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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/07/20 13:00:57 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@7e830e58708da700be8bbb849442180942b796ec)
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 36339f648e deploying docs (apache/tvm@7e830e58708da700be8bbb849442180942b796ec)
36339f648e is described below
commit 36339f648e455aab391de556891f77c5c915e831
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Thu Jul 20 13:00:50 2023 +0000
deploying docs (apache/tvm@7e830e58708da700be8bbb849442180942b796ec)
---
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docs/searchindex.js | 2 +-
.../vta/tutorials/autotvm/sg_execution_times.html | 4 +-
.../tutorials/frontend/deploy_classification.html | 4 +-
.../vta/tutorials/frontend/deploy_detection.html | 4 +-
.../vta/tutorials/frontend/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/matrix_multiply.html | 6 +-
.../vta/tutorials/optimize/convolution_opt.html | 6 +-
.../tutorials/optimize/matrix_multiply_opt.html | 6 +-
.../vta/tutorials/optimize/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/sg_execution_times.html | 4 +-
docs/topic/vta/tutorials/vta_get_started.html | 4 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 6 +-
docs/tutorial/autotvm_matmul_x86.html | 20 +-
docs/tutorial/autotvm_relay_x86.html | 268 ++---
docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 12 +-
docs/tutorial/sg_execution_times.html | 18 +-
docs/tutorial/tensor_expr_get_started.html | 63 +-
docs/tutorial/tensor_ir_blitz_course.html | 10 +-
364 files changed, 12415 insertions(+), 12350 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 74707fc11d..5c64af386d 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 33.684 seconds)
+ **Total running time of the script:** ( 1 minutes 32.546 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 63339fdff7..a02118cf22 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.zip1fd12e26-640f-4b08-8eb5-65b65c6fd171 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip35390e19-423b-4b54-b5f0-a24f751a8f7f 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 eb9886a591..61b8126244 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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92%|#########2| 38.3M/41.5M [00:01<00:00, 25.8MB/s]
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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 8d8c1c1c22..16cecab298 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 2.203 seconds)
+ **Total running time of the script:** ( 1 minutes 2.353 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 b136b061c5..7a047d7784 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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86%|########5 | 38.3M/44.7M [00:01<00:00, 31.4MB/s]
94%|#########4| 42.2M/44.7M [00:01<00:00, 31.2MB/s]
100%|##########| 44.7M/44.7M [00:01<00:00, 32.9MB/s]
+
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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 b845cbe965..f969e243df 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 39.681 seconds)
+ **Total running time of the script:** ( 1 minutes 35.586 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 4d8ca8269a..b5f51b26dd 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
=================
-**07:25.512** total execution time for **how_to_compile_models** files:
+**07:16.051** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:39.681 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:35.586 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:33.684 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:32.546 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.203 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.353 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.324 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.537 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:36.930 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.381 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:34.875 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:29.228 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.065 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.203 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.877 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.728 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.708 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.860 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.787 | 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 aa1079ef0b..cf947d1a8f 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)
- 4223.4739 4223.3115 4228.4866 4220.1845 2.2765
+ 4205.6209 4227.8086 4234.7956 4103.0899 49.1633
@@ -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 19.728 seconds)
+ **Total running time of the script:** ( 1 minutes 19.680 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 c9bfd0250b..978443b6d7 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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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 8f2b649a6e..760d4a7acc 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)
- 16.1803 16.0044 17.8246 15.7566 0.5667
+ 15.5669 15.2494 17.4863 15.1335 0.7048
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 b31f0d591d..b0676f519b 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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+
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]
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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 32.229 seconds)
+ **Total running time of the script:** ( 3 minutes 41.737 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 1442b714a9..69a6e98c0f 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]
59%|#####8 | 7.99M/13.6M [00:00<00:00, 38.3MB/s]
90%|########9 | 12.2M/13.6M [00:00<00:00, 33.3MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 37.4MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
47%|####6 | 6.30M/13.6M [00:00<00:00, 65.7MB/s]
93%|#########2| 12.6M/13.6M [00:00<00:00, 38.9MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 44.2MB/s]
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.4155 89.4265 90.0712 88.8519 0.2779
+ 88.7830 88.7270 89.6273 88.5885 0.1656
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.343 seconds)
+ **Total running time of the script:** ( 1 minutes 29.577 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 78111f41c2..23a88d9927 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 110.1314 110.0617 115.2141 109.6529 0.5545
+ 110.6743 110.6474 111.8093 109.9168 0.3296
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 3a768c42e4..6cb58a5c69 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 13.454 seconds)
+ **Total running time of the script:** ( 2 minutes 8.235 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 ab16cdc1b2..236ef6073e 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
=================
-**12:14.488** total execution time for **how_to_deploy_models** files:
+**12:22.948** 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``) | 03:32.229 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:41.737 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:13.454 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:08.235 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:28.343 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:29.577 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.728 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.680 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:58.186 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:58.591 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.321 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.669 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:50.403 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.472 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.519 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.195 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.299 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.786 | 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 1ca6be09b2..3dcc5b75b9 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.zip8ff35455-fc35-48fa-85e8-676b5e4ae3bf from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip9ec701a3-6083-49f5-87e5-076a5594399f from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt b/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
index 4a325bf575..99694af5f1 100644
--- a/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
@@ -97,7 +97,7 @@ our customized lowering pass to manipulate the IR directly instead of using sche
class Module:
@T.prim_func
def main(a: T.Buffer((128,), "float32"), b: T.Buffer((128,), "float32"), c: T.Buffer((128,), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i in range(128):
c[i] = a[i] + b[i]
@@ -254,7 +254,7 @@ Thus, a good place to put this transformation pass is just after Phase 1.
