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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/07/01 10:00:04 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@ab7c1a91d81ae91ad806c2f97c11f6b104ab2ec5)
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 0b978b356f deploying docs (apache/tvm@ab7c1a91d81ae91ad806c2f97c11f6b104ab2ec5)
0b978b356f is described below
commit 0b978b356f70e6de836b878e6bebe5fc3410f62d
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Mon Jul 1 09:59:57 2024 +0000
deploying docs (apache/tvm@ab7c1a91d81ae91ad806c2f97c11f6b104ab2ec5)
---
.../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 +-
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.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 20 +-
.../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 | 6 +-
.../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 +-
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docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
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docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
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docs/how_to/compile_models/from_oneflow.html | 17 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 12 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 20 +-
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329 files changed, 11174 insertions(+), 11129 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 69593bdb41..a8bd9e2fcd 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 42.369 seconds)
+ **Total running time of the script:** ( 1 minutes 37.056 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 bdb18186a6..638d6d24aa 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 973ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 895ms/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 060397f261..a2a596a819 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, 64.8MB/s] 30%|### | 12.5M/41.5M [00:00<00:00, 53.5MB/s] 43%|####2 | 17.7M/41.5M [00:00<00:00, 27.9MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 32.5MB/s] 63%|######3 | 26.3M/41.5M [00:00<00:00, 31.2MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 34.9MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 36.8MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 36.3MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:01, 36.3MB/s] 24%|##3 | 9.80M/41.5M [00:00<00:01, 31.9MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 36.4MB/s] 43%|####3 | 17.9M/41.5M [00:00<00:00, 32.8MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 28.4MB/s] 61%|###### | 25.2M/41.5M [00:00<00:00, 21.9MB/s] 77%|#######7 | 32.0M/41.5M [00:01<00:00, 26.5MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 25.7MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 28.4MB/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 48941e08cf..3e20145925 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 8.338 seconds)
+ **Total running time of the script:** ( 1 minutes 5.827 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 f833362581..59ed214f21 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
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+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 14%|#4 | 6.30M/44.7M [00:00<00:01, 37.2MB/s] 22%|##2 | 9.86M/44.7M [00:00<00:01, 35.2MB/s] 36%|###5 | 16.0M/44.7M [00:00<00:00, 39.2MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 49.8MB/s] 72%|#######1 | 32.0M/44.7M [00:00<00:00, 54.7MB/s] 83%|########3 | 37.3M/44.7M [00:00<00:00, 54.7MB/s] 95%|#########5| 42.5M/44.7M [00:01<00:00, 41.2MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 38.3MB/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 3556adf063..2a28eba88a 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 36.449 seconds)
+ **Total running time of the script:** ( 1 minutes 31.567 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 c2061a64ae..e766ca8960 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
=================
-**07:12.033** total execution time for **how_to_compile_models** files:
+**06:51.444** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:42.369 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:37.056 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:36.449 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:31.567 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:08.338 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:05.827 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:46.712 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:45.464 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:42.264 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:39.616 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:31.729 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.143 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.426 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:26.841 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.777 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.104 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.970 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.826 | 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 074a3c9c9a..ee7ec10026 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -673,7 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4121.5479 4121.5544 4123.7390 4119.9257 1.0993
+ 4028.2793 4027.8858 4032.1546 4024.9008 2.2532
@@ -681,7 +681,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 21.811 seconds)
+ **Total running time of the script:** ( 1 minutes 19.158 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 73f1c449ee..e24a969028 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
- 8192/102967424 [..............................] - ETA: 0s 6635520/102967424 [>.............................] - ETA: 1s 8380416/102967424 [=>............................] - ETA: 2s 15024128/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 2s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s
35741696/102967424 [=========>....................] - ETA: 1s 38993920/102967424 [==========>...................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 1s 50323456/102967424 [=============>................] - ETA: 1s 58695680/102967424 [================>.............] - ETA: 1s 58712064/102967424 [================>.............] -
ETA: 1s 65355776/102967424 [==================>...........] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 69296128/102967424 [===================>..........] - ETA: 0s 73744384/102967424 [====================>.........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 90521600/102967424 [=========================>....] - ETA: 0s 92266496/102967424
[=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 3s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 1s 15589376/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 23412736/102967424 [=====>........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s
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ETA: 0s 72540160/102967424 [====================>.........] - 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 eecc72a258..480d537005 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.0150 15.0203 15.5967 14.4492 0.4663
+ 15.0188 15.0072 15.1124 14.9500 0.0452
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 b946d0ea2f..ecd825b1ee 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:03, 55.0MB/s] 9%|8 | 14.7M/170M [00:00<00:02, 62.5MB/s] 12%|#2 | 20.8M/170M [00:00<00:02, 55.5MB/s] 15%|#5 | 26.2M/170M [00:00<00:03, 40.8MB/s] 18%|#7 | 30.6M/170M [00:00<00:03, 38.9MB/s] 21%|## | 35.3M/170M [00:00<00:03, 41.6MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 35.1MB/s] 28%|##8 | 48.0M/170M [00:01<00:03, 35.4MB/s] 33%|###3 | 56.1M/170M [00:01<00:02, 44.8MB/s] 37%|###6 | 62.3M/170M [00:01<00:02, 47.9MB/s] 40%|###9 | 67.3M/170M [00:01<00:02, 48.2MB/s] 43%|####2 | 72.2M/170M [00:01<00:02, 45.8MB/s] 47%|####7 | 80.0M/170M [00:01<00:02, 40.3MB/s] 51%|##### | 86.3M/170M [00:02<00:02, 35.8MB/s] 53%|#####3 | 90.0M/170M [00:02<00:02, 34.7MB/s] 57%|#####6 | 96.0M/170M [00:02<00:02, 35.0MB/s] 60%|###### | 102M/170M [00:02<00:01, 37.2MB/s
] 62%|######2 | 106M/170M [00:02<00:02, 31.1MB/s] 66%|######5 | 112M/170M [00:02<00:01, 36.4MB/s] 71%|####### | 120M/170M [00:03<00:01, 42.3MB/s] 74%|#######4 | 126M/170M [00:03<00:01, 36.3MB/s] 77%|#######6 | 130M/170M [00:03<00:01, 31.4MB/s] 79%|#######9 | 134M/170M [00:03<00:01, 30.2MB/s] 81%|######## | 137M/170M [00:03<00:01, 28.8MB/s] 85%|########4 | 144M/170M [00:03<00:00, 37.2MB/s] 89%|########9 | 152M/170M [00:04<00:00, 44.1MB/s] 93%|#########3| 158M/170M [00:04<00:00, 46.3MB/s] 96%|#########5| 163M/170M [00:04<00:00, 45.3MB/s] 99%|#########8| 167M/170M [00:04<00:00, 44.7MB/s] 100%|##########| 170M/170M [00:04<00:00, 39.5MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 4%|3 | 6.30M/170M [00:00<00:03, 45.0MB/s] 6%|6 | 10.6M/170M [00:00<00:05, 32.9MB/s] 8%|8 | 14.3M/170M [00:00<00:05, 31.9MB/s] 10%|# | 17.4M/170M [00:00<00:06, 25.6MB/s] 14%|#4 | 24.0M/170M [00:00<00:04, 36.9MB/s] 18%|#7 | 30.3M/170M [00:00<00:04, 36.4MB/s] 22%|##2 | 37.9M/170M [00:01<00:02, 46.7MB/s] 25%|##5 | 43.0M/170M [00:01<00:02, 47.9MB/s] 28%|##8 | 47.9M/170M [00:01<00:02, 44.3MB/s] 31%|###1 | 53.0M/170M [00:01<00:02, 46.5MB/s] 34%|###3 | 57.7M/170M [00:01<00:02, 46.6MB/s] 37%|###6 | 62.6M/170M [00:01<00:02, 47.9MB/s] 40%|###9 | 67.3M/170M [00:01<00:02, 46.5MB/s] 42%|####2 | 72.0M/170M [00:01<00:02, 40.2MB/s] 47%|####7 | 80.0M/170M [00:02<00:02, 41.2MB/s] 51%|##### | 86.3M/170M [00:02<00:02, 36.8MB/s] 53%|#####2 | 90.0M/170M [00:02<00:02, 35.4MB/
s] 56%|#####5 | 94.3M/170M [00:02<00:02, 28.5MB/s] 57%|#####7 | 97.3M/170M [00:02<00:02, 25.6MB/s] 60%|###### | 102M/170M [00:02<00:02, 27.0MB/s] 62%|######1 | 105M/170M [00:03<00:02, 25.9MB/s] 66%|######5 | 112M/170M [00:03<00:01, 35.2MB/s] 71%|####### | 120M/170M [00:03<00:01, 38.9MB/s] 75%|#######5 | 128M/170M [00:03<00:01, 39.6MB/s] 80%|######## | 136M/170M [00:03<00:00, 40.8MB/s] 85%|########4 | 144M/170M [00:03<00:00, 45.6MB/s] 88%|########8 | 150M/170M [00:04<00:00, 47.1MB/s] 91%|#########1| 155M/170M [00:04<00:00, 43.3MB/s] 94%|#########3| 159M/170M [00:04<00:00, 43.2MB/s] 96%|#########6| 163M/170M [00:04<00:00, 42.7MB/s] 99%|#########8| 167M/170M [00:04<00:00, 41.4MB/s] 100%|##########| 170M/170M [00:04<00:00, 38.7MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 47.973 seconds)
+ **Total running time of the script:** ( 3 minutes 32.708 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 ead4d4dfca..8ea3418f14 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, 47.8MB/s] 80%|######## | 10.9M/13.6M [00:00<00:00, 32.8MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 34.5MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 59%|#####8 | 7.99M/13.6M [00:00<00:00, 37.9MB/s] 90%|########9 | 12.2M/13.6M [00:00<00:00, 38.8MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 42.2MB/s]
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 88.9072 88.8142 89.8024 88.5988 0.2583
+ 85.9294 85.9129 89.8628 85.4341 0.4499
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.772 seconds)
+ **Total running time of the script:** ( 1 minutes 31.680 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 4a1f78348d..f543e62e52 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)
- 110.0343 110.0620 113.1740 108.5295 0.5187
+ 106.6789 106.8997 110.8443 103.3111 1.0737
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 d7b6c21085..d0c48eac03 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**10:31.315** total execution time for **how_to_deploy_models** files:
+**10:00.442** 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``) | 03:47.973 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:32.708 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:36.772 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:31.680 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.811 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.158 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:55.303 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:53.903 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:54.464 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.559 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.501 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.622 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.803 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.419 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:31.680 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.386 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.008 | 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 83fec787c5..8f4b2e8d9d 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:04.224** total execution time for **how_to_extend_tvm** files:
+**00:04.053** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.929 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.806 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.287 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.240 | 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 |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 5903ef93f4..08bf0f62b1 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: 25591us [25591us] (48.47%; 48.47%)
- FoldScaleAxis: 27204us [9us] (51.53%; 51.53%)
- FoldConstant: 27194us [1826us] (51.51%; 99.97%)
- InferType: 25369us [25369us] (48.05%; 93.29%)
+ InferType: 24718us [24718us] (48.40%; 48.40%)
+ FoldScaleAxis: 26352us [9us] (51.60%; 51.60%)
+ FoldConstant: 26342us [1742us] (51.58%; 99.97%)
+ InferType: 24600us [24600us] (48.17%; 93.39%)
@@ -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: 25288us [25288us] (47.90%; 47.90%)
- FoldScaleAxis: 27507us [9us] (52.10%; 52.10%)
- FoldConstant: 27498us [1907us] (52.09%; 99.97%)
- InferType: 25592us [25592us] (48.47%; 93.07%)
+ InferType: 24765us [24765us] (48.41%; 48.41%)
+ FoldScaleAxis: 26391us [9us] (51.59%; 51.59%)
+ FoldConstant: 26382us [1839us] (51.57%; 99.97%)
+ InferType: 24544us [24544us] (47.98%; 93.03%)
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 a0934ded99..6ad96dfb99 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: 49.404159 ms
+ Convolution: 46.263809 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 31a2f8a1a3..3162060568 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.224256 ms
+ conv2d with tensor core: 12.225475 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 754a05286d..873d73a5f8 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: 0.018352
- Baseline: 3.242398
+ Numpy running time: 0.013592
+ Baseline: 3.268959
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.309922
+ Opt1: 0.288502
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.291903
+ Opt2: 0.270170
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.113976
+ Opt3: 0.115226
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.105496
+ Opt4: 0.105673
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111628
+ Opt5: 0.095346
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.134260
+ Opt6: 0.113585
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 74c204a3c5..f4811e7287 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:33.933** total execution time for **how_to_optimize_operators** files:
+**00:31.649** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.736 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.516 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.011 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.980 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.185 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.153 | 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 80fd411dbb..98e25daefb 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
=================
-**03:50.601** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:41.138** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:39.403 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:36.539 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:15.682 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:12.519 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:21.224 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:20.101 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.970 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.031 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.220 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.849 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.100 | 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 d7367b259c..9935482ab0 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: 0.339 ms
+ Execution time of this operator: 0.343 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 5aa43dcaca..27de99e319 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)
- 3.1980 3.1980 3.1986 3.1976 0.0004
+ 3.2045 3.2040 3.2117 3.1979 0.0057
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 15.682 seconds)
+ **Total running time of the script:** ( 1 minutes 12.519 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 6bd2ea3907..037252b51e 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)
- 759.6549 759.5031 760.2037 759.2579 0.4008
+ 709.1685 708.8632 710.8094 707.8329 1.2342
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 39.403 seconds)
+ **Total running time of the script:** ( 1 minutes 36.539 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 329534498a..f78e655214 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:24.443** total execution time for **how_to_tune_with_autotvm** files:
+**00:22.861** 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``) | 00:24.404 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.824 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 6db8ca538b..b7003a7525 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: 0.037102
+ Time cost of this operator: 0.037109
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 4981121f67..7d0bfd4bd3 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 303.6 98.733 (1, 2, 10, 10, 3) 2 1 [303.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.943 0.957 (1, 6, 10, 10) 1 1 [2.943]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.311 (1, 1, 10, 10, 3) 1 1 [0.955]
- Total_time - 307.497 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.7 98.75 (1, 2, 10, 10, 3) 2 1 [296.7]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.828 0.941 (1, 6, 10, 10) 1 1 [2.828]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.928 0.309 (1, 1, 10, 10, 3) 1 1 [0.928]
+ Total_time - 300.455 - - - - -
@@ -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 102.8 97.507 (1, 6, 10, 10, 1) 2 1 [102.8]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.774 1.682 (1, 6, 10, 10) 1 1 [1.774]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.855 0.811 (1, 3, 10, 10, 1) 1 1 [0.855]
- Total_time - 105.428 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 99.375 97.416 (1, 6, 10, 10, 1) 2 1 [99.375]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.713 1.68 (1, 6, 10, 10) 1 1 [1.713]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.904 (1, 1, 10, 10, 3) 1 1 [0.923]
+ Total_time - 102.011 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 27.001 seconds)
+ **Total running time of the script:** ( 1 minutes 23.552 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 75930e9c97..5fc1ac27b1 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, 8.55MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 13.6MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 7.67MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 12.4MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -326,7 +326,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.090 seconds)
+ **Total running time of the script:** ( 1 minutes 29.758 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 5059eacd9b..37f33a3d02 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/tmpaa6w6xzw/images/random'
+ '/tmp/tmpugt5v0kh/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpaa6w6xzw/images/target contains 8144 images
- /tmp/tmpaa6w6xzw/images/random contains 5000 images
+ /tmp/tmpugt5v0kh/images/target contains 8144 images
+ /tmp/tmpugt5v0kh/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 47s - loss: 0.2075 - accuracy: 0.9272 - val_loss: 0.1082 - val_accuracy: 0.9645 - 47s/epoch - 142ms/step
+ 328/328 - 43s - loss: 0.2427 - accuracy: 0.9179 - val_loss: 0.2178 - val_accuracy: 0.9184 - 43s/epoch - 130ms/step
Epoch 2/3
- 328/328 - 44s - loss: 0.0951 - accuracy: 0.9670 - val_loss: 0.0912 - val_accuracy: 0.9690 - 44s/epoch - 134ms/step
+ 328/328 - 40s - loss: 0.1020 - accuracy: 0.9631 - val_loss: 0.1071 - val_accuracy: 0.9630 - 40s/epoch - 122ms/step
Epoch 3/3
- 328/328 - 44s - loss: 0.0664 - accuracy: 0.9758 - val_loss: 0.1049 - val_accuracy: 0.9641 - 44s/epoch - 134ms/step
+ 328/328 - 40s - loss: 0.0644 - accuracy: 0.9766 - val_loss: 0.0918 - val_accuracy: 0.9717 - 40s/epoch - 121ms/step
- <keras.callbacks.History object at 0x7f39aae822b0>
+ <keras.callbacks.History object at 0x7f4cd599c460>
@@ -861,7 +861,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 5 minutes 12.763 seconds)
+ **Total running time of the script:** ( 4 minutes 50.821 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 473034d521..4ce175f848 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
=================
-**08:46.385** total execution time for **how_to_work_with_microtvm** files:
+**08:14.433** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:12.763 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:50.821 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:34.090 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:29.758 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:27.001 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:23.552 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.568 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.891 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.877 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.348 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.086 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.063 | 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 41dd9cb09d..7a02e71bfc 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
=================
-**00:41.124** total execution time for **how_to_work_with_relay** files:
+**00:39.368** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.101 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.721 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:04.047 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.773 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.969 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.869 | 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 315added21..ff31417793 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 0x7f3ab516ddc0>
+ <function my_cuda_math_rule at 0x7f4d0c1ce0d0>
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 70e015868c..f2a80bbb6d 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
=================
-**00:05.528** total execution time for **how_to_work_with_schedules** files:
+**00:05.440** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.439 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.373 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.408 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.435 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.701 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.683 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.687 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.669 | 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_extern_op.py` (``extern_op.py``) | 00:00.116 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.073 | 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``) | 00:00.064 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.063 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.033 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 60560ab8f5..1d33143e8e 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 29.06s!
