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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/07/24 14:59:20 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@ae1be53d6dc08ad8a95ddf6af022880e836e8704)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new a20acc3e8e deploying docs (apache/tvm@ae1be53d6dc08ad8a95ddf6af022880e836e8704)
a20acc3e8e is described below
commit a20acc3e8e1372f97406260d9c031c86d95961a7
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Jul 24 14:59:13 2024 +0000
deploying docs (apache/tvm@ae1be53d6dc08ad8a95ddf6af022880e836e8704)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_keras.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 20 +-
.../deploy_models/deploy_model_on_adreno.rst.txt | 4 +-
.../deploy_model_on_adreno_tvmc.rst.txt | 2 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 18 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 8 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
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.../tune_with_autotvm/sg_execution_times.rst.txt | 8 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 2 +-
.../work_with_microtvm/micro_autotune.rst.txt | 18 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 14 +-
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.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../tutorials/frontend/deploy_detection.rst.txt | 7 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 4 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
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docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 62 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 18 +-
.../tutorial/tensor_expr_get_started.rst.txt | 40 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_keras.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 18 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 16 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 20 +-
.../deploy_models/deploy_model_on_adreno.html | 4 +-
.../deploy_models/deploy_model_on_adreno_tvmc.html | 24 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 42 +-
docs/how_to/deploy_models/deploy_prequantized.html | 11 +-
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docs/how_to/deploy_models/sg_execution_times.html | 18 +-
docs/how_to/extend_tvm/sg_execution_times.html | 8 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
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.../tune_conv2d_layer_cuda.html | 2 +-
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.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 2 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 18 +-
docs/how_to/work_with_microtvm/micro_pytorch.html | 6 +-
docs/how_to/work_with_microtvm/micro_train.html | 16 +-
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328 files changed, 9534 insertions(+), 9340 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 4ca25b5134..b45f4cfc5d 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 38.874 seconds)
+ **Total running time of the script:** ( 1 minutes 40.625 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index a1336bb6ff..2cc7bdddf3 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -232,7 +232,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
- 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 867ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 928ms/step
Keras top-1 id: 285, class name: Egyptian cat
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index 9feaad5aed..fff5092671 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, 42.1MB/s] 25%|##4 | 10.4M/41.5M [00:00<00:01, 26.9MB/s] 39%|###8 | 16.0M/41.5M [00:00<00:00, 29.5MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 36.8MB/s] 63%|######2 | 26.1M/41.5M [00:00<00:00, 33.4MB/s] 84%|########4 | 34.9M/41.5M [00:00<00:00, 47.9MB/s] 97%|#########6| 40.1M/41.5M [00:01<00:00, 41.1MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 39.1MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 6%|6 | 2.66M/41.5M [00:00<00:02, 17.2MB/s] 15%|#5 | 6.33M/41.5M [00:00<00:01, 22.5MB/s] 22%|##1 | 9.02M/41.5M [00:00<00:01, 24.5MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:01, 27.4MB/s] 41%|#### | 16.9M/41.5M [00:00<00:01, 24.0MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 27.3MB/s] 60%|###### | 24.9M/41.5M [00:01<00:00, 23.2MB/s] 77%|#######7 | 32.0M/41.5M [00:01<00:00, 31.2MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 34.1MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 29.6MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index f40eb190bb..4438cc5f4c 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.028 seconds)
+ **Total running time of the script:** ( 1 minutes 6.437 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 37a6dd8bf1..d9add687fd 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 14%|#4 | 6.47M/44.7M [00:00<00:00, 67.8MB/s] 29%|##8 | 12.9M/44.7M [00:00<00:00, 46.9MB/s] 40%|###9 | 17.8M/44.7M [00:00<00:01, 24.8MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 29.3MB/s] 68%|######7 | 30.3M/44.7M [00:01<00:00, 27.7MB/s] 75%|#######4 | 33.4M/44.7M [00:01<00:00, 24.4MB/s] 86%|########5 | 38.2M/44.7M [00:01<00:00, 29.2MB/s] 93%|#########2| 41.5M/44.7M [00:01<00:00, 27.0MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 30.8MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 66.9MB/s] 36%|###5 | 16.0M/44.7M [00:00<00:00, 70.3MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 71.0MB/s] 69%|######8 | 30.8M/44.7M [00:00<00:00, 44.3MB/s] 80%|######## | 35.9M/44.7M [00:00<00:00, 39.2MB/s] 90%|########9 | 40.2M/44.7M [00:01<00:00, 26.9MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 40.6MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 5bcc519c18..a1916a9ff2 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -424,7 +424,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 31.068 seconds)
+ **Total running time of the script:** ( 1 minutes 34.667 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 a36bd2c777..068c8b5a88 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
=================
-**06:45.920** total execution time for **how_to_compile_models** files:
+**07:03.047** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:38.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:40.625 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:31.068 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:34.667 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.028 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:06.437 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.295 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:45.990 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:39.924 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:41.154 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.509 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.359 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.758 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.410 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.548 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.529 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.916 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.875 | 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 274e5022fa..0213ed432d 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -664,7 +664,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4000.2870 4000.2443 4001.7779 3998.5194 1.0676
+ 3999.0015 3999.2471 4003.3998 3995.8450 2.4062
@@ -672,7 +672,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.689 seconds)
+ **Total running time of the script:** ( 1 minutes 19.690 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 bb41ae75b7..3df74d30e6 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: 1s 13230080/102967424 [==>...........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33210368/102967424 [========>.....................] - ETA: 0s 33546240/102967424 [========>.....................] - ETA: 1s
38395904/102967424 [==========>...................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 56967168/102967424 [===============>..............] - ETA: 0s 58712064/102967424 [================>.............] - ETA: 1s 60907520/102967424 [================>.............] -
ETA: 1s 67100672/102967424 [==================>...........] - ETA: 0s 72065024/102967424 [===================>..........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 82124800/102967424 [======================>.......] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 86917120/102967424 [========================>.....] - 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 5062656/102967424 [>.............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 2s 15024128/102967424 [===>..........................] - ETA: 3s 16769024/102967424 [===>..........................] - ETA: 3s 25157632/102967424 [======>.......................] - ETA: 2s 33546240/102967424 [========>.....................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s
41934848/102967424 [===========>..................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 56967168/102967424 [===============>..............] - ETA: 1s 58712064/102967424 [================>.............] - ETA: 1s 66977792/102967424 [==================>...........] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 69296128/102967424 [===================>..........] - ETA: 0s 73744384/102967424 [====================>.........] -
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 [==============================] - 3s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index a4c411fa26..b63e69c26f 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.0580 13.8153 15.0565 13.6570 0.4378
+ 14.2405 14.3120 14.5199 13.9393 0.2470
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 668d293afd..4fe3d5efe1 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:02, 70.8MB/s] 14%|#3 | 23.7M/170M [00:00<00:01, 122MB/s] 21%|## | 35.6M/170M [00:00<00:01, 102MB/s] 28%|##7 | 47.2M/170M [00:00<00:01, 109MB/s] 34%|###4 | 58.0M/170M [00:00<00:01, 76.8MB/s] 42%|####2 | 72.0M/170M [00:00<00:01, 82.7MB/s] 48%|####7 | 80.7M/170M [00:01<00:01, 71.7MB/s] 61%|######1 | 104M/170M [00:01<00:00, 103MB/s] 71%|####### | 120M/170M [00:01<00:00, 115MB/s] 79%|#######8 | 133M/170M [00:01<00:00, 122MB/s] 86%|########5 | 146M/170M [00:01<00:00, 102MB/s] 94%|#########4| 160M/170M [00:01<00:00, 97.4MB/s] 100%|##########| 170M/170M [00:01<00:00, 96.5MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 4%|3 | 6.30M/170M [00:00<00:03, 48.1MB/s] 6%|6 | 10.9M/170M [00:00<00:03, 44.9MB/s] 9%|8 | 15.2M/170M [00:00<00:05, 27.2MB/s] 11%|# | 18.2M/170M [00:00<00:05, 28.1MB/s] 14%|#4 | 24.0M/170M [00:00<00:04, 31.7MB/s] 20%|## | 34.2M/170M [00:00<00:02, 50.4MB/s] 24%|##3 | 40.0M/170M [00:00<00:02, 52.3MB/s] 28%|##8 | 48.0M/170M [00:01<00:03, 39.5MB/s] 33%|###2 | 56.0M/170M [00:01<00:02, 47.0MB/s] 37%|###6 | 62.3M/170M [00:01<00:02, 45.3MB/s] 40%|###9 | 67.2M/170M [00:01<00:02, 46.1MB/s] 42%|####2 | 72.1M/170M [00:01<00:02, 43.5MB/s] 47%|####7 | 80.0M/170M [00:02<00:02, 42.6MB/s] 52%|#####1 | 88.0M/170M [00:02<00:02, 39.9MB/s] 56%|#####5 | 94.3M/170M [00:02<00:02, 35.1MB/s] 58%|#####7 | 97.9M/170M [00:02<00:02, 35.1MB/s] 61%|######1 | 104M/170M [00:02<00:01, 36.5MB/s
] 66%|######5 | 112M/170M [00:02<00:01, 44.8MB/s] 71%|####### | 120M/170M [00:02<00:01, 49.7MB/s] 74%|#######4 | 126M/170M [00:03<00:00, 50.4MB/s] 79%|#######9 | 134M/170M [00:03<00:00, 49.6MB/s] 82%|########1 | 139M/170M [00:03<00:00, 45.9MB/s] 88%|########8 | 150M/170M [00:03<00:00, 58.7MB/s] 93%|#########3| 158M/170M [00:03<00:00, 51.1MB/s] 96%|#########6| 164M/170M [00:03<00:00, 51.4MB/s] 99%|#########9| 169M/170M [00:04<00:00, 44.1MB/s] 100%|##########| 170M/170M [00:04<00:00, 43.8MB/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).