class Module:
@T.prim_func
def main(a: T.Buffer((128,), "float32"), b: T.Buffer((128,), "float32"), c: T.Buffer((128,), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i_outer in range(16):
cse_var_1: T.int32 = i_outer * 8
c[cse_var_1:cse_var_1 + 8] = a[cse_var_1:cse_var_1 + 8] + b[cse_var_1:cse_var_1 + 8]
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 4ee672641a..acf18d7655 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
=================
-**00:57.549** total execution time for **how_to_extend_tvm** files:
+**00:59.501** 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``) | 00:53.672 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:55.504 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.699 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.781 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.171 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.209 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 97f73d9d56..b8e4a572d4 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: 23570us [23570us] (48.46%; 48.46%)
- FoldScaleAxis: 25069us [8us] (51.54%; 51.54%)
- FoldConstant: 25061us [1874us] (51.53%; 99.97%)
- InferType: 23187us [23187us] (47.67%; 92.52%)
+ InferType: 23644us [23644us] (48.30%; 48.30%)
+ FoldScaleAxis: 25312us [9us] (51.70%; 51.70%)
+ FoldConstant: 25304us [1889us] (51.69%; 99.97%)
+ InferType: 23415us [23415us] (47.83%; 92.54%)
@@ -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: 23113us [23113us] (48.31%; 48.31%)
- FoldScaleAxis: 24731us [6us] (51.69%; 51.69%)
- FoldConstant: 24725us [1814us] (51.68%; 99.98%)
- InferType: 22911us [22911us] (47.89%; 92.66%)
+ InferType: 23499us [23499us] (47.97%; 47.97%)
+ FoldScaleAxis: 25492us [9us] (52.03%; 52.03%)
+ FoldConstant: 25483us [1962us] (52.02%; 99.97%)
+ InferType: 23521us [23521us] (48.01%; 92.30%)
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 f297987197..33121533dc 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.544990 ms
+ Convolution: 49.347137 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 f512dbbbbb..1641f2cec8 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
@@ -439,7 +439,7 @@ one time.