+ yolov3-tiny inference graph built in 27.78s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.567 seconds)
+ **Total running time of the script:** ( 1 minutes 0.048 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 55ec9d57b6..0fdb6d76df 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**01:02.567** total execution time for **topic_vta_tutorials_frontend** files:
+**01:00.048** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:02.567 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:00.048 | 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 d2290fd82b..5c3612ca3d 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
=================
-**00:03.268** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.164** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.796 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.710 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.473 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.454 | 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 09987d5b47..0e38d5e586 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
=================
-**00:00.776** total execution time for **topic_vta_tutorials** files:
+**00:00.740** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.407 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.385 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.368 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.355 | 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 0d167fd4bf..61cc9d51dc 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 97.650 ms
+ Execution time of this operator: 92.740 ms
@@ -416,7 +416,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
-
+ *E
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 29.825 seconds)
+ **Total running time of the script:** ( 1 minutes 41.922 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 05863eaa35..605e94618a 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: 13.63/13.63 result: MeasureResult(costs=(0.019701419,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6129095554351807, timestamp=1719554783.533494) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 2 GFLOPS: 1.47/13.63 result: MeasureResult(costs=(0.18226653060000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.1744236946105957, timestamp=1719554786.7123275) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
- No: 3 GFLOPS: 16.31/16.31 result: MeasureResult(costs=(0.0164597944,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5627231597900391, timestamp=1719554787.2349677) [('tile_y', [-1, 16]), ('tile_x', [-1, 64])],None,64
- No: 4 GFLOPS: 1.64/16.31 result: MeasureResult(costs=(0.1639086896,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.8928186893463135, timestamp=1719554790.1199858) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
- No: 5 GFLOPS: 1.02/16.31 result: MeasureResult(costs=(0.2630227556,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.500633239746094, timestamp=1719554794.7365792) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
- No: 6 GFLOPS: 10.56/16.31 result: MeasureResult(costs=(0.0254154824,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6932358741760254, timestamp=1719554795.406453) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
- No: 7 GFLOPS: 3.81/16.31 result: MeasureResult(costs=(0.0704461804,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4027936458587646, timestamp=1719554796.800032) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
- No: 8 GFLOPS: 12.04/16.31 result: MeasureResult(costs=(0.022298537,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6477017402648926, timestamp=1719554797.4183245) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
- No: 9 GFLOPS: 10.91/16.31 result: MeasureResult(costs=(0.0246036962,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7030680179595947, timestamp=1719554798.2327766) [('tile_y', [-1, 512]), ('tile_x', [-1, 64])],None,69
- No: 10 GFLOPS: 12.40/16.31 result: MeasureResult(costs=(0.0216449158,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5976099967956543, timestamp=1719554798.8451285) [('tile_y', [-1, 32]), ('tile_x', [-1, 128])],None,75
+ No: 1 GFLOPS: 1.70/1.70 result: MeasureResult(costs=(0.15759977819999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.791661024093628, timestamp=1719821375.1003284) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
+ No: 2 GFLOPS: 12.62/12.62 result: MeasureResult(costs=(0.0212656062,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6515810489654541, timestamp=1719821375.7054868) [('tile_y', [-1, 16]), ('tile_x', [-1, 512])],None,94
+ No: 3 GFLOPS: 2.12/12.62 result: MeasureResult(costs=(0.1268745512,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3032922744750977, timestamp=1719821377.9905818) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
+ No: 4 GFLOPS: 9.88/12.62 result: MeasureResult(costs=(0.027156798600000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6984868049621582, timestamp=1719821378.6962092) [('tile_y', [-1, 512]), ('tile_x', [-1, 512])],None,99
+ No: 5 GFLOPS: 4.41/12.62 result: MeasureResult(costs=(0.0608888176,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2357099056243896, timestamp=1719821380.0640113) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
+ No: 6 GFLOPS: 8.51/12.62 result: MeasureResult(costs=(0.0315305662,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7921080589294434, timestamp=1719821380.834236) [('tile_y', [-1, 16]), ('tile_x', [-1, 8])],None,34
+ No: 7 GFLOPS: 3.21/12.62 result: MeasureResult(costs=(0.0834961964,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5950887203216553, timestamp=1719821382.4331458) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+ No: 8 GFLOPS: 1.56/12.62 result: MeasureResult(costs=(0.1721416484,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.0066511631011963, timestamp=1719821385.4471717) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
+ No: 9 GFLOPS: 10.77/12.62 result: MeasureResult(costs=(0.0249357992,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6354691982269287, timestamp=1719821386.1961343) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
+ No: 10 GFLOPS: 2.10/12.62 result: MeasureResult(costs=(0.1277370142,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.2844326496124268, timestamp=1719821388.4987261) [('tile_y', [-1, 128]), ('tile_x', [-1, 4])],None,27
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 1dc52fef06..8e09962570 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': 495.28697789000034, 'median': 495.4147258500029, 'std': 2.142740693440672}
+ {'mean': 467.5418617100013, 'median': 467.8203326500011, 'std': 1.3913844245490927}
@@ -582,29 +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: 20.92/ 20.92 GFLOPS | Progress: (4/20) | 9.32 s [Task 1/25] Current/Best: 17.01/ 20.92 GFLOPS | Progress: (8/20) | 11.88 s [Task 1/25] Current/Best: 15.56/ 20.92 GFLOPS | Progress: (12/20) | 14.70 s [Task 1/25] Current/Best: 1.93/ 23.94 GFLOPS | Progress: (16/20) | 19.05 s [Task 1/25] Current/Best: 14.05/ 23.94 GFLOPS | Progress: (20/20) | 22.55 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 17.32/ 19.26 GFLOPS | Progress: (4/20) | 4.54 s [Task 2/25] Current/Best: 19.31/ 19.31 GFLOPS | Progress: (8/20) | 6.14 s [Task 2/25] Current/Best: 3.79/ 19.31 GFLOPS | Progress: (12/20) | 7.82 s [Task 2/25] Current/Best: 12.02/ 19.31 GFLOPS | Progress: (16/20) | 10.47 s [Task 2/25] Current/Best: 13.46/ 19.31 GFLOPS | Progress: (20/20) | 12.21 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 11.49/ 12.11 GFLOPS | Progress: (4/20) | 6.29 s [Task 3/25] Current/Best: 12.39/ 14.80 GFLOPS | Progress: (8/20) | 8.91 s [Task 3/25] Current/Best: 5.38/ 16.99 GFLOPS | Progress: (12/20) | 11.94 s [Task 3/25] Current/Best: 14.96/ 18.74 GFLOPS | Progress: (16/20) | 14.86 s [Task 3/25] Current/Best: 11.09/ 20.46 GFLOPS | Progress: (20/20) | 16.96 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 11.60/ 15.17 GFLOPS | Progress: (4/20) | 5.70 s [Task 4/25] Current/Best: 13.09/ 17.71 GFLOPS | Progress: (8/20) | 8.42 s [Task 4/25] Current/Best: 11.64/ 17.71 GFLOPS | Progress: (12/20) | 13.11 s [Task 4/25] Current/Best: 20.60/ 20.60 GFLOPS | Progress: (16/20) | 14.90 s [Task 4/25] Current/Best: 11.63/ 20.60 GFLOPS | Progress: (20/20) | 20.35 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 7.14/ 19.28 GFLOPS | Progress: (4/20) | 4.54 s [Task 5/25] Current/Best: 17.58/ 19.28 GFLOPS | Progress: (8/20) | 6.73 s [Task 5/25] Current/Best: 15.87/ 19.28 GFLOPS | Progress: (12/20) | 10.00 s [Task 5/25] Current/Best: 3.41/ 19.28 GFLOPS | Progress: (16/20) | 12.48 s [Task 5/25] Current/Best: 13.34/ 19.28 GFLOPS | Progress: (20/20) | 15.04 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 14.81/ 14.81 GFLOPS | Progress: (4/20) | 6.10 s [Task 6/25] Current/Best: 11.52/ 14.96 GFLOPS | Progress: (8/20) | 8.65 s [Task 6/25] Current/Best: 14.97/ 14.97 GFLOPS | Progress: (12/20) | 11.93 s [Task 6/25] Current/Best: 13.55/ 17.50 GFLOPS | Progress: (16/20) | 14.08 s [Task 6/25] Current/Best: 14.28/ 17.50 GFLOPS | Progress: (20/20) | 19.17 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 18.86/ 21.44 GFLOPS | Progress: (4/20) | 4.97 s [Task 7/25] Current/Best: 13.90/ 21.58 GFLOPS | Progress: (8/20) | 7.14 s [Task 7/25] Current/Best: 20.29/ 21.58 GFLOPS | Progress: (12/20) | 9.28 s [Task 7/25] Current/Best: 20.51/ 21.58 GFLOPS | Progress: (16/20) | 11.59 s [Task 7/25] Current/Best: 15.27/ 21.58 GFLOPS | Progress: (20/20) | 13.70 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 20.71/ 20.71 GFLOPS | Progress: (4/20) | 5.05 s [Task 8/25] Current/Best: 12.57/ 20.71 GFLOPS | Progress: (8/20) | 8.99 s [Task 8/25] Current/Best: 7.60/ 20.71 GFLOPS | Progress: (12/20) | 21.00 s [Task 8/25] Current/Best: 9.64/ 20.71 GFLOPS | Progress: (16/20) | 30.43 s [Task 8/25] Current/Best: 11.60/ 20.71 GFLOPS | Progress: (20/20) | 33.66 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 18.86/ 18.86 GFLOPS | Progress: (4/20) | 9.01 s [Task 9/25] Current/Best: 6.33/ 18.86 GFLOPS | Progress: (8/20) | 11.16 s [Task 9/25] Current/Best: 16.26/ 18.86 GFLOPS | Progress: (12/20) | 12.90 s [Task 9/25] Current/Best: 13.71/ 19.09 GFLOPS | Progress: (16/20) | 14.97 s [Task 9/25] Current/Best: 7.23/ 20.81 GFLOPS | Progress: (20/20) | 17.49 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 14.01/ 14.33 GFLOPS | Progress: (4/20) | 5.02 s [Task 10/25] Current/Best: 16.57/ 16.57 GFLOPS | Progress: (8/20) | 7.05 s [Task 10/25] Current/Best: 13.68/ 16.86 GFLOPS | Progress: (12/20) | 9.07 s [Task 10/25] Current/Best: 2.95/ 18.44 GFLOPS | Progress: (16/20) | 11.10 s [Task 10/25] Current/Best: 17.30/ 18.44 GFLOPS | Progress: (20/20) | 12.88 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 16.10/ 16.10 GFLOPS | Progress: (4/20) | 4.98 s [Task 11/25] Current/Best: 23.36/ 23.36 GFLOPS | Progress: (8/20) | 7.89 s [Task 11/25] Current/Best: 6.39/ 23.36 GFLOPS | Progress: (12/20) | 10.25 s [Task 11/25] Current/Best: 23.11/ 23.36 GFLOPS | Progress: (16/20) | 12.58 s [Task 11/25] Current/Best: 9.16/ 23.36 GFLOPS | Progress: (20/20) | 15.00 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 11.52/ 15.91 GFLOPS | Progress: (4/20) | 5.75 s [Task 12/25] Current/Best: 17.89/ 17.89 GFLOPS | Progress: (8/20) | 8.44 s [Task 12/25] Current/Best: 12.35/ 17.89 GFLOPS | Progress: (12/20) | 10.72 s [Task 12/25] Current/Best: 15.16/ 17.89 GFLOPS | Progress: (16/20) | 13.45 s [Task 12/25] Current/Best: 3.92/ 17.89 GFLOPS | Progress: (20/20) | 17.08 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 11.63/ 18.48 GFLOPS | Progress: (4/20) | 6.14 s [Task 13/25] Current/Best: 18.09/ 18.48 GFLOPS | Progress: (8/20) | 8.53 s [Task 13/25] Current/Best: 12.79/ 18.48 GFLOPS | Progress: (12/20) | 12.44 s [Task 13/25] Current/Best: 7.36/ 18.81 GFLOPS | Progress: (16/20) | 15.86 s [Task 13/25] Current/Best: 17.84/ 18.81 GFLOPS | Progress: (20/20) | 18.86 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 17.59/ 17.59 GFLOPS | Progress: (4/20) | 7.66 s [Task 14/25] Current/Best: 9.74/ 17.59 GFLOPS | Progress: (8/20) | 11.24 s [Task 14/25] Current/Best: 12.70/ 19.02 GFLOPS | Progress: (12/20) | 13.95 s [Task 14/25] Current/Best: 17.14/ 19.02 GFLOPS | Progress: (16/20) | 16.56 s [Task 14/25] Current/Best: 10.91/ 19.02 GFLOPS | Progress: (20/20) | 25.36 s Done.
