277 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 311.0 98.802 (1, 2, 10, 10, 3) 2 1 [311.0]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.824 0.897 (1, 6, 10, 10) 1 1 [2.824]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.946 0.3 (1, 1, 10, 10, 3) 1 1 [0.946]
+ Total_time - 314.77 - - - - -
@@ -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 125.8 97.831 (1, 6, 10, 10, 1) 2 1 [125.8]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.873 1.457 (1, 6, 10, 10) 1 1 [1.873]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.915 0.712 (1, 1, 10, 10, 3) 1 1 [0.915]
- Total_time - 128.588 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.1 97.574 (1, 6, 10, 10, 1) 2 1 [100.1]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.684 1.641 (1, 6, 10, 10) 1 1 [1.684]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.805 0.785 (1, 3, 10, 10, 1) 1 1 [0.805]
+ Total_time - 102.589 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.818 seconds)
+ **Total running time of the script:** ( 1 minutes 20.386 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 ce141287c8..8efce27ae3 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, 18.6MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 28.8MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 203MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -326,7 +326,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 22.249 seconds)
+ **Total running time of the script:** ( 1 minutes 22.712 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 d97073d2d2..bb5734f606 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -217,7 +217,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpx54lepas/images/random'
+ '/tmp/tmprv61bh89/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpx54lepas/images/target contains 8144 images
- /tmp/tmpx54lepas/images/random contains 5000 images
+ /tmp/tmprv61bh89/images/target contains 8144 images
+ /tmp/tmprv61bh89/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 37s - loss: 0.2218 - accuracy: 0.9264 - val_loss: 0.1178 - val_accuracy: 0.9569 - 37s/epoch - 113ms/step
+ 328/328 - 37s - loss: 0.2098 - accuracy: 0.9275 - val_loss: 0.1204 - val_accuracy: 0.9600 - 37s/epoch - 113ms/step
Epoch 2/3
- 328/328 - 33s - loss: 0.1016 - accuracy: 0.9625 - val_loss: 0.0912 - val_accuracy: 0.9664 - 33s/epoch - 101ms/step
+ 328/328 - 34s - loss: 0.1052 - accuracy: 0.9619 - val_loss: 0.2153 - val_accuracy: 0.9196 - 34s/epoch - 103ms/step
Epoch 3/3
- 328/328 - 33s - loss: 0.0645 - accuracy: 0.9769 - val_loss: 0.0987 - val_accuracy: 0.9698 - 33s/epoch - 101ms/step
+ 328/328 - 34s - loss: 0.0669 - accuracy: 0.9769 - val_loss: 0.1004 - val_accuracy: 0.9649 - 34s/epoch - 102ms/step
- <keras.callbacks.History object at 0x7efc484f6a30>
+ <keras.callbacks.History object at 0x7fd15d0f99a0>
@@ -860,7 +860,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 14.892 seconds)
+ **Total running time of the script:** ( 4 minutes 19.894 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 4bf76bd5dd..62e12fa61f 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
=================
-**07:22.924** total execution time for **how_to_work_with_microtvm** files:
+**07:30.214** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:14.892 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:19.894 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:22.249 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:22.712 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:19.818 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:20.386 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.078 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.235 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.258 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.282 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:06.628 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.705 | 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 bbfbb3587d..dd5fe59121 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
=================
-**00:35.673** total execution time for **how_to_work_with_relay** files:
+**00:35.704** 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``) | 00:30.834 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:30.995 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.045 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:02.950 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.788 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.752 | 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 926f78c92b..7f450a1242 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 0x7efe2202a3a0>
+ <function my_cuda_math_rule at 0x7fd33d480670>
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 f23be6aa29..73165193bf 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:04.816** total execution time for **how_to_work_with_schedules** files:
+**00:05.170** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.305 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.329 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:00.933 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.243 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.656 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.666 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.645 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.654 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.114 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.115 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.066 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.067 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.063 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.065 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 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 65f40523df..b34286086a 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 24.43s!
+ yolov3-tiny inference graph built in 24.88s!
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 740ceef818..b5b4174b0e 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:55.881** total execution time for **topic_vta_tutorials_frontend** files:
+**00:56.435** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:55.881 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:56.435 | 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 f866e6f0df..61d0b7babd 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
=================
-**00:03.031** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.133** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.591 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.693 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.440 | 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 1553fe5d46..3910b8319d 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
=================
-**00:00.722** total execution time for **topic_vta_tutorials** files:
+**00:00.717** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.373 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.371 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.349 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.346 | 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 b58f585011..90843f8abe 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,6 +207,13 @@ trials, we can load the best schedule from the log file and apply it.