class Module:
@T.prim_func
def main(A: T.Buffer((16, 14, 14, 16, 16, 16), "float16"), W: T.Buffer((3, 3, 16, 32, 16, 16), "float16"), Conv: T.Buffer((16, 14, 14, 32, 16, 16), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_z = T.launch_thread("blockIdx.z", 196)
Conv_wmma_accumulator = T.allocate([2048], "float32", "wmma.accumulator")
Apad_shared = T.allocate([12288], "float16", "shared")
@@ -524,7 +524,7 @@ by mapping the 2D convolution to tensor intrinsics
class Module:
@T.prim_func
def main(A: T.Buffer((16, 14, 14, 16, 16, 16), "float16"), W: T.Buffer((3, 3, 16, 32, 16, 16), "float16"), Conv: T.Buffer((16, 14, 14, 32, 16, 16), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_z = T.launch_thread("blockIdx.z", 196)
Conv_wmma_accumulator = T.allocate([2048], "float32", "wmma.accumulator")
Apad_shared = T.allocate([12288], "float16", "shared")
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.276823 ms
+ conv2d with tensor core: 12.273459 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 3793417bd6..0625daee1e 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: 0.018669
- Baseline: 3.446875
+ Numpy running time: 0.019321
+ Baseline: 3.462748
@@ -167,7 +167,7 @@ Here is the generated IR using our baseline schedule.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m, n in T.grid(1024, 1024):
C_1 = T.Buffer((1048576,), data=C.data)
C_1[m * 1024 + n] = T.float32(0)
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.310505
+ Opt1: 0.316440
@@ -258,7 +258,7 @@ Here is the generated IR after blocking.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init, n_inner_init in T.grid(32, 32):
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.289689
+ Opt2: 0.304398
@@ -349,7 +349,7 @@ Here is the generated IR after vectorization.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init in range(32):
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.114256
+ Opt3: 0.117566
@@ -437,7 +437,7 @@ Here is the generated IR after loop permutation.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init in range(32):
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.106624
+ Opt4: 0.105768
@@ -554,7 +554,7 @@ Here is the generated IR after array packing.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for bigN in T.parallel(32):
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.112138
+ Opt5: 0.111828
@@ -666,7 +666,7 @@ Here is the generated IR after blocking.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
C_global = T.allocate([1024], "float32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.133126
+ Opt6: 0.132968
@@ -779,7 +779,7 @@ Here is the generated IR after parallelization.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for bigN in T.parallel(32):
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 8979a100e0..d071e24175 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
=================
-**00:34.765** total execution time for **how_to_optimize_operators** files:
+**00:35.165** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.296 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.681 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.076 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.113 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.393 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.372 | 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 774a323854..68b1b01ce9 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
=================
-**03:51.718** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:53.089** 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``) | 01:41.549 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:42.004 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:20.042 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:20.111 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.121 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.164 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.510 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.956 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.393 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.751 | 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 6356898a60..7ef75bc52e 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
@@ -244,7 +244,7 @@ cooperative fetching, unrolling and operator fusion.
class Module:
@T.prim_func
def main(data: T.Buffer((1, 512, 7, 7), "float32"), kernel: T.Buffer((512, 512, 3, 3), "float32"), bias: T.Buffer((1, 512, 1, 1), "float32"), compute: T.Buffer((1, 512, 7, 7), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_x = T.launch_thread("blockIdx.x", 28)
conv2d_nchw = T.allocate([14], "float32", "local")
pad_temp_shared = T.allocate([72], "float32", "shared")
@@ -767,7 +767,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.347 ms
+ Execution time of this operator: 0.351 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 9e53373f5a..ca1bf4a351 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)
- 8.1031 8.1045 8.1048 8.1001 0.0021
+ 8.1523 8.1525 8.1545 8.1500 0.0019
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.042 seconds)
+ **Total running time of the script:** ( 1 minutes 20.111 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 205857cc67..103e7b50cc 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)
- 766.0302 767.1045 767.1609 763.8254 1.5592
+ 766.1276 765.6038 770.8713 761.9077 3.6781
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 41.549 seconds)
+ **Total running time of the script:** ( 1 minutes 42.004 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 ab716b5617..24f7bcef15 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:23.501** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.563** 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``) | 00:23.463 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.526 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 cb0fec3b18..f6ea897b70 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: 0.036955
+ Time cost of this operator: 0.037149
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 3e9eeea16a..88548db054 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 306.2 98.736 (1, 2, 10, 10, 3) 2 1 [306.2]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.951 0.951 (1, 6, 10, 10) 1 1 [2.951]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.969 0.313 (1, 1, 10, 10, 3) 1 1 [0.969]
