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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/09/12 01:39:29 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@25b8a0798e6308b21191ead0739eac54d376806e)
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 afecf9f7d4 deploying docs (apache/tvm@25b8a0798e6308b21191ead0739eac54d376806e)
afecf9f7d4 is described below
commit afecf9f7d4c191f827c5a7005aa560a3ebecd368
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Tue Sep 12 01:39:22 2023 +0000
deploying docs (apache/tvm@25b8a0798e6308b21191ead0739eac54d376806e)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.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 +-
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.../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 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 22 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
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.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
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.../tune_network_cuda.rst.txt | 4 +-
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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 +-
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.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
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.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 11 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 58 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 22 +-
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docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 16 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 21 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 22 +-
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.../deploy_models/deploy_model_on_adreno_tvmc.html | 33 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 74 +-
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docs/how_to/work_with_microtvm/micro_train.html | 16 +-
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327 files changed, 11573 insertions(+), 11539 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 ee4c2219bc..f14a5834c7 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 35.240 seconds)
+ **Total running time of the script:** ( 1 minutes 40.931 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 7452791fb4..83339ecaa9 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -116,7 +116,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip7308da15-42b5-4532-a205-fef0862c2d49 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip2b246167-9e23-4399-b720-fd30fd5ca936 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
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 3cbe2067d0..5040f56913 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
-
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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 911454ad15..2a9a90d2dc 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.100 seconds)
+ **Total running time of the script:** ( 1 minutes 7.187 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 340a63eebf..4e7e0577d1 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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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 9f7b52d833..3cec570f48 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 32.303 seconds)
+ **Total running time of the script:** ( 1 minutes 34.681 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 2a7c57ba53..0db727e38f 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
@@ -5,26 +5,26 @@
Computation times
=================
-**07:13.238** total execution time for **how_to_compile_models** files:
+**07:36.928** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:35.240 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:40.931 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:32.303 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:34.681 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.100 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:07.187 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:41.510 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.524 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:36.333 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:39.479 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:33.518 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.261 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.201 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.733 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.876 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:29.634 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.309 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:14.560 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.849 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.938 | 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 b19239aca5..2f11f286b8 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)
- 4218.7213 4218.0683 4223.8765 4216.0642 2.2713
+ 4227.1443 4226.2691 4229.6240 4225.5971 1.5891
@@ -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 19.179 seconds)
+ **Total running time of the script:** ( 1 minutes 21.192 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 a82f7d2e52..354bea1c3c 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
-
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+
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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 b1b56bceec..73cd7899db 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)
- 15.5840 15.5806 15.7468 15.4171 0.1004
+ 15.3326 15.1779 16.3568 15.0942 0.3637
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 3030e3222e..c77eba320d 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
-
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60%|###### | 102M/170M [00:03<00:02, 27.8MB/s]
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80%|######## | 136M/170M [00:05<00:01, 23.7MB/s]
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 25.343 seconds)
+ **Total running time of the script:** ( 3 minutes 41.475 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 4041f48786..ea142efd5b 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, 37.5MB/s]
73%|#######2 | 9.88M/13.6M [00:00<00:00, 25.1MB/s]
91%|#########1| 12.4M/13.6M [00:00<00:00, 18.6MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 23.2MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
59%|#####8 | 7.99M/13.6M [00:00<00:00, 45.3MB/s]
91%|######### | 12.3M/13.6M [00:00<00:00, 27.0MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 31.9MB/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)
- 88.5201 88.4573 89.8281 88.3015 0.2439
+ 89.2064 89.1410 90.4811 88.7694 0.3194
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 24.756 seconds)
+ **Total running time of the script:** ( 1 minutes 29.115 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 eea428ed39..bf67cf6d9c 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)
- 107.4410 107.1817 130.4887 106.5048 2.3590
+ 110.2547 110.1715 114.9507 109.6149 0.5384
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index 27b1120a62..ac541eb7c3 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -257,7 +257,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 49.812 seconds)
+ **Total running time of the script:** ( 2 minutes 25.486 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
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 d545914f28..061b05b45f 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
@@ -5,26 +5,26 @@
Computation times
=================
-**12:24.454** total execution time for **how_to_deploy_models** files:
+**12:32.905** 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``) | 03:25.343 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:41.475 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:49.812 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:25.486 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:24.756 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.115 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.179 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.192 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:49.947 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.419 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:48.322 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.993 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:48.308 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:48.792 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.535 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.586 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.246 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.841 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 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/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 8acbc20df8..9d79c38cde 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -463,7 +463,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip41398fd1-55a6-4cbf-aab3-9360365a4ae4 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipba07a27b-88d9-4a5e-9d6e-76f5762539d0 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
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 974450bfb0..b5454bc7e4 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
@@ -5,14 +5,14 @@
Computation times
=================
-**00:55.439** total execution time for **how_to_extend_tvm** files:
+**00:58.962** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:51.675 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:54.958 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.628 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.799 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.130 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.197 | 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 aded4c6140..126d4b6a1d 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: 23277us [23277us] (48.70%; 48.70%)
- FoldScaleAxis: 24521us [6us] (51.30%; 51.30%)
- FoldConstant: 24514us [1672us] (51.29%; 99.97%)
- InferType: 22842us [22842us] (47.79%; 93.18%)
+ InferType: 23383us [23383us] (48.44%; 48.44%)
+ FoldScaleAxis: 24893us [8us] (51.56%; 51.56%)
+ FoldConstant: 24885us [1775us] (51.55%; 99.97%)
+ InferType: 23110us [23110us] (47.87%; 92.87%)
@@ -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: 22568us [22568us] (48.42%; 48.42%)
- FoldScaleAxis: 24041us [5us] (51.58%; 51.58%)
- FoldConstant: 24036us [1592us] (51.57%; 99.98%)
- InferType: 22444us [22444us] (48.15%; 93.38%)
+ InferType: 23034us [23034us] (48.18%; 48.18%)
+ FoldScaleAxis: 24776us [7us] (51.82%; 51.82%)
+ FoldConstant: 24768us [1806us] (51.81%; 99.97%)
+ InferType: 22962us [22962us] (48.03%; 92.71%)
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 854378caf3..5b579e31f2 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: 37.394462 ms
+ Convolution: 37.716030 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 fa585e66ff..86938d4bc2 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: 12.273107 ms
+ conv2d with tensor core: 12.264448 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 ff4d355300..a4406e7140 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: 0.017484
- Baseline: 3.200752
+ Numpy running time: 0.018754
+ Baseline: 3.433402
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.294598
+ Opt1: 0.317832
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.273858
+ Opt2: 0.304177
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.109366
+ Opt3: 0.119378
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.106350
+ Opt4: 0.108205
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.110870
+ Opt5: 0.112157
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.131637
+ Opt6: 0.134299
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 b4f2c4b200..be9ab6bb6e 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
=================
-**00:33.062** total execution time for **how_to_optimize_operators** files:
+**00:35.131** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:29.878 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.673 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.000 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.099 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.183 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.359 | 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 2f534798c7..ff6a118974 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
=================
-**03:30.279** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:38.594** 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``) | 01:30.059 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:34.397 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:11.139 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:13.332 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.478 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.549 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.043 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.774 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.458 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.440 | 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``) | 00:00.102 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index 308e1e8477..b2444d5fa2 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: 0.348 ms
+ Execution time of this operator: 0.342 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 6ab46e924f..2121216496 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
@@ -647,7 +647,7 @@ 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)
- 3.2687 3.2699 3.2712 3.2651 0.0026
+ 3.2445 3.2427 3.2497 3.2410 0.0038
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 11.139 seconds)
+ **Total running time of the script:** ( 1 minutes 13.332 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 9af8489c3b..a56ebaed67 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
@@ -666,7 +666,7 @@ 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)
- 756.5339 756.5140 757.0487 756.0391 0.4124
+ 767.1440 768.7540 770.2038 762.4741 3.3547
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 30.059 seconds)
+ **Total running time of the script:** ( 1 minutes 34.397 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 360fe03098..a509b17b8e 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
=================
-**00:23.696** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.864** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.654 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.825 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.025 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.023 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index cf00a067fa..c347dd11a7 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: 0.037294
+ Time cost of this operator: 0.037275
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 54cf1bc58e..fc09d3a0ef 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 302.8 98.75 (1, 2, 10, 10, 3) 2 1 [302.8]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.881 0.94 (1, 6, 10, 10) 1 1 [2.881]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.953 0.311 (1, 1, 10, 10, 3) 1 1 [0.953]
- Total_time - 306.634 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.6 98.762 (1, 2, 10, 10, 3) 2 1 [308.6]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.881 0.922 (1, 6, 10, 10) 1 1 [2.881]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.987 0.316 (1, 1, 10, 10, 3) 1 1 [0.987]
+ Total_time - 312.469 - - - - -
@@ -428,10 +428,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.3 97.33 (1, 6, 10, 10, 1) 2 1 [100.3]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.79 1.737 (1, 6, 10, 10) 1 1 [1.79]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 0.933 (1, 1, 10, 10, 3) 1 1 [0.962]
- Total_time - 103.052 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 104.6 97.447 (1, 6, 10, 10, 1) 2 1 [104.6]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.891 1.762 (1, 6, 10, 10) 1 1 [1.891]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.849 0.791 (1, 3, 10, 10, 1) 1 1 [0.849]
+ Total_time - 107.34 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 23.014 seconds)
+ **Total running time of the script:** ( 1 minutes 28.395 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 f1d557fb93..6d427fba29 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, 4.62MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 7.51MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 4.92MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 7.96MB/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 25.558 seconds)
+ **Total running time of the script:** ( 1 minutes 30.321 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 f4ed03513e..de40e982b5 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -217,7 +217,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmprgrucuqn/images/random'
+ '/tmp/tmpj6s0wly9/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmprgrucuqn/images/target contains 8144 images
- /tmp/tmprgrucuqn/images/random contains 5000 images
+ /tmp/tmpj6s0wly9/images/target contains 8144 images
+ /tmp/tmpj6s0wly9/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 40s - loss: 0.2188 - accuracy: 0.9230 - val_loss: 0.1100 - val_accuracy: 0.9585 - 40s/epoch - 123ms/step
+ 328/328 - 42s - loss: 0.2165 - accuracy: 0.9237 - val_loss: 0.1030 - val_accuracy: 0.9653 - 42s/epoch - 128ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.0986 - accuracy: 0.9633 - val_loss: 0.0946 - val_accuracy: 0.9622 - 35s/epoch - 107ms/step
+ 328/328 - 36s - loss: 0.1002 - accuracy: 0.9646 - val_loss: 0.1067 - val_accuracy: 0.9619 - 36s/epoch - 109ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0656 - accuracy: 0.9762 - val_loss: 0.0915 - val_accuracy: 0.9683 - 35s/epoch - 107ms/step
+ 328/328 - 36s - loss: 0.0649 - accuracy: 0.9747 - val_loss: 0.0956 - val_accuracy: 0.9698 - 36s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7fcd322ecb50>
+ <keras.callbacks.History object at 0x7f650fa97d30>
@@ -860,7 +860,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 5 minutes 34.435 seconds)
+ **Total running time of the script:** ( 5 minutes 22.994 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 5c78f37dbc..8f651005f4 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
=================
-**08:51.188** total execution time for **how_to_work_with_microtvm** files:
+**08:51.841** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:34.435 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:22.994 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:25.558 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:30.321 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:23.014 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:28.395 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.543 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.073 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.503 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.538 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.135 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.520 | 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 3ef31c4dd7..97abaa89a9 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:38.809** total execution time for **how_to_work_with_relay** files:
+**00:40.803** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.633 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.580 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.133 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.246 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.037 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.971 | 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 8759cb61d9..a766dcda07 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 0x7fce8b2fcee0>
+ <function my_cuda_math_rule at 0x7f651e9cca60>
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 51d980c1cb..a82bdaf2da 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
=================
-**00:06.389** total execution time for **how_to_work_with_schedules** files:
+**00:09.759** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.332 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:06.370 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.287 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.548 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.754 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.784 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.735 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.764 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.117 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.118 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.068 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.071 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.065 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.069 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.033 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index aeb420640a..706fade3b3 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:34.228** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:36.431** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:34.220 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:36.423 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 8f9eecc722..3a169445f4 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -293,7 +293,7 @@ The compilation steps are:
warnings.warn(
/workspace/vta/tutorials/frontend/deploy_classification.py:212: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
graph, lib, params = relay.build(
- resnet18_v1 inference graph built in 36.03s!