- [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 16.35/ 19.37 GFLOPS | Progress: (4/20) | 7.08 s [Task 15/25] Current/Best: 1.74/ 19.37 GFLOPS | Progress: (8/20) | 10.14 s [Task 15/25] Current/Best: 18.73/ 19.37 GFLOPS | Progress: (12/20) | 17.09 s [Task 15/25] Current/Best: 7.97/ 19.37 GFLOPS | Progress: (16/20) | 21.79 s [Task 15/25] Current/Best: 11.15/ 19.37 GFLOPS | Progress: (20/20) | 28.54 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 4.99/ 17.05 GFLOPS | Progress: (4/20) | 5.13 s [Task 16/25] Current/Best: 14.31/ 17.05 GFLOPS | Progress: (8/20) | 7.14 s [Task 16/25] Current/Best: 17.94/ 19.71 GFLOPS | Progress: (12/20) | 9.99 s [Task 16/25] Current/Best: 6.02/ 19.71 GFLOPS | Progress: (16/20) | 11.74 s [Task 16/25] Current/Best: 15.16/ 19.71 GFLOPS | Progress: (20/20) | 13.58 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 20.79/ 21.40 GFLOPS | Progress: (4/20) | 5.65 s [Task 17/25] Current/Best: 17.89/ 22.38 GFLOPS | Progress: (8/20) | 7.84 s [Task 17/25] Current/Best: 9.83/ 22.38 GFLOPS | Progress: (12/20) | 11.16 s [Task 17/25] Current/Best: 22.19/ 22.38 GFLOPS | Progress: (16/20) | 14.82 s [Task 17/25] Current/Best: 17.57/ 22.38 GFLOPS | Progress: (20/20) | 17.54 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 6.53/ 20.30 GFLOPS | Progress: (4/20) | 4.76 s [Task 18/25] Current/Best: 9.33/ 20.30 GFLOPS | Progress: (8/20) | 11.45 s [Task 18/25] Current/Best: 13.80/ 20.30 GFLOPS | Progress: (12/20) | 15.04 s [Task 18/25] Current/Best: 9.58/ 20.30 GFLOPS | Progress: (16/20) | 17.76 s [Task 18/25] Current/Best: 18.83/ 20.30 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: 17.03/ 22.05 GFLOPS | Progress: (4/20) | 5.55 s [Task 19/25] Current/Best: 13.17/ 22.05 GFLOPS | Progress: (8/20) | 10.15 s [Task 19/25] Current/Best: 11.04/ 22.05 GFLOPS | Progress: (12/20) | 13.61 s [Task 19/25] Current/Best: 7.62/ 22.05 GFLOPS | Progress: (16/20) | 17.28 s [Task 19/25] Current/Best: 6.14/ 22.50 GFLOPS | Progress: (20/20) | 20.82 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 11.19/ 16.19 GFLOPS | Progress: (4/20) | 14.34 s [Task 20/25] Current/Best: 13.97/ 16.24 GFLOPS | Progress: (8/20) | 17.81 s [Task 20/25] Current/Best: 8.92/ 16.24 GFLOPS | Progress: (12/20) | 21.15 s [Task 20/25] Current/Best: 9.63/ 16.24 GFLOPS | Progress: (16/20) | 32.42 s [Task 20/25] Current/Best: 14.74/ 16.24 GFLOPS | Progress: (20/20) | 45.19 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- [Task 21/25] Current/Best: 10.42/ 13.13 GFLOPS | Progress: (4/20) | 13.90 s [Task 21/25] Current/Best: 17.77/ 19.84 GFLOPS | Progress: (8/20) | 16.84 s [Task 21/25] Current/Best: 13.09/ 19.84 GFLOPS | Progress: (12/20) | 20.91 s [Task 21/25] Current/Best: 18.06/ 19.84 GFLOPS | Progress: (16/20) | 24.62 s [Task 21/25] Current/Best: 19.47/ 19.84 GFLOPS | Progress: (20/20) | 35.86 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 10.70/ 19.17 GFLOPS | Progress: (4/20) | 4.36 s [Task 22/25] Current/Best: 15.70/ 19.17 GFLOPS | Progress: (8/20) | 6.31 s [Task 22/25] Current/Best: 18.39/ 19.17 GFLOPS | Progress: (12/20) | 9.25 s [Task 22/25] Current/Best: 9.78/ 19.17 GFLOPS | Progress: (16/20) | 11.43 s [Task 22/25] Current/Best: 19.81/ 19.81 GFLOPS | Progress: (20/20) | 13.11 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 17.54/ 20.94 GFLOPS | Progress: (4/20) | 11.58 s [Task 23/25] Current/Best: 6.13/ 21.30 GFLOPS | Progress: (8/20) | 15.05 s [Task 23/25] Current/Best: 19.60/ 21.96 GFLOPS | Progress: (12/20) | 19.08 s [Task 23/25] Current/Best: 1.55/ 21.96 GFLOPS | Progress: (16/20) | 23.66 s [Task 23/25] Current/Best: 22.21/ 22.21 GFLOPS | Progress: (20/20) | 26.66 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 2.99/ 5.01 GFLOPS | Progress: (4/20) | 13.69 s [Task 24/25] Current/Best: 0.90/ 6.24 GFLOPS | Progress: (8/20) | 19.44 s [Task 24/25] Current/Best: 3.21/ 6.24 GFLOPS | Progress: (12/20) | 27.85 s [Task 24/25] Current/Best: 3.74/ 6.24 GFLOPS | Progress: (16/20) | 38.87 s [Task 24/25] Current/Best: 3.81/ 6.24 GFLOPS | Progress: (20/20) | 50.88 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 5.84/ 5.84 GFLOPS | Progress: (4/20) | 6.36 s [Task 25/25] Current/Best: 9.69/ 9.69 GFLOPS | Progress: (8/20) | 8.15 s [Task 25/25] Current/Best: 5.79/ 9.69 GFLOPS | Progress: (12/20) | 10.24 s [Task 25/25] Current/Best: 3.06/ 9.69 GFLOPS | Progress: (16/20) | 16.57 s [Task 25/25] Current/Best: 8.61/ 9.69 GFLOPS | Progress: (20/
20) | 27.53 s
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 16.14/ 22.67 GFLOPS | Progress: (4/20) | 10.31 s [Task 1/25] Current/Best: 1.99/ 22.67 GFLOPS | Progress: (8/20) | 14.33 s [Task 1/25] Current/Best: 21.94/ 22.67 GFLOPS | Progress: (12/20) | 16.28 s [Task 1/25] Current/Best: 7.91/ 24.17 GFLOPS | Progress: (16/20) | 18.34 s [Task 1/25] Current/Best: 13.16/ 24.17 GFLOPS | Progress: (20/20) | 21.19 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 5.84/ 14.89 GFLOPS | Progress: (4/20) | 4.46 s [Task 2/25] Current/Best: 17.92/ 17.92 GFLOPS | Progress: (8/20) | 6.08 s [Task 2/25] Current/Best: 11.97/ 23.41 GFLOPS | Progress: (12/20) | 7.49 s [Task 2/25] Current/Best: 15.14/ 23.41 GFLOPS | Progress: (16/20) | 9.08 s [Task 2/25] Current/Best: 20.70/ 23.41 GFLOPS | Progress: (20/20) | 10.70 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 7.39/ 15.10 GFLOPS | Progress: (4/20) | 5.63 s [Task 3/25] Current/Best: 23.73/ 23.73 GFLOPS | Progress: (8/20) | 7.60 s [Task 3/25] Current/Best: 10.67/ 23.94 GFLOPS | Progress: (12/20) | 9.86 s [Task 3/25] Current/Best: 15.15/ 23.94 GFLOPS | Progress: (16/20) | 11.83 s [Task 3/25] Current/Best: 23.80/ 23.94 GFLOPS | Progress: (20/20) | 13.91 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 14.51/ 20.83 GFLOPS | Progress: (4/20) | 4.49 s [Task 4/25] Current/Best: 12.98/ 20.83 GFLOPS | Progress: (8/20) | 6.47 s [Task 4/25] Current/Best: 15.48/ 20.83 GFLOPS | Progress: (12/20) | 14.01 s [Task 4/25] Current/Best: 22.53/ 22.53 GFLOPS | Progress: (16/20) | 15.88 s [Task 4/25] Current/Best: 16.16/ 22.53 GFLOPS | Progress: (20/20) | 17.99 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 11.65/ 16.54 GFLOPS | Progress: (4/20) | 4.68 s [Task 5/25] Current/Best: 23.63/ 23.85 GFLOPS | Progress: (8/20) | 6.47 s [Task 5/25] Current/Best: 10.93/ 23.85 GFLOPS | Progress: (12/20) | 9.15 s [Task 5/25] Current/Best: 4.20/ 23.85 GFLOPS | Progress: (16/20) | 11.44 s [Task 5/25] Current/Best: 15.31/ 23.85 GFLOPS | Progress: (20/20) | 13.60 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 13.80/ 18.88 GFLOPS | Progress: (4/20) | 4.83 s [Task 6/25] Current/Best: 4.05/ 18.88 GFLOPS | Progress: (8/20) | 8.09 s [Task 6/25] Current/Best: 12.03/ 18.88 GFLOPS | Progress: (12/20) | 13.06 s [Task 6/25] Current/Best: 21.88/ 21.88 GFLOPS | Progress: (16/20) | 15.41 s [Task 6/25] Current/Best: 5.19/ 21.88 GFLOPS | Progress: (20/20) | 17.72 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 20.19/ 20.19 GFLOPS | Progress: (4/20) | 4.87 s [Task 7/25] Current/Best: 3.23/ 23.79 GFLOPS | Progress: (8/20) | 7.41 s [Task 7/25] Current/Best: 14.38/ 23.79 GFLOPS | Progress: (12/20) | 9.80 s [Task 7/25] Current/Best: 6.35/ 23.79 GFLOPS | Progress: (16/20) | 12.09 s [Task 7/25] Current/Best: 9.80/ 23.79 GFLOPS | Progress: (20/20) | 14.67 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 20.09/ 20.09 GFLOPS | Progress: (4/20) | 4.98 s [Task 8/25] Current/Best: 13.63/ 20.09 GFLOPS | Progress: (8/20) | 7.41 s [Task 8/25] Current/Best: 3.23/ 20.09 GFLOPS | Progress: (12/20) | 10.57 s [Task 8/25] Current/Best: 15.74/ 20.09 GFLOPS | Progress: (16/20) | 16.46 s [Task 8/25] Current/Best: 9.90/ 20.09 GFLOPS | Progress: (20/20) | 23.99 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 8.11/ 19.77 GFLOPS | Progress: (4/20) | 4.32 s [Task 9/25] Current/Best: 10.12/ 19.77 GFLOPS | Progress: (8/20) | 15.42 s [Task 9/25] Current/Best: 15.43/ 19.77 GFLOPS | Progress: (12/20) | 18.31 s [Task 9/25] Current/Best: 13.76/ 19.77 GFLOPS | Progress: (16/20) | 20.35 s [Task 9/25] Current/Best: 7.50/ 20.67 GFLOPS | Progress: (20/20) | 21.91 s [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 21.28/ 21.28 GFLOPS | Progress: (4/20) | 4.42 s [Task 10/25] Current/Best: 4.06/ 21.28 GFLOPS | Progress: (8/20) | 6.69 s [Task 10/25] Current/Best: 3.58/ 21.28 GFLOPS | Progress: (12/20) | 8.71 s [Task 10/25] Current/Best: 17.53/ 21.28 GFLOPS | Progress: (16/20) | 11.61 s [Task 10/25] Current/Best: 12.13/ 21.28 GFLOPS | Progress: (20/20
) | 13.98 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 16.23/ 24.40 GFLOPS | Progress: (4/20) | 4.64 s [Task 11/25] Current/Best: 9.15/ 24.40 GFLOPS | Progress: (8/20) | 6.82 s [Task 11/25] Current/Best: 20.35/ 24.40 GFLOPS | Progress: (12/20) | 8.79 s [Task 11/25] Current/Best: 15.02/ 24.41 GFLOPS | Progress: (16/20) | 12.43 s [Task 11/25] Current/Best: 3.24/ 24.41 GFLOPS | Progress: (20/20) | 15.19 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 12.53/ 13.62 GFLOPS | Progress: (4/20) | 6.22 s [Task 12/25] Current/Best: 15.18/ 15.18 GFLOPS | Progress: (8/20) | 9.37 s [Task 12/25] Current/Best: 10.73/ 18.06 GFLOPS | Progress: (12/20) | 13.90 s [Task 12/25] Current/Best: 3.31/ 18.06 GFLOPS | Progress: (16/20) | 17.93 s [Task 12/25] Current/Best: 11.51/ 21.68 GFLOPS | Progress: (20/20) | 21.91 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 10.80/ 20.29 GFLOPS | Progress: (4/20) | 6.07 s [Task 13/25] Current/Best: 10.46/ 21.16 GFLOPS | Progress: (8/20) | 9.22 s [Task 13/25] Current/Best: 10.87/ 21.16 GFLOPS | Progress: (12/20) | 12.83 s [Task 13/25] Current/Best: 12.89/ 21.16 GFLOPS | Progress: (16/20) | 16.57 s [Task 13/25] Current/Best: 11.07/ 21.59 GFLOPS | Progress: (20/20) | 19.75 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 16.72/ 19.37 GFLOPS | Progress: (4/20) | 5.81 s [Task 14/25] Current/Best: 13.50/ 19.37 GFLOPS | Progress: (8/20) | 8.40 s [Task 14/25] Current/Best: 3.20/ 22.54 GFLOPS | Progress: (12/20) | 11.04 s [Task 14/25] Current/Best: 10.02/ 23.33 GFLOPS | Progress: (16/20) | 19.72 s [Task 14/25] Current/Best: 9.15/ 23.33 GFLOPS | Progress: (20/20) | 24.58 s Done.