- Total_time - 310.12 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.4 98.757 (1, 2, 10, 10, 3) 2 1 [309.4]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.916 0.931 (1, 6, 10, 10) 1 1 [2.916]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.979 0.312 (1, 1, 10, 10, 3) 1 1 [0.979]
+ Total_time - 313.295 - - - - -
@@ -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 177.3 98.385 (1, 1, 10, 10, 6) 2 1 [177.3]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.06 1.143 (1, 6, 10, 10) 1 1 [2.06]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.85 0.472 (1, 3, 10, 10, 1) 1 1 [0.85]
- Total_time - 180.21 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 101.4 97.334 (1, 6, 10, 10, 1) 2 1 [101.4]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.727 (1, 6, 10, 10) 1 1 [1.799]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.938 (1, 1, 10, 10, 3) 1 1 [0.978]
+ Total_time - 104.177 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.530 seconds)
+ **Total running time of the script:** ( 1 minutes 26.822 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 d867f3896f..f2f85c2398 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, 17.5MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 27.5MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 55.6MB/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 28.047 seconds)
+ **Total running time of the script:** ( 1 minutes 30.190 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 aab2752dbd..bfa6e3bf89 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/tmpf1dqv31q/images/random'
+ '/tmp/tmpy3urn_f8/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpf1dqv31q/images/target contains 8144 images
- /tmp/tmpf1dqv31q/images/random contains 5000 images
+ /tmp/tmpy3urn_f8/images/target contains 8144 images
+ /tmp/tmpy3urn_f8/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 42s - loss: 0.2119 - accuracy: 0.9288 - val_loss: 0.1281 - val_accuracy: 0.9547 - 42s/epoch - 128ms/step
+ 328/328 - 40s - loss: 0.2804 - accuracy: 0.9116 - val_loss: 0.1295 - val_accuracy: 0.9551 - 40s/epoch - 123ms/step
Epoch 2/3
- 328/328 - 36s - loss: 0.1014 - accuracy: 0.9640 - val_loss: 0.1191 - val_accuracy: 0.9543 - 36s/epoch - 108ms/step
+ 328/328 - 36s - loss: 0.1107 - accuracy: 0.9601 - val_loss: 0.1012 - val_accuracy: 0.9637 - 36s/epoch - 109ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0667 - accuracy: 0.9748 - val_loss: 0.1169 - val_accuracy: 0.9611 - 35s/epoch - 108ms/step
+ 328/328 - 36s - loss: 0.0733 - accuracy: 0.9733 - val_loss: 0.0910 - val_accuracy: 0.9694 - 36s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7f1e1253fcd0>
+ <keras.callbacks.History object at 0x7f301c6f68b0>
@@ -860,7 +860,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 43.330 seconds)
+ **Total running time of the script:** ( 4 minutes 51.463 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 38af059a88..2a3326eea6 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
=================
-**08:06.980** total execution time for **how_to_work_with_microtvm** files:
+**08:18.874** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:43.330 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:51.463 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:28.047 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:30.190 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:25.530 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.822 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.569 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.694 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.011 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.015 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.494 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.690 | 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 b1c3292d42..c5c03cda10 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:40.906** total execution time for **how_to_work_with_relay** files:
+**00:41.363** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.594 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:36.061 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.385 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.364 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.922 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.931 | 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 |
+| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.007 | 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 63ffc9f8ff..2b37ee0629 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
@@ -278,7 +278,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f1b66552160>
+ <function my_cuda_math_rule at 0x7f2ea47173a0>
diff --git a/docs/_sources/how_to/work_with_schedules/reduction.rst.txt b/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
index cfb40f1a91..9cac4806d8 100644
--- a/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
@@ -123,7 +123,7 @@ Before doing anything, let us print out the IR code of default schedule.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -169,7 +169,7 @@ axis by different factors. The result is a nested reduction.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -214,7 +214,7 @@ If we are building a GPU kernel, we can bind the rows of B to GPU threads.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -270,7 +270,7 @@ result B.rf. The factored dimension becomes the first dimension of B.rf.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -488,7 +488,7 @@ Here is an example for 2D convolution with filter size = [3, 3] and strides = [1
class Module:
@T.prim_func
def main(Input: T.handle, Filter: T.Buffer((3, 3), "float32"), Output: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
Input_1 = T.match_buffer(Input, (n, n), strides=("stride", "stride"), buffer_type="auto")
Output_1 = T.match_buffer(Output, (n - 2, n - 2))
diff --git a/docs/_sources/how_to/work_with_schedules/scan.rst.txt b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
index a922f66082..c214fab66c 100644
--- a/docs/_sources/how_to/work_with_schedules/scan.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
@@ -129,7 +129,7 @@ To split on the time iteration, user can schedule on scan_op.scan_axis instead.
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_1 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -243,7 +243,7 @@ the body of scan to be compute_at locations outside the scan loop.
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_1 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -310,7 +310,7 @@ The following example demonstrates how we can build recurrence with two states.