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ *E
+
+
@@ -318,7 +325,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 88.953 ms
+ Execution time of this operator: 91.880 ms
@@ -416,7 +423,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
- *E
+
@@ -434,7 +441,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.257 seconds)
+ **Total running time of the script:** ( 1 minutes 44.306 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 b8c5ddec24..421d86b1af 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: 11.36/11.36 result: MeasureResult(costs=(0.023635830200000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7260010242462158, timestamp=1709093967.8504858) [('tile_y', [-1, 512]), ('tile_x', [-1, 64])],None,69
- No: 2 GFLOPS: 2.17/11.36 result: MeasureResult(costs=(0.1239798772,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.251561403274536, timestamp=1709093970.084766) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
- No: 3 GFLOPS: 11.29/11.36 result: MeasureResult(costs=(0.0237726068,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6484413146972656, timestamp=1709093970.7149553) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
- No: 4 GFLOPS: 8.53/11.36 result: MeasureResult(costs=(0.0314578716,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7514944076538086, timestamp=1709093971.4657035) [('tile_y', [-1, 512]), ('tile_x', [-1, 16])],None,49
- No: 5 GFLOPS: 14.74/14.74 result: MeasureResult(costs=(0.0182087156,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5615394115447998, timestamp=1709093972.1515744) [('tile_y', [-1, 32]), ('tile_x', [-1, 128])],None,75
- No: 6 GFLOPS: 16.11/16.11 result: MeasureResult(costs=(0.0166613872,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5219171047210693, timestamp=1709093972.6751025) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 7 GFLOPS: 1.99/16.11 result: MeasureResult(costs=(0.1351390058,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.396437168121338, timestamp=1709093975.0840893) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
- No: 8 GFLOPS: 13.95/16.11 result: MeasureResult(costs=(0.0192468596,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5841596126556396, timestamp=1709093975.643369) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
- No: 9 GFLOPS: 14.65/16.11 result: MeasureResult(costs=(0.0183282398,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5461173057556152, timestamp=1709093976.297899) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
- No: 10 GFLOPS: 3.90/16.11 result: MeasureResult(costs=(0.0688767716,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.32258939743042, timestamp=1709093977.6452205) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 1 GFLOPS: 11.73/11.73 result: MeasureResult(costs=(0.022886010800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.624065637588501, timestamp=1709097004.0542984) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
+ No: 2 GFLOPS: 7.63/11.73 result: MeasureResult(costs=(0.0351615312,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.837968111038208, timestamp=1709097004.8601413) [('tile_y', [-1, 32]), ('tile_x', [-1, 16])],None,45
+ No: 3 GFLOPS: 2.20/11.73 result: MeasureResult(costs=(0.12197042039999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.2068393230438232, timestamp=1709097007.0593562) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
+ No: 4 GFLOPS: 15.14/15.14 result: MeasureResult(costs=(0.017735040599999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6364390850067139, timestamp=1709097007.5993776) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
+ No: 5 GFLOPS: 3.96/15.14 result: MeasureResult(costs=(0.0678655772,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3424408435821533, timestamp=1709097009.0520504) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
+ No: 6 GFLOPS: 13.09/15.14 result: MeasureResult(costs=(0.0205139784,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5776934623718262, timestamp=1709097009.6337156) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 7 GFLOPS: 10.12/15.14 result: MeasureResult(costs=(0.0265264838,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6751868724822998, timestamp=1709097010.30333) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
+ No: 8 GFLOPS: 0.51/15.14 result: MeasureResult(costs=(0.5231707908000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.603533029556274, timestamp=1709097018.922664) [('tile_y', [-1, 512]), ('tile_x', [-1, 1])],None,9
+ No: 9 GFLOPS: 9.48/15.14 result: MeasureResult(costs=(0.028327531,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6755623817443848, timestamp=1709097019.7080424) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
+ No: 10 GFLOPS: 8.67/15.14 result: MeasureResult(costs=(0.0309617792,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7026984691619873, timestamp=1709097020.4510086) [('tile_y', [-1, 2]), ('tile_x', [-1, 4])],None,21
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index a51e3f794b..2e02a987aa 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': 451.4457685699381, 'median': 451.7261080500248, 'std': 1.0400657207140818}
+ {'mean': 456.7010369400032, 'median': 456.24219435003397, 'std': 1.545224683946261}
@@ -582,30 +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: 13.01/ 21.94 GFLOPS | Progress: (4/20) | 8.15 s [Task 1/25] Current/Best: 20.88/ 24.50 GFLOPS | Progress: (8/20) | 10.28 s [Task 1/25] Current/Best: 6.21/ 24.75 GFLOPS | Progress: (12/20) | 13.49 s [Task 1/25] Current/Best: 7.02/ 24.75 GFLOPS | Progress: (16/20) | 16.74 s [Task 1/25] Current/Best: 23.72/ 24.75 GFLOPS | Progress: (20/20) | 19.14 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 6.91/ 18.29 GFLOPS | Progress: (4/20) | 4.18 s [Task 2/25] Current/Best: 16.06/ 18.46 GFLOPS | Progress: (8/20) | 5.77 s [Task 2/25] Current/Best: 13.48/ 21.28 GFLOPS | Progress: (12/20) | 7.17 s [Task 2/25] Current/Best: 14.54/ 21.28 GFLOPS | Progress: (16/20) | 9.09 s [Task 2/25] Current/Best: 7.24/ 21.28 GFLOPS | Progress: (20/20) | 10.60 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 11.88/ 17.13 GFLOPS | Progress: (4/20) | 4.67 s [Task 3/25] Current/Best: 3.38/ 21.47 GFLOPS | Progress: (8/20) | 8.10 s [Task 3/25] Current/Best: 10.25/ 21.47 GFLOPS | Progress: (12/20) | 10.83 s [Task 3/25] Current/Best: 19.81/ 21.47 GFLOPS | Progress: (16/20) | 12.81 s [Task 3/25] Current/Best: 11.52/ 21.79 GFLOPS | Progress: (20/20) | 15.00 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 13.79/ 21.79 GFLOPS | Progress: (4/20) | 6.46 s [Task 4/25] Current/Best: 15.78/ 21.79 GFLOPS | Progress: (8/20) | 8.32 s [Task 