@@ -288,7 +288,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 29.384 seconds)
+ **Total running time of the script:** ( 3 minutes 39.253 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 a186055b38..9fec285df6 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, 35.2MB/s] 71%|#######1 | 9.66M/13.6M [00:00<00:00, 25.7MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 25.7MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 29.4MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 48%|####7 | 6.50M/13.6M [00:00<00:00, 68.2MB/s] 96%|#########5| 13.0M/13.6M [00:00<00:00, 53.7MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 57.2MB/s]
@@ -400,7 +400,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 85.9449 85.8920 87.4023 85.5214 0.2609
+ 86.4515 86.3867 90.0166 86.1085 0.4085
@@ -448,7 +448,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 29.842 seconds)
+ **Total running time of the script:** ( 1 minutes 34.415 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 cfbb059e10..aaf03bbbcf 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)
- 104.6402 104.9834 106.6423 102.6870 1.2545
+ 107.3873 107.3637 108.1963 106.5327 0.3567
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 71e15f28ca..9f89c240d1 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
=================
-**09:50.602** total execution time for **how_to_deploy_models** files:
+**10:13.186** 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``) | 03:29.384 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:39.253 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.842 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:34.415 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.689 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.690 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:52.563 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:54.516 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.879 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.895 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.386 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:50.439 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.976 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.098 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.872 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index 1961025d9a..801dfd728d 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
=================
-**00:03.986** total execution time for **how_to_extend_tvm** files:
+**00:04.236** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.757 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.950 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.222 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.278 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 4da431f08f..f49fa285e2 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: 24625us [24625us] (48.58%; 48.58%)
- FoldScaleAxis: 26061us [7us] (51.42%; 51.42%)
- FoldConstant: 26053us [1747us] (51.40%; 99.97%)
- InferType: 24306us [24306us] (47.95%; 93.29%)
+ InferType: 25782us [25782us] (48.40%; 48.40%)
+ FoldScaleAxis: 27487us [9us] (51.60%; 51.60%)
+ FoldConstant: 27478us [1804us] (51.58%; 99.97%)
+ InferType: 25673us [25673us] (48.20%; 93.43%)
@@ -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: 24160us [24160us] (48.50%; 48.50%)
- FoldScaleAxis: 25657us [6us] (51.50%; 51.50%)
- FoldConstant: 25651us [1733us] (51.49%; 99.98%)
- InferType: 23917us [23917us] (48.01%; 93.24%)
+ InferType: 25271us [25271us] (48.59%; 48.59%)
+ FoldScaleAxis: 26737us [7us] (51.41%; 51.41%)
+ FoldConstant: 26730us [1808us] (51.40%; 99.97%)
+ InferType: 24921us [24921us] (47.92%; 93.23%)
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 5f361ec8ae..ab0ec377ac 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.425537 ms
+ Convolution: 53.428798 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 b2d47af165..58933bf1de 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: 11.083974 ms
+ conv2d with tensor core: 12.215990 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 85f947fb57..e99c74ac40 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: 0.013485
- Baseline: 3.148459
+ Numpy running time: 0.015034
+ Baseline: 3.366766
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.294563
+ Opt1: 0.308773
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.270745
+ Opt2: 0.302627
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.107219
+ Opt3: 0.111298
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.102499
+ Opt4: 0.104244
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.094166
+ Opt5: 0.095098
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.112657
+ Opt6: 0.114015
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 2d4263642d..123f267973 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:31.080** total execution time for **how_to_optimize_operators** files:
+**00:32.959** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.020 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:29.585 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.932 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.078 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.127 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.296 | 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 a0103d9821..afed41a774 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**03:37.080** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:45.074** 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``) | 01:33.895 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:38.126 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:11.691 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:13.717 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:19.703 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:20.689 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.660 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.497 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.034 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.946 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.097 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.099 | 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 e4320ab4cc..b8d000cb62 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: 0.337 ms
+ Execution time of this operator: 0.348 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 e1d6f91934..eef9beb4d1 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)
- 3.1977 3.1975 3.1982 3.1974 0.0003
+ 3.2653 3.2650 3.2677 3.2633 0.0018
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 11.691 seconds)
+ **Total running time of the script:** ( 1 minutes 13.717 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 149c1ff57d..b835c26b5c 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)
- 704.5372 704.4232 705.5015 703.6869 0.7452
+ 715.8231 715.7658 716.8739 714.8294 0.8356
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 33.895 seconds)
+ **Total running time of the script:** ( 1 minutes 38.126 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 428d694912..8408d99467 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**00:22.849** total execution time for **how_to_tune_with_autotvm** files:
+**00:22.716** 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``) | 00:22.812 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.679 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 ab153c6e02..6ddbcdd557 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: 0.037016
+ Time cost of this operator: 0.037018
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 f05bf3d7e8..637477a7cd 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -360,10 +360,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 295.0 98.745 (1, 2, 10, 10, 3) 2 1 [295.0]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.822 0.945 (1, 6, 10, 10) 1 1 [2.822]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.926 0.31 (1, 1, 10, 10, 3) 1 1 [0.926]
- Total_time - 298.748 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 317.8 98.832 (1, 2, 10, 10, 3) 2 1 [317.8]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.807 0.873 (1, 6, 10, 10) 1 1 [2.807]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.948 0.295 (1, 1, 10, 10, 3) 1 1 [0.948]
+ Total_time - 321.554 - - - - -
@@ -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 98.625 97.341 (1, 6, 10, 10, 1) 2 1 [98.625]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.737 1.714 (1, 6, 10, 10) 1 1 [1.737]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.945 (1, 1, 10, 10, 3) 1 1 [0.958]
- Total_time - 101.319 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.875 97.55 (1, 6, 10, 10, 1) 2 1 [100.875]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.717 1.66 (1, 6, 10, 10) 1 1 [1.717]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.817 0.79 (1, 3, 10, 10, 1) 1 1 [0.817]
+ Total_time - 103.409 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.705 seconds)
+ **Total running time of the script:** ( 1 minutes 26.682 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 23b4c1353a..ff9fbe3a67 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, 19.1MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 29.7MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 12.0MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 19.2MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
@@ -322,7 +322,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 27.992 seconds)
+ **Total running time of the script:** ( 1 minutes 32.865 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 8ebdc9cc75..6a5bcaeaca 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -218,7 +218,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpba7vshjt/images/random'
+ '/tmp/tmpk4ba3ziw/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpba7vshjt/images/target contains 8144 images
- /tmp/tmpba7vshjt/images/random contains 5000 images
+ /tmp/tmpk4ba3ziw/images/target contains 8144 images
+ /tmp/tmpk4ba3ziw/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 42s - loss: 0.2107 - accuracy: 0.9269 - val_loss: 0.1167 - val_accuracy: 0.9622 - 42s/epoch - 128ms/step
+ 328/328 - 44s - loss: 0.2233 - accuracy: 0.9249 - val_loss: 0.2023 - val_accuracy: 0.9286 - 44s/epoch - 134ms/step
Epoch 2/3
- 328/328 - 40s - loss: 0.0965 - accuracy: 0.9636 - val_loss: 0.0945 - val_accuracy: 0.9664 - 40s/epoch - 120ms/step
+ 328/328 - 40s - loss: 0.1028 - accuracy: 0.9614 - val_loss: 0.0973 - val_accuracy: 0.9668 - 40s/epoch - 123ms/step
Epoch 3/3
- 328/328 - 39s - loss: 0.0659 - accuracy: 0.9750 - val_loss: 0.1817 - val_accuracy: 0.9407 - 39s/epoch - 120ms/step
+ 328/328 - 40s - loss: 0.0670 - accuracy: 0.9736 - val_loss: 0.0838 - val_accuracy: 0.9732 - 40s/epoch - 123ms/step
- <keras.callbacks.History object at 0x7faa5c2cc9d0>
+ <keras.callbacks.History object at 0x7f5ed0a508b0>
@@ -861,7 +861,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 55.035 seconds)
+ **Total running time of the script:** ( 4 minutes 59.215 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 4e0d513280..27f56dd838 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**08:13.331** total execution time for **how_to_work_with_microtvm** files:
+**08:30.317** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:55.035 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:59.215 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:27.992 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:32.865 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:20.705 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.682 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.494 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.250 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.223 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.894 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:07.882 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.411 | 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 df0a873a20..1faaca289f 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:38.865** total execution time for **how_to_work_with_relay** files:
+**00:40.538** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.324 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.508 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.676 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.974 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.859 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.049 | 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 e84ed46317..aafdcfe964 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 0x7fab2bfff790>
+ <function my_cuda_math_rule at 0x7f5f03dc8ee0>
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 c888f8a416..5e6b00bae4 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:05.284** total execution time for **how_to_work_with_schedules** files:
+**00:05.391** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.331 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.477 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.349 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.215 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.670 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.709 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.658 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.697 | 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_extern_op.py` (``extern_op.py``) | 00:00.124 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.068 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.069 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.062 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.066 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.033 | 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 0d524c2253..2073437e89 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 27.16s!
+ yolov3-tiny inference graph built in 29.20s!
@@ -445,6 +445,11 @@ Download test image
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 2.058 seconds)
+
+
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
.. only:: html
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 57595ac734..ab737765b2 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:59.656** total execution time for **topic_vta_tutorials_frontend** files:
+**01:02.058** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:59.656 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:02.058 | 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 665bbf8f76..e64f19e00c 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
=================
-**00:03.246** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.269** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.781 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.767 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.465 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.502 | 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 7a63d3611e..29edff151b 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
=================
-**00:00.743** total execution time for **topic_vta_tutorials** files:
+**00:00.827** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.384 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.430 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.359 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.397 | 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 1cbe82b975..cd2755a4df 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,13 +207,6 @@ trials, we can load the best schedule from the log file and apply it.