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle, scan_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_2 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
diff --git a/docs/_sources/how_to/work_with_schedules/schedule_primitives.rst.txt b/docs/_sources/how_to/work_with_schedules/schedule_primitives.rst.txt
index bd7021b1bc..68064fd963 100644
--- a/docs/_sources/how_to/work_with_schedules/schedule_primitives.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/schedule_primitives.rst.txt
@@ -115,7 +115,7 @@ schedule computes tensor in a serial manner in a row-major order.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -168,7 +168,7 @@ split
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -213,7 +213,7 @@ contrary with :code:`factor`.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -259,7 +259,7 @@ axes.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -311,7 +311,7 @@ fuse
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -362,7 +362,7 @@ reorder
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -414,7 +414,7 @@ in gpu programming.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
A_1 = T.match_buffer(A, (n,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -461,7 +461,7 @@ tensors at the root separately by default.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -509,7 +509,7 @@ of computation of `C`.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -559,7 +559,7 @@ tensor is required.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -606,7 +606,7 @@ compute_root
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
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 685d7a56b1..a2bf17cd60 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
@@ -5,20 +5,20 @@
Computation times
=================
-**00:07.935** total execution time for **how_to_work_with_schedules** files:
+**00:07.738** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:04.676 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:04.491 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.428 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.405 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.775 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.776 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.759 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.763 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.121 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.083 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.089 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.061 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 7771c2e902..f2db0dcc82 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -94,7 +94,7 @@ The following lines describe the computation :code:`A * B^T` in TVM.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j in T.grid(1024, 512):
C_1 = T.Buffer((524288,), data=C.data)
C_1[i * 512 + j] = T.float32(0)
@@ -144,7 +144,7 @@ Thus we break down the matmul loops to make the innermost loops a (16x64) GEMV.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j_outer, j_inner in T.grid(1024, 32, 16):
C_1 = T.Buffer((524288,), data=C.data)
C_1[i * 512 + j_outer * 16 + j_inner] = T.float32(0)
@@ -264,7 +264,7 @@ such placeholder can be put to let TVM automatically bind the inferred value for
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j_outer in T.grid(1024, 32):
T.call_extern("int32", "gemv_update", T.tvm_access_ptr(T.type_annotation("float32"), C.data, i * 512 + j_outer * 16, 16, 2), T.tvm_access_ptr(T.type_annotation("float32"), A.data, i * 64, 64, 1), T.tvm_access_ptr(T.type_annotation("float32"), B.data, j_outer * 1024, 1024, 1), 16, 64, 64)
@@ -338,7 +338,7 @@ The importing needs to happen before the tensorized GEMV being executed.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
i = T.int32()
T.attr(T.iter_var(i, None, "DataPar", ""), "pragma_import_llvm", metadata["tir.StringImm"][0])
for i, j_outer in T.grid(1024, 32):
diff --git a/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt b/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
index 8d35d79d93..c403ec21f1 100644
--- a/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
@@ -88,7 +88,7 @@ together in the next schedule procedure.
class Module:
@T.prim_func
def main(A0: T.handle, A1: T.handle, B: T.handle, B_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A0_1 = T.match_buffer(A0, (m, n), strides=("stride", "stride"), buffer_type="auto")
A1_1 = T.match_buffer(A1, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -165,7 +165,7 @@ with :py:func:`te.comm_reducer` as below:
class Module:
@T.prim_func
def main(idx: T.handle, val: T.handle, T: T.handle, T_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
idx_1 = T.match_buffer(idx, (m, n), "int32", strides=("stride", "stride"), buffer_type="auto")
val_1 = T.match_buffer(val, (m, n), "int32", strides=("stride", "stride"), buffer_type="auto")
@@ -232,7 +232,7 @@ in terms of operation.
class Module:
@T.prim_func
def main(A0: T.handle, A1: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A0_1 = T.match_buffer(A0, (m, n), strides=("stride", "stride"), buffer_type="auto")
A1_1 = T.match_buffer(A1, (m, n), strides=("stride", "stride"), buffer_type="auto")
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 b12fd3c156..6b680b6c1f 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
=================
-**00:34.747** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:35.385** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:34.739 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.378 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 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 2379f8f7c9..cfe5277cfb 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 37.77s!
+ resnet18_v1 inference graph built in 38.98s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.096 seconds)
+ **Total running time of the script:** ( 1 minutes 6.316 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 24d1f25bff..61ae72bfbf 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 25.43s!