+ resnet18_v1 inference graph built in 38.65s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.145 seconds)
+ **Total running time of the script:** ( 1 minutes 6.068 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_classification.py:
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 b7a13a0b4c..9de2837218 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 24.87s!
+ yolov3-tiny inference graph built in 26.66s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 8.939 seconds)
+ **Total running time of the script:** ( 1 minutes 10.839 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 5649f78cc3..a2b8bad2b7 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
@@ -5,10 +5,10 @@
Computation times
=================
-**02:12.084** total execution time for **topic_vta_tutorials_frontend** files:
+**02:16.906** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:08.939 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:10.839 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:03.145 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:06.068 | 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 7d297febb0..b6fdb99daa 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
=================
-**00:03.392** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.455** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.867 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.905 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.526 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.549 | 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 a2c1ed1159..f120a2d866 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
=================
-**00:00.875** total execution time for **topic_vta_tutorials** files:
+**00:00.930** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.450 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.481 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.425 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.448 | 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 463d4e5564..6e16f4b363 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,13 +207,6 @@ trials, we can load the best schedule from the log file and apply it.
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- *E
-
-
@@ -325,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 95.261 ms
+ Execution time of this operator: 94.027 ms
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 32.887 seconds)
+ **Total running time of the script:** ( 1 minutes 29.828 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 13ef8cf087..f604070371 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 11.48/11.48 result: MeasureResult(costs=(0.0233807554,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6335878372192383, timestamp=1694458413.4182088) [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
- No: 2 GFLOPS: 5.95/11.48 result: MeasureResult(costs=(0.0451233262,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9610412120819092, timestamp=1694458414.3951511) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 3 GFLOPS: 12.11/12.11 result: MeasureResult(costs=(0.0221696152,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.629791259765625, timestamp=1694458415.0074625) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
- No: 4 GFLOPS: 9.05/12.11 result: MeasureResult(costs=(0.0296757462,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7296693325042725, timestamp=1694458415.7390516) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
- No: 5 GFLOPS: 13.74/13.74 result: MeasureResult(costs=(0.0195410336,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6325600147247314, timestamp=1694458416.534681) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
- No: 6 GFLOPS: 2.13/13.74 result: MeasureResult(costs=(0.1259638444,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.2803854942321777, timestamp=1694458418.808116) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
- No: 7 GFLOPS: 12.90/13.74 result: MeasureResult(costs=(0.020810600800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7001931667327881, timestamp=1694458419.405369) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
- No: 8 GFLOPS: 2.05/13.74 result: MeasureResult(costs=(0.1312056582,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3531384468078613, timestamp=1694458421.759663) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
- No: 9 GFLOPS: 11.10/13.74 result: MeasureResult(costs=(0.024180552,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6201024055480957, timestamp=1694458422.4953978) [('tile_y', [-1, 8]), ('tile_x', [-1, 512])],None,93
- No: 10 GFLOPS: 10.49/13.74 result: MeasureResult(costs=(0.0255952322,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6382443904876709, timestamp=1694458423.1593802) [('tile_y', [-1, 8]), ('tile_x', [-1, 16])],None,43
+ No: 1 GFLOPS: 10.97/10.97 result: MeasureResult(costs=(0.024480559,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6535546779632568, timestamp=1694478358.1835053) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+ No: 2 GFLOPS: 10.92/10.97 result: MeasureResult(costs=(0.0245758038,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6554069519042969, timestamp=1694478358.8346965) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
+ No: 3 GFLOPS: 11.12/11.12 result: MeasureResult(costs=(0.0241504482,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6345679759979248, timestamp=1694478359.4884417) [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
+ No: 4 GFLOPS: 1.62/11.12 result: MeasureResult(costs=(0.1659211502,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.9103546142578125, timestamp=1694478362.4038994) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
+ No: 5 GFLOPS: 11.19/11.19 result: MeasureResult(costs=(0.0239976282,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6705803871154785, timestamp=1694478363.199061) [('tile_y', [-1, 128]), ('tile_x', [-1, 32])],None,57
+ No: 6 GFLOPS: 0.51/11.19 result: MeasureResult(costs=(0.5280925008,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.717654466629028, timestamp=1694478371.9076557) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
+ No: 7 GFLOPS: 11.68/11.68 result: MeasureResult(costs=(0.0229882406,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6719496250152588, timestamp=1694478372.5450892) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
+ No: 8 GFLOPS: 10.68/11.68 result: MeasureResult(costs=(0.0251413438,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6791331768035889, timestamp=1694478373.2152972) [('tile_y', [-1, 256]), ('tile_x', [-1, 256])],None,88
+ No: 9 GFLOPS: 11.65/11.68 result: MeasureResult(costs=(0.0230323118,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6062197685241699, timestamp=1694478373.9330184) [('tile_y', [-1, 32]), ('tile_x', [-1, 32])],None,55
+ No: 10 GFLOPS: 3.64/11.68 result: MeasureResult(costs=(0.07369955619999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4113316535949707, timestamp=1694478375.3736925) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 366aeb84cb..e4b9c9bf3b 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': 488.051624159998, 'median': 487.0145931001389, 'std': 3.6031003135280066}
+ {'mean': 497.0907632900344, 'median': 497.0771257999331, 'std': 3.0660985668566254}
@@ -582,29 +582,28 @@ 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: 20.78/ 20.78 GFLOPS | Progress: (4/20) | 8.65 s
[Task 1/25] Current/Best: 12.71/ 20.78 GFLOPS | Progress: (8/20) | 11.36 s
[Task 1/25] Current/Best: 12.35/ 20.78 GFLOPS | Progress: (12/20) | 14.17 s
[Task 1/25] Current/Best: 6.34/ 20.78 GFLOPS | Progress: (16/20) | 16.75 s
[Task 1/25] Current/Best: 6.88/ 20.78 GFLOPS | Progress: (20/20) | 20.86 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 13.07/ 13.42 GFLOPS | Progress: (4/20) | 4.64 s
[Task 2/25] Current/Best: 17.28/ 19.32 GFLOPS | Progress: (8/20) | 6.23 s
[Task 2/25] Current/Best: 5.25/ 19.32 GFLOPS | Progress: (12/20) | 7.89 s
[Task 2/25] Current/Best: 22.67/ 22.67 GFLOPS | Progress: (16/20) | 9.53 s
[Task 2/25] Current/Best: 14.34/ 22.67 GFLOPS | Progress: (20/20) | 11.00 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 17.40/ 22.57 GFLOPS | Progress: (4/20) | 5.16 s
[Task 3/25] Current/Best: 12.64/ 23.20 GFLOPS | Progress: (8/20) | 7.63 s
[Task 3/25] Current/Best: 15.49/ 23.20 GFLOPS | Progress: (12/20) | 10.33 s
[Task 3/25] Current/Best: 14.14/ 23.20 GFLOPS | Progress: (16/20) | 12.78 s
[Task 3/25] Current/Best: 12.39/ 23.20 GFLOPS | Progress: (20/20) | 15.75 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.43/ 20.19 GFLOPS | Progress: (4/20) | 4.48 s
[Task 4/25] Current/Best: 7.03/ 20.19 GFLOPS | Progress: (8/20) | 6.57 s
[Task 4/25] Current/Best: 12.38/ 20.19 GFLOPS | Progress: (12/20) | 8.69 s
[Task 4/25] Current/Best: 21.06/ 21.06 GFLOPS | Progress: (16/20) | 11.60 s
[Task 4/25] Current/Best: 11.52/ 21.06 GFLOPS | Progress: (20/20) | 13.56 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 16.23/ 16.23 GFLOPS | Progress: (4/20) | 5.01 s
[Task 5/25] Current/Best: 20.44/ 20.44 GFLOPS | Progress: (8/20) | 7.37 s
[Task 5/25] Current/Best: 16.01/ 20.44 GFLOPS | Progress: (12/20) | 9.23 s
[Task 5/25] Current/Best: 4.02/ 23.03 GFLOPS | Progress: (16/20) | 11.43 s
[Task 5/25] Current/Best: 12.95/ 23.03 GFLOPS | Progress: (20/20) | 13.50 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.73/ 15.13 GFLOPS | Progress: (4/20) | 4.94 s
[Task 6/25] Current/Best: 18.74/ 18.74 GFLOPS | Progress: (8/20) | 6.87 s
[Task 6/25] Current/Best: 12.11/ 18.74 GFLOPS | Progress: (12/20) | 9.75 s