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-
- *E
-
@@ -325,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 95.198 ms
+ Execution time of this operator: 89.808 ms
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 30.217 seconds)
+ **Total running time of the script:** ( 1 minutes 34.135 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 b299a20974..9dbd3bc073 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: 7.25/7.25 result: MeasureResult(costs=(0.0370409684,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8932373523712158, timestamp=1721805631.8947172) [('tile_y', [-1, 2]), ('tile_x', [-1, 16])],None,41
- No: 2 GFLOPS: 12.29/12.29 result: MeasureResult(costs=(0.02183458,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7390377521514893, timestamp=1721805632.5006897) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
- No: 3 GFLOPS: 13.02/13.02 result: MeasureResult(costs=(0.0206218028,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7034733295440674, timestamp=1721805633.0989397) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
- No: 4 GFLOPS: 11.14/13.02 result: MeasureResult(costs=(0.0241017234,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7801656723022461, timestamp=1721805633.748537) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
- No: 5 GFLOPS: 4.21/13.02 result: MeasureResult(costs=(0.0637823354,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.284050703048706, timestamp=1721805635.413405) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
- No: 6 GFLOPS: 11.48/13.02 result: MeasureResult(costs=(0.023387401800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7701282501220703, timestamp=1721805636.0515773) [('tile_y', [-1, 2]), ('tile_x', [-1, 128])],None,71
- No: 7 GFLOPS: 12.17/13.02 result: MeasureResult(costs=(0.0220610828,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6159892082214355, timestamp=1721805636.6657047) [('tile_y', [-1, 2]), ('tile_x', [-1, 512])],None,91
- No: 8 GFLOPS: 15.32/15.32 result: MeasureResult(costs=(0.0175219458,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8138086795806885, timestamp=1721805637.201969) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
- No: 9 GFLOPS: 1.91/15.32 result: MeasureResult(costs=(0.1408836408,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.474242687225342, timestamp=1721805639.8011503) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
- No: 10 GFLOPS: 12.48/15.32 result: MeasureResult(costs=(0.0215069992,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5826966762542725, timestamp=1721805640.4078166) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
+ No: 1 GFLOPS: 11.03/11.03 result: MeasureResult(costs=(0.0243369924,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6601595878601074, timestamp=1721826780.0670645) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
+ No: 2 GFLOPS: 14.24/14.24 result: MeasureResult(costs=(0.0188523312,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6289374828338623, timestamp=1721826780.6352296) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
+ No: 3 GFLOPS: 11.01/14.24 result: MeasureResult(costs=(0.0243817892,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6617703437805176, timestamp=1721826781.295357) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
+ No: 4 GFLOPS: 7.93/14.24 result: MeasureResult(costs=(0.03386319040000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7934916019439697, timestamp=1721826782.1101282) [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
+ No: 5 GFLOPS: 3.89/14.24 result: MeasureResult(costs=(0.06902901,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3832976818084717, timestamp=1721826783.6567302) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
+ No: 6 GFLOPS: 4.38/14.24 result: MeasureResult(costs=(0.061240431399999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2524604797363281, timestamp=1721826784.9041798) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+ No: 7 GFLOPS: 1.05/14.24 result: MeasureResult(costs=(0.25445518219999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.348647832870483, timestamp=1721826789.2409468) [('tile_y', [-1, 256]), ('tile_x', [-1, 2])],None,18
+ No: 8 GFLOPS: 10.11/14.24 result: MeasureResult(costs=(0.026563443000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7576045989990234, timestamp=1721826789.9311967) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
+ No: 9 GFLOPS: 16.00/16.00 result: MeasureResult(costs=(0.0167773022,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5294044017791748, timestamp=1721826790.5714872) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 10 GFLOPS: 11.19/16.00 result: MeasureResult(costs=(0.0239872078,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6352133750915527, timestamp=1721826791.2283344) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index fa7a12b66c..43a91fa0c6 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -311,7 +311,7 @@ standard deviation.
.. code-block:: none
- {'mean': 465.42845639001825, 'median': 464.36597090005307, 'std': 1.8185930951413325}
+ {'mean': 471.49283153004944, 'median': 471.0764744999324, 'std': 1.5560659155590908}
@@ -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: 17.87/ 17.87 GFLOPS | Progress: (4/20) | 8.15 s [Task 1/25] Current/Best: 13.17/ 20.32 GFLOPS | Progress: (8/20) | 10.39 s [Task 1/25] Current/Best: 16.26/ 20.32 GFLOPS | Progress: (12/20) | 13.42 s [Task 1/25] Current/Best: 16.35/ 20.32 GFLOPS | Progress: (16/20) | 15.84 s [Task 1/25] Current/Best: 20.14/ 20.32 GFLOPS | Progress: (20/20) | 20.70 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 15.95/ 22.83 GFLOPS | Progress: (4/20) | 3.90 s [Task 2/25] Current/Best: 20.14/ 22.83 GFLOPS | Progress: (8/20) | 5.23 s [Task 2/25] Current/Best: 15.53/ 22.83 GFLOPS | Progress: (12/20) | 6.95 s [Task 2/25] Current/Best: 15.95/ 22.83 GFLOPS | Progress: (16/20) | 8.35 s [Task 2/25] Current/Best: 17.50/ 22.83 GFLOPS | Progress: (20/20) | 9.79 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 11.57/ 13.88 GFLOPS | Progress: (4/20) | 5.08 s [Task 3/25] Current/Best: 12.39/ 24.54 GFLOPS | Progress: (8/20) | 6.99 s [Task 3/25] Current/Best: 8.22/ 24.54 GFLOPS | Progress: (12/20) | 10.67 s [Task 3/25] Current/Best: 5.57/ 24.54 GFLOPS | Progress: (16/20) | 14.63 s [Task 3/25] Current/Best: 13.54/ 24.54 GFLOPS | Progress: (20/20) | 17.10 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 2.46/ 18.85 GFLOPS | Progress: (4/20) | 5.17 s [Task 4/25] Current/Best: 12.48/ 22.52 GFLOPS | Progress: (8/20) | 7.26 s [Task 4/25] Current/Best: 3.56/ 22.52 GFLOPS | Progress: (12/20) | 10.17 s [Task 4/25] Current/Best: 6.74/ 22.52 GFLOPS | Progress: (16/20) | 12.02 s [Task 4/25] Current/Best: 15.31/ 22.52 GFLOPS | Progress: (20/20) | 16.60 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 1.63/ 16.39 GFLOPS | Progress: (4/20) | 5.90 s [Task 5/25] Current/Best: 13.58/ 16.39 GFLOPS | Progress: (8/20) | 8.21 s [Task 5/25] Current/Best: 18.37/ 18.37 GFLOPS | Progress: (12/20) | 10.52 s [Task 5/25] Current/Best: 15.05/ 18.37 GFLOPS | Progress: (16/20) | 12.37 s [Task 5/25] Current/Best: 9.52/ 18.37 GFLOPS | Progress: (20/20) | 14.50 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 6.81/ 20.64 GFLOPS | Progress: (4/20) | 4.76 s [Task 6/25] Current/Best: 10.03/ 20.64 GFLOPS | Progress: (8/20) | 9.01 s [Task 6/25] Current/Best: 16.89/ 20.64 GFLOPS | Progress: (12/20) | 12.09 s [Task 6/25] Current/Best: 12.63/ 21.62 GFLOPS | Progress: (16/20) | 15.47 s [Task 6/25] Current/Best: 17.39/ 21.62 GFLOPS | Progress: (20/20) | 18.50 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 5.35/ 19.06 GFLOPS | Progress: (4/20) | 5.87 s [Task 7/25] Current/Best: 5.14/ 19.06 GFLOPS | Progress: (8/20) | 8.54 s [Task 7/25] Current/Best: 13.27/ 19.06 GFLOPS | Progress: (12/20) | 11.40 s [Task 7/25] Current/Best: 17.74/ 22.32 GFLOPS | Progress: (16/20) | 13.52 s [Task 7/25] Current/Best: 13.55/ 22.32 GFLOPS | Progress: (20/20) | 15.98 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 20.16/ 20.16 GFLOPS | Progress: (4/20) | 4.60 s [Task 8/25] Current/Best: 7.95/ 20.16 GFLOPS | Progress: (8/20) | 16.37 s [Task 8/25] Current/Best: 11.60/ 20.16 GFLOPS | Progress: (12/20) | 21.38 s [Task 8/25] Current/Best: 8.80/ 20.16 GFLOPS | Progress: (16/20) | 24.36 s [Task 8/25] Current/Best: 19.16/ 20.16 GFLOPS | Progress: (20/20) | 27.30 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 