+ yolov3-tiny inference graph built in 26.06s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 8.959 seconds)
+ **Total running time of the script:** ( 1 minutes 8.675 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 a13d8599c6..b09a95f62b 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
=================
-**02:14.055** total execution time for **topic_vta_tutorials_frontend** files:
+**02:14.991** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:08.959 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:08.675 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:05.096 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:06.316 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/topic/vta/tutorials/matrix_multiply.rst.txt
index b61e24636d..e8cfa7abd6 100644
--- a/docs/_sources/topic/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/matrix_multiply.rst.txt
@@ -353,7 +353,7 @@ After we construct the schedule, by default the schedule computes
class Module:
@T.prim_func
def main(A: T.Buffer((1, 16, 1, 16), "int8"), B: T.Buffer((16, 16, 16, 16), "int8"), C: T.Buffer((1, 16, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
A_buf = T.allocate([256], "int8", "global")
B_buf = T.allocate([65536], "int8", "global")
C_buf = T.allocate([256], "int32", "global")
@@ -501,7 +501,7 @@ moving the copy operations into the matrix multiplication loop.
class Module:
@T.prim_func
def main(A: T.Buffer((1, 16, 1, 16), "int8"), B: T.Buffer((16, 16, 16, 16), "int8"), C: T.Buffer((1, 16, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
C_buf = T.allocate([256], "int32", "local.acc_buffer")
A_buf = T.allocate([16], "int8", "local.inp_buffer")
B_buf = T.allocate([16], "int8", "local.wgt_buffer")
@@ -584,7 +584,7 @@ by the VTA runtime JIT compiler.
class Module:
@T.prim_func
def main(A: T.Buffer((1, 16, 1, 16), "int8"), B: T.Buffer((16, 16, 16, 16), "int8"), C: T.Buffer((1, 16, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
vta = T.int32()
with T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 2):
with T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_uop_scope", "VTAPushGEMMOp"):
diff --git a/docs/_sources/topic/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/convolution_opt.rst.txt
index 979e6994e7..f0c9d678ee 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -281,7 +281,7 @@ Those include:
class Module:
@T.prim_func
def main(data: T.Buffer((1, 16, 14, 14, 1, 16), "int8"), kernel: T.Buffer((16, 16, 3, 3, 16, 16), "int8"), res: T.Buffer((1, 16, 14, 14, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
data_buf = T.allocate([65536], "int8", "global")
kernel_buf = T.allocate([589824], "int8", "global")
res_conv = T.allocate([50176], "int32", "global")
@@ -435,7 +435,7 @@ below.
class Module:
@T.prim_func
def main(data: T.Buffer((1, 16, 14, 14, 1, 16), "int8"), kernel: T.Buffer((16, 16, 3, 3, 16, 16), "int8"), res: T.Buffer((1, 16, 14, 14, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
data_buf = T.allocate([65536], "int8", "global")
kernel_buf = T.allocate([589824], "int8", "global")
res_conv = T.allocate([25088], "int32", "global")
@@ -577,7 +577,7 @@ and mapping the shift, and clipping computation to the vector ALU.
class Module:
@T.prim_func
def main(data: T.Buffer((1, 16, 14, 14, 1, 16), "int8"), kernel: T.Buffer((16, 16, 3, 3, 16, 16), "int8"), res: T.Buffer((1, 16, 14, 14, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
T.tir.vta.coproc_dep_push(3, 2)
T.tir.vta.coproc_dep_push(3, 2)
for i2_outer in range(2):
diff --git a/docs/_sources/topic/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index 9200c2b485..9e662e60fb 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -218,7 +218,7 @@ Those include:
class Module:
@T.prim_func
def main(data: T.Buffer((1, 64, 1, 16), "int8"), weight: T.Buffer((64, 64, 16, 16), "int8"), res: T.Buffer((1, 64, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
data_buf = T.allocate([1024], "int8", "global")
weight_buf = T.allocate([1048576], "int8", "global")
res_gem = T.allocate([1024], "int32", "global")
@@ -365,7 +365,7 @@ below:
class Module:
@T.prim_func
def main(data: T.Buffer((1, 64, 1, 16), "int8"), weight: T.Buffer((64, 64, 16, 16), "int8"), res: T.Buffer((1, 64, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
data_buf = T.allocate([1024], "int8", "global")
weight_buf = T.allocate([1048576], "int8", "global")
res_gem = T.allocate([256], "int32", "global")
@@ -490,7 +490,7 @@ and mapping the shift, and clipping computation to the vector ALU.