4/25] Current/Best: 19.81/ 21.79 GFLOPS | Progress: (12/20) | 10.22 s [Task 4/25] Current/Best: 4.16/ 23.97 GFLOPS | Progress: (16/20) | 12.21 s [Task 4/25] Current/Best: 13.56/ 23.97 GFLOPS | Progress: (20/20) | 16.24 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 18.79/ 18.79 GFLOPS | Progress: (4/20) | 4.48 s [Task 5/25] Current/Best: 15.52/ 18.79 GFLOPS | Progress: (8/20) | 6.38 s [Task 5/25] Current/Best: 15.54/ 18.79 GFLOPS | Progress: (12/20) | 8.39 s [Task 5/25] Current/Best: 18.98/ 24.32 GFLOPS | Progress: (16/20) | 10.05 s [Task 5/25] Current/Best: 3.36/ 24.32 GFLOPS | Progress: (20/20) | 12.41 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 11.86/ 14.35 GFLOPS | Progress: (4/20) | 4.91 s [Task 6/25] Current/Best: 18.99/ 18.99 GFLOPS | Progress: (8/20) | 7.15 s [Task 6/25] Current/Best: 12.72/ 21.39 GFLOPS | Progress: (12/20) | 9.19 s [Task 6/25] Current/Best: 17.83/ 22.32 GFLOPS | Progress: (16/20) | 11.13 s [Task 6/25] Current/Best: 13.11/ 23.87 GFLOPS | Progress: (20/20) | 14.32 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 1.70/ 24.13 GFLOPS | Progress: (4/20) | 5.57 s [Task 7/25] Current/Best: 15.55/ 24.13 GFLOPS | Progress: (8/20) | 8.27 s [Task 7/25] Current/Best: 18.72/ 24.13 GFLOPS | Progress: (12/20) | 11.16 s [Task 7/25] Current/Best: 5.94/ 24.13 GFLOPS | Progress: (16/20) | 14.21 s [Task 7/25] Current/Best: 9.88/ 24.13 GFLOPS | Progress: (20/20) | 16.36 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 6.19/ 13.62 GFLOPS | Progress: (4/20) | 5.64 s [Task 8/25] Current/Best: 3.47/ 13.62 GFLOPS | Progress: (8/20) | 15.31 s [Task 8/25] Current/Best: 2.65/ 14.68 GFLOPS | Progress: (12/20) | 18.23 s [Task 8/25] Current/Best: 15.50/ 15.50 GFLOPS | Progress: (16/20) | 20.43 s [Task 8/25] Current/Best: 11.24/ 15.50 GFLOPS | Progress: (20/20) | 23.17 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 8.27/ 14.05 GFLOPS | Progress: (4/20) | 6.86 s [Task 9/25] Current/Best: 18.17/ 23.68 GFLOPS | Progress: (8/20) | 8.44 s [Task 9/25] Current/Best: 14.91/ 23.68 GFLOPS | Progress: (12/20) | 13.44 s [Task 9/25] Current/Best: 9.70/ 23.68 GFLOPS | Progress: (16/20) | 20.31 s [Task 9/25] Current/Best: 13.47/ 23.68 GFLOPS | Progress: (20/20) | 23.44 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 5.28/ 17.86 GFLOPS | Progress: (4/20) | 4.07 s [Task 10/25] Current/Best: 9.27/ 17.86 GFLOPS | Progress: (8/20) | 5.76 s [Task 10/25] Current/Best: 21.25/ 21.25 GFLOPS | Progress: (12/20) | 8.15 s [Task 10/25] Current/Best: 13.55/ 21.25 GFLOPS | Progress: (16/20) | 11.53 s [Task 10/25] Current/Best: 15.93/ 21.25 GFLOPS | Progress: (20/20) | 13.57 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 12.13/ 21.20 GFLOPS | Progress: (4/20) | 5.06 s [Task 11/25] Current/Best: 21.68/ 21.68 GFLOPS | Progress: (8/20) | 7.78 s [Task 11/25] Current/Best: 19.22/ 21.68 GFLOPS | Progress: (12/20) | 10.13 s [Task 11/25] Current/Best: 12.58/ 21.68 GFLOPS | Progress: (16/20) | 12.66 s [Task 11/25] Current/Best: 6.57/ 23.41 GFLOPS | Progress: (20/20) | 16.16 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 13.84/ 16.37 GFLOPS | Progress: (4/20) | 5.48 s [Task 12/25] Current/Best: 14.79/ 16.37 GFLOPS | Progress: (8/20) | 9.45 s [Task 12/25] Current/Best: 2.29/ 16.37 GFLOPS | Progress: (12/20) | 13.40 s [Task 12/25] Current/Best: 11.61/ 16.37 GFLOPS | Progress: (16/20) | 17.26 s [Task 12/25] Current/Best: 9.52/ 16.37 GFLOPS | Progress: (20/20) | 19.70 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 14.74/ 19.78 GFLOPS | Progress: (4/20) | 4.60 s [Task 13/25] Current/Best: 3.29/ 21.38 GFLOPS | Progress: (8/20) | 8.12 s [Task 13/25] Current/Best: 12.91/ 21.38 GFLOPS | Progress: (12/20) | 12.48 s [Task 13/25] Current/Best: 21.21/ 21.88 GFLOPS | Progress: (16/20) | 14.84 s [Task 13/25] Current/Best: 10.82/ 21.88 GFLOPS | Progress: (20/20) | 17.72 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 12.47/ 12.47 GFLOPS | Progress: (4/20) | 14.39 s [Task 14/25] Current/Best: 11.64/ 12.47 GFLOPS | Progress: (8/20) | 27.29 s [Task 14/25] Current/Best: 4.70/ 22.34 GFLOPS | Progress: (12/20) | 38.23 s [Task 14/25] Current/Best: 9.72/ 22.34 GFLOPS | Progress: (16/20) | 44.54 s [Task 14/25] Current/Best: 7.61/ 22.34 GFLOPS | Progress: (20/20) | 52.81 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 19.00/ 19.00 GFLOPS | Progress: (4/20) | 5.85 s [Task 15/25] Current/Best: 14.15/ 19.00 GFLOPS | Progress: (8/20) | 8.66 s [Task 15/25] Current/Best: 23.93/ 23.93 GFLOPS | Progress: (12/20) | 18.06 s [Task 15/25] Current/Best: 16.50/ 23.93 GFLOPS | Progress: (16/20) | 19.62 s [Task 15/25] Current/Best: 7.58/ 23.93 GFLOPS | Progress: (20/
20) | 26.64 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 12.60/ 19.98 GFLOPS | Progress: (4/20) | 4.58 s [Task 16/25] Current/Best: 18.39/ 19.98 GFLOPS | Progress: (8/20) | 6.60 s [Task 16/25] Current/Best: 17.88/ 23.67 GFLOPS | Progress: (12/20) | 8.02 s [Task 16/25] Current/Best: 6.23/ 23.67 GFLOPS | Progress: (16/20) | 11.58 s [Task 16/25] Current/Best: 5.21/ 23.67 GFLOPS | Progress: (20/20) | 14.18 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 3.30/ 19.57 GFLOPS | Progress: (4/20) | 6.35 s [Task 17/25] Current/Best: 21.71/ 21.71 GFLOPS | Progress: (8/20) | 8.63 s [Task 17/25] Current/Best: 13.12/ 21.71 GFLOPS | Progress: (12/20) | 11.19 s [Task 17/25] Current/Best: 20.64/ 21.71 GFLOPS | Progress: (16/20) | 13.28 s [Task 17/25] Current/Best: 23.87/ 23.87 GFLOPS | Progress: (20/20) | 15.60 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 22.50/ 22.50 GFLOPS | Progress: (4/20) | 4.83 s [Task 18/25] Current/Best: 12.38/ 22.50 GFLOPS | Progress: (8/20) | 8.98 s [Task 18/25] Current/Best: 4.91/ 22.50 GFLOPS | Progress: (12/20) | 11.91 s [Task 18/25] Current/Best: 11.63/ 22.50 GFLOPS | Progress: (16/20) | 15.39 s [Task 18/25] Current/Best: 15.72/ 22.50 GFLOPS | Progress: (20/20) | 22.34 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 3.29/ 20.99 GFLOPS | Progress: (4/20) | 5.01 s [Task 19/25] Current/Best: 10.92/ 20.99 GFLOPS | Progress: (8/20) | 8.41 s [Task 19/25] Current/Best: 14.93/ 20.99 GFLOPS | Progress: (12/20) | 12.39 s [Task 19/25] Current/Best: 5.11/ 23.78 GFLOPS | Progress: (16/20) | 16.68 s [Task 19/25] Current/Best: 9.30/ 23.78 GFLOPS | Progress: (20/20) | 20.58 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 10.38/ 22.41 GFLOPS | Progress: (4/20) | 5.98 s [Task 20/25] Current/Best: 22.29/ 22.41 GFLOPS | Progress: (8/20) | 9.69 s [Task 20/25] Current/Best: 5.73/ 22.41 GFLOPS | Progress: (12/20) | 17.42 s [Task 20/25] Current/Best: 7.73/ 22.41 GFLOPS | Progress: (16/20) | 22.41 s [Task 20/25] Current/Best: 12.57/ 22.41 GFLOPS | Progress: (20/20) | 26.72 s Done.