15.60/ 15.60 GFLOPS | Progress: (4/20) | 7.54 s [Task 9/25] Current/Best: 13.30/ 15.60 GFLOPS | Progress: (8/20) | 12.95 s [Task 9/25] Current/Best: 7.80/ 23.56 GFLOPS | Progress: (12/20) | 20.62 s [Task 9/25] Current/Best: 18.17/ 23.56 GFLOPS | Progress: (16/20) | 24.03 s [Task 9/25] Current/Best: 6.19/ 23.56 GFLOPS | Progress: (20/20) | 27.24 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 3.10/ 16.01 GFLOPS | Progress: (4/20) | 4.87 s [Task 10/25] Current/Best: 16.09/ 16.09 GFLOPS | Progress: (8/20) | 7.17 s [Task 10/25] Current/Best: 15.90/ 17.15 GFLOPS | Progress: (12/20) | 8.91 s [Task 10/25] Current/Best: 16.25/ 21.80 GFLOPS | Progress: (16/20) | 10.78 s [Task 10/25] Current/Best: 15.10/ 21.80 GFLOPS | Progress: (20/20) | 12.91 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 6.97/ 23.85 GFLOPS | Progress: (4/20) | 4.81 s [Task 11/25] Current/Best: 15.12/ 23.85 GFLOPS | Progress: (8/20) | 7.66 s [Task 11/25] Current/Best: 20.03/ 25.02 GFLOPS | Progress: (12/20) | 9.52 s [Task 11/25] Current/Best: 11.59/ 25.02 GFLOPS | Progress: (16/20) | 12.32 s [Task 11/25] Current/Best: 12.63/ 25.02 GFLOPS | Progress: (20/20) | 14.60 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 12.75/ 18.67 GFLOPS | Progress: (4/20) | 6.93 s [Task 12/25] Current/Best: 16.02/ 18.67 GFLOPS | Progress: (8/20) | 9.56 s [Task 12/25] Current/Best: 9.97/ 19.67 GFLOPS | Progress: (12/20) | 14.38 s [Task 12/25] Current/Best: 3.18/ 23.00 GFLOPS | Progress: (16/20) | 17.49 s [Task 12/25] Current/Best: 13.47/ 23.00 GFLOPS | Progress: (20/20) | 20.83 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 24.54/ 24.54 GFLOPS | Progress: (4/20) | 5.41 s [Task 13/25] Current/Best: 8.89/ 24.54 GFLOPS | Progress: (8/20) | 9.24 s [Task 13/25] Current/Best: 7.81/ 24.54 GFLOPS | Progress: (12/20) | 12.83 s [Task 13/25] Current/Best: 23.23/ 24.54 GFLOPS | Progress: (16/20) | 15.36 s [Task 13/25] Current/Best: 8.81/ 24.54 GFLOPS | Progress: (20/20) | 17.55 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 8.06/ 8.06 GFLOPS | Progress: (4/20) | 10.09 s [Task 14/25] Current/Best: 14.59/ 20.87 GFLOPS | Progress: (8/20) | 14.11 s [Task 14/25] Current/Best: 11.63/ 20.87 GFLOPS | Progress: (12/20) | 20.60 s [Task 14/25] Current/Best: 15.25/ 20.87 GFLOPS | Progress: (16/20) | 32.08 s [Task 14/25] Current/Best: 19.22/ 20.87 GFLOPS | Progress: (20/20) | 37.19 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 19.63/ 19.63 GFLOPS | Progress: (4/20) | 10.65 s [Task 15/25] Current/Best: 3.37/ 19.63 GFLOPS | Progress: (8/20) | 17.46 s [Task 15/25] Current/Best: 6.20/ 20.65 GFLOPS | Progress: (12/20) | 21.53 s [Task 15/25] Current/Best: 13.76/ 20.65 GFLOPS | Progress: (16/20) | 23.23 s [Task 15/25] Current/Best: 13.76/ 20.65 GFLOPS | Progress: (20/20) | 31.24 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 11.14/ 14.93 GFLOPS | Progress: (4/20) | 5.37 s [Task 16/25] Current/Best: 17.97/ 17.97 GFLOPS | Progress: (8/20) | 7.06 s [Task 16/25] Current/Best: 10.79/ 19.96 GFLOPS | Progress: (12/20) | 10.81 s [Task 16/25] Current/Best: 15.24/ 19.96 GFLOPS | Progress: (16/20) | 13.27 s [Task 16/25] Current/Best: 18.32/ 19.96 GFLOPS | Progress: (20/20) | 15.76 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 20.82/ 23.13 GFLOPS | Progress: (4/20) | 4.76 s [Task 17/25] Current/Best: 7.39/ 23.13 GFLOPS | Progress: (8/20) | 7.05 s [Task 17/25] Current/Best: 20.07/ 23.13 GFLOPS | Progress: (12/20) | 10.59 s [Task 17/25] Current/Best: 19.59/ 23.13 GFLOPS | Progress: (16/20) | 12.85 s [Task 17/25] Current/Best: 23.85/ 23.85 GFLOPS | Progress: (20/20) | 14.90 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 19.75/ 19.75 GFLOPS | Progress: (4/20) | 7.28 s [Task 18/25] Current/Best: 18.37/ 21.59 GFLOPS | Progress: (8/20) | 10.70 s [Task 18/25] Current/Best: 3.23/ 21.59 GFLOPS | Progress: (12/20) | 13.32 s [Task 18/25] Current/Best: 17.88/ 22.63 GFLOPS | Progress: (16/20) | 16.08 s [Task 18/25] Current/Best: 11.46/ 22.63 GFLOPS | Progress: (20/20) | 19.92 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 6.04/ 23.01 GFLOPS | Progress: (4/20) | 7.19 s [Task 19/25] Current/Best: 12.66/ 23.41 GFLOPS | Progress: (8/20) | 9.85 s [Task 19/25] Current/Best: 10.94/ 23.41 GFLOPS | Progress: (12/20) | 13.69 s [Task 19/25] Current/Best: 10.86/ 23.41 GFLOPS | Progress: (16/20) | 17.96 s [Task 19/25] Current/Best: 6.33/ 23.41 GFLOPS | Progress: (20/20) | 20.78 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 9.09/ 19.41 GFLOPS | Progress: (4/20) | 5.72 s [Task 20/25] Current/Best: 17.36/ 19.41 GFLOPS | Progress: (8/20) | 13.24 s [Task 20/25] Current/Best: 15.20/ 19.41 GFLOPS | Progress: (12/20) | 15.50 s [Task 20/25] Current/Best: 7.69/ 19.41 GFLOPS | Progress: (16/20) | 26.98 s [Task 20/25] Current/Best: 15.97/ 19.41 GFLOPS | Progress: (20/20) | 31.51 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 3.73/ 10.22 GFLOPS | Progress: (4/20) | 9.55 s [Task 21/25] Current/Best: 7.64/ 17.18 GFLOPS | Progress: (8/20) | 11.98 s [Task 21/25] Current/Best: 10.91/ 20.17 GFLOPS | Progress: (12/20) | 13.94 s [Task 21/25] Current/Best: 7.14/ 20.17 GFLOPS | Progress: (16/20) | 25.46 s [Task 21/25] Current/Best: 13.97/ 20.17 GFLOPS | Progress: (20/
20) | 27.57 s Done.
- [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 21.07/ 21.07 GFLOPS | Progress: (4/20) | 4.76 s [Task 22/25] Current/Best: 11.46/ 22.08 GFLOPS | Progress: (8/20) | 7.45 s [Task 22/25] Current/Best: 16.60/ 22.08 GFLOPS | Progress: (12/20) | 9.42 s [Task 22/25] Current/Best: 20.59/ 22.16 GFLOPS | Progress: (16/20) | 11.07 s [Task 22/25] Current/Best: 22.64/ 22.64 GFLOPS | Progress: (20/20) | 17.45 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 10.08/ 22.91 GFLOPS | Progress: (4/20) | 4.96 s [Task 23/25] Current/Best: 20.24/ 22.91 GFLOPS | Progress: (8/20) | 8.44 s [Task 23/25] Current/Best: 12.39/ 22.91 GFLOPS | Progress: (12/20) | 12.82 s [Task 23/25] Current/Best: 12.59/ 22.91 GFLOPS | Progress: (16/20) | 15.74 s [Task 23/25] Current/Best: 21.14/ 24.62 GFLOPS | Progress: (20/20) | 18.63 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 4.33/ 4.33 GFLOPS | Progress: (4/20) | 7.52 s [Task 24/25] Current/Best: 3.94/ 5.88 GFLOPS | Progress: (8/20) | 18.50 s [Task 24/25] Current/Best: 6.11/ 10.96 GFLOPS | Progress: (12/20) | 21.17 s [Task 24/25] Current/Best: 1.87/ 10.96 GFLOPS | Progress: (16/20) | 22.78 s Done.
- [Task 24/25] Current/Best: 2.22/ 10.96 GFLOPS | Progress: (20/20) | 33.79 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 6.08/ 9.59 GFLOPS | Progress: (4/20) | 12.55 s [Task 25/25] Current/Best: 5.94/ 9.59 GFLOPS | Progress: (8/20) | 23.51 s [Task 25/25] Current/Best: 6.32/ 9.59 GFLOPS | Progress: (12/20) | 34.47 s [Task 25/25] Current/Best: 3.13/ 9.88 GFLOPS | Progress: (16/20) | 45.46 s [Task 25/25] Current/Best: 8.52/ 10.30 GFLOPS | Progress: (20/20) | 56.37 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 18.48/ 18.48 GFLOPS | Progress: (4/20) | 9.45 s [Task 1/25] Current/Best: 17.90/ 23.87 GFLOPS | Progress: (8/20) | 12.54 s [Task 1/25] Current/Best: 18.27/ 23.87 GFLOPS | Progress: (12/20) | 15.66 s [Task 1/25] Current/Best: 8.44/ 24.15 GFLOPS | Progress: (16/20) | 18.05 s [Task 1/25] Current/Best: 19.21/ 24.15 GFLOPS | Progress: (20/20) | 21.78 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 20.15/ 20.15 GFLOPS | Progress: (4/20) | 4.33 s [Task 2/25] Current/Best: 13.93/ 20.15 GFLOPS | Progress: (8/20) | 6.03 s [Task 2/25] Current/Best: 15.55/ 20.15 GFLOPS | Progress: (12/20) | 7.74 s [Task 2/25] Current/Best: 19.08/ 20.15 GFLOPS | Progress: (16/20) | 9.54 s [Task 2/25] Current/Best: 14.64/ 20.15 GFLOPS | Progress: (20/20) | 11.10 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 6.54/ 13.07 GFLOPS | Progress: (4/20) | 4.91 s [Task 3/25] Current/Best: 6.34/ 21.59 GFLOPS | Progress: (8/20) | 7.19 s [Task 3/25] Current/Best: 11.28/ 23.38 GFLOPS | Progress: (12/20) | 9.59 s [Task 3/25] Current/Best: 7.19/ 23.38 GFLOPS | Progress: (16/20) | 12.59 s [Task 3/25] Current/Best: 10.60/ 23.38 GFLOPS | Progress: (20/20) | 15.38 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 8.71/ 17.35 GFLOPS | Progress: (4/20) | 4.52 s [Task 4/25] Current/Best: 12.53/ 17.35 GFLOPS | Progress: (8/20) | 7.29 s [Task 4/25] Current/Best: 18.80/ 18.92 GFLOPS | Progress: (12/20) | 12.80 s [Task 4/25] Current/Best: 7.79/ 20.64 GFLOPS | Progress: (16/20) | 14.66 s [Task 4/25] Current/Best: 6.73/ 20.64 GFLOPS | Progress: (20/20) | 20.10 