class Module:
@T.prim_func
def main(data: T.Buffer((1, 64, 1, 16), "int8"), weight: T.Buffer((64, 64, 16, 16), "int8"), res: T.Buffer((1, 64, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
T.tir.vta.coproc_dep_push(3, 2)
for i1_outer in range(4):
vta = T.int32()
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 d191a9cfcf..656d7e39af 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
=================
-**00:03.440** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.379** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.892 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.825 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.548 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.554 | 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 c97758a03d..f52a070167 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
=================
-**00:00.935** total execution time for **topic_vta_tutorials** files:
+**00:00.939** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.482 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.453 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.458 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
index 9f5d700858..55cbadb262 100644
--- a/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
@@ -359,7 +359,7 @@ After we construct the schedule, by default the schedule computes
class Module:
@T.prim_func
def main(A: T.Buffer((1, 64, 1, 16), "int32"), B: T.Buffer((1, 64, 1, 16), "int32"), C: T.Buffer((1, 64, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
A_buf = T.allocate([1024], "int32", "global")
B_buf = T.allocate([1024], "int32", "global")
A_buf_1 = T.Buffer((1024,), "int32", data=A_buf)
@@ -494,7 +494,7 @@ with an :code:`env.alu` pragma.
class Module:
@T.prim_func
def main(A: T.Buffer((1, 64, 1, 16), "int32"), B: T.Buffer((1, 64, 1, 16), "int32"), C: T.Buffer((1, 64, 1, 16), "int8")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
vta = T.int32()
with T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 2):
T.call_extern("int32", "VTALoadBuffer2D", T.tvm_thread_context(T.tir.vta.command_handle()), A.data, 0, 64, 1, 64, 0, 0, 0, 0, 0, 3)
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 8f8016f00f..4cd2f07aa7 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -243,7 +243,7 @@ operator fusion.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32"), out: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
auto_scheduler_layout_transform = T.allocate([1048576], "float32", "global")
auto_scheduler_layout_transform_1 = T.Buffer((1048576,), data=auto_scheduler_layout_transform)
for ax0_ax1_fused_ax2_fused in T.parallel(128):
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.166 ms
+ Execution time of this operator: 92.929 ms
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.911 seconds)
+ **Total running time of the script:** ( 1 minutes 26.092 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 50d4c6ab64..2270d65240 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: 10.39/10.39 result: MeasureResult(costs=(0.025830498599999994,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6647439002990723, timestamp=1689807524.7162478) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
- No: 2 GFLOPS: 1.33/10.39 result: MeasureResult(costs=(0.20220007020000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.4881787300109863, timestamp=1689807528.2151625) [('tile_y', [-1, 2]), ('tile_x', [-1, 1])],None,1
- No: 3 GFLOPS: 11.47/11.47 result: MeasureResult(costs=(0.0234092196,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6499099731445312, timestamp=1689807528.8585424) [('tile_y', [-1, 16]), ('tile_x', [-1, 256])],None,84
- No: 4 GFLOPS: 12.13/12.13 result: MeasureResult(costs=(0.0221291554,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.626690149307251, timestamp=1689807529.4743218) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
- No: 5 GFLOPS: 11.99/12.13 result: MeasureResult(costs=(0.022391012000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6968846321105957, timestamp=1689807530.2971423) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
- No: 6 GFLOPS: 0.52/12.13 result: MeasureResult(costs=(0.5190988378,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.573720693588257, timestamp=1689807538.8627875) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
- No: 7 GFLOPS: 13.13/13.13 result: MeasureResult(costs=(0.0204512414,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6893689632415771, timestamp=1689807539.461333) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