[Task 6/25] Current/Best: 4.32/ 18.74 GFLOPS | Progress: (16/20) | 12.62 s
[Task 6/25] Current/Best: 12.68/ 18.74 GFLOPS | Progress: (20/20) | 14.89 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 5.64/ 20.70 GFLOPS | Progress: (4/20) | 5.18 s
[Task 7/25] Current/Best: 15.57/ 20.70 GFLOPS | Progress: (8/20) | 7.21 s
[Task 7/25] Current/Best: 13.41/ 20.70 GFLOPS | Progress: (12/20) | 9.76 s
[Task 7/25] Current/Best: 2.79/ 21.51 GFLOPS | Progress: (16/20) | 13.27 s
[Task 7/25] Current/Best: 21.35/ 23.26 GFLOPS | Progress: (20/20) | 15.52 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 17.29/ 17.29 GFLOPS | Progress: (4/20) | 5.32 s
[Task 8/25] Current/Best: 10.31/ 17.29 GFLOPS | Progress: (8/20) | 8.05 s
[Task 8/25] Current/Best: 14.74/ 17.29 GFLOPS | Progress: (12/20) | 11.02 s
[Task 8/25] Current/Best: 10.26/ 17.53 GFLOPS | Progress: (16/20) | 16.26 s
[Task 8/25] Current/Best: 11.93/ 17.53 GFLOPS | Progress: (20/20) | 19.71 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 11.69/ 17.14 GFLOPS | Progress: (4/20) | 5.71 s
[Task 9/25] Current/Best: 18.34/ 19.42 GFLOPS | Progress: (8/20) | 8.94 s
[Task 9/25] Current/Best: 21.96/ 21.96 GFLOPS | Progress: (12/20) | 19.91 s
[Task 9/25] Current/Best: 10.16/ 21.96 GFLOPS | Progress: (16/20) | 24.99 s
[Task 9/25] Current/Best: 10.56/ 21.96 GFLOPS | Progress: (20/20) | 35.87 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 13.87/ 15.94 GFLOPS | Progress: (4/20) | 4.90 s
[Task 10/25] Current/Best: 7.43/ 15.94 GFLOPS | Progress: (8/20) | 7.06 s
[Task 10/25] Current/Best: 15.84/ 15.94 GFLOPS | Progress: (12/20) | 9.37 s
[Task 10/25] Current/Best: 14.45/ 16.39 GFLOPS | Progress: (16/20) | 11.65 s
[Task 10/25] Current/Best: 13.67/ 16.39 GFLOPS | Progress: (20/20) | 14.35 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 23.71/ 23.71 GFLOPS | Progress: (4/20) | 5.67 s
[Task 11/25] Current/Best: 20.09/ 23.71 GFLOPS | Progress: (8/20) | 9.21 s
[Task 11/25] Current/Best: 6.29/ 23.71 GFLOPS | Progress: (12/20) | 11.45 s
[Task 11/25] Current/Best: 22.26/ 23.71 GFLOPS | Progress: (16/20) | 13.60 s
[Task 11/25] Current/Best: 21.31/ 23.71 GFLOPS | Progress: (20/20) | 17.02 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 9.60/ 14.75 GFLOPS | Progress: (4/20) | 7.45 s
[Task 12/25] Current/Best: 12.90/ 18.45 GFLOPS | Progress: (8/20) | 10.00 s
[Task 12/25] Current/Best: 5.33/ 18.45 GFLOPS | Progress: (12/20) | 12.53 s
[Task 12/25] Current/Best: 3.19/ 18.45 GFLOPS | Progress: (16/20) | 15.40 s
[Task 12/25] Current/Best: 14.68/ 18.45 GFLOPS | Progress: (20/20) | 20.81 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 12.29/ 16.55 GFLOPS | Progress: (4/20) | 6.30 s
[Task 13/25] Current/Best: 9.46/ 20.13 GFLOPS | Progress: (8/20) | 10.06 s
[Task 13/25] Current/Best: 7.97/ 21.59 GFLOPS | Progress: (12/20) | 13.01 s
[Task 13/25] Current/Best: 18.45/ 21.59 GFLOPS | Progress: (16/20) | 16.47 s
[Task 13/25] Current/Best: 8.34/ 21.59 GFLOPS | Progress: (20/20) | 19.58 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 8.30/ 13.36 GFLOPS | Progress: (4/20) | 12.18 s
[Task 14/25] Current/Best: 13.10/ 15.09 GFLOPS | Progress: (8/20) | 15.31 s
[Task 14/25] Current/Best: 14.80/ 15.09 GFLOPS | Progress: (12/20) | 18.96 s
[Task 14/25] Current/Best: 7.92/ 15.09 GFLOPS | Progress: (16/20) | 26.43 s
[Task 14/25] Current/Best: 14.84/ 16.97 GFLOPS | Progress: (20/20) | 28.63 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 17.85/ 22.40 GFLOPS | Progress: (4/20) | 6.52 s
[Task 15/25] Current/Best: 19.68/ 22.40 GFLOPS | Progress: (8/20) | 8.62 s
[Task 15/25] Current/Best: 12.90/ 22.40 GFLOPS | Progress: (12/20) | 10.44 s
[Task 15/25] Current/Best: 7.05/ 22.40 GFLOPS | Progress: (16/20) | 21.63 s
[Task 15/25] Current/Best: 15.09/ 22.40 GFLOPS | Progress: (20/20) | 24.82 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 21.53/ 21.53 GFLOPS | Progress: (4/20) | 4.51 s
[Task 16/25] Current/Best: 17.14/ 21.53 GFLOPS | Progress: (8/20) | 6.84 s
[Task 16/25] Current/Best: 9.30/ 21.53 GFLOPS | Progress: (12/20) | 10.02 s
[Task 16/25] Current/Best: 18.23/ 21.53 GFLOPS | Progress: (16/20) | 12.83 s
[Task 16/25] Current/Best: 16.18/ 21.53 GFLOPS | Progress: (20/20
) | 14.65 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 11.52/ 18.33 GFLOPS | Progress: (4/20) | 5.48 s
[Task 17/25] Current/Best: 12.18/ 20.17 GFLOPS | Progress: (8/20) | 8.47 s
[Task 17/25] Current/Best: 10.62/ 20.72 GFLOPS | Progress: (12/20) | 10.83 s
[Task 17/25] Current/Best: 3.09/ 22.24 GFLOPS | Progress: (16/20) | 13.43 s
[Task 17/25] Current/Best: 18.52/ 22.24 GFLOPS | Progress: (20/20) | 16.59 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 17.59/ 19.45 GFLOPS | Progress: (4/20) | 4.80 s
[Task 18/25] Current/Best: 10.96/ 19.45 GFLOPS | Progress: (8/20) | 7.74 s
[Task 18/25] Current/Best: 7.79/ 19.45 GFLOPS | Progress: (12/20) | 10.02 s
[Task 18/25] Current/Best: 17.87/ 19.45 GFLOPS | Progress: (16/20) | 15.87 s
[Task 18/25] Current/Best: 20.99/ 20.99 GFLOPS | Progress: (20/20) | 18.21 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 5.38/ 19.36 GFLOPS | Progress: (4/20) | 5.71 s
[Task 19/25] Current/Best: 10.74/ 21.34 GFLOPS | Progress: (8/20) | 8.49 s
[Task 19/25] Current/Best: 18.61/ 21.85 GFLOPS | Progress: (12/20) | 11.09 s
[Task 19/25] Current/Best: 19.16/ 21.85 GFLOPS | Progress: (16/20) | 14.23 s
[Task 19/25] Current/Best: 8.62/ 22.44 GFLOPS | Progress: (20/20) | 16.73 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 2.71/ 8.30 GFLOPS | Progress: (4/20) | 15.75 s
[Task 20/25] Current/Best: 14.69/ 18.67 GFLOPS | Progress: (8/20) | 19.28 s
[Task 20/25] Current/Best: 10.38/ 21.12 GFLOPS | Progress: (12/20) | 22.11 s
[Task 20/25] Current/Best: 6.43/ 21.12 GFLOPS | Progress: (16/20) | 24.28 s
[Task 20/25] Current/Best: 21.65/ 21.65 GFLOPS | Progress: (20/20) | 36.08 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
- Done.
-
[Task 21/25] Current/Best: 22.74/ 22.74 GFLOPS | Progress: (4/20) | 5.60 s
[Task 21/25] Current/Best: 7.27/ 22.74 GFLOPS | Progress: (8/20) | 11.85 s
[Task 21/25] Current/Best: 6.40/ 22.74 GFLOPS | Progress: (12/20) | 13.69 s
[Task 21/25] Current/Best: 6.34/ 22.74 GFLOPS | Progress: (16/20) | 24.90 s
[Task 21/25] Current/Best: 12.45/ 22.74 GFLOPS | Progress: (20/20) | 32.19 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 10.07/ 15.11 GFLOPS | Progress: (4/20) | 5.17 s
[Task 22/25] Current/Best: 10.28/ 17.85 GFLOPS | Progress: (8/20) | 6.87 s
[Task 22/25] Current/Best: 8.01/ 17.85 GFLOPS | Progress: (12/20) | 11.94 s
[Task 22/25] Current/Best: 20.66/ 20.79 GFLOPS | Progress: (16/20) | 13.68 s
[Task 22/25] Current/Best: 9.12/ 20.79 GFLOPS | Progress: (20/20) | 18.47 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 10.79/ 23.21 GFLOPS | Progress: (4/20) | 9.52 s
[Task 23/25] Current/Best: 22.04/ 23.21 GFLOPS | Progress: (8/20) | 12.04 s
[Task 23/25] Current/Best: 17.56/ 23.21 GFLOPS | Progress: (12/20) | 18.33 s
[Task 23/25] Current/Best: 2.50/ 24.13 GFLOPS | Progress: (16/20) | 21.64 s
[Task 23/25] Current/Best: 10.68/ 24.13 GFLOPS | Progress: (20/20) | 26.06 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 2.22/ 4.29 GFLOPS | Progress: (4/20) | 7.29 s
[Task 24/25] Current/Best: 1.87/ 10.28 GFLOPS | Progress: (8/20) | 11.00 s
[Task 24/25] Current/Best: 3.76/ 10.28 GFLOPS | Progress: (12/20) | 13.48 s
[Task 24/25] Current/Best: 4.44/ 10.28 GFLOPS | Progress: (16/20) | 24.17 s
[Task 24/25] Current/Best: 4.12/ 10.28 GFLOPS | Progress: (20/20) | 28.90 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 5.99/ 9.80 GFLOPS | Progress: (4/20) | 7.68 s
[Task 25/25] Current/Best: 4.60/ 9.80 GFLOPS | Progress: (8/20) | 18.63 s
[Task 25/25] Current/Best: 7.09/ 9.80 GFLOPS | Progress: (12/20) | 29.59 s
[Task 25/25] Current/Best: 6.27/ 9.80 GFLOPS | Progress: (16/20) | 32.65 s
[Task 25/25] Current/Best: 2.80/ 9.80 GFLOPS | Progress: (20/
20) | 34.82 s
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 13.32/ 20.98 GFLOPS | Progress: (4/20) | 9.52 s
[Task 1/25] Current/Best: 9.41/ 20.98 GFLOPS | Progress: (8/20) | 13.24 s
[Task 1/25] Current/Best: 18.46/ 20.98 GFLOPS | Progress: (12/20) | 15.63 s
[Task 1/25] Current/Best: 16.57/ 20.98 GFLOPS | Progress: (16/20) | 18.46 s
[Task 1/25] Current/Best: 18.68/ 23.44 GFLOPS | Progress: (20/20) | 21.07 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 18.20/ 18.20 GFLOPS | Progress: (4/20) | 4.48 s
[Task 2/25] Current/Best: 11.27/ 19.82 GFLOPS | Progress: (8/20) | 6.27 s
[Task 2/25] Current/Best: 14.81/ 19.82 GFLOPS | Progress: (12/20) | 7.76 s
[Task 2/25] Current/Best: 9.54/ 19.82 GFLOPS | Progress: (16/20) | 9.17 s
[Task 2/25] Current/Best: 10.98/ 19.82 GFLOPS | Progress: (20/20) | 11.30 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 20.46/ 20.46 GFLOPS | Progress: (4/20) | 5.00 s
[Task 3/25] Current/Best: 9.70/ 20.46 GFLOPS | Progress: (8/20) | 7.38 s
[Task 3/25] Current/Best: 13.43/ 23.24 GFLOPS | Progress: (12/20) | 9.92 s
[Task 3/25] Current/Best: 23.36/ 23.36 GFLOPS | Progress: (16/20) | 12.13 s
[Task 3/25] Current/Best: 19.06/ 23.36 GFLOPS | Progress: (20/20) | 14.73 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.74/ 12.20 GFLOPS | Progress: (4/20) | 5.79 s
[Task 4/25] Current/Best: 12.03/ 12.20 GFLOPS | Progress: (8/20) | 13.58 s
[Task 4/25] Current/Best: 11.08/ 18.53 GFLOPS | Progress: (12/20) | 15.45 s
[Task 4/25] Current/Best: 14.01/ 18.53 GFLOPS | Progress: (16/20) | 17.46 s
[Task 4/25] Current/Best: 10.45/ 18.53 GFLOPS | Progress: (20/20) | 22.13 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 8.70/ 22.56 GFLOPS | Progress: (4/20) | 4.61 s
[Task 5/25] Current/Best: 4.99/ 22.56 GFLOPS | Progress: (8/20) | 6.45 s