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 17.61/ 20.97 GFLOPS | Progress: (4/20) | 4.43 s [Task 5/25] Current/Best: 4.61/ 20.97 GFLOPS | Progress: (8/20) | 6.72 s [Task 5/25] Current/Best: 16.60/ 20.97 GFLOPS | Progress: (12/20) | 8.50 s [Task 5/25] Current/Best: 12.85/ 21.52 GFLOPS | Progress: (16/20) | 10.59 s [Task 5/25] Current/Best: 12.05/ 21.52 GFLOPS | Progress: (20/20) | 12.78 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 15.15/ 15.36 GFLOPS | Progress: (4/20) | 5.04 s [Task 6/25] Current/Best: 19.29/ 19.29 GFLOPS | Progress: (8/20) | 7.30 s [Task 6/25] Current/Best: 13.18/ 21.16 GFLOPS | Progress: (12/20) | 10.67 s [Task 6/25] Current/Best: 14.19/ 21.16 GFLOPS | Progress: (16/20) | 13.31 s [Task 6/25] Current/Best: 16.28/ 21.16 GFLOPS | Progress: (20/20) | 16.54 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 12.89/ 18.21 GFLOPS | Progress: (4/20) | 4.68 s [Task 7/25] Current/Best: 14.62/ 18.21 GFLOPS | Progress: (8/20) | 7.34 s [Task 7/25] Current/Best: 21.59/ 21.59 GFLOPS | Progress: (12/20) | 10.39 s [Task 7/25] Current/Best: 19.22/ 21.59 GFLOPS | Progress: (16/20) | 12.48 s [Task 7/25] Current/Best: 14.25/ 21.59 GFLOPS | Progress: (20/20) | 15.42 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 18.83/ 18.83 GFLOPS | Progress: (4/20) | 6.27 s [Task 8/25] Current/Best: 9.43/ 18.83 GFLOPS | Progress: (8/20) | 11.66 s [Task 8/25] Current/Best: 22.20/ 22.20 GFLOPS | Progress: (12/20) | 14.13 s [Task 8/25] Current/Best: 15.38/ 22.20 GFLOPS | Progress: (16/20) | 16.41 s [Task 8/25] Current/Best: 8.38/ 22.20 GFLOPS | Progress: (20/20) | 21.21 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 15.92/ 17.51 GFLOPS | Progress: (4/20) | 9.49 s [Task 9/25] Current/Best: 17.01/ 17.51 GFLOPS | Progress: (8/20) | 17.61 s [Task 9/25] Current/Best: 6.10/ 17.51 GFLOPS | Progress: (12/20) | 28.74 s [Task 9/25] Current/Best: 6.80/ 23.09 GFLOPS | Progress: (16/20) | 30.49 s [Task 9/25] Current/Best: 24.01/ 24.01 GFLOPS | Progress: (20/20) | 33.42 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 12.43/ 17.75 GFLOPS | Progress: (4/20) | 4.49 s [Task 10/25] Current/Best: 12.92/ 17.75 GFLOPS | Progress: (8/20) | 6.46 s [Task 10/25] Current/Best: 6.49/ 17.75 GFLOPS | Progress: (12/20) | 8.50 s [Task 10/25] Current/Best: 16.07/ 17.75 GFLOPS | Progress: (16/20) | 10.87 s [Task 10/25] Current/Best: 21.10/ 21.10 GFLOPS | Progress: (20/20) | 12.81 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 8.67/ 21.72 GFLOPS | Progress: (4/20) | 4.58 s [Task 11/25] Current/Best: 19.07/ 24.00 GFLOPS | Progress: (8/20) | 6.55 s [Task 11/25] Current/Best: 12.16/ 24.00 GFLOPS | Progress: (12/20) | 8.80 s [Task 11/25] Current/Best: 21.08/ 24.00 GFLOPS | Progress: (16/20) | 10.97 s [Task 11/25] Current/Best: 7.35/ 24.00 GFLOPS | Progress: (20/20) | 14.19 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 16.60/ 16.60 GFLOPS | Progress: (4/20) | 5.21 s [Task 12/25] Current/Best: 11.71/ 17.09 GFLOPS | Progress: (8/20) | 11.24 s [Task 12/25] Current/Best: 12.83/ 19.57 GFLOPS | Progress: (12/20) | 14.70 s [Task 12/25] Current/Best: 9.33/ 20.09 GFLOPS | Progress: (16/20) | 23.07 s [Task 12/25] Current/Best: 5.16/ 20.09 GFLOPS | Progress: (20/20) | 25.86 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 12.07/ 19.28 GFLOPS | Progress: (4/20) | 5.62 s [Task 13/25] Current/Best: 17.29/ 19.28 GFLOPS | Progress: (8/20) | 9.20 s [Task 13/25] Current/Best: 24.41/ 24.41 GFLOPS | Progress: (12/20) | 11.47 s [Task 13/25] Current/Best: 21.63/ 24.41 GFLOPS | Progress: (16/20) | 13.59 s [Task 13/25] Current/Best: 20.29/ 24.41 GFLOPS | Progress: (20/20) | 16.41 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 3.16/ 18.97 GFLOPS | Progress: (4/20) | 5.01 s [Task 14/25] Current/Best: 14.70/ 21.26 GFLOPS | Progress: (8/20) | 7.17 s [Task 14/25] Current/Best: 13.29/ 21.26 GFLOPS | Progress: (12/20) | 10.28 s [Task 14/25] Current/Best: 16.88/ 21.26 GFLOPS | Progress: (16/20) | 12.39 s [Task 14/25] Current/Best: 14.12/ 21.26 GFLOPS | Progress: (20/20) | 14.68 s Done.
+ [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 6.97/ 17.63 GFLOPS | Progress: (4/20) | 11.45 s [Task 15/25] Current/Best: 14.32/ 19.57 GFLOPS | Progress: (8/20) | 13.38 s [Task 15/25] Current/Best: 18.81/ 19.57 GFLOPS | Progress: (12/20) | 24.48 s [Task 15/25] Current/Best: 20.22/ 20.22 GFLOPS | Progress: (16/20) | 27.17 s [Task 15/25] Current/Best: 11.30/ 20.22 GFLOPS | Progress: (20/20) | 34.39 s [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 15.97/ 17.61 GFLOPS | Progress: (4/20) | 4.89 s [Task 16/25] Current/Best: 16.21/ 20.97 GFLOPS | Progress: (8/20) | 7.44 s [Task 16/25] Current/Best: 15.62/ 20.97 GFLOPS | Progress: (12/20) | 9.34 s [Task 16/25] Current/Best: 19.35/ 21.98 GFLOPS | Progress: (16/20) | 10.83 s [Task 16/25] Current/Best: 10.10/ 21.98 GFLOPS | Progress: (20/2
0) | 14.05 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 14.97/ 19.94 GFLOPS | Progress: (4/20) | 4.69 s [Task 17/25] Current/Best: 21.86/ 21.86 GFLOPS | Progress: (8/20) | 7.19 s [Task 17/25] Current/Best: 19.44/ 21.86 GFLOPS | Progress: (12/20) | 9.53 s [Task 17/25] Current/Best: 22.46/ 22.46 GFLOPS | Progress: (16/20) | 13.86 s [Task 17/25] Current/Best: 1.61/ 22.46 GFLOPS | Progress: (20/20) | 19.12 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 5.98/ 21.05 GFLOPS | Progress: (4/20) | 4.97 s [Task 18/25] Current/Best: 14.36/ 21.05 GFLOPS | Progress: (8/20) | 7.21 s [Task 18/25] Current/Best: 15.91/ 21.05 GFLOPS | Progress: (12/20) | 9.25 s [Task 18/25] Current/Best: 8.79/ 22.87 GFLOPS | Progress: (16/20) | 13.23 s [Task 18/25] Current/Best: 19.59/ 22.87 GFLOPS | Progress: (20/20) | 16.40 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 5.02/ 20.40 GFLOPS | Progress: (4/20) | 8.37 s [Task 19/25] Current/Best: 9.33/ 20.40 GFLOPS | Progress: (8/20) | 11.13 s [Task 19/25] Current/Best: 18.28/ 20.40 GFLOPS | Progress: (12/20) | 14.75 s [Task 19/25] Current/Best: 21.96/ 21.96 GFLOPS | Progress: (16/20) | 17.64 s [Task 19/25] Current/Best: 8.97/ 21.96 GFLOPS | Progress: (20/20) | 21.56 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 10.85/ 17.69 GFLOPS | Progress: (4/20) | 5.11 s [Task 20/25] Current/Best: 16.71/ 21.31 GFLOPS | Progress: (8/20) | 16.50 s [Task 20/25] Current/Best: 18.55/ 21.47 GFLOPS | Progress: (12/20) | 19.54 s [Task 20/25] Current/Best: 9.61/ 21.47 GFLOPS | Progress: (16/20) | 21.76 s [Task 20/25] Current/Best: 7.61/ 21.47 GFLOPS | Progress: (20/20) | 31.26 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ Done.
+ [Task 21/25] Current/Best: 2.82/ 14.99 GFLOPS | Progress: (4/20) | 13.94 s [Task 21/25] Current/Best: 6.66/ 22.51 GFLOPS | Progress: (8/20) | 18.63 s [Task 21/25] Current/Best: 16.96/ 22.51 GFLOPS | Progress: (12/20) | 23.38 s [Task 21/25] Current/Best: 8.29/ 22.51 GFLOPS | Progress: (16/20) | 30.86 s [Task 21/25] Current/Best: 7.28/ 22.51 GFLOPS | Progress: (20/20) | 36.89 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 15.44/ 24.12 GFLOPS | Progress: (4/20) | 4.16 s [Task 22/25] Current/Best: 11.29/ 24.12 GFLOPS | Progress: (8/20) | 7.46 s [Task 22/25] Current/Best: 18.05/ 24.12 GFLOPS | Progress: (12/20) | 9.39 s [Task 22/25] Current/Best: 15.02/ 24.12 GFLOPS | Progress: (16/20) | 13.96 s [Task 22/25] Current/Best: 15.39/ 24.12 GFLOPS | Progress: (20/20) | 15.96 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 23.17/ 23.17 GFLOPS | Progress: (4/20) | 4.68 s [Task 23/25] Current/Best: 10.23/ 23.17 GFLOPS | Progress: (8/20) | 7.22 s [Task 23/25] Current/Best: 12.25/ 23.17 GFLOPS | Progress: (12/20) | 12.24 s [Task 23/25] Current/Best: 2.78/ 23.17 GFLOPS | Progress: (16/20) | 17.82 s [Task 23/25] Current/Best: 20.35/ 23.17 GFLOPS | Progress: (20/20) | 21.46 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 9.35/ 10.02 GFLOPS | Progress: (4/20) | 13.53 s [Task 24/25] Current/Best: 0.88/ 10.02 GFLOPS | Progress: (8/20) | 25.77 s [Task 24/25] Current/Best: 3.66/ 10.02 GFLOPS | Progress: (12/20) | 36.79 s [Task 24/25] Current/Best: 8.12/ 10.02 GFLOPS | Progress: (16/20) | 47.50 s [Task 24/25] Current/Best: 10.03/ 10.03 GFLOPS | Progress: (20/20) | 50.16 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 1.57/ 8.41 GFLOPS | Progress: (4/20) | 4.65 s [Task 25/25] Current/Best: 1.59/ 9.73 GFLOPS | Progress: (8/20) | 10.85 s [Task 25/25] Current/Best: 3.11/ 9.73 GFLOPS | Progress: (12/20) | 21.99 s [Task 25/25] Current/Best: 1.60/ 9.73 GFLOPS | Progress: (16/20) | 27.75 s Done.