- [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 18.06/ 18.06 GFLOPS | Progress: (4/20) | 4.47 s [Task 21/25] Current/Best: 7.05/ 18.06 GFLOPS | Progress: (8/20) | 15.77 s Done.
- [Task 21/25] Current/Best: 2.80/ 22.00 GFLOPS | Progress: (12/20) | 27.22 s [Task 21/25] Current/Best: 5.04/ 22.09 GFLOPS | Progress: (16/20) | 39.04 s [Task 21/25] Current/Best: 7.33/ 22.09 GFLOPS | Progress: (20/20) | 45.94 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 21.66/ 23.49 GFLOPS | Progress: (4/20) | 4.00 s [Task 22/25] Current/Best: 11.46/ 23.71 GFLOPS | Progress: (8/20) | 6.10 s [Task 22/25] Current/Best: 13.83/ 23.71 GFLOPS | Progress: (12/20) | 9.00 s [Task 22/25] Current/Best: 21.39/ 23.71 GFLOPS | Progress: (16/20) | 11.60 s [Task 22/25] Current/Best: 15.08/ 23.71 GFLOPS | Progress: (20/20) | 13.40 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 2.87/ 23.35 GFLOPS | Progress: (4/20) | 5.31 s [Task 23/25] Current/Best: 19.83/ 23.92 GFLOPS | Progress: (8/20) | 7.43 s [Task 23/25] Current/Best: 25.09/ 25.09 GFLOPS | Progress: (12/20) | 10.23 s [Task 23/25] Current/Best: 10.09/ 25.09 GFLOPS | Progress: (16/20) | 14.37 s [Task 23/25] Current/Best: 21.04/ 25.09 GFLOPS | Progress: (20/20) | 20.20 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.65/ 10.09 GFLOPS | Progress: (4/20) | 13.38 s [Task 24/25] Current/Best: 1.64/ 10.09 GFLOPS | Progress: (8/20) | 24.40 s [Task 24/25] Current/Best: 6.34/ 10.09 GFLOPS | Progress: (12/20) | 35.43 s [Task 24/25] Current/Best: 2.24/ 10.09 GFLOPS | Progress: (16/20) | 46.40 s [Task 24/25] Current/Best: 3.84/ 10.09 GFLOPS | Progress: (20/20) | 57.11 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- [Task 25/25] Current/Best: 6.18/ 6.75 GFLOPS | Progress: (4/20) | 5.91 s [Task 25/25] Current/Best: 4.32/ 9.89 GFLOPS | Progress: (8/20) | 16.82 s [Task 25/25] Current/Best: 6.04/ 9.89 GFLOPS | Progress: (12/20) | 19.29 s [Task 25/25] Current/Best: 5.90/ 9.89 GFLOPS | Progress: (16/20) | 29.92 s [Task 25/25] Current/Best: 5.84/ 9.89 GFLOPS | Progress: (20/20) | 37.10 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 7.63/ 21.68 GFLOPS | Progress: (4/20) | 7.43 s [Task 1/25] Current/Best: 16.04/ 21.68 GFLOPS | Progress: (8/20) | 10.61 s [Task 1/25] Current/Best: 2.01/ 21.68 GFLOPS | Progress: (12/20) | 16.54 s [Task 1/25] Current/Best: 16.49/ 21.68 GFLOPS | Progress: (16/20) | 21.14 s [Task 1/25] Current/Best: 7.72/ 21.68 GFLOPS | Progress: (20/20) | 23.73 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 6.49/ 19.52 GFLOPS | Progress: (4/20) | 3.90 s [Task 2/25] Current/Best: 20.47/ 20.47 GFLOPS | Progress: (8/20) | 5.52 s [Task 2/25] Current/Best: 16.79/ 20.47 GFLOPS | Progress: (12/20) | 7.06 s [Task 2/25] Current/Best: 15.73/ 20.47 GFLOPS | Progress: (16/20) | 8.54 s [Task 2/25] Current/Best: 12.64/ 20.47 GFLOPS | Progress: (20/20) | 10.25 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 20.63/ 25.27 GFLOPS | Progress: (4/20) | 5.30 s [Task 3/25] Current/Best: 14.66/ 25.27 GFLOPS | Progress: (8/20) | 7.59 s [Task 3/25] Current/Best: 6.70/ 25.43 GFLOPS | Progress: (12/20) | 12.40 s [Task 3/25] Current/Best: 24.01/ 25.43 GFLOPS | Progress: (16/20) | 14.19 s [Task 3/25] Current/Best: 14.51/ 25.43 GFLOPS | Progress: (20/20) | 16.63 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 13.03/ 18.36 GFLOPS | Progress: (4/20) | 4.21 s [Task 4/25] Current/Best: 9.81/ 19.84 GFLOPS | Progress: (8/20) | 7.58 s [Task 4/25] Current/Best: 11.31/ 19.84 GFLOPS | Progress: (12/20) | 12.39 s [Task 4/25] Current/Best: 9.94/ 19.84 GFLOPS | Progress: (16/20) | 14.04 s [Task 4/25] Current/Best: 7.83/ 19.84 GFLOPS | Progress: (20/20) | 15.69 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 14.78/ 16.78 GFLOPS | Progress: (4/20) | 4.31 s [Task 5/25] Current/Best: 9.82/ 18.48 GFLOPS | Progress: (8/20) | 6.13 s [Task 5/25] Current/Best: 3.39/ 18.48 GFLOPS | Progress: (12/20) | 8.09 s [Task 5/25] Current/Best: 12.86/ 18.48 GFLOPS | Progress: (16/20) | 10.05 s [Task 5/25] Current/Best: 22.90/ 22.90 GFLOPS | Progress: (20/20) | 12.17 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 18.99/ 18.99 GFLOPS | Progress: (4/20) | 4.45 s [Task 6/25] Current/Best: 6.51/ 18.99 GFLOPS | Progress: (8/20) | 6.94 s [Task 6/25] Current/Best: 5.28/ 18.99 GFLOPS | Progress: (12/20) | 11.77 s [Task 6/25] Current/Best: 17.65/ 22.24 GFLOPS | Progress: (16/20) | 14.63 s [Task 6/25] Current/Best: 12.93/ 22.24 GFLOPS | Progress: (20/20) | 17.12 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 23.65/ 23.65 GFLOPS | Progress: (4/20) | 4.74 s [Task 7/25] Current/Best: 12.83/ 23.65 GFLOPS | Progress: (8/20) | 7.18 s [Task 7/25] Current/Best: 15.08/ 23.65 GFLOPS | Progress: (12/20) | 9.92 s [Task 7/25] Current/Best: 17.33/ 23.65 GFLOPS | Progress: (16/20) | 12.64 s [Task 7/25] Current/Best: 7.38/ 23.65 GFLOPS | Progress: (20/20) | 15.03 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 15.42/ 15.42 GFLOPS | Progress: (4/20) | 5.26 s [Task 8/25] Current/Best: 13.39/ 15.42 GFLOPS | Progress: (8/20) | 10.23 s [Task 8/25] Current/Best: 9.17/ 20.08 GFLOPS | Progress: (12/20) | 17.76 s [Task 8/25] Current/Best: 16.26/ 20.08 GFLOPS | Progress: (16/20) | 20.46 s [Task 8/25] Current/Best: 13.25/ 20.08 GFLOPS | Progress: (20/20) | 31.98 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 12.76/ 22.44 GFLOPS | Progress: (4/20) | 12.64 s [Task 9/25] Current/Best: 11.71/ 22.44 GFLOPS | Progress: (8/20) | 16.27 s [Task 9/25] Current/Best: 15.86/ 22.44 GFLOPS | Progress: (12/20) | 22.14 s [Task 9/25] Current/Best: 23.02/ 23.02 GFLOPS | Progress: (16/20) | 24.70 s [Task 9/25] Current/Best: 16.25/ 23.02 GFLOPS | Progress: (20
/20) | 35.47 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 3.27/ 19.23 GFLOPS | Progress: (4/20) | 4.38 s [Task 10/25] Current/Best: 17.89/ 19.23 GFLOPS | Progress: (8/20) | 6.32 s [Task 10/25] Current/Best: 17.81/ 21.30 GFLOPS | Progress: (12/20) | 8.90 s [Task 10/25] Current/Best: 18.87/ 21.30 GFLOPS | Progress: (16/20) | 10.78 s [Task 10/25] Current/Best: 20.89/ 21.30 GFLOPS | Progress: (20/20) | 13.40 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 6.98/ 21.59 GFLOPS | Progress: (4/20) | 4.56 s [Task 11/25] Current/Best: 19.26/ 21.59 GFLOPS | Progress: (8/20) | 6.73 s [Task 11/25] Current/Best: 24.58/ 24.58 GFLOPS | Progress: (12/20) | 9.67 s [Task 11/25] Current/Best: 10.16/ 25.33 GFLOPS | Progress: (16/20) | 11.82 s [Task 11/25] Current/Best: 6.47/ 25.33 GFLOPS | Progress: (20/20) | 14.17 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 4.84/ 22.84 GFLOPS | Progress: (4/20) | 8.18 s [Task 12/25] Current/Best: 11.53/ 22.84 GFLOPS | Progress: (8/20) | 11.06 s [Task 12/25] Current/Best: 13.28/ 22.84 GFLOPS | Progress: (12/20) | 16.70 s [Task 12/25] Current/Best: 3.61/ 22.84 GFLOPS | Progress: (16/20) | 20.30 s [Task 12/25] Current/Best: 12.03/ 22.84 GFLOPS | Progress: (20/20) | 24.00 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 15.58/ 23.95 GFLOPS | Progress: (4/20) | 5.39 s [Task 13/25] Current/Best: 22.13/ 23.95 GFLOPS | Progress: (8/20) | 8.63 s [Task 13/25] Current/Best: 13.73/ 23.95 GFLOPS | Progress: (12/20) | 11.82 s [Task 13/25] Current/Best: 20.85/ 23.95 GFLOPS | Progress: (16/20) | 14.11 s [Task 13/25] Current/Best: 1.63/ 23.95 GFLOPS | Progress: (20/20) | 18.56 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 9.10/ 9.96 GFLOPS | Progress: (4/20) | 14.25 s [Task 14/25] Current/Best: 8.15/ 13.15 GFLOPS | Progress: (8/20) | 25.84 s [Task 14/25] Current/Best: 1.70/ 14.18 GFLOPS | Progress: (12/20) | 30.12 s [Task 14/25] Current/Best: 17.66/ 18.93 GFLOPS | Progress: (16/20) | 37.02 s [Task 14/25] Current/Best: 10.88/ 18.93 GFLOPS | Progress: (20/20) | 40.62 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 7.46/ 14.25 GFLOPS | Progress: (4/20) | 6.23 s [Task 15/25] Current/Best: 19.10/ 22.11 GFLOPS | Progress: (8/20) | 9.06 s [Task 15/25] Current/Best: 13.43/ 22.11 GFLOPS | Progress: (12/20) | 11.75 s [Task 15/25] Current/Best: 14.70/ 22.11 GFLOPS | Progress: (16/20) | 15.12 s [Task 15/25] Current/Best: 7.80/ 23.87 GFLOPS | Progress: (20/