+ [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 16.68/ 20.38 GFLOPS | Progress: (4/20) | 4.23 s [Task 15/25] Current/Best: 6.82/ 20.38 GFLOPS | Progress: (8/20) | 10.69 s [Task 15/25] Current/Best: 8.39/ 20.38 GFLOPS | Progress: (12/20) | 12.32 s [Task 15/25] Current/Best: 15.79/ 20.38 GFLOPS | Progress: (16/20) | 15.92 s [Task 15/25] Current/Best: 12.71/ 20.38 GFLOPS | Progress: (20/20) | 23.82 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 15.34/ 15.34 GFLOPS | Progress: (4/20) | 4.72 s [Task 16/25] Current/Best: 18.29/ 18.29 GFLOPS | Progress: (8/20) | 7.84 s [Task 16/25] Current/Best: 11.41/ 18.29 GFLOPS | Progress: (12/20) | 10.51 s [Task 16/25] Current/Best: 6.12/ 18.65 GFLOPS | Progress: (16/20) | 14.19 s [Task 16/25] Current/Best: 16.43/ 18.65 GFLOPS | Progress: (20/20) | 16.55 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 11.00/ 23.59 GFLOPS | Progress: (4/20) | 5.73 s [Task 17/25] Current/Best: 19.20/ 23.59 GFLOPS | Progress: (8/20) | 9.73 s [Task 17/25] Current/Best: 12.73/ 23.59 GFLOPS | Progress: (12/20) | 12.72 s [Task 17/25] Current/Best: 19.00/ 23.59 GFLOPS | Progress: (16/20) | 15.27 s [Task 17/25] Current/Best: 20.51/ 23.59 GFLOPS | Progress: (20/20) | 17.37 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 4.78/ 18.66 GFLOPS | Progress: (4/20) | 8.22 s [Task 18/25] Current/Best: 19.67/ 19.67 GFLOPS | Progress: (8/20) | 10.29 s [Task 18/25] Current/Best: 8.21/ 19.67 GFLOPS | Progress: (12/20) | 13.62 s [Task 18/25] Current/Best: 18.64/ 19.67 GFLOPS | Progress: (16/20) | 15.81 s [Task 18/25] Current/Best: 14.16/ 19.67 GFLOPS | Progress: (20/20) | 18.10 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (4/20) | 5.57 s [Task 19/25] Current/Best: 5.53/ 21.90 GFLOPS | Progress: (8/20) | 8.68 s [Task 19/25] Current/Best: 20.80/ 21.90 GFLOPS | Progress: (12/20) | 11.82 s [Task 19/25] Current/Best: 3.19/ 21.90 GFLOPS | Progress: (16/20) | 16.81 s [Task 19/25] Current/Best: 10.09/ 21.90 GFLOPS | Progress: (20/20) | 20.49 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 9.16/ 12.88 GFLOPS | Progress: (4/20) | 7.01 s [Task 20/25] Current/Best: 12.33/ 12.88 GFLOPS | Progress: (8/20) | 15.84 s [Task 20/25] Current/Best: 19.33/ 22.44 GFLOPS | Progress: (12/20) | 18.29 s [Task 20/25] Current/Best: 10.57/ 22.44 GFLOPS | Progress: (16/20) | 30.18 s [Task 20/25] Current/Best: 17.18/ 22.44 GFLOPS | Progress: (20/20) | 41.62 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ Done.
+ [Task 21/25] Current/Best: 18.25/ 20.25 GFLOPS | Progress: (4/20) | 5.26 s [Task 21/25] Current/Best: 10.01/ 20.25 GFLOPS | Progress: (8/20) | 16.70 s [Task 21/25] Current/Best: 9.97/ 20.25 GFLOPS | Progress: (12/20) | 20.39 s [Task 21/25] Current/Best: 17.60/ 20.25 GFLOPS | Progress: (16/20) | 22.54 s [Task 21/25] Current/Best: 8.66/ 20.25 GFLOPS | Progress: (20/20) | 27.23 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 20.42/ 20.42 GFLOPS | Progress: (4/20) | 4.88 s [Task 22/25] Current/Best: 15.01/ 20.42 GFLOPS | Progress: (8/20) | 6.87 s [Task 22/25] Current/Best: 19.83/ 21.87 GFLOPS | Progress: (12/20) | 8.59 s [Task 22/25] Current/Best: 15.43/ 21.87 GFLOPS | Progress: (16/20) | 11.36 s [Task 22/25] Current/Best: 16.92/ 21.87 GFLOPS | Progress: (20/20) | 14.15 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 10.10/ 23.68 GFLOPS | Progress: (4/20) | 9.27 s [Task 23/25] Current/Best: 19.32/ 23.85 GFLOPS | Progress: (8/20) | 11.71 s [Task 23/25] Current/Best: 10.81/ 23.85 GFLOPS | Progress: (12/20) | 14.90 s [Task 23/25] Current/Best: 19.57/ 23.85 GFLOPS | Progress: (16/20) | 20.49 s [Task 23/25] Current/Best: 9.86/ 23.85 GFLOPS | Progress: (20/20) | 23.92 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 8.22/ 10.27 GFLOPS | Progress: (4/20) | 13.64 s [Task 24/25] Current/Best: 1.14/ 10.27 GFLOPS | Progress: (8/20) | 26.09 s [Task 24/25] Current/Best: 8.83/ 10.27 GFLOPS | Progress: (12/20) | 38.26 s [Task 24/25] Current/Best: 9.68/ 10.27 GFLOPS | Progress: (16/20) | 44.02 s [Task 24/25] Current/Best: 3.30/ 10.27 GFLOPS | Progress: (20/20) | 52.04 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 3.72/ 9.89 GFLOPS | Progress: (4/20) | 13.58 s Done.
+ Done.
+ [Task 25/25] Current/Best: 8.04/ 9.89 GFLOPS | Progress: (8/20) | 24.55 s [Task 25/25] Current/Best: 1.59/ 9.89 GFLOPS | Progress: (12/20) | 35.54 s [Task 25/25] Current/Best: 7.81/ 9.89 GFLOPS | Progress: (16/20) | 46.52 s [Task 25/25] Current/Best: 9.92/ 9.92 GFLOPS | Progress: (20/20) | 57.36 s
@@ -673,8 +675,6 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
- Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 431.62747654000214, 'median': 431.2601891999975, 'std': 1.0925074141299869}
- unoptimized: {'mean': 495.28697789000034, 'median': 495.4147258500029, 'std': 2.142740693440672}
+ optimized: {'mean': 373.27328309999984, 'median': 373.012040399999, 'std': 1.3773208562065975}
+ unoptimized: {'mean': 467.5418617100013, 'median': 467.8203326500011, 'std': 1.3913844245490927}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 51.256 seconds)
+ **Total running time of the script:** ( 13 minutes 34.678 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 308e72a3c4..ace56df8bc 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
- 1.162e-07 secs/op
+ 1.232e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 9dda698785..34813dcff6 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, 0x8627b90)), stage(b, placeholder(b, 0x9168c50)), 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.R [...]
+ [stage(a, placeholder(a, 0x2950d2e0)), stage(b, placeholder(b, 0xed37240)), 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 2538334b90..178c6932c1 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,31 +5,31 @@
Computation times
=================
-**17:29.794** total execution time for **tutorial** files:
+**17:22.386** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:51.256 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:34.678 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:29.825 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:41.922 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.236 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.158 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:45.454 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.864 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:22.236 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:22.257 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.702 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.445 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.893 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.866 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.194 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.196 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 925ccabd06..2ab1d179c9 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000007
- naive: 0.000007
+ Numpy running time: 0.000005
+ naive: 0.000006
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000007
+ parallel: 0.000008
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000040
+ 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 7.486859999517037e-06 1.0
- naive 6.811399999999999e-06 0.9097806023405526
- parallel 7.1591e-06 0.9562219676155049
- vector 3.99416e-05 5.334893400247441
+ numpy 5.191380000724166e-06 1.0
+ naive 5.615599999999999e-06 1.081716229445091
+ parallel 8.073099999999999e-06 1.5550971030581175
+ vector 3.87041e-05 7.455455003217066
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018772
+ Numpy running time: 0.013779
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.227239
+ none: 3.269734
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.302809
+ blocking: 0.293221
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.289890
+ vectorization: 0.270945
# 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: 0.120290
+ loop permutation: 0.107718
# 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: 0.106158
+ array packing: 0.101499
# 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: 0.111363
+ block caching: 0.094156
# 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: 0.133586
+ parallelization: 0.113293
# 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 3.2272391636999997 1.0
- blocking 0.3028091261 0.0938291557396794
- vectorization 0.2898895711 0.08982587171123826
- loop permutation 0.12029046080000001 0.037273488173119505
- array packing 0.10615786199999999 0.03289432750880818
- block caching 0.1113632674 0.03450728680186288
- parallelization 0.13358606680000001 0.04139329625847898
+ none 3.2697338987 1.0
+ blocking 0.2932206543 0.08967722248485736
+ vectorization 0.2709449567 0.08286452815249702
+ loop permutation 0.10771797280000002 0.03294395695100056
+ array packing 0.1014994119 0.03104210160354478
+ block caching 0.0941561482 0.02879627245429212
+ parallelization 0.11329265869999998 0.0346488926040873
diff --git a/docs/commit_hash b/docs/commit_hash
index 91c0bdf9ed..45ae4a959a 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-4a5e22e869e92b9c12b3bda8b88a0ce8c69b8d30
+ab7c1a91d81ae91ad806c2f97c11f6b104ab2ec5
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index aa1b1786e3..d53c26b814 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 42.369 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 37.056 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
index 05ee394d39..2b8338e10f 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
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+1/1 [==============================] - 1s 895ms/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
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diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 6b3b29dae8..3458338cff 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -478,14 +478,15 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 2d56a5314a..48ca94e1fa 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 8.338 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 5.827 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
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<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 0bde44dd4d..d5ca474d6f 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -461,10 +461,14 @@ 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 af32ca59ef..61e016ea43 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 36.449 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.567 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
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--- 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>07:12.033</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:51.444</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -379,39 +379,39 @@
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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>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
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+<td><p>01:05.827</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
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</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
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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>00:02.970</p></td>
+<td><p>00:02.826</p></td>
<td><p>0.0 MB</p></td>
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diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 920b0bc74d..40143ec278 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -860,10 +860,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4121.5479 4121.5544 4123.7390 4119.9257 1.0993
+ 4028.2793 4027.8858 4032.1546 4024.9008 2.2532
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.158 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
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--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -468,26 +468,24 @@ 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 1eb7671b01..c066dbb9fb 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)
- 15.0150 15.0203 15.5967 14.4492 0.4663
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</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
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--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -484,36 +484,38 @@ 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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+ 75%|#######5 | 128M/170M [00:03<00:01, 39.6MB/s]
+ 80%|######## | 136M/170M [00:03<00:00, 40.8MB/s]
+ 85%|########4 | 144M/170M [00:03<00:00, 45.6MB/s]
+ 88%|########8 | 150M/170M [00:04<00:00, 47.1MB/s]
+ 91%|#########1| 155M/170M [00:04<00:00, 43.3MB/s]
+ 94%|#########3| 159M/170M [00:04<00:00, 43.2MB/s]
+ 96%|#########6| 163M/170M [00:04<00:00, 42.7MB/s]
+ 99%|#########8| 167M/170M [00:04<00:00, 41.4MB/s]
+100%|##########| 170M/170M [00:04<00:00, 38.7MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -604,7 +606,7 @@ 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 47.973 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 32.708 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 3e34176bbd..b89590dda5 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -525,9 +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, 47.8MB/s]
- 80%|######## | 10.9M/13.6M [00:00<00:00, 32.8MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 34.5MB/s]
+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 37.9MB/s]
+ 90%|########9 | 12.2M/13.6M [00:00<00:00, 38.8MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 42.2MB/s]
</pre></div>
</div>
</div>
@@ -618,7 +618,7 @@ 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)
- 88.9072 88.8142 89.8024 88.5988 0.2583
+ 85.9294 85.9129 89.8628 85.4341 0.4499
</pre></div>
</div>
<div class="admonition note">
@@ -657,7 +657,7 @@ 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 36.772 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.680 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 d5206c2400..f232d8b104 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)
- 110.0343 110.0620 113.1740 108.5295 0.5187
+ 106.6789 106.8997 110.8443 103.3111 1.0737
</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 4a9622cebc..2cfb160d02 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>10:31.315</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:00.442</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -379,39 +379,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:47.973</p></td>
+<td><p>03:32.708</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>01:36.772</p></td>
+<td><p>01:31.680</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:21.811</p></td>
+<td><p>01:19.158</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>00:55.303</p></td>
+<td><p>00:53.903</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>00:54.464</p></td>
+<td><p>00:52.559</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>00:51.501</p></td>
+<td><p>00:49.622</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:31.803</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
+<td><p>00:30.419</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:31.680</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:30.386</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.008</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 894e60285e..6458cfb937 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>00:04.224</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.053</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>00:02.929</p></td>
+<td><p>00:02.806</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>00:01.287</p></td>
+<td><p>00:01.240</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>00:00.008</p></td>
+<td><p>00:00.007</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 87c0d22b26..78349bf7d6 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: 25591us [25591us] (48.47%; 48.47%)
-FoldScaleAxis: 27204us [9us] (51.53%; 51.53%)
- FoldConstant: 27194us [1826us] (51.51%; 99.97%)
- InferType: 25369us [25369us] (48.05%; 93.29%)
+InferType: 24718us [24718us] (48.40%; 48.40%)
+FoldScaleAxis: 26352us [9us] (51.60%; 51.60%)
+ FoldConstant: 26342us [1742us] (51.58%; 99.97%)
+ InferType: 24600us [24600us] (48.17%; 93.39%)
</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: 25288us [25288us] (47.90%; 47.90%)
-FoldScaleAxis: 27507us [9us] (52.10%; 52.10%)
- FoldConstant: 27498us [1907us] (52.09%; 99.97%)
- InferType: 25592us [25592us] (48.47%; 93.07%)
+InferType: 24765us [24765us] (48.41%; 48.41%)
+FoldScaleAxis: 26391us [9us] (51.59%; 51.59%)
+ FoldConstant: 26382us [1839us] (51.57%; 99.97%)
+ InferType: 24544us [24544us] (47.98%; 93.03%)
</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 2ffca3dbe4..cc9a947607 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: 49.404159 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 46.263809 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 26541eb243..1889ae21dc 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -887,7 +887,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.224256 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.225475 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 82c38b028f..f003e41beb 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: 0.018352
-Baseline: 3.242398
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013592
+Baseline: 3.268959
</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: 0.309922
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.288502
</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: 0.291903
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.270170
</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: 0.113976
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.115226
</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: 0.105496
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.105673
</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: 0.111628
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.095346
</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: 0.134260
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.113585
</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 7b4159de40..b680555946 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>00:33.933</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:31.649</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 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:30.736</p></td>
+<td><p>00:28.516</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
-<td><p>00:02.011</p></td>
+<td><p>00:01.980</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.185</p></td>
+<td><p>00:01.153</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 526540302d..f547dd7ca9 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:50.601</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:41.138</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -379,27 +379,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:39.403</p></td>
+<td><p>01:36.539</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:15.682</p></td>
+<td><p>01:12.519</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:21.224</p></td>
+<td><p>00:20.101</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:17.970</p></td>
+<td><p>00:17.031</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:16.220</p></td>
+<td><p>00:14.849</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.103</p></td>
+<td><p>00:00.100</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index cd8b6a5f40..2f0092454b 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1042,7 +1042,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.339 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.343 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index 3a99f3f654..8b9b74251b 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)
- 3.1980 3.1980 3.1986 3.1976 0.0004
+ 3.2045 3.2040 3.2117 3.1979 0.0057
</pre></div>
</div>
</div>
@@ -955,7 +955,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 15.682 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.519 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 0e1ef10f90..2ff95aa90a 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -955,7 +955,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 759.6549 759.5031 760.2037 759.2579 0.4008
+ 709.1685 708.8632 710.8094 707.8329 1.2342
</pre></div>
</div>
</div>
@@ -977,7 +977,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.403 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.539 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index a275f9a178..8c5b5beff3 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -370,7 +370,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:24.443</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:22.861</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -379,11 +379,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:24.404</p></td>
+<td><p>00:22.824</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.022</p></td>
+<td><p>00:00.021</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 79b05dc849..114bbce933 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
-Time cost of this operator: 0.037102
+Time cost of this operator: 0.037109
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 261872fb37..5377e7665d 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -674,10 +674,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 303.6 98.733 (1, 2, 10, 10, 3) 2 1 [303.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.943 0.957 (1, 6, 10, 10) 1 1 [2.943]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.311 (1, 1, 10, 10, 3) 1 1 [0.955]
-Total_time - 307.497 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.7 98.75 (1, 2, 10, 10, 3) 2 1 [296.7]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.828 0.941 (1, 6, 10, 10) 1 1 [2.828]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.928 0.309 (1, 1, 10, 10, 3) 1 1 [0.928]
+Total_time - 300.455 - - - - -
</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 102.8 97.507 (1, 6, 10, 10, 1) 2 1 [102.8]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.774 1.682 (1, 6, 10, 10) 1 1 [1.774]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.855 0.811 (1, 3, 10, 10, 1) 1 1 [0.855]
-Total_time - 105.428 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 99.375 97.416 (1, 6, 10, 10, 1) 2 1 [99.375]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.713 1.68 (1, 6, 10, 10) 1 1 [1.713]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.904 (1, 1, 10, 10, 3) 1 1 [0.923]
+Total_time - 102.011 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.001 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.552 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 11f2f9ebe4..c77a93d7d5 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -485,8 +485,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 8.55MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 13.6MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 7.67MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 12.4MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -614,7 +614,7 @@ 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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diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
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+++ 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 [...]