+ [Task 25/25] Current/Best: 6.17/ 9.73 GFLOPS | Progress: (20/20) | 29.25 s Done.
+
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
- Done.
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621103
- class='n02123159 tiger cat' with probability=0.356379
+ class='n02123045 tabby, tabby cat' with probability=0.621105
+ class='n02123159 tiger cat' with probability=0.356377
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 376.7080979500133, 'median': 375.22594424999625, 'std': 2.9530615367644653}
- unoptimized: {'mean': 465.42845639001825, 'median': 464.36597090005307, 'std': 1.8185930951413325}
+ optimized: {'mean': 377.25800551001157, 'median': 377.06377325012, 'std': 1.2347146196571983}
+ unoptimized: {'mean': 471.49283153004944, 'median': 471.0764744999324, 'std': 1.5560659155590908}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 27.230 seconds)
+ **Total running time of the script:** ( 13 minutes 18.160 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 35c0283fa2..9163d3d227 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
- 1.129e-07 secs/op
+ 1.284e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 1add3af1d4..7c52d2cef0 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, 0x10469f70)), stage(b, placeholder(b, 0x243d8170)), 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, 0x2401f5a0)), stage(b, placeholder(b, 0x10603880)), 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 e30e13aa29..c612277637 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
=================
-**16:54.564** total execution time for **tutorial** files:
+**16:55.778** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:27.230 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:18.160 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:30.217 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:34.135 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:56.407 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.534 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.380 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:44.223 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:15.477 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:17.763 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.852 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.884 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.821 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.871 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.180 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.208 | 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 68a0261128..ee10d0dcbf 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000038
+ vector: 0.000039
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 5.542210001294734e-06 1.0
- naive 5.6644e-06 1.0220471614530524
- parallel 5.8994e-06 1.064449019185816
- vector 3.84473e-05 6.937178488548467
+ numpy 5.951830025878735e-06 1.0
+ naive 5.6404e-06 0.9476749126697792
+ parallel 5.8939e-06 0.9902668547947683
+ vector 3.88382e-05 6.525421564649921
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.014222
+ Numpy running time: 0.014826
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.214302
+ none: 3.419086
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.296757
+ blocking: 0.302880
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.261110
+ vectorization: 0.286259
# 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: 0.110433
+ loop permutation: 0.109829
# 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: 0.102822
+ array packing: 0.101500
# 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: 0.093887
+ block caching: 0.094624
# 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: 0.112556
+ parallelization: 0.113094
# 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 3.2143017147000004 1.0
- blocking 0.29675724940000003 0.09232401801076637
- vectorization 0.261109582 0.08123368780406169
- loop permutation 0.11043250689999999 0.034356608900451946
- array packing 0.1028215646 0.03198877197176763
- block caching 0.093886562 0.029209007222510443
- parallelization 0.112555746 0.03501716888780154
+ none 3.4190861984 1.0
+ blocking 0.302879664 0.08858497458816217
+ vectorization 0.2862593286 0.08372392855551822
+ loop permutation 0.109829001 0.032122325857533426
+ array packing 0.1015000313 0.029686303711061186
+ block caching 0.094623616 0.027675118587030704
+ parallelization 0.1130935757 0.03307713498212576
diff --git a/docs/commit_hash b/docs/commit_hash
index 9abdffd34b..13d9c39a1a 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-9a07870b2e6480a533dbebe8d10e945fc173cf59
+ae1be53d6dc08ad8a95ddf6af022880e836e8704
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 0d3113ed39..575d700342 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -614,7 +614,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.874 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.625 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
index aa89862f0d..9d92f2bd00 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -535,7 +535,7 @@ Tensorflow is also required since it’s used as the default backend of keras.</
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 867ms/step
+1/1 [==============================] - 1s 928ms/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index aaff494e43..a587fc51be 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -478,14 +478,16 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 15%|#5 | 6.33M/41.5M [00:00<00:00, 42.1MB/s]
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index f4defbe905..688051e6ed 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -513,7 +513,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.028 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 6.437 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 49d12c932a..bd9a218322 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -461,15 +461,13 @@ 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 0def09b8c6..e1debba556 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -678,7 +678,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.068 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.667 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 2afc76a665..7753c75072 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:45.920</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:03.047</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
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<tr 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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<td><p>0.0 MB</p></td>
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<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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<tr class="row-even"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
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<td><p>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>00:02.916</p></td>
+<td><p>00:02.875</p></td>
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diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 9432f74e5c..a1d12390bb 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -854,10 +854,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4000.2870 4000.2443 4001.7779 3998.5194 1.0676
+ 3999.0015 3999.2471 4003.3998 3995.8450 2.4062
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.689 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.690 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
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<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index cd88c08485..3488bc7704 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -468,32 +468,30 @@ 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 fea61a65e2..7078e66972 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -692,7 +692,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.0580 13.8153 15.0565 13.6570 0.4378
+ 14.2405 14.3120 14.5199 13.9393 0.2470
</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 f84f504af6..0a0dd14bc4 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -484,19 +484,33 @@ 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).
@@ -583,7 +597,7 @@ 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 29.384 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 39.253 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 66e3a34d3b..f3dd83ace3 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -525,10 +525,9 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
0%| | 0.00/13.6M [00:00<?, ?B/s]
- 47%|####6 | 6.30M/13.6M [00:00<00:00, 35.2MB/s]
- 71%|#######1 | 9.66M/13.6M [00:00<00:00, 25.7MB/s]
- 90%|########9 | 12.2M/13.6M [00:00<00:00, 25.7MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 29.4MB/s]
+ 48%|####7 | 6.50M/13.6M [00:00<00:00, 68.2MB/s]
+ 96%|#########5| 13.0M/13.6M [00:00<00:00, 53.7MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 57.2MB/s]
</pre></div>
</div>
</div>
@@ -613,7 +612,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 85.9449 85.8920 87.4023 85.5214 0.2609
+ 86.4515 86.3867 90.0166 86.1085 0.4085
</pre></div>
</div>
<div class="admonition note">
@@ -652,7 +651,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.842 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.415 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 b171983400..7499ee65bb 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -610,7 +610,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 104.6402 104.9834 106.6423 102.6870 1.2545
+ 107.3873 107.3637 108.1963 106.5327 0.3567
</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 d735944ca5..c424050b0b 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:50.602</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:13.186</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -379,35 +379,35 @@
</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>03:29.384</p></td>
+<td><p>03:39.253</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></td>
-<td><p>01:29.842</p></td>
+<td><p>01:34.415</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:18.689</p></td>
+<td><p>01:19.690</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:52.563</p></td>
+<td><p>00:54.516</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:50.879</p></td>
+<td><p>00:52.895</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>00:49.386</p></td>
+<td><p>00:50.439</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:29.976</p></td>
+<td><p>00:31.098</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:29.874</p></td>
+<td><p>00:30.872</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 4809672d7b..f899930086 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.986</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.236</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,15 +379,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:02.757</p></td>
+<td><p>00:02.950</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>00:01.222</p></td>
+<td><p>00:01.278</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.008</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index d0d01b54d9..bff505ddcc 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -555,10 +555,10 @@ profile the execution time of each passes.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 24625us [24625us] (48.58%; 48.58%)
-FoldScaleAxis: 26061us [7us] (51.42%; 51.42%)
- FoldConstant: 26053us [1747us] (51.40%; 99.97%)
- InferType: 24306us [24306us] (47.95%; 93.29%)
+InferType: 25782us [25782us] (48.40%; 48.40%)
+FoldScaleAxis: 27487us [9us] (51.60%; 51.60%)
+ FoldConstant: 27478us [1804us] (51.58%; 99.97%)
+ InferType: 25673us [25673us] (48.20%; 93.43%)
</pre></div>
</div>
</div>
@@ -580,10 +580,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 24160us [24160us] (48.50%; 48.50%)
-FoldScaleAxis: 25657us [6us] (51.50%; 51.50%)
- FoldConstant: 25651us [1733us] (51.49%; 99.98%)
- InferType: 23917us [23917us] (48.01%; 93.24%)
+InferType: 25271us [25271us] (48.59%; 48.59%)
+FoldScaleAxis: 26737us [7us] (51.41%; 51.41%)
+ FoldConstant: 26730us [1808us] (51.40%; 99.97%)
+ InferType: 24921us [24921us] (47.92%; 93.23%)
</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 7086284e8a..4679383418 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -605,7 +605,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.425537 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.428798 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 75a33de6e8..fc2f2d8ded 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -887,7 +887,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.083974 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.215990 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 2d658442b1..da069d334e 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -502,8 +502,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013485
-Baseline: 3.148459
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.015034
+Baseline: 3.366766
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -562,7 +562,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.294563
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.308773
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -619,7 +619,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.270745
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.302627
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -674,7 +674,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.107219
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.111298
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -751,7 +751,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.102499
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.104244
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -829,7 +829,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.094166
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.095098
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -909,7 +909,7 @@ class Module:
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.112657
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.114015
</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 bfd97ae27d..cf70a290f1 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:31.080</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:32.959</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,15 +379,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