20) | 25.13 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 22.65/ 22.65 GFLOPS | Progress: (4/20) | 4.06 s [Task 16/25] Current/Best: 21.15/ 22.65 GFLOPS | Progress: (8/20) | 6.93 s [Task 16/25] Current/Best: 12.70/ 22.65 GFLOPS | Progress: (12/20) | 9.23 s [Task 16/25] Current/Best: 19.83/ 22.65 GFLOPS | Progress: (16/20) | 10.75 s [Task 16/25] Current/Best: 12.61/ 22.65 GFLOPS | Progress: (20/20) | 12.40 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 10.47/ 22.20 GFLOPS | Progress: (4/20) | 6.14 s [Task 17/25] Current/Best: 11.80/ 22.20 GFLOPS | Progress: (8/20) | 9.04 s [Task 17/25] Current/Best: 19.91/ 22.20 GFLOPS | Progress: (12/20) | 11.65 s [Task 17/25] Current/Best: 3.25/ 22.20 GFLOPS | Progress: (16/20) | 15.28 s [Task 17/25] Current/Best: 12.41/ 22.20 GFLOPS | Progress: (20/20) | 17.48 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 15.72/ 15.72 GFLOPS | Progress: (4/20) | 6.16 s [Task 18/25] Current/Best: 10.75/ 15.72 GFLOPS | Progress: (8/20) | 10.97 s [Task 18/25] Current/Best: 16.53/ 22.62 GFLOPS | Progress: (12/20) | 13.21 s [Task 18/25] Current/Best: 6.87/ 22.62 GFLOPS | Progress: (16/20) | 18.34 s [Task 18/25] Current/Best: 7.28/ 22.62 GFLOPS | Progress: (20/20) | 20.25 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 23.06/ 23.06 GFLOPS | Progress: (4/20) | 5.62 s [Task 19/25] Current/Best: 8.30/ 23.06 GFLOPS | Progress: (8/20) | 9.50 s Done.
+ Done.
+ [Task 19/25] Current/Best: 18.21/ 23.06 GFLOPS | Progress: (12/20) | 12.30 s [Task 19/25] Current/Best: 13.15/ 23.70 GFLOPS | Progress: (16/20) | 14.85 s [Task 19/25] Current/Best: 9.33/ 23.70 GFLOPS | Progress: (20/20) | 18.84 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 9.35/ 12.74 GFLOPS | Progress: (4/20) | 13.97 s [Task 20/25] Current/Best: 20.14/ 20.14 GFLOPS | Progress: (8/20) | 21.06 s [Task 20/25] Current/Best: 7.25/ 20.14 GFLOPS | Progress: (12/20) | 23.63 s [Task 20/25] Current/Best: 11.54/ 20.14 GFLOPS | Progress: (16/20) | 30.73 s [Task 20/25] Current/Best: 4.81/ 20.14 GFLOPS | Progress: (20/20) | 33.26 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 10.79/ 13.28 GFLOPS | Progress: (4/20) | 13.84 s [Task 21/25] Current/Best: 19.03/ 19.09 GFLOPS | Progress: (8/20) | 16.70 s [Task 21/25] Current/Best: 7.83/ 21.52 GFLOPS | Progress: (12/20) | 18.45 s [Task 21/25] Current/Best: 10.49/ 21.52 GFLOPS | Progress: (16/20) | 24.57 s [Task 21/25] Current/Best: 7.76/ 21.52 GFLOPS | Progress: (2
0/20) | 26.59 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 20.69/ 20.69 GFLOPS | Progress: (4/20) | 3.94 s [Task 22/25] Current/Best: 5.35/ 20.69 GFLOPS | Progress: (8/20) | 6.37 s [Task 22/25] Current/Best: 11.85/ 22.17 GFLOPS | Progress: (12/20) | 8.91 s [Task 22/25] Current/Best: 16.71/ 22.17 GFLOPS | Progress: (16/20) | 10.63 s [Task 22/25] Current/Best: 13.24/ 23.73 GFLOPS | Progress: (20/20) | 12.17 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 12.43/ 14.97 GFLOPS | Progress: (4/20) | 5.55 s [Task 23/25] Current/Best: 14.94/ 14.97 GFLOPS | Progress: (8/20) | 8.74 s [Task 23/25] Current/Best: 14.13/ 14.97 GFLOPS | Progress: (12/20) | 13.53 s [Task 23/25] Current/Best: 18.83/ 22.12 GFLOPS | Progress: (16/20) | 16.17 s [Task 23/25] Current/Best: 21.57/ 23.30 GFLOPS | Progress: (20/20) | 18.56 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.67/ 5.09 GFLOPS | Progress: (4/20) | 7.97 s [Task 24/25] Current/Best: 2.71/ 5.09 GFLOPS | Progress: (8/20) | 18.95 s [Task 24/25] Current/Best: 7.02/ 10.92 GFLOPS | Progress: (12/20) | 21.36 s [Task 24/25] Current/Best: 7.55/ 10.92 GFLOPS | Progress: (16/20) | 31.68 s [Task 24/25] Current/Best: 2.67/ 10.92 GFLOPS | Progress: (20/20) | 42.64 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 3.19/ 5.98 GFLOPS | Progress: (4/20) | 8.40 s Done.
+ Done.
+ Done.