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</pre></div>
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@@ -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>
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+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpugt5v0kh/images/target contains 8144 images
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@@ -727,13 +727,13 @@ the time on our validation set).</p>
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Epoch 2/3
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Epoch 3/3
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+328/328 - 40s - loss: 0.0644 - accuracy: 0.9766 - val_loss: 0.0918 - val_accuracy: 0.9717 - 40s/epoch - 121ms/step
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</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>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
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index 1a3f42e304..f5f769af0c 100644
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diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
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--- 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
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</pre></div>
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<p>Register the rule to TVM with override option to override existing rule.
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<div class="section" id="computation-times">
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<li>Call()
: <a class="el" href="classtvm_1_1relax_1_1Call.html#ad290bc43f966baed02ef4e1fa11ca4ea">tvm::relax::Call</a>
diff --git a/docs/reference/api/doxygen/functions_f.html b/docs/reference/api/doxygen/functions_f.html
index d9610bbd18..8b90a261e2 100644
--- a/docs/reference/api/doxygen/functions_f.html
+++ b/docs/reference/api/doxygen/functions_f.html
@@ -758,7 +758,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Fuse.html#a10b77eec10eb7dbc536b0c8d65163f9c">tvm::te::Fuse</a>
</li>
<li>fuse()
-: <a class="el" href="classtvm_1_1te_1_1Stage.html#a07b721494aa3c0c79e8a8654c433708f">tvm::te::Stage</a>
+: <a class="el" href="classtvm_1_1te_1_1Stage.html#a5658065d9cbbee620bbd107d30c4ae72">tvm::te::Stage</a>
</li>
<li>Fuse()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a5b9a41d190be0f8d817b4936732bd0ef">tvm::tir::ScheduleNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_g.html b/docs/reference/api/doxygen/functions_func_g.html
index 000ac184f4..799e80520a 100644
--- a/docs/reference/api/doxygen/functions_func_g.html
+++ b/docs/reference/api/doxygen/functions_func_g.html
@@ -120,8 +120,8 @@ $(function() {
, <a class="el" href="classtvm_1_1TargetKind.html#a11c894b5e17dd940287648261d5efc0d">tvm::TargetKind</a>
, <a class="el" href="classtvm_1_1TargetTag.html#af90a7c152674c7dfe96f6e5aac2f9b25">tvm::TargetTag</a>
, <a class="el" href="classtvm_1_1tir_1_1InstructionKind.html#a6d165507be268ad41aa5221d48062da7">tvm::tir::InstructionKind</a>
-, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a8980b8d6362947fe93a890e1a97028d4">tvm::tir::LayoutAxis</a>
-, <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#aec0dd6e11e5c3cd85766a5a5ad9acd41">tvm::tir::ScheduleNode</a>
+, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a273d3cfc07193e551292450bc2b0e56f">tvm::tir::LayoutAxis</a>
+, <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#ae2229f72137786e7ab892aa211191251">tvm::tir::ScheduleNode</a>
</li>
<li>get()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleState.html#a0b041658b4298a97db360c8259d03f0a">tvm::tir::ScheduleState</a>
diff --git a/docs/reference/api/doxygen/functions_g.html b/docs/reference/api/doxygen/functions_g.html
index ffedb79207..6128a9e4d1 100644
--- a/docs/reference/api/doxygen/functions_g.html
+++ b/docs/reference/api/doxygen/functions_g.html
@@ -123,7 +123,7 @@ $(function() {
, <a class="el" href="classtvm_1_1TargetKind.html#a11c894b5e17dd940287648261d5efc0d">tvm::TargetKind</a>
, <a class="el" href="classtvm_1_1TargetTag.html#af90a7c152674c7dfe96f6e5aac2f9b25">tvm::TargetTag</a>
, <a class="el" href="classtvm_1_1tir_1_1InstructionKind.html#a6d165507be268ad41aa5221d48062da7">tvm::tir::InstructionKind</a>
-, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a273d3cfc07193e551292450bc2b0e56f">tvm::tir::LayoutAxis</a>
+, <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a8980b8d6362947fe93a890e1a97028d4">tvm::tir::LayoutAxis</a>
, <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a1d34e09f35c6ca2f179165a041037c4c">tvm::tir::ScheduleNode</a>
</li>
<li>get()
@@ -136,7 +136,7 @@ $(function() {
</li>
<li>get()
: <a class="el" href="classtvm_1_1tir_1_1Var.html#a018469fa554bee82399594e7d6bfad1d">tvm::tir::Var</a>
-, <a class="el" href="classtvm_1_1With.html#a54ab86b1e1f78e9304d8b89c97eb07af">tvm::With< ContextType ></a>
+, <a class="el" href="classtvm_1_1With.html#acc09c3d117aa5500f8579e18866209f8">tvm::With< ContextType ></a>
</li>
<li>get_c_struct_name()
: <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadataNode.html#a00880e8239fec43504324e48552d8632">tvm::runtime::metadata::ConstantInfoMetadataNode</a>
diff --git a/docs/reference/api/doxygen/functions_i.html b/docs/reference/api/doxygen/functions_i.html
index 382773ee57..a0640c3bad 100644
--- a/docs/reference/api/doxygen/functions_i.html
+++ b/docs/reference/api/doxygen/functions_i.html
@@ -292,6 +292,9 @@ $(function() {
: <a class="el" href="structtvm_1_1relax_1_1CallInplacePackedAttrs.html#aa59d804e491b4b451cc9841ae7451bf7">tvm::relax::CallInplacePackedAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1CallTIRInplaceAttrs.html#a8be99a5894d4de1dd537cb44dbed416d">tvm::relax::CallTIRInplaceAttrs</a>
</li>
+<li>input_axis_separators
+: <a class="el" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7">tvm::relax::LayoutTransformAttrs</a>
+</li>
<li>input_placeholders
: <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html#ae5c3fa995ba59e0e001d6b8f92e39c7a">tvm::te::ExternOpNode</a>
</li>
@@ -323,7 +326,7 @@ $(function() {
, <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html#aa08974f313719690d7db3f0f89e8db2e">tvm::te::TensorComputeOpNode</a>
</li>
<li>insert()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a78b7d88d169c1305f5dac0818f34141f">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#ab07747919e590f2a5b80c5c2f9bde6ce">tvm::runtime::Array< T, typename ></a>
</li>
<li>InsertMaybeReHash()
: <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">tvm::runtime::MapNode</a>
@@ -673,8 +676,8 @@ $(function() {
<li>IsPrimal()
: <a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html#a4a8e9b07fbdfccc187ea41141b373ebe">tvm::tir::LayoutAxis</a>
</li>
-<li>IsPrimitiveOp
-: <a class="el" href="classtvm_1_1OpNode.html#aee9090e54dff3e72ed272b981e036ae6">tvm::OpNode</a>
+<li>IsPrimitiveOp()
+: <a class="el" href="classtvm_1_1OpNode.html#a3ba51353d63c4674eac080a3560a4412">tvm::OpNode</a>
</li>
<li>IsRegionCoveredConsumer()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html#a9596efdecacb172c531a53b1f21717ad">tvm::tir::ScheduleStateNode</a>
@@ -760,7 +763,7 @@ $(function() {
<li>iterator
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a98e5ad633b8195d954c98067213ae29f">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a75e3f2657cdb7cc613bf922429983165">tvm::runtime::MapNode::iterator</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ad605c9f9aaed23e669c2a3c595d08ba4">tvm::runtime::MapNode::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html#a79703afd52032715cc2d9d4c6830147f">tvm::runtime::metadata::ArrayAccessor< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html#a3216d91ef7fb0771463e76c9a0afcd1c">tvm::runtime::metadata::ArrayAccessor< const char *, ::tvm::runtime::String ></a>
, <a class="el" href="classtvm_1_1support_1_1Span.html#af6fbb6a54a3ecf8673146acae0c8228b">tvm::support::Span< T, W ></a>
diff --git a/docs/reference/api/doxygen/functions_vars_i.html b/docs/reference/api/doxygen/functions_vars_i.html
index 248fb97792..472282a193 100644
--- a/docs/reference/api/doxygen/functions_vars_i.html
+++ b/docs/reference/api/doxygen/functions_vars_i.html
@@ -169,6 +169,9 @@ $(function() {
: <a class="el" href="structtvm_1_1relax_1_1CallInplacePackedAttrs.html#aa59d804e491b4b451cc9841ae7451bf7">tvm::relax::CallInplacePackedAttrs</a>
, <a class="el" href="structtvm_1_1relax_1_1CallTIRInplaceAttrs.html#a8be99a5894d4de1dd537cb44dbed416d">tvm::relax::CallTIRInplaceAttrs</a>
</li>
+<li>input_axis_separators
+: <a class="el" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7">tvm::relax::LayoutTransformAttrs</a>
+</li>
<li>input_placeholders
: <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html#ae5c3fa995ba59e0e001d6b8f92e39c7a">tvm::te::ExternOpNode</a>
</li>
diff --git a/docs/reference/api/doxygen/manipulate_8h_source.html b/docs/reference/api/doxygen/manipulate_8h_source.html
index da32feb0c1..060d7602ed 100644
--- a/docs/reference/api/doxygen/manipulate_8h_source.html
+++ b/docs/reference/api/doxygen/manipulate_8h_source.html
@@ -124,93 +124,96 @@ $(function() {
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// needs to be revisited in case PrimValue is evolved to represent symbolic expression in future.</span></div>
<div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a53d32c9319c4de82f130207ccf76bfc8"> 61</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<PrimValue></a> <a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a53d32c9319c4de82f130207ccf76bfc8">pad_value</a>;</div>
<div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea4d82c073ad5110f768f54419bff8e3"> 68</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<IntImm></a>> <a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea4d82c073ad5110f768f54419bff8e3">axis_separators</a>;</div>
-<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  </div>
-<div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c"> 70</a></span>  <a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a>, <span class="stringliteral">"relax.attrs.LayoutTransformAttrs"</span> [...]