[Task 5/25] Current/Best: 6.00/ 22.56 GFLOPS | Progress: (12/20) | 8.55 s
[Task 5/25] Current/Best: 11.27/ 22.56 GFLOPS | Progress: (16/20) | 10.58 s
[Task 5/25] Current/Best: 5.55/ 22.56 GFLOPS | Progress: (20/20) | 12.66 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 2.19/ 11.09 GFLOPS | Progress: (4/20) | 6.60 s
[Task 6/25] Current/Best: 11.16/ 12.02 GFLOPS | Progress: (8/20) | 10.96 s
[Task 6/25] Current/Best: 5.98/ 13.66 GFLOPS | Progress: (12/20) | 13.46 s
[Task 6/25] Current/Best: 12.54/ 14.22 GFLOPS | Progress: (16/20) | 15.93 s
[Task 6/25] Current/Best: 12.11/ 14.22 GFLOPS | Progress: (20/20) | 18.77 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 16.96/ 21.07 GFLOPS | Progress: (4/20) | 5.00 s
[Task 7/25] Current/Best: 14.42/ 21.07 GFLOPS | Progress: (8/20) | 7.36 s
[Task 7/25] Current/Best: 19.98/ 21.07 GFLOPS | Progress: (12/20) | 11.16 s
[Task 7/25] Current/Best: 18.88/ 22.77 GFLOPS | Progress: (16/20) | 13.19 s
[Task 7/25] Current/Best: 20.40/ 22.77 GFLOPS | Progress: (20/20) | 15.56 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 4.80/ 14.04 GFLOPS | Progress: (4/20) | 6.06 s
[Task 8/25] Current/Best: 15.05/ 15.05 GFLOPS | Progress: (8/20) | 12.26 s
[Task 8/25] Current/Best: 21.79/ 21.79 GFLOPS | Progress: (12/20) | 15.62 s
[Task 8/25] Current/Best: 18.97/ 21.79 GFLOPS | Progress: (16/20) | 18.17 s
[Task 8/25] Current/Best: 16.32/ 21.79 GFLOPS | Progress: (20/20) | 20.29 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.37/ 18.09 GFLOPS | Progress: (4/20) | 4.45 s
[Task 9/25] Current/Best: 15.82/ 18.09 GFLOPS | Progress: (8/20) | 8.11 s
[Task 9/25] Current/Best: 11.20/ 18.09 GFLOPS | Progress: (12/20) | 10.47 s
[Task 9/25] Current/Best: 12.54/ 19.76 GFLOPS | Progress: (16/20) | 12.73 s
[Task 9/25] Current/Best: 19.17/ 19.76 GFLOPS | Progress: (20/20) | 14.52 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 3.17/ 12.50 GFLOPS | Progress: (4/20) | 5.60 s
[Task 10/25] Current/Best: 10.31/ 16.98 GFLOPS | Progress: (8/20) | 8.03 s
[Task 10/25] Current/Best: 12.16/ 16.98 GFLOPS | Progress: (12/20) | 11.33 s
[Task 10/25] Current/Best: 9.54/ 19.02 GFLOPS | Progress: (16/20) | 13.14 s
[Task 10/25] Current/Best: 22.05/ 22.05 GFLOPS | Progress: (20/20) | 15.47 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 13.04/ 22.96 GFLOPS | Progress: (4/20) | 5.02 s
[Task 11/25] Current/Best: 21.69/ 22.96 GFLOPS | Progress: (8/20) | 7.37 s
[Task 11/25] Current/Best: 8.90/ 22.96 GFLOPS | Progress: (12/20) | 10.16 s
[Task 11/25] Current/Best: 8.64/ 22.96 GFLOPS | Progress: (16/20) | 12.78 s
[Task 11/25] Current/Best: 18.22/ 22.96 GFLOPS | Progress: (20/20) | 15.03 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 3.67/ 18.21 GFLOPS | Progress: (4/20) | 5.47 s
[Task 12/25] Current/Best: 15.00/ 18.34 GFLOPS | Progress: (8/20) | 8.25 s
[Task 12/25] Current/Best: 14.20/ 18.34 GFLOPS | Progress: (12/20) | 11.32 s
[Task 12/25] Current/Best: 11.48/ 18.34 GFLOPS | Progress: (16/20) | 15.63 s
[Task 12/25] Current/Best: 15.23/ 18.34 GFLOPS | Progress: (20/20) | 17.69 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 22.14/ 22.14 GFLOPS | Progress: (4/20) | 6.54 s
[Task 13/25] Current/Best: 12.60/ 22.14 GFLOPS | Progress: (8/20) | 9.51 s
[Task 13/25] Current/Best: 6.02/ 22.14 GFLOPS | Progress: (12/20) | 11.95 s
[Task 13/25] Current/Best: 19.76/ 22.14 GFLOPS | Progress: (16/20) | 14.52 s
[Task 13/25] Current/Best: 20.19/ 22.14 GFLOPS | Progress: (20/20) | 16.44 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 12.57/ 18.93 GFLOPS | Progress: (4/20) | 5.04 s
[Task 14/25] Current/Best: 14.76/ 18.93 GFLOPS | Progress: (8/20) | 12.96 s
[Task 14/25] Current/Best: 12.22/ 18.93 GFLOPS | Progress: (12/20) | 24.40 s
[Task 14/25] Current/Best: 19.46/ 19.46 GFLOPS | Progress: (16/20) | 35.82 s
[Task 14/25] Current/Best: 14.79/ 19.46 GFLOPS | Progress: (20/20) | 39.45 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 9.37/ 18.82 GFLOPS | Progress: (4/20) | 6.16 s
[Task 15/25] Current/Best: 9.71/ 18.93 GFLOPS | Progress: (8/20) | 12.79 s
[Task 15/25] Current/Best: 7.97/ 20.88 GFLOPS | Progress: (12/20) | 14.42 s
[Task 15/25] Current/Best: 9.57/ 20.88 GFLOPS | Progress: (16/20) | 16.16 s
[Task 15/25] Current/Best: 14.51/ 20.88 GFLOPS | Progress: (20/
20) | 18.03 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 14.69/ 14.69 GFLOPS | Progress: (4/20) | 5.26 s
[Task 16/25] Current/Best: 14.23/ 16.94 GFLOPS | Progress: (8/20) | 7.08 s
[Task 16/25] Current/Best: 7.20/ 17.71 GFLOPS | Progress: (12/20) | 9.10 s
[Task 16/25] Current/Best: 10.92/ 17.71 GFLOPS | Progress: (16/20) | 11.47 s
[Task 16/25] Current/Best: 14.27/ 17.71 GFLOPS | Progress: (20/20) | 13.26 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 6.14/ 23.14 GFLOPS | Progress: (4/20) | 5.48 s
[Task 17/25] Current/Best: 18.81/ 23.14 GFLOPS | Progress: (8/20) | 8.16 s
[Task 17/25] Current/Best: 19.40/ 23.14 GFLOPS | Progress: (12/20) | 10.52 s
[Task 17/25] Current/Best: 11.02/ 23.14 GFLOPS | Progress: (16/20) | 14.44 s
[Task 17/25] Current/Best: 18.18/ 23.14 GFLOPS | Progress: (20/20) | 17.35 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 10.46/ 18.59 GFLOPS | Progress: (4/20) | 5.03 s
[Task 18/25] Current/Best: 6.17/ 18.59 GFLOPS | Progress: (8/20) | 7.30 s
[Task 18/25] Current/Best: 2.92/ 18.59 GFLOPS | Progress: (12/20) | 11.61 s
[Task 18/25] Current/Best: 12.27/ 18.59 GFLOPS | Progress: (16/20) | 15.92 s
[Task 18/25] Current/Best: 9.38/ 18.59 GFLOPS | Progress: (20/20) | 23.15 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 11.98/ 20.13 GFLOPS | Progress: (4/20) | 5.60 s
[Task 19/25] Current/Best: 15.23/ 20.13 GFLOPS | Progress: (8/20) | 8.80 s
[Task 19/25] Current/Best: 8.23/ 22.02 GFLOPS | Progress: (12/20) | 11.42 s
[Task 19/25] Current/Best: 6.15/ 22.02 GFLOPS | Progress: (16/20) | 14.76 s
[Task 19/25] Current/Best: 7.62/ 22.02 GFLOPS | Progress: (20/20) | 18.14 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 6.29/ 15.31 GFLOPS | Progress: (4/20) | 10.75 s
[Task 20/25] Current/Best: 6.42/ 15.93 GFLOPS | Progress: (8/20) | 14.60 s
[Task 20/25] Current/Best: 13.22/ 17.59 GFLOPS | Progress: (12/20) | 26.03 s Done.
+
[Task 20/25] Current/Best: 19.99/ 19.99 GFLOPS | Progress: (16/20) | 30.58 s
[Task 20/25] Current/Best: 7.72/ 19.99 GFLOPS | Progress: (20/20) | 42.13 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 17.07/ 17.44 GFLOPS | Progress: (4/20) | 7.69 s
[Task 21/25] Current/Best: 17.45/ 18.23 GFLOPS | Progress: (8/20) | 9.92 s
[Task 21/25] Current/Best: 10.72/ 18.23 GFLOPS | Progress: (12/20) | 21.44 s
[Task 21/25] Current/Best: 4.75/ 20.10 GFLOPS | Progress: (16/20) | 34.00 s
[Task 21/25] Current/Best: 12.51/ 20.10 GFLOPS | Progress: (20/20) | 43.40 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 8.71/ 8.71 GFLOPS | Progress: (4/20) | 6.63 s
[Task 22/25] Current/Best: 10.93/ 19.65 GFLOPS | Progress: (8/20) | 8.94 s
[Task 22/25] Current/Best: 9.28/ 19.65 GFLOPS | Progress: (12/20
) | 12.24 s
[Task 22/25] Current/Best: 20.51/ 20.51 GFLOPS | Progress: (16/20) | 14.63 s
[Task 22/25] Current/Best: 18.38/ 21.35 GFLOPS | Progress: (20/20) | 18.33 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 6.13/ 22.66 GFLOPS | Progress: (4/20) | 6.90 s
[Task 23/25] Current/Best: 20.85/ 22.66 GFLOPS | Progress: (8/20) | 8.92 s
[Task 23/25] Current/Best: 5.37/ 22.66 GFLOPS | Progress: (12/20) | 12.30 s
[Task 23/25] Current/Best: 20.44/ 22.66 GFLOPS | Progress: (16/20) | 15.16 s
[Task 23/25] Current/Best: 8.74/ 22.89 GFLOPS | Progress: (20/20) | 20.33 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 0.99/ 9.40 GFLOPS | Progress: (4/20) | 9.47 s
[Task 24/25] Current/Best: 4.14/ 9.40 GFLOPS | Progress: (8/20) | 14.73 s
[Task 24/25] Current/Best: 3.62/ 9.40 GFLOPS | Progress: (12/20) | 18.61 s
[Task 24/25] Current/Best: 2.96/ 9.40 GFLOPS | Progress: (16/20) | 23.00 s
[Task 24/25] Current/Best: 7.21/ 9.40 GFLOPS | Progress: (20/20) | 34.00 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 2.95/ 8.10 GFLOPS | Progress: (4/20) | 7.80 s
[Task 25/25] Current/Best: 5.87/ 8.10 GFLOPS | Progress: (8/20) | 16.84 s
[Task 25/25] Current/Best: 1.54/ 9.23 GFLOPS | Progress: (12/20) | 20.14 s
[Task 25/25] Current/Best: 2.51/ 9.23 GFLOPS | Progress: (16/20) | 25.37 s
[Task 25/25] Current/Best: 7.17/ 9.23 GFLOPS | Progress: (20/
20) | 36.05 s
@@ -675,6 +674,7 @@ model using optimized operators to speed up our computations.
Done.
Done.
Done.
+ Done.
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621104
- class='n02123159 tiger cat' with probability=0.356378
+ class='n02123045 tabby, tabby cat' with probability=0.621103
+ class='n02123159 tiger cat' with probability=0.356379
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 406.27946542994323, 'median': 404.5121894499971, 'std': 3.4501309615297613}
- unoptimized: {'mean': 488.051624159998, 'median': 487.0145931001389, 'std': 3.6031003135280066}
+ optimized: {'mean': 409.69271746998857, 'median': 409.63112874997023, 'std': 2.6985752746225575}
+ unoptimized: {'mean': 497.0907632900344, 'median': 497.0771257999331, 'std': 3.0660985668566254}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 12 minutes 52.070 seconds)
+ **Total running time of the script:** ( 13 minutes 13.290 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 fed913002a..182881495e 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -274,7 +274,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.174e-07 secs/op
+ 1.118e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 17c43d9e49..93f23d8df8 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, 0x24da3630)), stage(b, placeholder(b, 0x163301e0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T [...]