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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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+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
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<div class="section" id="computation-times">
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<table class="docutils align-default">
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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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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
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+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1042,7 +1042,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
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</pre></div>
</div>
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diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index c35cb22674..54a32bbe96 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -933,7 +933,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.1977 3.1975 3.1982 3.1974 0.0003
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</pre></div>
</div>
</div>
@@ -955,7 +955,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
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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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@@ -955,7 +955,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
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</pre></div>
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@@ -977,7 +977,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
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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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@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:22.849</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
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<td><p>0.0 MB</p></td>
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diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index dbfae83f97..b7aa4725a7 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -640,7 +640,7 @@ and measure running time.</p>
Best config:
,None
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--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -674,10 +674,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
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-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.926 0.31 (1, 1, 10, 10, 3) 1 1 [0.926]
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</pre></div>
</div>
</div>
@@ -729,13 +729,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 98.625 97.341 (1, 6, 10, 10, 1) 2 1 [98.625]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.737 1.714 (1, 6, 10, 10) 1 1 [1.737]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.945 (1, 1, 10, 10, 3) 1 1 [0.958]
-Total_time - 101.319 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.875 97.55 (1, 6, 10, 10, 1) 2 1 [100.875]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.717 1.66 (1, 6, 10, 10) 1 1 [1.717]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.817 0.79 (1, 3, 10, 10, 1) 1 1 [0.817]
+Total_time - 103.409 - - - - -
</pre></div>
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index d3b9ec014c..0caa5cf80a 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -485,8 +485,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
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-100%|##########| 3.42M/3.42M [00:00<00:00, 29.7MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 12.0MB/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,
</pre></div>
@@ -610,7 +610,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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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 7f39010b17..b4d84d4430 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -554,7 +554,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpba7vshjt/images/random'
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</pre></div>
</div>
</div>
@@ -614,8 +614,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpba7vshjt/images/target contains 8144 images
-/tmp/tmpba7vshjt/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/tmpk4ba3ziw/images/target contains 8144 images
+/tmp/tmpk4ba3ziw/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -727,13 +727,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 42s - loss: 0.2107 - accuracy: 0.9269 - val_loss: 0.1167 - val_accuracy: 0.9622 - 42s/epoch - 128ms/step
+328/328 - 44s - loss: 0.2233 - accuracy: 0.9249 - val_loss: 0.2023 - val_accuracy: 0.9286 - 44s/epoch - 134ms/step
Epoch 2/3
-328/328 - 40s - loss: 0.0965 - accuracy: 0.9636 - val_loss: 0.0945 - val_accuracy: 0.9664 - 40s/epoch - 120ms/step
+328/328 - 40s - loss: 0.1028 - accuracy: 0.9614 - val_loss: 0.0973 - val_accuracy: 0.9668 - 40s/epoch - 123ms/step
Epoch 3/3
-328/328 - 39s - loss: 0.0659 - accuracy: 0.9750 - val_loss: 0.1817 - val_accuracy: 0.9407 - 39s/epoch - 120ms/step
+328/328 - 40s - loss: 0.0670 - accuracy: 0.9736 - val_loss: 0.0838 - val_accuracy: 0.9732 - 40s/epoch - 123ms/step
-<keras.callbacks.History object at 0x7faa5c2cc9d0>
+<keras.callbacks.History object at 0x7f5ed0a508b0>
</pre></div>
</div>
</div>
@@ -997,7 +997,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 55.035 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 59.215 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 51b586da33..132c19f5a9 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:13.331</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>08:30.317</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
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@@ -379,27 +379,27 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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-<td><p>01:27.992</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:20.705</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:11.494</p></td>
+<td><p>00:12.250</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
-<td><p>00:10.223</p></td>
+<td><p>00:10.894</p></td>
<td><p>0.0 MB</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>
-<td><p>00:07.882</p></td>
+<td><p>00:08.411</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 15972acb21..f4598cf43e 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:38.865</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:40.538</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -379,15 +379,15 @@
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:33.324</p></td>
+<td><p>00:34.508</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
-<td><p>00:03.676</p></td>
+<td><p>00:03.974</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
-<td><p>00:01.859</p></td>
+<td><p>00:02.049</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 101c37f867..f02553a5e4 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -582,7 +582,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<a href="../../reference/api/python/ir.html#tvm.ir.register_intrin_lowering" title="tvm.ir.register_intrin_lowering" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-function"><span class="n">register_intrin_lowering</span></a><span class="p">(</span><span class="s2">"tir.exp"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">f</span><span class="o">= [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fab2bfff790>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f5f03dc8ee0>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index 4b6c54aaef..a71dd34604 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:05.284</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.391</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -379,35 +379,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
-<td><p>00:01.349</p></td>
+<td><p>00:01.215</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
-<td><p>00:00.670</p></td>
+<td><p>00:00.709</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
-<td><p>00:00.658</p></td>
+<td><p>00:00.697</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
-<td><p>00:00.115</p></td>
+<td><p>00:00.124</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
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+<td><p>00:00.069</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
-<td><p>00:00.062</p></td>
+<td><p>00:00.066</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
-<td><p>00:00.031</p></td>
+<td><p>00:00.033</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_r.html b/docs/reference/api/doxygen/namespacemembers_func_r.html
index 2695acf9d4..8ddae3d727 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_r.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_r.html
@@ -76,6 +76,12 @@ $(function() {
<li>RealizeVDevice()
: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a032567f2efa23079fc71029d4cb13ca0">tvm::relax::transform</a>
</li>
+<li>RecvFromPrevGroup()
+: <a class="el" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">tvm::runtime</a>
+</li>
+<li>RecvFromWorker()
+: <a class="el" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">tvm::runtime</a>
+</li>
<li>RecvFromWorker0()
: <a class="el" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">tvm::runtime</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_s.html b/docs/reference/api/doxygen/namespacemembers_func_s.html
index 52a2ae320a..9ee42781da 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_s.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_s.html
@@ -141,6 +141,12 @@ $(function() {
<li>ScheduleToModule()
: <a class="el" href="namespacetvm.html#a16d68709766f3c808c5a8921d4f72274">tvm</a>
</li>
+<li>SendToNextGroup()
+: <a class="el" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">tvm::runtime</a>
+</li>
+<li>SendToWorker()
+: <a class="el" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">tvm::runtime</a>
+</li>
<li>sequence_mask()
: <a class="el" href="namespacetvm_1_1topi.html#a037a112cc5c556107797e36e7feb0873">tvm::topi</a>
</li>
@@ -190,7 +196,7 @@ $(function() {
: <a class="el" href="namespacetvm.html#a15f25703cfce73c75cb4cd33c74ea8f0">tvm</a>
</li>
<li>ShapeString()
-: <a class="el" href="namespacetvm_1_1runtime_1_1profiling.html#a0db7b8aaaf50308b175b79c8fee98f13">tvm::runtime::profiling</a>
+: <a class="el" href="namespacetvm_1_1runtime_1_1profiling.html#ad031e70431cc716c8ff9c36aaad18197">tvm::runtime::profiling</a>
</li>
<li>shift_left()
: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a26472adf05d821f1929cfbc02bc3c231">tvm::tir::builtin</a>
@@ -353,7 +359,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1arith.html#a07096c818b466b42171bc93c1784cdf8">tvm::arith</a>
</li>
<li>Substitute()
-: <a class="el" href="namespacetvm_1_1tir.html#ab2e5f81ff91f66f0dbbdbb442cec9d2f">tvm::tir</a>
+: <a class="el" href="namespacetvm_1_1tir.html#a9e1301a585fe9a09feb38a15b3300d8b">tvm::tir</a>
</li>
<li>SubstituteBoundVars()
: <a class="el" href="namespacetvm_1_1relay.html#aa65d5cde84db61b456ce982b5328fae2">tvm::relay</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_r.html b/docs/reference/api/doxygen/namespacemembers_r.html
index badab11f12..d10abf24d8 100644
--- a/docs/reference/api/doxygen/namespacemembers_r.html
+++ b/docs/reference/api/doxygen/namespacemembers_r.html
@@ -79,6 +79,12 @@ $(function() {
<li>RealizeVDevice()
: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a032567f2efa23079fc71029d4cb13ca0">tvm::relax::transform</a>
</li>
+<li>RecvFromPrevGroup()
+: <a class="el" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">tvm::runtime</a>
+</li>
+<li>RecvFromWorker()
+: <a class="el" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">tvm::runtime</a>
+</li>
<li>RecvFromWorker0()
: <a class="el" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">tvm::runtime</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_s.html b/docs/reference/api/doxygen/namespacemembers_s.html
index 6989b522e3..d1428fbd94 100644
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+++ b/docs/reference/api/doxygen/namespacemembers_s.html
@@ -159,6 +159,12 @@ $(function() {
<li>ScriptDtypePrintLocation
: <a class="el" href="namespacetvm_1_1tir.html#ae52a7f5a7dfc8ed07258b2f526df603e">tvm::tir</a>
</li>
+<li>SendToNextGroup()
+: <a class="el" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">tvm::runtime</a>
+</li>
+<li>SendToWorker()
+: <a class="el" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">tvm::runtime</a>
+</li>
<li>sequence_mask()
: <a class="el" href="namespacetvm_1_1topi.html#a037a112cc5c556107797e36e7feb0873">tvm::topi</a>
</li>
@@ -419,7 +425,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1arith.html#a07096c818b466b42171bc93c1784cdf8">tvm::arith</a>
</li>
<li>Substitute()
-: <a class="el" href="namespacetvm_1_1tir.html#a1a31a676a35932c9653081e7fd062301">tvm::tir</a>
+: <a class="el" href="namespacetvm_1_1tir.html#a71cc8d64e0d78149a19413953dec6230">tvm::tir</a>
</li>
<li>SubstituteBoundVars()
: <a class="el" href="namespacetvm_1_1relay.html#aa65d5cde84db61b456ce982b5328fae2">tvm::relay</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1runtime.html b/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
index 6ce7393cf1..a7bad3ba54 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1runtime.html
@@ -554,6 +554,18 @@ Functions</h2></td></tr>
<tr class="memitem:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">RecvFromWorker0</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer)</td></tr>
<tr class="memdesc:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from worker-0. No-op if the current worker is worker-0. <a href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">More...</a><br /></td></tr>
<tr class="separator:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a26269143666ad651b16d445a49863ddf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">SendToNextGroup</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer)</td></tr>
+<tr class="memdesc:a26269143666ad651b16d445a49863ddf"><td class="mdescLeft"> </td><td class="mdescRight">Send a buffer to the corresponding worker in the next group. An error is thrown if the worker is already in the last group. <a href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">More...</a><br /></td></tr>