+ [Task 25/25] Current/Best: 1.62/ 8.17 GFLOPS | Progress: (8/20) | 19.34 s [Task 25/25] Current/Best: 8.09/ 8.17 GFLOPS | Progress: (12/20) | 23.93 s [Task 25/25] Current/Best: 10.21/ 10.21 GFLOPS | Progress: (16/20) | 25.79 s [Task 25/25] Current/Best: 6.15/ 10.23 GFLOPS | Progress: (20/20) | 27.55 s
@@ -765,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 373.1435294699986, 'median': 371.8562300000485, 'std': 2.0801811563045867}
- unoptimized: {'mean': 451.4457685699381, 'median': 451.7261080500248, 'std': 1.0400657207140818}
+ optimized: {'mean': 374.89591800999733, 'median': 375.18698344993027, 'std': 1.7541073565697605}
+ unoptimized: {'mean': 456.7010369400032, 'median': 456.24219435003397, 'std': 1.545224683946261}
@@ -789,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 24.273 seconds)
+ **Total running time of the script:** ( 12 minutes 58.633 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 d7c454668d..182881495e 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
- 1.179e-07 secs/op
+ 1.118e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index ce1e0c1646..bdb4a7aaa0 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, 0x6e84f70)), stage(b, placeholder(b, 0x284c2a40)), 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, 0x10349970)), stage(b, placeholder(b, 0x2d33acb0)), 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 5816f4d65a..63c6e4d015 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
=================
-**16:46.811** total execution time for **tutorial** files:
+**16:44.721** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:24.273 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:58.633 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:28.257 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:44.306 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:55.989 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:56.429 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:40.235 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:40.687 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:16.143 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:22.834 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.909 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.843 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.836 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.826 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.169 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.163 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index eed5c1f68d..de37e26daa 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000006
- naive: 0.000006
+ Numpy running time: 0.000005
+ naive: 0.000005
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000006
+ parallel: 0.000007
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 5.78084000153467e-06 1.0
- naive 5.6484e-06 0.9770898344359106
- parallel 5.722e-06 0.9898215481627151
- vector 3.80682e-05 6.585236745852478
+ numpy 5.03038999340788e-06 1.0
+ naive 5.473499999999999e-06 1.0880866110128233
+ parallel 6.8955e-06 1.3707684710402714
+ vector 3.8076799999999994e-05 7.569353479531027
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.013258
+ Numpy running time: 0.013323
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.220557
+ none: 3.241507
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.282271
+ blocking: 0.286457
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.253588
+ vectorization: 0.264949
# 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: 0.107062
+ loop permutation: 0.108885
# 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: 0.100985
+ array packing: 0.101488
# 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: 0.093585
+ block caching: 0.092008
# 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: 0.111752
+ parallelization: 0.109926
# 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 3.2205568944 1.0
- blocking 0.282271345 0.08764675000488946
- vectorization 0.2535879161 0.07874039317266718
- loop permutation 0.1070618021 0.0332432574894616
- array packing 0.100985383 0.031356497124952636
- block caching 0.0935853093 0.029058734985470656
- parallelization 0.111752135 0.034699630736012746
+ none 3.2415065217 1.0
+ blocking 0.2864572082 0.08837162790891694
+ vectorization 0.26494915769999994 0.08173642592613019
+ loop permutation 0.1088852954 0.03359095367264459
+ array packing 0.1014884998 0.03130905309632838
+ block caching 0.0920076728 0.02838423189466446
+ parallelization 0.10992579970000001 0.03391194772063877
diff --git a/docs/commit_hash b/docs/commit_hash
index 0bd5eb1a57..cc7246363d 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-b3fa6cb873c71bfc15054bc9abbcc111c8413c9b
+563ef9587cfa913cf96f9ec061cdab43ce744b70
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 975a880238..dfe3e76bc9 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -604,7 +604,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.877 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.056 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 8488a8c236..563f26942c 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -468,15 +468,14 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 4d548c4413..ab80f7aff6 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -451,13 +451,12 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 4ef1ada43c..076a77af9f 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -671,7 +671,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 24.944 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.358 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 714b3b7072..b8e3c219b5 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:12.389</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:16.312</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -369,39 +369,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
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+<td><p>01:28.056</p></td>
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+<td><p>01:26.358</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
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<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>
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+<td><p>00:25.706</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>00:11.376</p></td>
+<td><p>00:11.252</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>00:02.652</p></td>
+<td><p>00:02.673</p></td>
<td><p>0.0 MB</p></td>
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</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 33ce0200aa..ea5b7ca90d 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -850,10 +850,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3869.3919 3868.9551 3874.8382 3866.4956 2.4531
+ 3931.6974 3931.9528 3938.9781 3927.5907 3.1503
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.507 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 15.441 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 3d3cfd8810..a372af5e67 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -458,27 +458,29 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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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 c7b0828abf..854c30de63 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -682,7 +682,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 13.8329 13.7768 14.2839 13.3256 0.3437
+ 13.6361 13.6103 13.8469 13.5288 0.1074
</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 b52812edd8..2f9bf2a8a1 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -474,32 +474,28 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -590,7 +586,7 @@ 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 15.032 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 15.004 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 558a7b51c7..80bf6cc37d 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -515,9 +515,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
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@@ -608,7 +607,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 83.2266 83.1432 83.8235 82.8631 0.2519
+ 84.5716 84.3256 99.2646 84.1383 1.6463
</pre></div>
</div>
<div class="admonition note">
@@ -647,7 +646,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.623 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.280 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 b86e310873..bb0f5464fb 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -600,7 +600,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 99.9112 99.8940 101.5831 99.2885 0.2883
+ 102.0743 102.0062 104.1448 101.7435 0.2825
</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 c5c1bee3e2..9e9b36e3dc 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:07.286</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>09:10.662</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -369,35 +369,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:15.032</p></td>
+<td><p>03:15.004</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>01:22.623</p></td>
+<td><p>01:23.280</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>01:14.507</p></td>
+<td><p>01:15.441</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:47.941</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:47.806</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:47.354</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
+<td><p>00:47.766</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:44.310</p></td>
+<td><p>00:44.777</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:27.858</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:28.326</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:27.653</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
+<td><p>00:28.254</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 e0f93d1d4f..ef1fd42142 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.692</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:03.754</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,11 +369,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:02.562</p></td>
+<td><p>00:02.612</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>00:01.122</p></td>
+<td><p>00:01.134</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 f32bcb506a..2e1fa676cf 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -545,10 +545,10 @@ profile the execution time of each passes.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 23998us [23998us] (49.03%; 49.03%)
-FoldScaleAxis: 24945us [6us] (50.97%; 50.97%)
- FoldConstant: 24939us [1705us] (50.95%; 99.97%)
- InferType: 23233us [23233us] (47.47%; 93.16%)
+InferType: 24150us [24150us] (48.86%; 48.86%)
+FoldScaleAxis: 25279us [6us] (51.14%; 51.14%)
+ FoldConstant: 25273us [1713us] (51.13%; 99.98%)
+ InferType: 23560us [23560us] (47.66%; 93.22%)
</pre></div>
</div>
</div>
@@ -570,10 +570,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 22993us [22993us] (48.45%; 48.45%)
-FoldScaleAxis: 24465us [5us] (51.55%; 51.55%)
- FoldConstant: 24459us [1567us] (51.54%; 99.98%)