+ [stage(a, placeholder(a, 0xc72f050)), stage(b, placeholder(b, 0x15a62410)), 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 109ca8902c..2ae1afc64d 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,31 +5,31 @@
Computation times
=================
-**16:22.660** total execution time for **tutorial** files:
+**16:53.768** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:52.070 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:13.290 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:32.887 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:29.828 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.737 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.077 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:41.064 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:42.956 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:16.877 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:24.560 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.988 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.006 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.849 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.852 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.188 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.199 | 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 959d2dda9f..396748d65d 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: 0.000008
+ naive: 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 7.3681700087036e-06 1.0
- naive 6.6974e-06 0.9089638257652499
- parallel 7.0057e-06 0.9508059656230197
- vector 3.95141e-05 5.362810569425548
+ numpy 7.706819997110869e-06 1.0
+ naive 8.226200000000001e-06 1.0673922581666415
+ parallel 7.3352e-06 0.9517803715085882
+ vector 3.93811e-05 5.109902659561684
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017548
+ Numpy running time: 0.018427
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.252857
+ none: 3.445608
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.291531
+ blocking: 0.313910
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.269656
+ vectorization: 0.301106
# 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: 0.113240
+ loop permutation: 0.115910
# 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: 0.106775
+ array packing: 0.106509
# 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: 0.110606
+ block caching: 0.111519
# 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: 0.132033
+ parallelization: 0.132100
# 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 3.25285727 1.0
- blocking 0.2915307195 0.08962296691855773
- vectorization 0.2696557751 0.08289812700573856
- loop permutation 0.1132398652 0.03481242975041447
- array packing 0.10677501569999999 0.03282499256415268
- block caching 0.11060617449999999 0.03400277519708081
- parallelization 0.13203342580000002 0.04058998438625007
+ none 3.4456082048999996 1.0
+ blocking 0.3139097878 0.09110431863773394
+ vectorization 0.30110615550000003 0.08738839055229697
+ loop permutation 0.1159101786 0.033639976371998455
+ array packing 0.1065087079 0.030911439016349556
+ block caching 0.11151873059999999 0.03236547046800307
+ parallelization 0.13209956250000002 0.03833853260279019
@@ -1594,6 +1594,11 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.077 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/api/rust/help.html b/docs/api/rust/help.html
index b7e9afb4d7..6efdf48682 100644
--- a/docs/api/rust/help.html
+++ b/docs/api/rust/help.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
diff --git a/docs/api/rust/settings.html b/docs/api/rust/settings.html
index 6212392595..95bf831b71 100644
--- a/docs/api/rust/settings.html
+++ b/docs/api/rust/settings.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
diff --git a/docs/commit_hash b/docs/commit_hash
index e5b0d66636..e6911f50be 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-2032b44f427dec2028e0d2a98be1d5210ed3033b
+25b8a0798e6308b21191ead0739eac54d376806e
diff --git a/docs/genindex.html b/docs/genindex.html
index 7d3b511b02..fa12b01ba6 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -1246,6 +1246,8 @@
<li><a href="reference/api/python/meta_schedule.html#tvm.meta_schedule.TaskScheduler.create">(tvm.meta_schedule.TaskScheduler static method)</a>
</li>
</ul></li>
+ <li><a href="reference/api/python/tir.html#tvm.tir.create_barriers">create_barriers() (in module tvm.tir)</a>
+</li>
<li><a href="reference/api/python/contrib.html#tvm.contrib.xcode.create_dylib">create_dylib() (in module tvm.contrib.xcode)</a>
</li>
<li><a href="reference/api/python/contrib.html#tvm.contrib.cc.create_executable">create_executable() (in module tvm.contrib.cc)</a>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 08e5e2037e..d45d6f819d 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -600,7 +600,7 @@ 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 35.240 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.931 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_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index 47d5a3aa7f..953ec8c39b 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -454,7 +454,7 @@
<span class="nb">print</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip7308da15-42b5-4532-a205-fef0862c2d49 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip2b246167-9e23-4399-b720-fd30fd5ca936 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index e94d3c928f..91a46becbe 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -464,14 +464,14 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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- 92%|#########2| 38.3M/41.5M [00:01<00:00, 27.9MB/s]
-100%|##########| 41.5M/41.5M [00:02<00:00, 21.4MB/s]
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+ 62%|######2 | 25.9M/41.5M [00:00<00:00, 29.3MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:01<00:00, 31.4MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 32.9MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 31.9MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 2a2574a9d5..c9ab360b99 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -499,7 +499,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.100 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.187 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 6e8af17a75..6ca0deb7f9 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -447,16 +447,17 @@ 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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- 86%|########5 | 38.3M/44.7M [00:01<00:00, 27.9MB/s]
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-100%|##########| 44.7M/44.7M [00:01<00:00, 28.8MB/s]
+ 14%|#4 | 6.30M/44.7M [00:00<00:01, 30.0MB/s]
+ 21%|## | 9.17M/44.7M [00:00<00:01, 24.7MB/s]
+ 32%|###2 | 14.3M/44.7M [00:00<00:01, 27.9MB/s]
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+ 54%|#####3 | 24.0M/44.7M [00:00<00:00, 26.4MB/s]
+ 59%|#####9 | 26.4M/44.7M [00:01<00:00, 26.1MB/s]
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+ 86%|########5 | 38.3M/44.7M [00:01<00:00, 22.8MB/s]
+ 91%|######### | 40.4M/44.7M [00:01<00:00, 19.9MB/s]
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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 ab4720ae16..5cf27308ef 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -667,7 +667,7 @@ 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 32.303 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.681 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 29c2c7e679..8cdb905166 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -355,7 +355,7 @@
<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>07:13.238</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:36.928</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -364,43 +364,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:35.240</p></td>
+<td><p>01:40.931</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
-<td><p>01:32.303</p></td>
+<td><p>01:34.681</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:02.100</p></td>
+<td><p>01:07.187</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:41.510</p></td>
+<td><p>00:42.524</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
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+<td><p>00:39.479</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></td>
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+<td><p>00:35.261</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:28.201</p></td>
+<td><p>00:29.733</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:27.876</p></td>
+<td><p>00:29.634</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
-<td><p>00:13.309</p></td>
+<td><p>00:14.560</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:02.849</p></td>
+<td><p>00:02.938</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index c8873cd1f4..68c2315d9f 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -845,10 +845,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4218.7213 4218.0683 4223.8765 4216.0642 2.2713
+ 4227.1443 4226.2691 4229.6240 4225.5971 1.5891
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.179 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.192 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 2fba17e863..ed09e97340 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -453,34 +453,33 @@ 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 322e6ecbb6..c0b8ba1180 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -677,7 +677,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.5840 15.5806 15.7468 15.4171 0.1004
+ 15.3326 15.1779 16.3568 15.0942 0.3637
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index aeff4a256b..2e6ffa8433 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -469,44 +469,42 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -600,7 +598,7 @@ 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 25.343 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 41.475 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 5cc5fe60e7..8524f52669 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -510,10 +510,9 @@ 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
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</pre></div>
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@@ -604,7 +603,7 @@ 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)
- 88.5201 88.4573 89.8281 88.3015 0.2439
+ 89.2064 89.1410 90.4811 88.7694 0.3194
</pre></div>
</div>
<div class="admonition note">
@@ -643,7 +642,7 @@ 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 24.756 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.115 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 ae1fc8f8c6..0f4f3d6cf2 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -595,7 +595,7 @@ 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)
- 107.4410 107.1817 130.4887 106.5048 2.3590
+ 110.2547 110.1715 114.9507 109.6149 0.5384
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index 45ec583edc..6b133373df 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -536,7 +536,7 @@ for calibration. But the accuracy might be impacted.</p>
warnings.warn(
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 49.812 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 25.486 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.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_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 91eed0246c..4ffa49252f 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -355,7 +355,7 @@
<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>12:24.454</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:32.905</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -364,43 +364,43 @@
</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>03:25.343</p></td>
+<td><p>03:41.475</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></td>
-<td><p>02:49.812</p></td>
+<td><p>02:25.486</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>01:24.756</p></td>
+<td><p>01:29.115</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.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>01:19.179</p></td>
+<td><p>01:21.192</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>00:49.947</p></td>
+<td><p>00:52.419</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:48.322</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:51.993</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:48.308</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
+<td><p>00:48.792</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:29.535</p></td>
+<td><p>00:31.586</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:29.246</p></td>
+<td><p>00:30.841</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:00.006</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/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index 5444316939..74657699e4 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -634,7 +634,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
<span class="n">module</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="n">get_mobilenet</span><span class="p">()</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip41398fd1-55a6-4cbf-aab3-9360365a4ae4 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipba07a27b-88d9-4a5e-9d6e-76f5762539d0 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index b8118e69d3..eb83280f3f 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -355,7 +355,7 @@
<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>00:55.439</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:58.962</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -364,15 +364,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:51.675</p></td>
+<td><p>00:54.958</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:02.628</p></td>
+<td><p>00:02.799</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:01.130</p></td>
+<td><p>00:01.197</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 4fd4fa490f..2a0f758ec6 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -541,10 +541,10 @@ 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: 23277us [23277us] (48.70%; 48.70%)
-FoldScaleAxis: 24521us [6us] (51.30%; 51.30%)
- FoldConstant: 24514us [1672us] (51.29%; 99.97%)
- InferType: 22842us [22842us] (47.79%; 93.18%)
+InferType: 23383us [23383us] (48.44%; 48.44%)
+FoldScaleAxis: 24893us [8us] (51.56%; 51.56%)
+ FoldConstant: 24885us [1775us] (51.55%; 99.97%)
+ InferType: 23110us [23110us] (47.87%; 92.87%)
</pre></div>
</div>
</div>
@@ -566,10 +566,10 @@ 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: 22568us [22568us] (48.42%; 48.42%)
-FoldScaleAxis: 24041us [5us] (51.58%; 51.58%)
- FoldConstant: 24036us [1592us] (51.57%; 99.98%)
- InferType: 22444us [22444us] (48.15%; 93.38%)
+InferType: 23034us [23034us] (48.18%; 48.18%)
+FoldScaleAxis: 24776us [7us] (51.82%; 51.82%)
+ FoldConstant: 24768us [1806us] (51.81%; 99.97%)
+ InferType: 22962us [22962us] (48.03%; 92.71%)
</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 0b6099e9f4..7d6e66fa79 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -590,7 +590,7 @@ 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: 37.394462 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 37.716030 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 e9a7811edd..2ab08b52f1 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -872,7 +872,7 @@ 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: 12.273107 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.264448 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 e3ca898864..c47577ccde 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -487,8 +487,8 @@ 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: 0.017484
-Baseline: 3.200752
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018754
+Baseline: 3.433402
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -547,7 +547,7 @@ 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: 0.294598
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.317832
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -604,7 +604,7 @@ 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: 0.273858
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.304177
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -659,7 +659,7 @@ 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: 0.109366
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.119378
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -736,7 +736,7 @@ 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: 0.106350
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.108205
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -814,7 +814,7 @@ 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: 0.110870
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112157
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -894,7 +894,7 @@ 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: 0.131637
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.134299
</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 59ac3efd8a..eab044786e 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -355,7 +355,7 @@
<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>00:33.062</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.131</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -364,15 +364,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:29.878</p></td>
+<td><p>00:31.673</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>00:02.000</p></td>
+<td><p>00:02.099</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>00:01.183</p></td>
+<td><p>00:01.359</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 f8414e9e9c..c67e78c60c 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -355,7 +355,7 @@
<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>03:30.279</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:38.594</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -364,27 +364,27 @@
</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>01:30.059</p></td>
+<td><p>01:34.397</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>01:11.139</p></td>
+<td><p>01:13.332</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>00:17.478</p></td>
+<td><p>00:17.549</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:16.043</p></td>
+<td><p>00:16.774</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:15.458</p></td>
+<td><p>00:16.440</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>00:00.102</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index eefa130f7c..8935c79772 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
@@ -1027,7 +1027,7 @@ 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: 0.348 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.342 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 60734e1950..0c8b5ac83d 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -931,7 +931,7 @@ 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)
- 3.2687 3.2699 3.2712 3.2651 0.0026
+ 3.2445 3.2427 3.2497 3.2410 0.0038
</pre></div>
</div>
</div>
@@ -953,7 +953,7 @@ 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 11.139 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 13.332 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 d5d818644f..b452e7a47e 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -950,7 +950,7 @@ 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)
- 756.5339 756.5140 757.0487 756.0391 0.4124
+ 767.1440 768.7540 770.2038 762.4741 3.3547
</pre></div>
</div>
</div>
@@ -972,7 +972,7 @@ 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 30.059 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.397 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 03ca643e64..4053d93eb7 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -355,7 +355,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:23.696</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.864</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -364,11 +364,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:23.654</p></td>
+<td><p>00:23.825</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>00:00.025</p></td>
+<td><p>00:00.023</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.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 d9aa1c2b48..596405199b 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -625,7 +625,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.037294
+Time cost of this operator: 0.037275
</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 f1f3d43ff8..30b38c495d 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -659,10 +659,10 @@ 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 302.8 98.75 (1, 2, 10, 10, 3) 2 1 [302.8]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.881 0.94 (1, 6, 10, 10) 1 1 [2.881]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.953 0.311 (1, 1, 10, 10, 3) 1 1 [0.953]
-Total_time - 306.634 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.6 98.762 (1, 2, 10, 10, 3) 2 1 [308.6]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.881 0.922 (1, 6, 10, 10) 1 1 [2.881]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.987 0.316 (1, 1, 10, 10, 3) 1 1 [0.987]
+Total_time - 312.469 - - - - -
</pre></div>
</div>
</div>
@@ -714,13 +714,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.3 97.33 (1, 6, 10, 10, 1) 2 1 [100.3]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.79 1.737 (1, 6, 10, 10) 1 1 [1.79]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 0.933 (1, 1, 10, 10, 3) 1 1 [0.962]
-Total_time - 103.052 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 104.6 97.447 (1, 6, 10, 10, 1) 2 1 [104.6]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.891 1.762 (1, 6, 10, 10) 1 1 [1.891]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.849 0.791 (1, 3, 10, 10, 1) 1 1 [0.849]
+Total_time - 107.34 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.014 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.395 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 452eaff05b..347298f455 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -470,8 +470,8 @@ 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, 4.62MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 7.51MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 4.92MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 7.96MB/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.