+<tr class="separator:a26269143666ad651b16d445a49863ddf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a14019542f71b83cdb46b9961341463c7"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">RecvFromPrevGroup</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer)</td></tr>
+<tr class="memdesc:a14019542f71b83cdb46b9961341463c7"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from the corresponding worker in the previous group. An error is thrown if the worker is already in the first group. <a href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">More...</a><br /></td></tr>
+<tr class="separator:a14019542f71b83cdb46b9961341463c7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">SendToWorker</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer, int receiver_id)</td></tr>
+<tr class="memdesc:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="mdescLeft"> </td><td class="mdescRight">Send a buffer to the target receiver worker (globally across all groups). <a href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">More...</a><br /></td></tr>
+<tr class="separator:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">RecvFromWorker</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer, int sender_id)</td></tr>
+<tr class="memdesc:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from the target sender worker (globally across all groups). <a href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">More...</a><br /></td></tr>
+<tr class="separator:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">WorkerId</a> ()</td></tr>
<tr class="memdesc:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="mdescLeft"> </td><td class="mdescRight">Get the local worker id. <a href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">More...</a><br /></td></tr>
<tr class="separator:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="memSeparator" colspan="2"> </td></tr>
@@ -3757,6 +3769,69 @@ template<typename T > </div>
</dl>
<dl class="section return"><dt>Returns</dt><dd>type code of custom type parsed </dd></dl>
+</div>
+</div>
+<a id="a14019542f71b83cdb46b9961341463c7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a14019542f71b83cdb46b9961341463c7">◆ </a></span>RecvFromPrevGroup()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void tvm::runtime::RecvFromPrevGroup </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> </td>
+ <td class="paramname"><em>buffer</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Receive a buffer from the corresponding worker in the previous group. An error is thrown if the worker is already in the first group. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">buffer</td><td>The receiving buffer. </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a9783e81b1ee8377ee885f8c3e6c9545f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a9783e81b1ee8377ee885f8c3e6c9545f">◆ </a></span>RecvFromWorker()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void tvm::runtime::RecvFromWorker </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> </td>
+ <td class="paramname"><em>buffer</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>sender_id</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Receive a buffer from the target sender worker (globally across all groups). </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">buffer</td><td>The receiving buffer. </td></tr>
+ <tr><td class="paramname">sender_id</td><td>The global sender worker id. </td></tr>
+ </table>
+ </dd>
+</dl>
+
</div>
</div>
<a id="ab6b211d99323b4e09a76bdb6b6f133cb"></a>
@@ -4023,6 +4098,69 @@ template<typename T > </div>
</dd>
</dl>
+</div>
+</div>
+<a id="a26269143666ad651b16d445a49863ddf"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a26269143666ad651b16d445a49863ddf">◆ </a></span>SendToNextGroup()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void tvm::runtime::SendToNextGroup </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> </td>
+ <td class="paramname"><em>buffer</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Send a buffer to the corresponding worker in the next group. An error is thrown if the worker is already in the last group. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">buffer</td><td>The sending buffer. </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="ac7c367bc94b21f5ba168b292b88d0c1a"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ac7c367bc94b21f5ba168b292b88d0c1a">◆ </a></span>SendToWorker()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void tvm::runtime::SendToWorker </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> </td>
+ <td class="paramname"><em>buffer</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>receiver_id</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Send a buffer to the target receiver worker (globally across all groups). </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">buffer</td><td>The sending buffer. </td></tr>
+ <tr><td class="paramname">receiver_id</td><td>The global receiver worker id. </td></tr>
+ </table>
+ </dd>
+</dl>
+
</div>
</div>
<a id="a277f104e659f71cd8885744700016341"></a>
diff --git a/docs/reference/api/doxygen/runtime_2disco_2builtin_8h.html b/docs/reference/api/doxygen/runtime_2disco_2builtin_8h.html
index 95981efd44..9f4af7551c 100644
--- a/docs/reference/api/doxygen/runtime_2disco_2builtin_8h.html
+++ b/docs/reference/api/doxygen/runtime_2disco_2builtin_8h.html
@@ -137,6 +137,18 @@ Functions</h2></td></tr>
<tr class="memitem:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">tvm::runtime::RecvFromWorker0</a> (NDArray buffer)</td></tr>
<tr class="memdesc:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from worker-0. No-op if the current worker is worker-0. <a href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">More...</a><br /></td></tr>
<tr class="separator:ab6b211d99323b4e09a76bdb6b6f133cb"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a26269143666ad651b16d445a49863ddf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">tvm::runtime::SendToNextGroup</a> (NDArray buffer)</td></tr>
+<tr class="memdesc:a26269143666ad651b16d445a49863ddf"><td class="mdescLeft"> </td><td class="mdescRight">Send a buffer to the corresponding worker in the next group. An error is thrown if the worker is already in the last group. <a href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">More...</a><br /></td></tr>
+<tr class="separator:a26269143666ad651b16d445a49863ddf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a14019542f71b83cdb46b9961341463c7"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">tvm::runtime::RecvFromPrevGroup</a> (NDArray buffer)</td></tr>
+<tr class="memdesc:a14019542f71b83cdb46b9961341463c7"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from the corresponding worker in the previous group. An error is thrown if the worker is already in the first group. <a href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">More...</a><br /></td></tr>
+<tr class="separator:a14019542f71b83cdb46b9961341463c7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">tvm::runtime::SendToWorker</a> (NDArray buffer, int receiver_id)</td></tr>
+<tr class="memdesc:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="mdescLeft"> </td><td class="mdescRight">Send a buffer to the target receiver worker (globally across all groups). <a href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">More...</a><br /></td></tr>
+<tr class="separator:ac7c367bc94b21f5ba168b292b88d0c1a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">tvm::runtime::RecvFromWorker</a> (NDArray buffer, int sender_id)</td></tr>
+<tr class="memdesc:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="mdescLeft"> </td><td class="mdescRight">Receive a buffer from the target sender worker (globally across all groups). <a href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">More...</a><br /></td></tr>
+<tr class="separator:a9783e81b1ee8377ee885f8c3e6c9545f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">tvm::runtime::WorkerId</a> ()</td></tr>
<tr class="memdesc:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="mdescLeft"> </td><td class="mdescRight">Get the local worker id. <a href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">More...</a><br /></td></tr>
<tr class="separator:a11d7c335ce64b50b3e6cee085d55c8fc"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/runtime_2disco_2builtin_8h_source.html b/docs/reference/api/doxygen/runtime_2disco_2builtin_8h_source.html
index d882ee3df8..7bfa875f17 100644
--- a/docs/reference/api/doxygen/runtime_2disco_2builtin_8h_source.html
+++ b/docs/reference/api/doxygen/runtime_2disco_2builtin_8h_source.html
@@ -131,13 +131,17 @@ $(function() {
<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a88632a9de4c6a6d15bdcb6da199ceb4c"> 103</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a88632a9de4c6a6d15bdcb6da199ceb4c">ScatterFromWorker0</a>(<a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<NDArray></a> send, <span class="keywordtype">bool</span> in_group, <a class="code" href="class [...]
<div class="line"><a name="l00111"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a3546229edf3fb5ef16273bc83a7596e8"> 111</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a3546229edf3fb5ef16273bc83a7596e8">GatherToWorker0</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> send, <span class="keywordtype">bool</span> in_group, <a class="code" href="classtvm_1_1runtime_1_1Op [...]
<div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb"> 116</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#ab6b211d99323b4e09a76bdb6b6f133cb">RecvFromWorker0</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer);</div>
-<div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc"> 118</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">WorkerId</a>();</div>
-<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a65f369b9882f8ec57826acd5f86c4404"> 124</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a65f369b9882f8ec57826acd5f86c4404">SyncWorker</a>();</div>
-<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  </div>
-<div class="line"><a name="l00126"></a><span class="lineno"> 126</span> } <span class="comment">// namespace runtime</span></div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_DISCO_BUILTIN_H_</span></div>
+<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf"> 122</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">SendToNextGroup</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer);</div>
+<div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7"> 128</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">RecvFromPrevGroup</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer);</div>
+<div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a"> 134</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#ac7c367bc94b21f5ba168b292b88d0c1a">SendToWorker</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer, <span class="keywordtype">int</span> receiver_id);</div>
+<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f"> 140</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">RecvFromWorker</a>(<a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> buffer, <span class="keywordtype">int</span> sender_id);</div>
+<div class="line"><a name="l00142"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc"> 142</a></span> TVM_DLL <span class="keywordtype">int</span> <a class="code" href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">WorkerId</a>();</div>
+<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#a65f369b9882f8ec57826acd5f86c4404"> 148</a></span> TVM_DLL <span class="keywordtype">void</span> <a class="code" href="namespacetvm_1_1runtime.html#a65f369b9882f8ec57826acd5f86c4404">SyncWorker</a>();</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  </div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> } <span class="comment">// namespace runtime</span></div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  </div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_DISCO_BUILTIN_H_</span></div>
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<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM.</div><div class="ttdef"><b>Definition:</b> module.h:79</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1NDArray_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1NDArray.html">tvm::runtime::NDArray</a></div><div class="ttdoc">Managed NDArray. The array is backed by reference counted blocks.</div><div class="ttdef"><b>Definition:</b> ndarray.h:51</div></div>
@@ -146,7 +150,9 @@ $(function() {
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<div class="ttc" id="anamespacetvm_1_1runtime_html_a11d7c335ce64b50b3e6cee085d55c8fc"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a11d7c335ce64b50b3e6cee085d55c8fc">tvm::runtime::WorkerId</a></div><div class="ttdeci">int WorkerId()</div><div class="ttdoc">Get the local worker id.</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_html_a14019542f71b83cdb46b9961341463c7"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a14019542f71b83cdb46b9961341463c7">tvm::runtime::RecvFromPrevGroup</a></div><div class="ttdeci">void RecvFromPrevGroup(NDArray buffer)</div><div class="ttdoc">Receive a buffer from the corresponding worker in the previous group. An error is thrown if the worke...</div></div>
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+<div class="ttc" id="anamespacetvm_1_1runtime_html_a26269143666ad651b16d445a49863ddf"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a26269143666ad651b16d445a49863ddf">tvm::runtime::SendToNextGroup</a></div><div class="ttdeci">void SendToNextGroup(NDArray buffer)</div><div class="ttdoc">Send a buffer to the corresponding worker in the next group. An error is thrown if the worker is alre...</div></div>
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+<div class="ttc" id="anamespacetvm_1_1runtime_html_a9783e81b1ee8377ee885f8c3e6c9545f"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a9783e81b1ee8377ee885f8c3e6c9545f">tvm::runtime::RecvFromWorker</a></div><div class="ttdeci">void RecvFromWorker(NDArray buffer, int sender_id)</div><div class="ttdoc">Receive a buffer from the target sender worker (globally across all groups).</div></div>
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<div class="ttc" id="anamespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript.</div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
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