-<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea47ca86852da0c95169073baccba982">index_map</a>).describe(<span class="stringliteral">"The layout transformation to apply."</span>);</div>
-<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a53d32c9319c4de82f130207ccf76bfc8">pad_value</a>).describe(</div>
-<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="stringliteral">"The specific value to be used to pad if the layout transform would result in implicit "</span></div>
-<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="stringliteral">"padding. If not specified, the compiler is free to choose any value."</span>);</div>
-<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea4d82c073ad5110f768f54419bff8e3">axis_separators</a>)</div>
-<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  .describe(<span class="stringliteral">"The separators between input axes when generating flat output axes"</span>);</div>
-<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div>
-<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> }; <span class="comment">// struct LayoutTransformAttrs</span></div>
-<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  </div>
-<div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html"> 81</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">PermuteDimsAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><PermuteDimsAttrs> {</div>
-<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854"> 82</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<Integer></a>> <a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">axes</a>;</div>
-<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  </div>
-<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8"> 84</a></span>  <a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">PermuteDimsAttrs</a>, <span class="stringliteral">"relax.attrs.PermuteDimsAttrs"</span>) {</div>
-<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">axes</a>).describe(<span class="stringliteral">"The target axes order, reverse order if not specified."</span>);</div>
-<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div>
-<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> }; <span class="comment">// struct PermuteDimsAttrs</span></div>
-<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  </div>
-<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html"> 90</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html">SplitAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><SplitAttrs> {</div>
-<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb"> 91</a></span>  <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">indices_or_sections</a>;</div>
-<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e"> 92</a></span>  <span class="keywordtype">int</span> <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">axis</a>;</div>
-<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  </div>
-<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade"> 94</a></span>  <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html">SplitAttrs</a>, <span class="stringliteral">"relax.attrs.SplitAttrs"</span>) {</div>
-<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">indices_or_sections</a>)</div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  .describe(<span class="stringliteral">"The input array of indices or the number of split sections."</span>);</div>
-<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">axis</a>).describe(<span class="stringliteral">"The axis to be splitted"</span>);</div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div>
-<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> }; <span class="comment">// struct SplitAttrs</span></div>
-<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  </div>
-<div class="line"><a name="l00102"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html"> 102</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html">SqueezeAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><SqueezeAttrs> {</div>
-<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926"> 103</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<Integer></a>> <a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">axis</a>;</div>
-<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  </div>
-<div class="line"><a name="l00105"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013"> 105</a></span>  <a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html">SqueezeAttrs</a>, <span class="stringliteral">"relax.attrs.SqueezeAttrs"</span>) {</div>
-<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">axis</a>).describe(</div>
-<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="stringliteral">"The axis to squeeze in the input tensor."</span></div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="stringliteral">"If `axis = None`, all axis of dimension 1 get squeezed;"</span></div>
-<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="stringliteral">"Else, the dimension in axes get squeezed."</span></div>
-<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="stringliteral">"It is an error if an axis does not has dimension 1."</span>);</div>
-<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  }</div>
-<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> }; <span class="comment">// struct SqueezeAttrs</span></div>
-<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  </div>
-<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html"> 115</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html">RepeatAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><RepeatAttrs> {</div>
-<div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1"> 116</a></span>  <span class="keywordtype">int</span> <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">repeats</a>;</div>
-<div class="line"><a name="l00117"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd"> 117</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Integer></a> <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">axis</a>;</div>
-<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  </div>
-<div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c"> 119</a></span>  <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html">RepeatAttrs</a>, <span class="stringliteral">"relax.attrs.RepeatAttrs"</span>) {</div>
-<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">repeats</a>).describe(<span class="stringliteral">"The number of repetitions."</span>);</div>
-<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">axis</a>).describe(</div>
-<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="stringliteral">"The axis along which to repeat values. The negative numbers are interpreted "</span></div>
-<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="stringliteral">"counting from the backward. By default, use the flattened input array, and "</span></div>
-<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="stringliteral">"return a flat output array."</span>);</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">// struct RepeatAttrs</span></div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  </div>
-<div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html"> 129</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1TileAttrs.html">TileAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><TileAttrs> {</div>
-<div class="line"><a name="l00130"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f"> 130</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Integer></a> <a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">repeats</a>;</div>
-<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  </div>
-<div class="line"><a name="l00132"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9"> 132</a></span>  <a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1TileAttrs.html">TileAttrs</a>, <span class="stringliteral">"relax.attrs.TileAttrs"</span>) {</div>
-<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">repeats</a>).describe(<span class="stringliteral">"The number of repetitions of data along each axis."</span>);</div>
-<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div>
-<div class="line"><a name="l00135"></a><span class="lineno"> 135</span> }; <span class="comment">// struct TileAttrs</span></div>
-<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  </div>
-<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html"> 138</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html">FlipAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><FlipAttrs> {</div>
-<div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a"> 139</a></span>  <a class="code" href="classtvm_1_1Integer.html">Integer</a> <a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">axis</a>;</div>
-<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b"> 140</a></span>  <a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html">FlipAttrs</a>, <span class="stringliteral">"relax.attrs.FlipAttrs"</span>) {</div>
-<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">axis</a>)</div>
-<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  .set_default(NullValue<Integer>())</div>
-<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  .describe(<span class="stringliteral">"The axis along which to flip over."</span>);</div>
-<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div>
-<div class="line"><a name="l00145"></a><span class="lineno"> 145</span> }; <span class="comment">// struct FlipAttrs</span></div>
-<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  </div>
-<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html"> 148</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">ScatterElementsAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><ScatterElementsAttrs> {</div>
-<div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002"> 149</a></span>  <a class="code" href="classtvm_1_1Integer.html">Integer</a> <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">axis</a>;</div>
-<div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2"> 150</a></span>  <a class="code" href="classtvm_1_1runtime_1_1String.html">String</a> <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">reduction</a>;</div>
-<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  </div>
-<div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c"> 152</a></span>  <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">ScatterElementsAttrs</a>, <span class="stringliteral">"relax.attrs.ScatterElementsAttrs"</span> [...]
-<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">axis</a>).set_default(0).describe(<span class="stringliteral">"The axis over which to select values."</span>);</div>
-<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">reduction</a>).set_default(<span class="stringliteral">"update"</span>).describe(</div>
-<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="stringliteral">"Reduction mode of the scatter elements, "</span></div>
-<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="stringliteral">"either \"update\", \"add\", \"mul\", \"mean\", \"min\" or \"max\"."</span>);</div>
-<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  }</div>
-<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> }; <span class="comment">// struct ScatterElementsAttrs</span></div>
-<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> } <span class="comment">// namespace relax</span></div>
-<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  </div>
-<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ATTRS_MANIPULATE_H_</span></div>
+<div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7"> 74</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<IntImm></a>> <a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7">input_axis_separators</a>;</div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  </div>
+<div class="line"><a name="l00076"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c"> 76</a></span>  <a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a>, <span class="stringliteral">"relax.attrs.LayoutTransformAttrs"</span> [...]
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea47ca86852da0c95169073baccba982">index_map</a>).describe(<span class="stringliteral">"The layout transformation to apply."</span>);</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a53d32c9319c4de82f130207ccf76bfc8">pad_value</a>).describe(</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="stringliteral">"The specific value to be used to pad if the layout transform would result in implicit "</span></div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="stringliteral">"padding. If not specified, the compiler is free to choose any value."</span>);</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea4d82c073ad5110f768f54419bff8e3">axis_separators</a>)</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  .describe(<span class="stringliteral">"The separators between input axes when generating flat output axes"</span>);</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7">input_axis_separators</a>)</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  .describe(<span class="stringliteral">"The separators between axes to regenerate output"</span>);</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  }</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> }; <span class="comment">// struct LayoutTransformAttrs</span></div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  </div>
+<div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html"> 89</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">PermuteDimsAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><PermuteDimsAttrs> {</div>
+<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854"> 90</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<Integer></a>> <a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">axes</a>;</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  </div>
+<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8"> 92</a></span>  <a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">PermuteDimsAttrs</a>, <span class="stringliteral">"relax.attrs.PermuteDimsAttrs"</span>) {</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">axes</a>).describe(<span class="stringliteral">"The target axes order, reverse order if not specified."</span>);</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  }</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> }; <span class="comment">// struct PermuteDimsAttrs</span></div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  </div>
+<div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html"> 98</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html">SplitAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><SplitAttrs> {</div>
+<div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb"> 99</a></span>  <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">indices_or_sections</a>;</div>
+<div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e"> 100</a></span>  <span class="keywordtype">int</span> <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">axis</a>;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  </div>
+<div class="line"><a name="l00102"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade"> 102</a></span>  <a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html">SplitAttrs</a>, <span class="stringliteral">"relax.attrs.SplitAttrs"</span>) {</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">indices_or_sections</a>)</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  .describe(<span class="stringliteral">"The input array of indices or the number of split sections."</span>);</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">axis</a>).describe(<span class="stringliteral">"The axis to be splitted"</span>);</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> }; <span class="comment">// struct SplitAttrs</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  </div>
+<div class="line"><a name="l00110"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html"> 110</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html">SqueezeAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><SqueezeAttrs> {</div>
+<div class="line"><a name="l00111"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926"> 111</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Array<Integer></a>> <a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">axis</a>;</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  </div>
+<div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013"> 113</a></span>  <a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html">SqueezeAttrs</a>, <span class="stringliteral">"relax.attrs.SqueezeAttrs"</span>) {</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">axis</a>).describe(</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="stringliteral">"The axis to squeeze in the input tensor."</span></div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="stringliteral">"If `axis = None`, all axis of dimension 1 get squeezed;"</span></div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="stringliteral">"Else, the dimension in axes get squeezed."</span></div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="stringliteral">"It is an error if an axis does not has dimension 1."</span>);</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> }; <span class="comment">// struct SqueezeAttrs</span></div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  </div>
+<div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html"> 123</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html">RepeatAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><RepeatAttrs> {</div>
+<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1"> 124</a></span>  <span class="keywordtype">int</span> <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">repeats</a>;</div>
+<div class="line"><a name="l00125"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd"> 125</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<Integer></a> <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">axis</a>;</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  </div>
+<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c"> 127</a></span>  <a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html">RepeatAttrs</a>, <span class="stringliteral">"relax.attrs.RepeatAttrs"</span>) {</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">repeats</a>).describe(<span class="stringliteral">"The number of repetitions."</span>);</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">axis</a>).describe(</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="stringliteral">"The axis along which to repeat values. The negative numbers are interpreted "</span></div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="stringliteral">"counting from the backward. By default, use the flattened input array, and "</span></div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="stringliteral">"return a flat output array."</span>);</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> }; <span class="comment">// struct RepeatAttrs</span></div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  </div>
+<div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html"> 137</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1TileAttrs.html">TileAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><TileAttrs> {</div>
+<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f"> 138</a></span>  <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Integer></a> <a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">repeats</a>;</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  </div>
+<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9"> 140</a></span>  <a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1TileAttrs.html">TileAttrs</a>, <span class="stringliteral">"relax.attrs.TileAttrs"</span>) {</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">repeats</a>).describe(<span class="stringliteral">"The number of repetitions of data along each axis."</span>);</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  }</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span> }; <span class="comment">// struct TileAttrs</span></div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  </div>
+<div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html"> 146</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html">FlipAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><FlipAttrs> {</div>
+<div class="line"><a name="l00147"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a"> 147</a></span>  <a class="code" href="classtvm_1_1Integer.html">Integer</a> <a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">axis</a>;</div>
+<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b"> 148</a></span>  <a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html">FlipAttrs</a>, <span class="stringliteral">"relax.attrs.FlipAttrs"</span>) {</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">axis</a>)</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  .set_default(NullValue<Integer>())</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  .describe(<span class="stringliteral">"The axis along which to flip over."</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="comment">// struct FlipAttrs</span></div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  </div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html"> 156</a></span> <span class="keyword">struct </span><a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">ScatterElementsAttrs</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a><ScatterElementsAttrs> {</div>
+<div class="line"><a name="l00157"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002"> 157</a></span>  <a class="code" href="classtvm_1_1Integer.html">Integer</a> <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">axis</a>;</div>
+<div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2"> 158</a></span>  <a class="code" href="classtvm_1_1runtime_1_1String.html">String</a> <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">reduction</a>;</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  </div>
+<div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c"> 160</a></span>  <a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c">TVM_DECLARE_ATTRS</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">ScatterElementsAttrs</a>, <span class="stringliteral">"relax.attrs.ScatterElementsAttrs"</span> [...]
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">axis</a>).set_default(0).describe(<span class="stringliteral">"The axis over which to select values."</span>);</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a>(<a class="code" href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">reduction</a>).set_default(<span class="stringliteral">"update"</span>).describe(</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="stringliteral">"Reduction mode of the scatter elements, "</span></div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="stringliteral">"either \"update\", \"add\", \"mul\", \"mean\", \"min\" or \"max\"."</span>);</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> }; <span class="comment">// struct ScatterElementsAttrs</span></div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> } <span class="comment">// namespace relax</span></div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  </div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_ATTRS_MANIPULATE_H_</span></div>
<div class="ttc" id="aclasstvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use "curiously recurring template pattern".</div><div class="ttdef"><b>Definition:</b> attrs.h:830</div></div>
<div class="ttc" id="aclasstvm_1_1Integer_html"><div class="ttname"><a href="classtvm_1_1Integer.html">tvm::Integer</a></div><div class="ttdoc">Container of constant int that adds more constructors.</div><div class="ttdef"><b>Definition:</b> expr.h:632</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs.</div><div class="ttdef"><b>Definition:</b> array.h:289</div></div>
@@ -228,35 +231,36 @@ $(function() {