- InferType: 22893us [22893us] (48.24%; 93.59%)
+InferType: 23261us [23261us] (48.55%; 48.55%)
+FoldScaleAxis: 24655us [5us] (51.45%; 51.45%)
+ FoldConstant: 24650us [1588us] (51.44%; 99.98%)
+ InferType: 23061us [23061us] (48.13%; 93.56%)
</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 d7db0007db..07c5c0fc84 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -595,7 +595,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 33.933055 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.526657 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 fdd754da4e..0aff3c4300 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -877,7 +877,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.276394 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.126416 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 b99944b5fb..97996c5a9b 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -492,8 +492,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013185
-Baseline: 3.228810
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013319
+Baseline: 3.238822
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -552,7 +552,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.282098
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.275729
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -609,7 +609,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.253881
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.261426
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -664,7 +664,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.102643
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.106849
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -741,7 +741,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.098629
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.099563
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -819,7 +819,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.093493
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.092628
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -899,7 +899,7 @@ class Module:
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.112774
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.109652
</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 280d70ff8a..127f357626 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:30.686</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:30.540</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,15 +369,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
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diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
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<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>
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<table class="docutils align-default">
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<col style="width: 85%" />
@@ -369,27 +369,27 @@
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<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>
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<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>
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<td><p>0.0 MB</p></td>
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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 fcd28ca35c..28c71739be 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1032,7 +1032,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.349 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.337 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 5422079e86..94ea2a2c46 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -923,7 +923,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.2591 3.2615 3.2626 3.2531 0.0042
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</pre></div>
</div>
</div>
@@ -945,7 +945,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
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<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
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--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
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@@ -945,7 +945,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
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</pre></div>
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</div>
@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
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<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
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+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:22.505</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -369,11 +369,11 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
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diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
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--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -630,7 +630,7 @@ and measure running time.</p>
Best config:
,None
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--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -664,10 +664,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
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+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.824 0.897 (1, 6, 10, 10) 1 1 [2.824]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.946 0.3 (1, 1, 10, 10, 3) 1 1 [0.946]
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</pre></div>
</div>
</div>
@@ -719,13 +719,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 125.8 97.831 (1, 6, 10, 10, 1) 2 1 [125.8]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.873 1.457 (1, 6, 10, 10) 1 1 [1.873]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.915 0.712 (1, 1, 10, 10, 3) 1 1 [0.915]
-Total_time - 128.588 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.1 97.574 (1, 6, 10, 10, 1) 2 1 [100.1]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.684 1.641 (1, 6, 10, 10) 1 1 [1.684]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.805 0.785 (1, 3, 10, 10, 1) 1 1 [0.805]
+Total_time - 102.589 - - - - -
</pre></div>
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<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 d5d9e066f4..497257e5af 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -475,8 +475,7 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
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-100%|##########| 3.42M/3.42M [00:00<00:00, 28.8MB/s]
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -604,7 +603,7 @@ 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>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.712 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">
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<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index fd340cf145..1c065bb7a8 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -543,7 +543,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpx54lepas/images/random'
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</pre></div>
</div>
</div>
@@ -603,8 +603,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
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-/tmp/tmpx54lepas/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/tmprv61bh89/images/target contains 8144 images
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</pre></div>
</div>
</div>
@@ -716,13 +716,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
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Epoch 2/3
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+328/328 - 34s - loss: 0.1052 - accuracy: 0.9619 - val_loss: 0.2153 - val_accuracy: 0.9196 - 34s/epoch - 103ms/step
Epoch 3/3
-328/328 - 33s - loss: 0.0645 - accuracy: 0.9769 - val_loss: 0.0987 - val_accuracy: 0.9698 - 33s/epoch - 101ms/step
+328/328 - 34s - loss: 0.0669 - accuracy: 0.9769 - val_loss: 0.1004 - val_accuracy: 0.9649 - 34s/epoch - 102ms/step
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@@ -986,7 +986,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
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<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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<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<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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--- a/docs/how_to/work_with_schedules/intrin_math.html
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@@ -572,7 +572,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
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<p>Register the rule to TVM with override option to override existing rule.
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<title>Node45->Node21</title>
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<title>Node45->Node22</title>
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<title>Node45->Node33</title>
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<title>Node52->Node35</title>
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<title>Node52->Node53</title>
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<title>Node66->Node24</title>
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<title>Node67->Node2</title>
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<title>Node67->Node4</title>
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<title>Node67->Node17</title>
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<title>Node67->Node18</title>
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<title>Node67->Node36</title>
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<title>Node67->Node47</title>
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<title>Node68->Node4</title>
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<title>Node69</title>
<g id="a_node69"><a xlink:href="target_8h.html" target="_top" xlink:title="Compilation target object.">
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<title>Node68->Node69</title>
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<title>Node69->Node4</title>
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<!-- Node69->Node6 -->
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<title>Node69->Node6</title>
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<title>Node69->Node17</title>
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<title>Node69->Node21</title>
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<g id="node70" class="node">
<title>Node70</title>
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<title>Node69->Node70</title>
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<g id="node71" class="node">
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diff --git a/docs/reference/api/doxygen/algorithm_8h_source.html b/docs/reference/api/doxygen/algorithm_8h_source.html
index 90232edaa8..49665682fc 100644
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+++ b/docs/reference/api/doxygen/algorithm_8h_source.html
@@ -162,8 +162,8 @@ $(function() {
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