@@ -599,7 +599,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.558 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.321 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index fa5c04aae5..43f7f0a374 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -538,7 +538,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmprgrucuqn/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpj6s0wly9/images/random'
</pre></div>
</div>
</div>
@@ -598,8 +598,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmprgrucuqn/images/target contains 8144 images
-/tmp/tmprgrucuqn/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpj6s0wly9/images/target contains 8144 images
+/tmp/tmpj6s0wly9/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -711,13 +711,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 40s - loss: 0.2188 - accuracy: 0.9230 - val_loss: 0.1100 - val_accuracy: 0.9585 - 40s/epoch - 123ms/step
+328/328 - 42s - loss: 0.2165 - accuracy: 0.9237 - val_loss: 0.1030 - val_accuracy: 0.9653 - 42s/epoch - 128ms/step
Epoch 2/3
-328/328 - 35s - loss: 0.0986 - accuracy: 0.9633 - val_loss: 0.0946 - val_accuracy: 0.9622 - 35s/epoch - 107ms/step
+328/328 - 36s - loss: 0.1002 - accuracy: 0.9646 - val_loss: 0.1067 - val_accuracy: 0.9619 - 36s/epoch - 109ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0656 - accuracy: 0.9762 - val_loss: 0.0915 - val_accuracy: 0.9683 - 35s/epoch - 107ms/step
+328/328 - 36s - loss: 0.0649 - accuracy: 0.9747 - val_loss: 0.0956 - val_accuracy: 0.9698 - 36s/epoch - 108ms/step
-<keras.callbacks.History object at 0x7fcd322ecb50>
+<keras.callbacks.History object at 0x7f650fa97d30>
</pre></div>
</div>
</div>
@@ -981,7 +981,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 34.435 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 22.994 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
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index fe05d9f271..f0b32c36ce 100644
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -355,7 +355,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<table class="docutils align-default">
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:08.503</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
-<td><p>00:08.135</p></td>
+<td><p>00:08.520</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 19dd3cdbca..905762ccd3 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -355,7 +355,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:38.809</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
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<table class="docutils align-default">
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<col style="width: 84%" />
@@ -364,15 +364,15 @@
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
-<td><p>00:02.037</p></td>
+<td><p>00:01.971</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 23200326a5..9cdc0d356e 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -567,7 +567,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<a href="../../reference/api/python/ir.html#tvm.ir.register_intrin_lowering" title="tvm.ir.register_intrin_lowering" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-function"><span class="n">register_intrin_lowering</span></a><span class="p">(</span><span class="s2">"tir.exp"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">f</span><span class="o">= [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fce8b2fcee0>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f651e9cca60>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index 61b8f9ba46..51f7a39f03 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -355,7 +355,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:06.389</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
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<table class="docutils align-default">
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@@ -364,35 +364,35 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
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</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
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diff --git a/docs/install/nnpack.html b/docs/install/nnpack.html
index f593cbe13b..e6565cbaae 100644
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+++ b/docs/install/nnpack.html
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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Installing TVM</a><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="docker.html">Docker Images</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#conditions">Conditions</a></li>
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Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/reference/api/doxygen/builtin_8h.html b/docs/reference/api/doxygen/builtin_8h.html
index c1640cddd7..ca96086887 100644
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<tr class="memitem:a89bf47c3237df742ada0b599e3e4787a"><td class="memItemLeft" align="right" valign="top">const Op & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">tvm::tir::builtin::ptx_wait_barrier</a> ()</td></tr>
<tr class="memdesc:a89bf47c3237df742ada0b599e3e4787a"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsics for ptx barrier wait using mbarrier.try_wait <a href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">More...</a><br /></td></tr>
<tr class="separator:a89bf47c3237df742ada0b599e3e4787a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="memItemLeft" align="right" valign="top">const Op & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">tvm::tir::builtin::create_barriers</a> ()</td></tr>
+<tr class="memdesc:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsics to create N barriers <a href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">More...</a><br /></td></tr>
+<tr class="separator:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a772fb68f083e71e635c50bb503903f22"><td class="memItemLeft" align="right" valign="top">const Op & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22">tvm::tir::builtin::mma_store</a> ()</td></tr>
<tr class="memdesc:a772fb68f083e71e635c50bb503903f22"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsic for storing the result of PTX MMA into a destination pointer. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 4 elements from the result of m16xn8xk16 MMA in its registers, this intrinsic can be used to store the result in a 16x8 region in shared or global memory. <a hre [...]
<tr class="separator:a772fb68f083e71e635c50bb503903f22"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/builtin_8h_source.html b/docs/reference/api/doxygen/builtin_8h_source.html
index fbd37385bf..3d65ec7c4b 100644
--- a/docs/reference/api/doxygen/builtin_8h_source.html
+++ b/docs/reference/api/doxygen/builtin_8h_source.html
@@ -218,77 +218,79 @@ $(function() {
<div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  </div>
<div class="line"><a name="l00656"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a7eaa1b0b7d7cb550f6a7eaf5efe8d493"> 656</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a7eaa1b0b7d7cb550f6a7eaf5efe8d493">ptx_cp_async</a>();</div>
<div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  </div>
-<div class="line"><a name="l00669"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a5a2befd1e97765919a6bd98ae314993e"> 669</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a5a2befd1e97765919a6bd98ae314993e">ptx_cp_async_bulk</a>();</div>
-<div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  </div>
-<div class="line"><a name="l00678"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a21346695611f032cadc3fad6615fe63b"> 678</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a21346695611f032cadc3fad6615fe63b">ptx_commit_group</a>();</div>
-<div class="line"><a name="l00679"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aed0dcc804c2d1b939969c9e48aa448f9"> 679</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aed0dcc804c2d1b939969c9e48aa448f9">ptx_wait_group</a>();</div>
-<div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  </div>
-<div class="line"><a name="l00687"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a2b8d9de5b924dc2dd2dd35a110f767b3"> 687</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a2b8d9de5b924dc2dd2dd35a110f767b3">ptx_cp_async_barrier</a>();</div>
-<div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  </div>
-<div class="line"><a name="l00695"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#acbd805fd2396ed0803800644f5ecdaa5"> 695</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#acbd805fd2396ed0803800644f5ecdaa5">ptx_init_barrier_thread_count</a>();</div>
-<div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  </div>
-<div class="line"><a name="l00703"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ac21d88612f6c2c21562411b99e4d24d1"> 703</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ac21d88612f6c2c21562411b99e4d24d1">ptx_arrive_barrier</a>();</div>
-<div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  </div>
-<div class="line"><a name="l00711"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aaf28239ff1cf7b756610e4d3b32bdfcd"> 711</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aaf28239ff1cf7b756610e4d3b32bdfcd">ptx_arrive_barrier_expect_tx</a>();</div>
-<div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  </div>
-<div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a"> 719</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">ptx_wait_barrier</a>();</div>
-<div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  </div>
-<div class="line"><a name="l00733"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22"> 733</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22">mma_store</a>();</div>
-<div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  </div>
-<div class="line"><a name="l00746"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab"> 746</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">mma_fill</a>();</div>
-<div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  </div>
-<div class="line"><a name="l00748"></a><span class="lineno"> 748</span> <span class="comment">// TODO(tvm-team) replace the usage of the vector operations by Shuffle.</span></div>
-<div class="line"><a name="l00752"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25"> 752</a></span> <span class="comment"></span>TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25">vectorhigh</a>();</div>
-<div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  </div>
-<div class="line"><a name="l00757"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a7ed64a9fb0a7f575fc63e1e0395e96a6"> 757</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a7ed64a9fb0a7f575fc63e1e0395e96a6">vectorlow</a>();</div>
-<div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  </div>
-<div class="line"><a name="l00762"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43"> 762</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">vectorcombine</a>();</div>
-<div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  </div>
-<div class="line"><a name="l00767"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7"> 767</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7">atomic_add</a>();</div>
-<div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a9603510b7eff067c253eb340310c2996"> 771</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a9603510b7eff067c253eb340310c2996">nd_mem_alloc_with_scope</a>();</div>
-<div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  </div>
-<div class="line"><a name="l00776"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a6ea87b06a1eb1050b7503ef6cc1bab34"> 776</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a6ea87b06a1eb1050b7503ef6cc1bab34">texture2d_store</a>();</div>
-<div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  </div>
-<div class="line"><a name="l00781"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ae52feb3e7828262a1b8a31062642ae36"> 781</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ae52feb3e7828262a1b8a31062642ae36">texture2d_load</a>();</div>
-<div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  </div>
-<div class="line"><a name="l00794"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#afd453e860a10562ac2692473edde6707"> 794</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#afd453e860a10562ac2692473edde6707">dma_copy</a>();</div>
-<div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  </div>
-<div class="line"><a name="l00802"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a13aefe6686345fee7d4a1633dd059806"> 802</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a13aefe6686345fee7d4a1633dd059806">dma_wait</a>();</div>
-<div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  </div>
-<div class="line"><a name="l00813"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ace1c09d0bcfa95c52dd509e0c40b1824"> 813</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ace1c09d0bcfa95c52dd509e0c40b1824">dma_start_group</a>();</div>
-<div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  </div>
-<div class="line"><a name="l00826"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a3397d82a9ffb11859615f298f529f538"> 826</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a3397d82a9ffb11859615f298f529f538">dma_end_group</a>();</div>
-<div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  </div>
-<div class="line"><a name="l00835"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a0b3d8f41abba4b7dd4a9af8ec2f9acba"> 835</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a0b3d8f41abba4b7dd4a9af8ec2f9acba">assume</a>();</div>
-<div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  </div>
-<div class="line"><a name="l00843"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877"> 843</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877">undef</a>();</div>
-<div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  </div>
-<div class="line"><a name="l00848"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aa63c16281d1010a82a06eb771268b4e2"> 848</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aa63c16281d1010a82a06eb771268b4e2">start_profile_intrinsic</a>();</div>
-<div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  </div>
-<div class="line"><a name="l00853"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a6c57e9701977267ff96d514bd3c2a5e6"> 853</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a6c57e9701977267ff96d514bd3c2a5e6">end_profile_intrinsic</a>();</div>
-<div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  </div>
-<div class="line"><a name="l00856"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c"> 856</a></span> <span class="keyword">enum</span> <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">TVMStructFieldKind</a> : <span class="keywordtype">int</span> {</div>
-<div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="comment">// array head address</span></div>
-<div class="line"><a name="l00858"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"> 858</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">kArrAddr</a>,</div>
-<div class="line"><a name="l00859"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"> 859</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">kArrData</a>,</div>
-<div class="line"><a name="l00860"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"> 860</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">kArrShape</a>,</div>
-<div class="line"><a name="l00861"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"> 861</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">kArrStrides</a>,</div>