<div class="ttc" id="astructtvm_1_1relax_1_1ExpandDimsAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1ExpandDimsAttrs.html">tvm::relax::ExpandDimsAttrs</a></div><div class="ttdoc">Attributes used in expand_dims operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:45</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1ExpandDimsAttrs_html_a0322e2e00ebf26b6a04f1802649fedd0"><div class="ttname"><a href="structtvm_1_1relax_1_1ExpandDimsAttrs.html#a0322e2e00ebf26b6a04f1802649fedd0">tvm::relax::ExpandDimsAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(ExpandDimsAttrs, "relax.attrs.ExpandDimsAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:48</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1ExpandDimsAttrs_html_adc86728d477d932e3c1f07bac6386801"><div class="ttname"><a href="structtvm_1_1relax_1_1ExpandDimsAttrs.html#adc86728d477d932e3c1f07bac6386801">tvm::relax::ExpandDimsAttrs::axis</a></div><div class="ttdeci">Array< Integer > axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:46</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html">tvm::relax::FlipAttrs</a></div><div class="ttdoc">Attributes used in flip operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:138</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html_a1ba3bb4d10fe9739ed2d8ecefcd9c59b"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b">tvm::relax::FlipAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(FlipAttrs, "relax.attrs.FlipAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:140</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html_abda176f983bfdf59be4f2ac648272b3a"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">tvm::relax::FlipAttrs::axis</a></div><div class="ttdeci">Integer axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:139</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html">tvm::relax::FlipAttrs</a></div><div class="ttdoc">Attributes used in flip operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:146</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html_a1ba3bb4d10fe9739ed2d8ecefcd9c59b"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html#a1ba3bb4d10fe9739ed2d8ecefcd9c59b">tvm::relax::FlipAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(FlipAttrs, "relax.attrs.FlipAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:148</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1FlipAttrs_html_abda176f983bfdf59be4f2ac648272b3a"><div class="ttname"><a href="structtvm_1_1relax_1_1FlipAttrs.html#abda176f983bfdf59be4f2ac648272b3a">tvm::relax::FlipAttrs::axis</a></div><div class="ttdeci">Integer axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:147</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html">tvm::relax::LayoutTransformAttrs</a></div><div class="ttdoc">Attributes used in layout_transform operator.</div><div class="ttdef"><b>Definition:</b> manipulate.h:57</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_a53d32c9319c4de82f130207ccf76bfc8"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a53d32c9319c4de82f130207ccf76bfc8">tvm::relax::LayoutTransformAttrs::pad_value</a></div><div class="ttdeci">Optional< PrimValue > pad_value</div><div class="ttdef"><b>Definition:</b> manipulate.h:61</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_a8984c95e7c558ed5cd519964295e9b1c"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c">tvm::relax::LayoutTransformAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(LayoutTransformAttrs, "relax.attrs.LayoutTransformAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:70</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_a8984c95e7c558ed5cd519964295e9b1c"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#a8984c95e7c558ed5cd519964295e9b1c">tvm::relax::LayoutTransformAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(LayoutTransformAttrs, "relax.attrs.LayoutTransformAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:76</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_ab963bc115ec6450ee337b754edbd85d7"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#ab963bc115ec6450ee337b754edbd85d7">tvm::relax::LayoutTransformAttrs::input_axis_separators</a></div><div class="ttdeci">Optional< Array< IntImm > > input_axis_separators</div><div class="ttdef"><b>Definition:</b> manipulate.h:74</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_aea47ca86852da0c95169073baccba982"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea47ca86852da0c95169073baccba982">tvm::relax::LayoutTransformAttrs::index_map</a></div><div class="ttdeci">tir::IndexMap index_map</div><div class="ttdef"><b>Definition:</b> manipulate.h:58</div></div>
<div class="ttc" id="astructtvm_1_1relax_1_1LayoutTransformAttrs_html_aea4d82c073ad5110f768f54419bff8e3"><div class="ttname"><a href="structtvm_1_1relax_1_1LayoutTransformAttrs.html#aea4d82c073ad5110f768f54419bff8e3">tvm::relax::LayoutTransformAttrs::axis_separators</a></div><div class="ttdeci">Optional< Array< IntImm > > axis_separators</div><div class="ttdef"><b>Definition:</b> manipulate.h:68</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">tvm::relax::PermuteDimsAttrs</a></div><div class="ttdoc">Attributes used in permute_dims operator.</div><div class="ttdef"><b>Definition:</b> manipulate.h:81</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html_a122de68144f029db7c92b8ead77a6df8"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8">tvm::relax::PermuteDimsAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(PermuteDimsAttrs, "relax.attrs.PermuteDimsAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:84</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html_af0e1e3d683ebed0a6dd53f3821610854"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">tvm::relax::PermuteDimsAttrs::axes</a></div><div class="ttdeci">Optional< Array< Integer > > axes</div><div class="ttdef"><b>Definition:</b> manipulate.h:82</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html">tvm::relax::RepeatAttrs</a></div><div class="ttdoc">Attributes used in repeat operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:115</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a08aade1a53c6894f34a2acb4cf563bcd"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">tvm::relax::RepeatAttrs::axis</a></div><div class="ttdeci">Optional< Integer > axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:117</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a8014893fa1472f27b8dc0e4d5f6d2c4c"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c">tvm::relax::RepeatAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(RepeatAttrs, "relax.attrs.RepeatAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:119</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a924e5ff0773bc90b70289053d5058ca1"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">tvm::relax::RepeatAttrs::repeats</a></div><div class="ttdeci">int repeats</div><div class="ttdef"><b>Definition:</b> manipulate.h:116</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">tvm::relax::ScatterElementsAttrs</a></div><div class="ttdoc">Attributes used in scatter_elements operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:148</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_a282dcd392045359be94ac1944f31cbf2"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">tvm::relax::ScatterElementsAttrs::reduction</a></div><div class="ttdeci">String reduction</div><div class="ttdef"><b>Definition:</b> manipulate.h:150</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_a2e3e81244938c16d1631bc9ebf6e2002"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">tvm::relax::ScatterElementsAttrs::axis</a></div><div class="ttdeci">Integer axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:149</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_ade8d1d752029a431b67aaa5ba6e12f4c"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c">tvm::relax::ScatterElementsAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(ScatterElementsAttrs, "relax.attrs.ScatterElementsAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:152</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html">tvm::relax::SplitAttrs</a></div><div class="ttdoc">Attributes used in split operator.</div><div class="ttdef"><b>Definition:</b> manipulate.h:90</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_a5156e12411e02872d964d17e734bf1eb"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">tvm::relax::SplitAttrs::indices_or_sections</a></div><div class="ttdeci">ObjectRef indices_or_sections</div><div class="ttdef"><b>Definition:</b> manipulate.h:91</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_a605cfb73921ebad24acfe3cdb714dade"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade">tvm::relax::SplitAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(SplitAttrs, "relax.attrs.SplitAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:94</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_afd049c1622d7c4854893aa5e25885b1e"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">tvm::relax::SplitAttrs::axis</a></div><div class="ttdeci">int axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:92</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html">tvm::relax::SqueezeAttrs</a></div><div class="ttdoc">Attributes used in squeeze operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:102</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html_a14a530976d5869931ad00873e8590926"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">tvm::relax::SqueezeAttrs::axis</a></div><div class="ttdeci">Optional< Array< Integer > > axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:103</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html_a33ce4acb151ee651e2223cebafbf3013"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013">tvm::relax::SqueezeAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(SqueezeAttrs, "relax.attrs.SqueezeAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:105</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html">tvm::relax::TileAttrs</a></div><div class="ttdoc">Attributes used in tile operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:129</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html_a8a97c821e92070a104682c71510f041f"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">tvm::relax::TileAttrs::repeats</a></div><div class="ttdeci">Array< Integer > repeats</div><div class="ttdef"><b>Definition:</b> manipulate.h:130</div></div>
-<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html_afd7caf6dcb5210175060aee7985586f9"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9">tvm::relax::TileAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(TileAttrs, "relax.attrs.TileAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:132</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html">tvm::relax::PermuteDimsAttrs</a></div><div class="ttdoc">Attributes used in permute_dims operator.</div><div class="ttdef"><b>Definition:</b> manipulate.h:89</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html_a122de68144f029db7c92b8ead77a6df8"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#a122de68144f029db7c92b8ead77a6df8">tvm::relax::PermuteDimsAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(PermuteDimsAttrs, "relax.attrs.PermuteDimsAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:92</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1PermuteDimsAttrs_html_af0e1e3d683ebed0a6dd53f3821610854"><div class="ttname"><a href="structtvm_1_1relax_1_1PermuteDimsAttrs.html#af0e1e3d683ebed0a6dd53f3821610854">tvm::relax::PermuteDimsAttrs::axes</a></div><div class="ttdeci">Optional< Array< Integer > > axes</div><div class="ttdef"><b>Definition:</b> manipulate.h:90</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html">tvm::relax::RepeatAttrs</a></div><div class="ttdoc">Attributes used in repeat operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:123</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a08aade1a53c6894f34a2acb4cf563bcd"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a08aade1a53c6894f34a2acb4cf563bcd">tvm::relax::RepeatAttrs::axis</a></div><div class="ttdeci">Optional< Integer > axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:125</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a8014893fa1472f27b8dc0e4d5f6d2c4c"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a8014893fa1472f27b8dc0e4d5f6d2c4c">tvm::relax::RepeatAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(RepeatAttrs, "relax.attrs.RepeatAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:127</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1RepeatAttrs_html_a924e5ff0773bc90b70289053d5058ca1"><div class="ttname"><a href="structtvm_1_1relax_1_1RepeatAttrs.html#a924e5ff0773bc90b70289053d5058ca1">tvm::relax::RepeatAttrs::repeats</a></div><div class="ttdeci">int repeats</div><div class="ttdef"><b>Definition:</b> manipulate.h:124</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html">tvm::relax::ScatterElementsAttrs</a></div><div class="ttdoc">Attributes used in scatter_elements operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:156</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_a282dcd392045359be94ac1944f31cbf2"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a282dcd392045359be94ac1944f31cbf2">tvm::relax::ScatterElementsAttrs::reduction</a></div><div class="ttdeci">String reduction</div><div class="ttdef"><b>Definition:</b> manipulate.h:158</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_a2e3e81244938c16d1631bc9ebf6e2002"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#a2e3e81244938c16d1631bc9ebf6e2002">tvm::relax::ScatterElementsAttrs::axis</a></div><div class="ttdeci">Integer axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:157</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1ScatterElementsAttrs_html_ade8d1d752029a431b67aaa5ba6e12f4c"><div class="ttname"><a href="structtvm_1_1relax_1_1ScatterElementsAttrs.html#ade8d1d752029a431b67aaa5ba6e12f4c">tvm::relax::ScatterElementsAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(ScatterElementsAttrs, "relax.attrs.ScatterElementsAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:160</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html">tvm::relax::SplitAttrs</a></div><div class="ttdoc">Attributes used in split operator.</div><div class="ttdef"><b>Definition:</b> manipulate.h:98</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_a5156e12411e02872d964d17e734bf1eb"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#a5156e12411e02872d964d17e734bf1eb">tvm::relax::SplitAttrs::indices_or_sections</a></div><div class="ttdeci">ObjectRef indices_or_sections</div><div class="ttdef"><b>Definition:</b> manipulate.h:99</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_a605cfb73921ebad24acfe3cdb714dade"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#a605cfb73921ebad24acfe3cdb714dade">tvm::relax::SplitAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(SplitAttrs, "relax.attrs.SplitAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:102</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SplitAttrs_html_afd049c1622d7c4854893aa5e25885b1e"><div class="ttname"><a href="structtvm_1_1relax_1_1SplitAttrs.html#afd049c1622d7c4854893aa5e25885b1e">tvm::relax::SplitAttrs::axis</a></div><div class="ttdeci">int axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:100</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html">tvm::relax::SqueezeAttrs</a></div><div class="ttdoc">Attributes used in squeeze operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:110</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html_a14a530976d5869931ad00873e8590926"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html#a14a530976d5869931ad00873e8590926">tvm::relax::SqueezeAttrs::axis</a></div><div class="ttdeci">Optional< Array< Integer > > axis</div><div class="ttdef"><b>Definition:</b> manipulate.h:111</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1SqueezeAttrs_html_a33ce4acb151ee651e2223cebafbf3013"><div class="ttname"><a href="structtvm_1_1relax_1_1SqueezeAttrs.html#a33ce4acb151ee651e2223cebafbf3013">tvm::relax::SqueezeAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(SqueezeAttrs, "relax.attrs.SqueezeAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:113</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html">tvm::relax::TileAttrs</a></div><div class="ttdoc">Attributes used in tile operators.</div><div class="ttdef"><b>Definition:</b> manipulate.h:137</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html_a8a97c821e92070a104682c71510f041f"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html#a8a97c821e92070a104682c71510f041f">tvm::relax::TileAttrs::repeats</a></div><div class="ttdeci">Array< Integer > repeats</div><div class="ttdef"><b>Definition:</b> manipulate.h:138</div></div>
+<div class="ttc" id="astructtvm_1_1relax_1_1TileAttrs_html_afd7caf6dcb5210175060aee7985586f9"><div class="ttname"><a href="structtvm_1_1relax_1_1TileAttrs.html#afd7caf6dcb5210175060aee7985586f9">tvm::relax::TileAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(TileAttrs, "relax.attrs.TileAttrs")</div><div class="ttdef"><b>Definition:</b> manipulate.h:140</div></div>
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
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diff --git a/docs/reference/api/doxygen/namespacemembers.html b/docs/reference/api/doxygen/namespacemembers.html
index 8158abf694..482e279a7e 100644
--- a/docs/reference/api/doxygen/namespacemembers.html
+++ b/docs/reference/api/doxygen/namespacemembers.html
@@ -135,7 +135,7 @@ $(function() {
, <a class="el" href="namespacetvm_1_1relay.html#a6b1e94cf8d97514fe4a9493a0dec1559">tvm::relay</a>
</li>
<li>AlterOpImpl()
-: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">tvm::relax::transform</a>
+: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">tvm::relax::transform</a>
</li>
<li>AlterOpLayout()
: <a class="el" href="namespacetvm_1_1relay_1_1transform.html#a64e0426cfa5887f11769a057c09ce8ba">tvm::relay::transform</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_func.html b/docs/reference/api/doxygen/namespacemembers_func.html
index c4ba338e54..7e2d31a272 100644
--- a/docs/reference/api/doxygen/namespacemembers_func.html
+++ b/docs/reference/api/doxygen/namespacemembers_func.html
@@ -132,7 +132,7 @@ $(function() {
, <a class="el" href="namespacetvm_1_1relay.html#a6b1e94cf8d97514fe4a9493a0dec1559">tvm::relay</a>
</li>
<li>AlterOpImpl()
-: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">tvm::relax::transform</a>
+: <a class="el" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">tvm::relax::transform</a>
</li>
<li>AlterOpLayout()
: <a class="el" href="namespacetvm_1_1relay_1_1transform.html#a64e0426cfa5887f11769a057c09ce8ba">tvm::relay::transform</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1relax_1_1transform.html b/docs/reference/api/doxygen/namespacetvm_1_1relax_1_1transform.html
index 7fb67ff1cd..2d5cfb935e 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1relax_1_1transform.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1relax_1_1transform.html
@@ -203,9 +203,9 @@ Functions</h2></td></tr>
<tr class="memitem:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#aa3d28b2e48b6d5708052909943dd3282">Pass</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c">DecomposeOpsForTraining</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1r [...]
<tr class="memdesc:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="mdescLeft"> </td><td class="mdescRight">Decompose composite operators during training. For example, The result of batch norm (a triple) will be simplified. Operators like Attention, Erf, etc. can be also simplified into several operators as well. <a href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c">More...</a><br /></td></tr>
<tr class="separator:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9994428123b73cedf8b1feddfe830883"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#aa3d28b2e48b6d5708052909943dd3282">Pass</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">AlterOpImpl</a> (const <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String [...]
-<tr class="memdesc:a9994428123b73cedf8b1feddfe830883"><td class="mdescLeft"> </td><td class="mdescRight">Returns a pass which replaces PrimFuncs which have matching kOperatorName attribute in <code>op_impl_map</code>, with replacement PrimFunc that could possibly have different layouts on i/o buffers. The layout transformations on i/o buffers is present in the <code>op_buffer_transforms</code>. The pass inserts the layout transformations in the call sites of PrimFuncs being replaced [...]
-<tr class="separator:a9994428123b73cedf8b1feddfe830883"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7046b43a48dab74382800701ef713aab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#aa3d28b2e48b6d5708052909943dd3282">Pass</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">AlterOpImpl</a> (const <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String [...]
+<tr class="memdesc:a7046b43a48dab74382800701ef713aab"><td class="mdescLeft"> </td><td class="mdescRight">Returns a pass which replaces PrimFuncs which have matching kOperatorName attribute in <code>op_impl_map</code>, with replacement PrimFunc that could possibly have different layouts on i/o buffers. The layout transformations on i/o buffers is present in the <code>op_buffer_transforms</code>. The pass inserts the layout transformations in the call sites of PrimFuncs being replaced [...]
+<tr class="separator:a7046b43a48dab74382800701ef713aab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#aa3d28b2e48b6d5708052909943dd3282">Pass</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">ConvertLayout</a> (<a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.htm [...]
<tr class="memdesc:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="mdescLeft"> </td><td class="mdescRight">Layout conversion pass. <a href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">More...</a><br /></td></tr>
<tr class="separator:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="memSeparator" colspan="2"> </td></tr>
@@ -300,8 +300,8 @@ Functions</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Function Documentation</h2>
-<a id="a9994428123b73cedf8b1feddfe830883"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a9994428123b73cedf8b1feddfe830883">◆ </a></span>AlterOpImpl()</h2>
+<a id="a7046b43a48dab74382800701ef713aab"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a7046b43a48dab74382800701ef713aab">◆ </a></span>AlterOpImpl()</h2>
<div class="memitem">
<div class="memproto">
@@ -322,7 +322,13 @@ Functions</h2></td></tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1IntImm.html">IntImm</a> >>> & </td>
- <td class="paramname"><em>axis_separators</em> </td>
+ <td class="paramname"><em>axis_separators</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1IntImm.html">IntImm</a> >>> & </td>
+ <td class="paramname"><em>input_axis_separators</em> </td>
</tr>
<tr>
<td></td>
@@ -338,6 +344,7 @@ Functions</h2></td></tr>
<tr><td class="paramname">op_impl_map</td><td>Map from kOperatorName attr (e.g., relax.conv2d) to replacement PrimFunc </td></tr>
<tr><td class="paramname">op_buffer_transforms</td><td>Map from kOperatorName attr to layout transformations on each of the PrimFunc i/o buffers. </td></tr>
<tr><td class="paramname">axis_separators</td><td>Map from kOperatorName attr to axis_separators of each buffer_transforms </td></tr>
+ <tr><td class="paramname">input_axis_separators</td><td>Map from kOperatorName attr to axis_separator for input buffer </td></tr>
</table>
</dd>
</dl>
diff --git a/docs/reference/api/doxygen/relax_2transform_8h.html b/docs/reference/api/doxygen/relax_2transform_8h.html
index 4a6e08ba0b..19d5fddece 100644
--- a/docs/reference/api/doxygen/relax_2transform_8h.html
+++ b/docs/reference/api/doxygen/relax_2transform_8h.html
@@ -230,9 +230,9 @@ Functions</h2></td></tr>
<tr class="memitem:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="memItemLeft" align="right" valign="top">Pass </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c">tvm::relax::transform::DecomposeOpsForTraining</a> (Optional< String > func_name)</td></tr>
<tr class="memdesc:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="mdescLeft"> </td><td class="mdescRight">Decompose composite operators during training. For example, The result of batch norm (a triple) will be simplified. Operators like Attention, Erf, etc. can be also simplified into several operators as well. <a href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c">More...</a><br /></td></tr>
<tr class="separator:a32ebc2bcef84b022e7c35a2a2678b99c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9994428123b73cedf8b1feddfe830883"><td class="memItemLeft" align="right" valign="top">Pass </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">tvm::relax::transform::AlterOpImpl</a> (const Map< String, tir::PrimFunc > &op_impl_map, const Map< String, Array< tir::IndexMap >> &op_buffer_transforms, const Map< String, Array< Array< IntImm >&g [...]
-<tr class="memdesc:a9994428123b73cedf8b1feddfe830883"><td class="mdescLeft"> </td><td class="mdescRight">Returns a pass which replaces PrimFuncs which have matching kOperatorName attribute in <code>op_impl_map</code>, with replacement PrimFunc that could possibly have different layouts on i/o buffers. The layout transformations on i/o buffers is present in the <code>op_buffer_transforms</code>. The pass inserts the layout transformations in the call sites of PrimFuncs being replaced [...]
-<tr class="separator:a9994428123b73cedf8b1feddfe830883"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7046b43a48dab74382800701ef713aab"><td class="memItemLeft" align="right" valign="top">Pass </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">tvm::relax::transform::AlterOpImpl</a> (const Map< String, tir::PrimFunc > &op_impl_map, const Map< String, Array< tir::IndexMap >> &op_buffer_transforms, const Map< String, Array< Array< IntImm >&g [...]
+<tr class="memdesc:a7046b43a48dab74382800701ef713aab"><td class="mdescLeft"> </td><td class="mdescRight">Returns a pass which replaces PrimFuncs which have matching kOperatorName attribute in <code>op_impl_map</code>, with replacement PrimFunc that could possibly have different layouts on i/o buffers. The layout transformations on i/o buffers is present in the <code>op_buffer_transforms</code>. The pass inserts the layout transformations in the call sites of PrimFuncs being replaced [...]
+<tr class="separator:a7046b43a48dab74382800701ef713aab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="memItemLeft" align="right" valign="top">Pass </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">tvm::relax::transform::ConvertLayout</a> (Map< String, Array< String >> desired_layouts)</td></tr>
<tr class="memdesc:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="mdescLeft"> </td><td class="mdescRight">Layout conversion pass. <a href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">More...</a><br /></td></tr>
<tr class="separator:a844f4c41bfb8380a8f6e6e3d017aed94"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/relax_2transform_8h_source.html b/docs/reference/api/doxygen/relax_2transform_8h_source.html
index f2875d0aa3..e2c0971f46 100644
--- a/docs/reference/api/doxygen/relax_2transform_8h_source.html
+++ b/docs/reference/api/doxygen/relax_2transform_8h_source.html
@@ -248,30 +248,31 @@ $(function() {
<div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  </div>
<div class="line"><a name="l00549"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c"> 549</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a32ebc2bcef84b022e7c35a2a2678b99c">DecomposeOpsForTraining</a>(<a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<String></a> func_name);</div>
<div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  </div>
-<div class="line"><a name="l00564"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883"> 564</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">AlterOpImpl</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<String, tir::PrimFunc></a [...]
-<div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<tir::IndexMap></a>>& op_buffer_transforms,</div>
-<div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<IntImm></a>>>& <a class="code" href="namespacetvm_1_1tir_1_1attr.html [...]
-<div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  </div>
-<div class="line"><a name="l00574"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94"> 574</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">ConvertLayout</a>(<a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html" [...]
-<div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  </div>
-<div class="line"><a name="l00583"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#adf34717bb1990ef17ddb03db00360ddc"> 583</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#adf34717bb1990ef17ddb03db00360ddc">ConvertToDataflow</a>(<span class="keywordtype">int</span> min_size = 2);</div>
-<div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  </div>
-<div class="line"><a name="l00601"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a6f2eb123a3384119da2671fdb5c550a9"> 601</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a6f2eb123a3384119da2671fdb5c550a9">DeadCodeElimination</a>(<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<runtime::String></a> entry_functions = {});</div>
-<div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  </div>
-<div class="line"><a name="l00612"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a11f82e9471203ddc307c67bc858a28d8"> 612</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a11f82e9471203ddc307c67bc858a28d8">DataflowUseInplaceCalls</a>();</div>
-<div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  </div>
-<div class="line"><a name="l00624"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a3876e2a757378b1f9d195af8cbba1e52"> 624</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a3876e2a757378b1f9d195af8cbba1e52">ToMixedPrecision</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1DataType.html">DataType</a>& out_dty [...]
-<div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<String></a>> fp16_input_names = <a class="code" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a>);</div>
-<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  </div>
-<div class="line"><a name="l00632"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#ad769f00c33bd5eac4fa5e608ce92b87b"> 632</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#ad769f00c33bd5eac4fa5e608ce92b87b">RewriteCUDAGraph</a>();</div>
-<div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  </div>
-<div class="line"><a name="l00644"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a47edcc1f5a163d830f6a428663d82875"> 644</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a47edcc1f5a163d830f6a428663d82875">FewShotTuning</a>(<span class="keywordtype">int</span> valid_count, <span class="keywordtype">bool</span> benchmark);</div>
-<div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  </div>
-<div class="line"><a name="l00646"></a><span class="lineno"> 646</span> } <span class="comment">// namespace transform</span></div>
-<div class="line"><a name="l00647"></a><span class="lineno"> 647</span> } <span class="comment">// namespace relax</span></div>
-<div class="line"><a name="l00648"></a><span class="lineno"> 648</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  </div>
-<div class="line"><a name="l00650"></a><span class="lineno"> 650</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_TRANSFORM_H_</span></div>
+<div class="line"><a name="l00565"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab"> 565</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">AlterOpImpl</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map<String, tir::PrimFunc></a [...]
+<div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<tir::IndexMap></a>>& op_buffer_transforms,</div>
+<div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<IntImm></a>>>& <a class="code" href="namespacetvm_1_1tir_1_1attr.html [...]
+<div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<IntImm></a>>>& input_axis_separators);</div>
+<div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  </div>
+<div class="line"><a name="l00576"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94"> 576</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">ConvertLayout</a>(<a class="code" href="classtvm_1_1runtime_1_1Map.html">Map</a><<a class="code" href="classtvm_1_1runtime_1_1String.html" [...]
+<div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  </div>
+<div class="line"><a name="l00585"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#adf34717bb1990ef17ddb03db00360ddc"> 585</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#adf34717bb1990ef17ddb03db00360ddc">ConvertToDataflow</a>(<span class="keywordtype">int</span> min_size = 2);</div>
+<div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  </div>
+<div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a6f2eb123a3384119da2671fdb5c550a9"> 603</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a6f2eb123a3384119da2671fdb5c550a9">DeadCodeElimination</a>(<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<runtime::String></a> entry_functions = {});</div>
+<div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  </div>
+<div class="line"><a name="l00614"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a11f82e9471203ddc307c67bc858a28d8"> 614</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a11f82e9471203ddc307c67bc858a28d8">DataflowUseInplaceCalls</a>();</div>
+<div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  </div>
+<div class="line"><a name="l00626"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a3876e2a757378b1f9d195af8cbba1e52"> 626</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a3876e2a757378b1f9d195af8cbba1e52">ToMixedPrecision</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1DataType.html">DataType</a>& out_dty [...]
+<div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<String></a>> fp16_input_names = <a class="code" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a>);</div>
+<div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  </div>
+<div class="line"><a name="l00634"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#ad769f00c33bd5eac4fa5e608ce92b87b"> 634</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#ad769f00c33bd5eac4fa5e608ce92b87b">RewriteCUDAGraph</a>();</div>
+<div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  </div>
+<div class="line"><a name="l00646"></a><span class="lineno"><a class="line" href="namespacetvm_1_1relax_1_1transform.html#a47edcc1f5a163d830f6a428663d82875"> 646</a></span> TVM_DLL <a class="code" href="classtvm_1_1transform_1_1Pass.html">Pass</a> <a class="code" href="namespacetvm_1_1relax_1_1transform.html#a47edcc1f5a163d830f6a428663d82875">FewShotTuning</a>(<span class="keywordtype">int</span> valid_count, <span class="keywordtype">bool</span> benchmark);</div>
+<div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  </div>
+<div class="line"><a name="l00648"></a><span class="lineno"> 648</span> } <span class="comment">// namespace transform</span></div>
+<div class="line"><a name="l00649"></a><span class="lineno"> 649</span> } <span class="comment">// namespace relax</span></div>
+<div class="line"><a name="l00650"></a><span class="lineno"> 650</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  </div>
+<div class="line"><a name="l00652"></a><span class="lineno"> 652</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_TRANSFORM_H_</span></div>
<div class="ttc" id="aclasstvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
<div class="ttc" id="aclasstvm_1_1Bool_html"><div class="ttname"><a href="classtvm_1_1Bool.html">tvm::Bool</a></div><div class="ttdoc">Boolean constant.</div><div class="ttdef"><b>Definition:</b> expr.h:597</div></div>
<div class="ttc" id="aclasstvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode.</div><div class="ttdef"><b>Definition:</b> module.h:366</div></div>
@@ -338,6 +339,7 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a619a3e900288c40a35594c7c52d8d048"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a619a3e900288c40a35594c7c52d8d048">tvm::relax::transform::CreateDataflowBlockPass</a></div><div class="ttdeci">Pass CreateDataflowBlockPass(const runtime::TypedPackedFunc< DataflowBlock(DataflowBlock, IRModule, PassContext)> &pass_func, int opt_level, String name, tvm::Array< String > required, bool traceable [...]
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a6ca6a56c220e2984deb346caeba5d871"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a6ca6a56c220e2984deb346caeba5d871">tvm::relax::transform::AttachGlobalSymbol</a></div><div class="ttdeci">Pass AttachGlobalSymbol()</div><div class="ttdoc">Attach global_symbol to Relax functions and TIR Primfuncs for codegen.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a6f2eb123a3384119da2671fdb5c550a9"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a6f2eb123a3384119da2671fdb5c550a9">tvm::relax::transform::DeadCodeElimination</a></div><div class="ttdeci">Pass DeadCodeElimination(Array< runtime::String > entry_functions={})</div><div class="ttdoc">Dead code elimination.</div></div>
+<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a7046b43a48dab74382800701ef713aab"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a7046b43a48dab74382800701ef713aab">tvm::relax::transform::AlterOpImpl</a></div><div class="ttdeci">Pass AlterOpImpl(const Map< String, tir::PrimFunc > &op_impl_map, const Map< String, Array< tir::IndexMap >> &op_buffer_transforms, const Map< String, Array< Array< IntImm >>> &ax [...]
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a7155cec2bad0e8e64bd78d1459843b88"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a7155cec2bad0e8e64bd78d1459843b88">tvm::relax::transform::FuseOps</a></div><div class="ttdeci">Pass FuseOps(int fuse_opt_level=-1)</div><div class="ttdoc">This pass groups bindings in a dataflow block of Relax functions and generates a new grouped Relax fu...</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a73639cc09dc072ab9ffcae0f8e97ff6b"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a73639cc09dc072ab9ffcae0f8e97ff6b">tvm::relax::transform::FuseOpsByPattern</a></div><div class="ttdeci">Pass FuseOpsByPattern(const tvm::Array< FusionPattern > &patterns, bool bind_constants=true, bool annotate_codegen=false, const tvm::Array< String > &entry_function_names={})</div><div class="ttdoc">App [...]
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a739adf2cff75a2e14b305f6fbefa982b"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a739adf2cff75a2e14b305f6fbefa982b">tvm::relax::transform::AnnotateTIROpPattern</a></div><div class="ttdeci">Pass AnnotateTIROpPattern()</div><div class="ttdoc">Annotate Op Pattern Kind for TIR functions, which is used in FuseOps.</div></div>
@@ -346,7 +348,6 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a844f4c41bfb8380a8f6e6e3d017aed94"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a844f4c41bfb8380a8f6e6e3d017aed94">tvm::relax::transform::ConvertLayout</a></div><div class="ttdeci">Pass ConvertLayout(Map< String, Array< String >> desired_layouts)</div><div class="ttdoc">Layout conversion pass.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a93a3eef153bbae5089e940ba27e598cf"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a93a3eef153bbae5089e940ba27e598cf">tvm::relax::transform::RemoveUnusedOutputs</a></div><div class="ttdeci">Pass RemoveUnusedOutputs()</div><div class="ttdoc">Remove unused outputs from internal functions.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a9578408fcfdc920ca97d54ae699c431f"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a9578408fcfdc920ca97d54ae699c431f">tvm::relax::transform::ExpandTupleArguments</a></div><div class="ttdeci">Pass ExpandTupleArguments()</div><div class="ttdoc">Expand tuple arguments to internal functions.</div></div>
-<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a9994428123b73cedf8b1feddfe830883"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a9994428123b73cedf8b1feddfe830883">tvm::relax::transform::AlterOpImpl</a></div><div class="ttdeci">Pass AlterOpImpl(const Map< String, tir::PrimFunc > &op_impl_map, const Map< String, Array< tir::IndexMap >> &op_buffer_transforms, const Map< String, Array< Array< IntImm >>> &ax [...]
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_a9a4dfbde47ed73e61446a4f13ffa1d57"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#a9a4dfbde47ed73e61446a4f13ffa1d57">tvm::relax::transform::DecomposeOpsForInference</a></div><div class="ttdeci">Pass DecomposeOpsForInference(Optional< String > func_name)</div><div class="ttdoc">Decompose composite operators during inference. For example, The result of batch norm (a triple) will...</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_aa26d9a1b7b6b9b7bc575ad8edccb7ca7"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#aa26d9a1b7b6b9b7bc575ad8edccb7ca7">tvm::relax::transform::FoldConstant</a></div><div class="ttdeci">Pass FoldConstant()</div><div class="ttdoc">Fold constant expressions within dataflow blocks.</div></div>
<div class="ttc" id="anamespacetvm_1_1relax_1_1transform_html_aa697394f51af1181825cc5223746f18c"><div class="ttname"><a href="namespacetvm_1_1relax_1_1transform.html#aa697394f51af1181825cc5223746f18c">tvm::relax::transform::CanonicalizeBindings</a></div><div class="ttdeci">Pass CanonicalizeBindings()</div><div class="ttdoc">Simplify a Relax module by folding var bindings and match shape nodes, as well as tuple indices....</div></div>
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index 4d95fb1feb..8598dfae90 100644
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