-<div class="line"><a name="l00862"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"> 862</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">kArrNDim</a>,</div>
-<div class="line"><a name="l00863"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"> 863</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">kArrTypeCode</a>,</div>
-<div class="line"><a name="l00864"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"> 864</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">kArrTypeBits</a>,</div>
-<div class="line"><a name="l00865"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"> 865</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">kArrTypeLanes</a>,</div>
-<div class="line"><a name="l00866"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"> 866</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">kArrByteOffset</a>,</div>
-<div class="line"><a name="l00867"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74"> 867</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74">kArrDeviceId</a>,</div>
-<div class="line"><a name="l00868"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"> 868</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">kArrDeviceType</a>,</div>
-<div class="line"><a name="l00869"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"> 869</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">kArrKindBound_</a>,</div>
-<div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="comment">// TVMValue field</span></div>
-<div class="line"><a name="l00871"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"> 871</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">kTVMValueContent</a>,</div>
-<div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">kTVMValueKindBound_</a></div>
-<div class="line"><a name="l00873"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"> 873</a></span> };</div>
-<div class="line"><a name="l00874"></a><span class="lineno"> 874</span> } <span class="comment">// namespace builtin</span></div>
-<div class="line"><a name="l00875"></a><span class="lineno"> 875</span> } <span class="comment">// namespace tir</span></div>
-<div class="line"><a name="l00876"></a><span class="lineno"> 876</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TIR_BUILTIN_H_</span></div>
+<div class="line"><a name="l00668"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a5a2befd1e97765919a6bd98ae314993e"> 668</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a5a2befd1e97765919a6bd98ae314993e">ptx_cp_async_bulk</a>();</div>
+<div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  </div>
+<div class="line"><a name="l00677"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a21346695611f032cadc3fad6615fe63b"> 677</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a21346695611f032cadc3fad6615fe63b">ptx_commit_group</a>();</div>
+<div class="line"><a name="l00678"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aed0dcc804c2d1b939969c9e48aa448f9"> 678</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aed0dcc804c2d1b939969c9e48aa448f9">ptx_wait_group</a>();</div>
+<div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  </div>
+<div class="line"><a name="l00686"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a2b8d9de5b924dc2dd2dd35a110f767b3"> 686</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a2b8d9de5b924dc2dd2dd35a110f767b3">ptx_cp_async_barrier</a>();</div>
+<div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  </div>
+<div class="line"><a name="l00694"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#acbd805fd2396ed0803800644f5ecdaa5"> 694</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#acbd805fd2396ed0803800644f5ecdaa5">ptx_init_barrier_thread_count</a>();</div>
+<div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  </div>
+<div class="line"><a name="l00702"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ac21d88612f6c2c21562411b99e4d24d1"> 702</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ac21d88612f6c2c21562411b99e4d24d1">ptx_arrive_barrier</a>();</div>
+<div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  </div>
+<div class="line"><a name="l00710"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aaf28239ff1cf7b756610e4d3b32bdfcd"> 710</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aaf28239ff1cf7b756610e4d3b32bdfcd">ptx_arrive_barrier_expect_tx</a>();</div>
+<div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  </div>
+<div class="line"><a name="l00718"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a"> 718</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">ptx_wait_barrier</a>();</div>
+<div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  </div>
+<div class="line"><a name="l00726"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee"> 726</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">create_barriers</a>();</div>
+<div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  </div>
+<div class="line"><a name="l00740"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22"> 740</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22">mma_store</a>();</div>
+<div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  </div>
+<div class="line"><a name="l00753"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab"> 753</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">mma_fill</a>();</div>
+<div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  </div>
+<div class="line"><a name="l00755"></a><span class="lineno"> 755</span> <span class="comment">// TODO(tvm-team) replace the usage of the vector operations by Shuffle.</span></div>
+<div class="line"><a name="l00759"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25"> 759</a></span> <span class="comment"></span>TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25">vectorhigh</a>();</div>
+<div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  </div>
+<div class="line"><a name="l00764"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a7ed64a9fb0a7f575fc63e1e0395e96a6"> 764</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a7ed64a9fb0a7f575fc63e1e0395e96a6">vectorlow</a>();</div>
+<div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  </div>
+<div class="line"><a name="l00769"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43"> 769</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">vectorcombine</a>();</div>
+<div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  </div>
+<div class="line"><a name="l00774"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7"> 774</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7">atomic_add</a>();</div>
+<div class="line"><a name="l00778"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a9603510b7eff067c253eb340310c2996"> 778</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a9603510b7eff067c253eb340310c2996">nd_mem_alloc_with_scope</a>();</div>
+<div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  </div>
+<div class="line"><a name="l00783"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a6ea87b06a1eb1050b7503ef6cc1bab34"> 783</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a6ea87b06a1eb1050b7503ef6cc1bab34">texture2d_store</a>();</div>
+<div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  </div>
+<div class="line"><a name="l00788"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ae52feb3e7828262a1b8a31062642ae36"> 788</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ae52feb3e7828262a1b8a31062642ae36">texture2d_load</a>();</div>
+<div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  </div>
+<div class="line"><a name="l00801"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#afd453e860a10562ac2692473edde6707"> 801</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#afd453e860a10562ac2692473edde6707">dma_copy</a>();</div>
+<div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  </div>
+<div class="line"><a name="l00809"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a13aefe6686345fee7d4a1633dd059806"> 809</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a13aefe6686345fee7d4a1633dd059806">dma_wait</a>();</div>
+<div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  </div>
+<div class="line"><a name="l00820"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ace1c09d0bcfa95c52dd509e0c40b1824"> 820</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ace1c09d0bcfa95c52dd509e0c40b1824">dma_start_group</a>();</div>
+<div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  </div>
+<div class="line"><a name="l00833"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a3397d82a9ffb11859615f298f529f538"> 833</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a3397d82a9ffb11859615f298f529f538">dma_end_group</a>();</div>
+<div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  </div>
+<div class="line"><a name="l00842"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a0b3d8f41abba4b7dd4a9af8ec2f9acba"> 842</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a0b3d8f41abba4b7dd4a9af8ec2f9acba">assume</a>();</div>
+<div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  </div>
+<div class="line"><a name="l00850"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877"> 850</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877">undef</a>();</div>
+<div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  </div>
+<div class="line"><a name="l00855"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#aa63c16281d1010a82a06eb771268b4e2"> 855</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#aa63c16281d1010a82a06eb771268b4e2">start_profile_intrinsic</a>();</div>
+<div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  </div>
+<div class="line"><a name="l00860"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#a6c57e9701977267ff96d514bd3c2a5e6"> 860</a></span> TVM_DLL <span class="keyword">const</span> <a class="code" href="classtvm_1_1Op.html">Op</a>& <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#a6c57e9701977267ff96d514bd3c2a5e6">end_profile_intrinsic</a>();</div>
+<div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  </div>
+<div class="line"><a name="l00863"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c"> 863</a></span> <span class="keyword">enum</span> <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">TVMStructFieldKind</a> : <span class="keywordtype">int</span> {</div>
+<div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// array head address</span></div>
+<div class="line"><a name="l00865"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"> 865</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">kArrAddr</a>,</div>
+<div class="line"><a name="l00866"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"> 866</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">kArrData</a>,</div>
+<div class="line"><a name="l00867"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"> 867</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">kArrShape</a>,</div>
+<div class="line"><a name="l00868"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"> 868</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">kArrStrides</a>,</div>
+<div class="line"><a name="l00869"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"> 869</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">kArrNDim</a>,</div>
+<div class="line"><a name="l00870"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"> 870</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">kArrTypeCode</a>,</div>
+<div class="line"><a name="l00871"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"> 871</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">kArrTypeBits</a>,</div>
+<div class="line"><a name="l00872"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"> 872</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">kArrTypeLanes</a>,</div>
+<div class="line"><a name="l00873"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"> 873</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">kArrByteOffset</a>,</div>
+<div class="line"><a name="l00874"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74"> 874</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74">kArrDeviceId</a>,</div>
+<div class="line"><a name="l00875"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"> 875</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">kArrDeviceType</a>,</div>
+<div class="line"><a name="l00876"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"> 876</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">kArrKindBound_</a>,</div>
+<div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="comment">// TVMValue field</span></div>
+<div class="line"><a name="l00878"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"> 878</a></span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">kTVMValueContent</a>,</div>
+<div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">kTVMValueKindBound_</a></div>
+<div class="line"><a name="l00880"></a><span class="lineno"><a class="line" href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"> 880</a></span> };</div>
+<div class="line"><a name="l00881"></a><span class="lineno"> 881</span> } <span class="comment">// namespace builtin</span></div>
+<div class="line"><a name="l00882"></a><span class="lineno"> 882</span> } <span class="comment">// namespace tir</span></div>
+<div class="line"><a name="l00883"></a><span class="lineno"> 883</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00884"></a><span class="lineno"> 884</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TIR_BUILTIN_H_</span></div>
<div class="ttc" id="aclasstvm_1_1Op_html"><div class="ttname"><a href="classtvm_1_1Op.html">tvm::Op</a></div><div class="ttdoc">Managed reference class to OpNode.</div><div class="ttdef"><b>Definition:</b> op.h:165</div></div>
<div class="ttc" id="air_2op_8h_html"><div class="ttname"><a href="ir_2op_8h.html">op.h</a></div><div class="ttdoc">Primitive operators(builtin intrinsics) and registry for them.</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a0117a4a76af962576a6a3bbf32f97b36"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0117a4a76af962576a6a3bbf32f97b36">tvm::tir::builtin::tvm_call_packed_lowered</a></div><div class="ttdeci">const Op & tvm_call_packed_lowered()</div><div class="ttdoc">Lowered version of call packed, the space of value and type codes are explicitly allocated.</div></div>
@@ -312,6 +314,7 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a28f99e6dd767482765b854ee9fc71f2c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a28f99e6dd767482765b854ee9fc71f2c">tvm::tir::builtin::tvm_stack_make_array</a></div><div class="ttdeci">const Op & tvm_stack_make_array()</div><div class="ttdoc">Allocate a NDArray(DLTensor) on stack, return the handle.</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a2b8d9de5b924dc2dd2dd35a110f767b3"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a2b8d9de5b924dc2dd2dd35a110f767b3">tvm::tir::builtin::ptx_cp_async_barrier</a></div><div class="ttdeci">const Op & ptx_cp_async_barrier()</div><div class="ttdoc">tvm intrinsics for ptx async copy barrier using cp.async.mbarrier.arrive</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a2c13c6e4b2f92e17f357665f9f11736c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a2c13c6e4b2f92e17f357665f9f11736c">tvm::tir::builtin::tvm_call_packed</a></div><div class="ttdeci">const Op & tvm_call_packed()</div><div class="ttdoc">See pesudo code.</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a2dd4fbe8995788ddcd3dfb1f788869ee"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">tvm::tir::builtin::create_barriers</a></div><div class="ttdeci">const Op & create_barriers()</div><div class="ttdoc">tvm intrinsics to create N barriers</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a307667c449c54cef747d781771f79bab"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">tvm::tir::builtin::mma_fill</a></div><div class="ttdeci">const Op & mma_fill()</div><div class="ttdoc">tvm intrinsic for zero-initalizing an MMA accumulation registor. For example, if each thread in a war...</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a30dff65bc2c142b57fae7f60e378ff43"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">tvm::tir::builtin::vectorcombine</a></div><div class="ttdeci">const Op & vectorcombine()</div><div class="ttdoc">Concat two vectors.</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_a3131992ea9acd964fb7d3ca782d74805"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a3131992ea9acd964fb7d3ca782d74805">tvm::tir::builtin::tvm_call_cpacked_lowered</a></div><div class="ttdeci">const Op & tvm_call_cpacked_lowered()</div><div class="ttdoc">Lowered version of call c-packed, the space of value and type codes are explicitly allocated.</div></div>
@@ -361,21 +364,21 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_aca44a85c87273dfab1731421f4edd2bf"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#aca44a85c87273dfab1731421f4edd2bf">tvm::tir::builtin::tvm_warp_shuffle</a></div><div class="ttdeci">const Op & tvm_warp_shuffle()</div><div class="ttdoc">See pseudo code.</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_acbd805fd2396ed0803800644f5ecdaa5"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#acbd805fd2396ed0803800644f5ecdaa5">tvm::tir::builtin::ptx_init_barrier_thread_count</a></div><div class="ttdeci">const Op & ptx_init_barrier_thread_count()</div><div class="ttdoc">tvm intrinsics for ptx barrier initialization of thread count using mbarrier.init</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ace1c09d0bcfa95c52dd509e0c40b1824"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ace1c09d0bcfa95c52dd509e0c40b1824">tvm::tir::builtin::dma_start_group</a></div><div class="ttdeci">const Op & dma_start_group()</div><div class="ttdoc">Start a group of DMA copies.</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">tvm::tir::builtin::TVMStructFieldKind</a></div><div class="ttdeci">TVMStructFieldKind</div><div class="ttdoc">The kind of structure field info used in intrinsic.</div><div class="ttdef"><b>Definition:</b> builtin.h:856</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">tvm::tir::builtin::kArrAddr</a></div><div class="ttdeci">@ kArrAddr</div><div class="ttdef"><b>Definition:</b> builtin.h:858</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">tvm::tir::builtin::kArrTypeLanes</a></div><div class="ttdeci">@ kArrTypeLanes</div><div class="ttdef"><b>Definition:</b> builtin.h:865</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">tvm::tir::builtin::kArrTypeBits</a></div><div class="ttdeci">@ kArrTypeBits</div><div class="ttdef"><b>Definition:</b> builtin.h:864</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">tvm::tir::builtin::kArrKindBound_</a></div><div class="ttdeci">@ kArrKindBound_</div><div class="ttdef"><b>Definition:</b> builtin.h:869</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">tvm::tir::builtin::kArrShape</a></div><div class="ttdeci">@ kArrShape</div><div class="ttdef"><b>Definition:</b> builtin.h:860</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">tvm::tir::builtin::kArrTypeCode</a></div><div class="ttdeci">@ kArrTypeCode</div><div class="ttdef"><b>Definition:</b> builtin.h:863</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">tvm::tir::builtin::kTVMValueContent</a></div><div class="ttdeci">@ kTVMValueContent</div><div class="ttdef"><b>Definition:</b> builtin.h:871</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">tvm::tir::builtin::kArrData</a></div><div class="ttdeci">@ kArrData</div><div class="ttdef"><b>Definition:</b> builtin.h:859</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74">tvm::tir::builtin::kArrDeviceId</a></div><div class="ttdeci">@ kArrDeviceId</div><div class="ttdef"><b>Definition:</b> builtin.h:867</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">tvm::tir::builtin::kArrStrides</a></div><div class="ttdeci">@ kArrStrides</div><div class="ttdef"><b>Definition:</b> builtin.h:861</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">tvm::tir::builtin::kTVMValueKindBound_</a></div><div class="ttdeci">@ kTVMValueKindBound_</div><div class="ttdef"><b>Definition:</b> builtin.h:872</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">tvm::tir::builtin::kArrDeviceType</a></div><div class="ttdeci">@ kArrDeviceType</div><div class="ttdef"><b>Definition:</b> builtin.h:868</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">tvm::tir::builtin::kArrNDim</a></div><div class="ttdeci">@ kArrNDim</div><div class="ttdef"><b>Definition:</b> builtin.h:862</div></div>
-<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">tvm::tir::builtin::kArrByteOffset</a></div><div class="ttdeci">@ kArrByteOffset</div><div class="ttdef"><b>Definition:</b> builtin.h:866</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">tvm::tir::builtin::TVMStructFieldKind</a></div><div class="ttdeci">TVMStructFieldKind</div><div class="ttdoc">The kind of structure field info used in intrinsic.</div><div class="ttdef"><b>Definition:</b> builtin.h:863</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">tvm::tir::builtin::kArrAddr</a></div><div class="ttdeci">@ kArrAddr</div><div class="ttdef"><b>Definition:</b> builtin.h:865</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">tvm::tir::builtin::kArrTypeLanes</a></div><div class="ttdeci">@ kArrTypeLanes</div><div class="ttdef"><b>Definition:</b> builtin.h:872</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">tvm::tir::builtin::kArrTypeBits</a></div><div class="ttdeci">@ kArrTypeBits</div><div class="ttdef"><b>Definition:</b> builtin.h:871</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">tvm::tir::builtin::kArrKindBound_</a></div><div class="ttdeci">@ kArrKindBound_</div><div class="ttdef"><b>Definition:</b> builtin.h:876</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">tvm::tir::builtin::kArrShape</a></div><div class="ttdeci">@ kArrShape</div><div class="ttdef"><b>Definition:</b> builtin.h:867</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">tvm::tir::builtin::kArrTypeCode</a></div><div class="ttdeci">@ kArrTypeCode</div><div class="ttdef"><b>Definition:</b> builtin.h:870</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">tvm::tir::builtin::kTVMValueContent</a></div><div class="ttdeci">@ kTVMValueContent</div><div class="ttdef"><b>Definition:</b> builtin.h:878</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">tvm::tir::builtin::kArrData</a></div><div class="ttdeci">@ kArrData</div><div class="ttdef"><b>Definition:</b> builtin.h:866</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9539d6f9103aa0c177cd8cee72c39d74">tvm::tir::builtin::kArrDeviceId</a></div><div class="ttdeci">@ kArrDeviceId</div><div class="ttdef"><b>Definition:</b> builtin.h:874</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">tvm::tir::builtin::kArrStrides</a></div><div class="ttdeci">@ kArrStrides</div><div class="ttdef"><b>Definition:</b> builtin.h:868</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">tvm::tir::builtin::kTVMValueKindBound_</a></div><div class="ttdeci">@ kTVMValueKindBound_</div><div class="ttdef"><b>Definition:</b> builtin.h:879</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">tvm::tir::builtin::kArrDeviceType</a></div><div class="ttdeci">@ kArrDeviceType</div><div class="ttdef"><b>Definition:</b> builtin.h:875</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">tvm::tir::builtin::kArrNDim</a></div><div class="ttdeci">@ kArrNDim</div><div class="ttdef"><b>Definition:</b> builtin.h:869</div></div>
+<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">tvm::tir::builtin::kArrByteOffset</a></div><div class="ttdeci">@ kArrByteOffset</div><div class="ttdef"><b>Definition:</b> builtin.h:873</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ad99599849e8572bcd46e22636f908fca"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad99599849e8572bcd46e22636f908fca">tvm::tir::builtin::ptx_mma</a></div><div class="ttdeci">const Op & ptx_mma()</div><div class="ttdoc">tvm intrinsic for ptx tensor core mma instructions.</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ae0470bd69bb03047aae4cb52e1e6e337"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae0470bd69bb03047aae4cb52e1e6e337">tvm::tir::builtin::tvm_warp_shuffle_up</a></div><div class="ttdeci">const Op & tvm_warp_shuffle_up()</div></div>
<div class="ttc" id="anamespacetvm_1_1tir_1_1builtin_html_ae2add6e324d391782d367360a68ccf51"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae2add6e324d391782d367360a68ccf51">tvm::tir::builtin::call_pure_extern</a></div><div class="ttdeci">const Op & call_pure_extern()</div><div class="ttdoc">Call an pure extern C function with given name and signature from the types of args in the runtime en...</div></div>
diff --git a/docs/reference/api/doxygen/namespacemembers_c.html b/docs/reference/api/doxygen/namespacemembers_c.html
index 08edad52e7..1d878a703e 100644
--- a/docs/reference/api/doxygen/namespacemembers_c.html
+++ b/docs/reference/api/doxygen/namespacemembers_c.html
@@ -178,7 +178,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">tvm::arith</a>
</li>
<li>compute()
-: <a class="el" href="namespacetvm_1_1te.html#afe4f57aeb3dd5ae9c0b58135e14d67ca">tvm::te</a>
+: <a class="el" href="namespacetvm_1_1te.html#a1e3160ba59375548c1423fdd55a67353">tvm::te</a>
</li>
<li>compute_scope
: <a class="el" href="namespacetvm_1_1tir_1_1attr.html#a00a6b89838348f152d844cead81b5016">tvm::tir::attr</a>
@@ -263,6 +263,9 @@ $(function() {
<li>crc16_compute()
: <a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html#aee88d771b775a6c7caf709c47880ad3d">tvm::runtime::micro_rpc</a>
</li>
+<li>create_barriers()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">tvm::tir::builtin</a>
+</li>
<li>create_schedule()
: <a class="el" href="namespacetvm_1_1te.html#a485034766309df280239e0994913b34b">tvm::te</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_c.html b/docs/reference/api/doxygen/namespacemembers_func_c.html
index 0cad075d0f..b68f1f8caf 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_c.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_c.html
@@ -224,6 +224,9 @@ $(function() {
<li>crc16_compute()
: <a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html#aee88d771b775a6c7caf709c47880ad3d">tvm::runtime::micro_rpc</a>
</li>
+<li>create_barriers()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">tvm::tir::builtin</a>
+</li>
<li>create_schedule()
: <a class="el" href="namespacetvm_1_1te.html#a485034766309df280239e0994913b34b">tvm::te</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html b/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
index 44aae0ceb3..b4d5199823 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
@@ -300,6 +300,9 @@ Functions</h2></td></tr>
<tr class="memitem:a89bf47c3237df742ada0b599e3e4787a"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">ptx_wait_barrier</a> ()</td></tr>
<tr class="memdesc:a89bf47c3237df742ada0b599e3e4787a"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsics for ptx barrier wait using mbarrier.try_wait <a href="namespacetvm_1_1tir_1_1builtin.html#a89bf47c3237df742ada0b599e3e4787a">More...</a><br /></td></tr>
<tr class="separator:a89bf47c3237df742ada0b599e3e4787a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">create_barriers</a> ()</td></tr>
+<tr class="memdesc:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsics to create N barriers <a href="namespacetvm_1_1tir_1_1builtin.html#a2dd4fbe8995788ddcd3dfb1f788869ee">More...</a><br /></td></tr>
+<tr class="separator:a2dd4fbe8995788ddcd3dfb1f788869ee"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a772fb68f083e71e635c50bb503903f22"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22">mma_store</a> ()</td></tr>
<tr class="memdesc:a772fb68f083e71e635c50bb503903f22"><td class="mdescLeft"> </td><td class="mdescRight">tvm intrinsic for storing the result of PTX MMA into a destination pointer. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 4 elements from the result of m16xn8xk16 MMA in its registers, this intrinsic can be used to store the result in a 16x8 region in shared or global memory. <a hre [...]
<tr class="separator:a772fb68f083e71e635c50bb503903f22"><td class="memSeparator" colspan="2"> </td></tr>
@@ -627,6 +630,26 @@ Functions</h2></td></tr>
<p><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> call_spirv_pure_glsl450(intrin_id, args...) { return dlsym(name)(args...); }</p>
<dl class="section note"><dt>Note</dt><dd>This op does not provide any type checking. </dd></dl>
+</div>
+</div>
+<a id="a2dd4fbe8995788ddcd3dfb1f788869ee"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a2dd4fbe8995788ddcd3dfb1f788869ee">◆ </a></span>create_barriers()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">const <a class="el" href="classtvm_1_1Op.html">Op</a>& tvm::tir::builtin::create_barriers </td>
+ <td>(</td>
+ <td class="paramname"></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>tvm intrinsics to create N barriers </p>
+<p>ptx_wait_barrier(int barrier_count) </p>
+
</div>
</div>
<a id="afd453e860a10562ac2692473edde6707"></a>
@@ -986,7 +1009,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsics for ptx barrier arrival using mbarrier.arrive </p>
-<p>ptx_arrive_barrier(Var barrier_ptr, Expr barrier_offset) </p>
+<p>ptx_arrive_barrier(int barrier_id) </p>
</div>
</div>
@@ -1006,7 +1029,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsic for ptx barrier arrival with expect tx using mbarrier.arrive.expect_tx </p>
-<p>ptx_arrive_barrier_expect_tx(Var barrier_ptr, Expr barrier_offset, int byte_count) </p>
+<p>ptx_arrive_barrier_expect_tx(int barrier_id, int byte_count) </p>
</div>
</div>
@@ -1066,7 +1089,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsics for ptx async copy barrier using cp.async.mbarrier.arrive </p>
-<p>ptx_cp_async_barrier(Var barrier_ptr, Expr barrier_offset) </p>
+<p>ptx_cp_async_barrier(int barrier_id) </p>
</div>
</div>
@@ -1086,7 +1109,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsics for ptx async copy from global to shared memory using cp.async.bulk </p>
-<p>void ptx_cp_async(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> shared_ptr, Expr shared_offset, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> global_ptr, Expr global_offset, size_t bytes, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> barrier_ptr, Expr barrier_offset); </p>
+<p>void ptx_cp_async(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> shared_ptr, Expr shared_offset, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> global_ptr, Expr global_offset, size_t bytes, int barrier_id); </p>
</div>
</div>
@@ -1106,7 +1129,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsics for ptx barrier initialization of thread count using mbarrier.init </p>
-<p>ptx_init_barrier_thread_count(Var barrier_ptr, Expr barrier_offset, int thread_count) </p>
+<p>ptx_init_barrier_thread_count(int barrier_id, int thread_count) </p>
</div>
</div>
@@ -1205,7 +1228,7 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>tvm intrinsics for ptx barrier wait using mbarrier.try_wait </p>
-<p>ptx_wait_barrier(Var barrier_ptr, Expr barrier_offset) </p>
+<p>ptx_wait_barrier(int barrier_id) </p>
</div>
</div>
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