- No: 8 GFLOPS: 2.06/13.13 result: MeasureResult(costs=(0.13019679180000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.361342191696167, timestamp=1689807541.8042316) [('tile_y', [-1, 128]), ('tile_x', [-1, 4])],None,27
- No: 9 GFLOPS: 3.65/13.13 result: MeasureResult(costs=(0.0735933632,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4052186012268066, timestamp=1689807543.3265834) [('tile_y', [-1, 256]), ('tile_x', [-1, 8])],None,38
- No: 10 GFLOPS: 11.63/13.13 result: MeasureResult(costs=(0.023078855,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6186504364013672, timestamp=1689807543.968993) [('tile_y', [-1, 4]), ('tile_x', [-1, 256])],None,82
+ No: 1 GFLOPS: 1.96/1.96 result: MeasureResult(costs=(0.1370513622,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.447744846343994, timestamp=1689853895.9770539) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
+ No: 2 GFLOPS: 1.36/1.96 result: MeasureResult(costs=(0.1967742674,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.379549503326416, timestamp=1689853899.387403) [('tile_y', [-1, 2]), ('tile_x', [-1, 1])],None,1
+ No: 3 GFLOPS: 1.66/1.96 result: MeasureResult(costs=(0.1615791724,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.862670660018921, timestamp=1689853902.2396967) [('tile_y', [-1, 16]), ('tile_x', [-1, 1])],None,4
+ No: 4 GFLOPS: 3.42/3.42 result: MeasureResult(costs=(0.0784709888,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5047032833099365, timestamp=1689853903.7555401) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
+ No: 5 GFLOPS: 8.78/8.78 result: MeasureResult(costs=(0.030574477,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7542030811309814, timestamp=1689853904.6430407) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 6 GFLOPS: 16.31/16.31 result: MeasureResult(costs=(0.0164575574,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5425384044647217, timestamp=1689853905.1679053) [('tile_y', [-1, 16]), ('tile_x', [-1, 64])],None,64
+ No: 7 GFLOPS: 10.51/16.31 result: MeasureResult(costs=(0.025549617200000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6915848255157471, timestamp=1689853905.8385055) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
+ No: 8 GFLOPS: 9.94/16.31 result: MeasureResult(costs=(0.0269936748,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6864285469055176, timestamp=1689853906.538544) [('tile_y', [-1, 1]), ('tile_x', [-1, 512])],None,90
+ No: 9 GFLOPS: 11.06/16.31 result: MeasureResult(costs=(0.0242696112,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.629758358001709, timestamp=1689853907.3116148) [('tile_y', [-1, 64]), ('tile_x', [-1, 256])],None,86
+ No: 10 GFLOPS: 12.88/16.31 result: MeasureResult(costs=(0.0208406542,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6108803749084473, timestamp=1689853907.9156177) [('tile_y', [-1, 128]), ('tile_x', [-1, 128])],None,77
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 4f1861b23c..683a378101 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': 490.87725040000805, 'median': 490.83310535006603, 'std': 2.1082866969456844}
+ {'mean': 494.926992339997, 'median': 494.77054239996505, 'std': 1.3447543944605553}
@@ -582,29 +582,30 @@ 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: 11.18/ 12.25 GFLOPS | Progress: (4/20) | 9.75 s
[Task 1/25] Current/Best: 17.88/ 23.35 GFLOPS | Progress: (8/20) | 12.60 s
[Task 1/25] Current/Best: 11.58/ 23.35 GFLOPS | Progress: (12/20) | 16.24 s
[Task 1/25] Current/Best: 23.55/ 23.55 GFLOPS | Progress: (16/20) | 18.37 s
[Task 1/25] Current/Best: 6.50/ 23.55 GFLOPS | Progress: (20/20) | 23.03 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 17.57/ 17.57 GFLOPS | Progress: (4/20) | 5.56 s
[Task 2/25] Current/Best: 15.14/ 17.57 GFLOPS | Progress: (8/20) | 7.43 s
[Task 2/25] Current/Best: 6.47/ 18.72 GFLOPS | Progress: (12/20) | 9.02 s
[Task 2/25] Current/Best: 6.22/ 18.72 GFLOPS | Progress: (16/20) | 10.45 s
[Task 2/25] Current/Best: 5.16/ 18.72 GFLOPS | Progress: (20/20) | 11.97 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 13.65/ 14.77 GFLOPS | Progress: (4/20) | 4.94 s
[Task 3/25] Current/Best: 15.94/ 20.53 GFLOPS | Progress: (8/20) | 7.68 s
[Task 3/25] Current/Best: 7.57/ 20.53 GFLOPS | Progress: (12/20) | 10.24 s
[Task 3/25] Current/Best: 7.16/ 21.33 GFLOPS | Progress: (16/20) | 12.62 s
[Task 3/25] Current/Best: 19.40/ 22.74 GFLOPS | Progress: (20/20) | 14.74 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 12.64/ 16.74 GFLOPS | Progress: (4/20) | 6.84 s
[Task 4/25] Current/Best: 10.23/ 20.50 GFLOPS | Progress: (8/20) | 12.02 s
[Task 4/25] Current/Best: 12.83/ 20.50 GFLOPS | Progress: (12/20) | 13.66 s
[Task 4/25] Current/Best: 9.91/ 20.50 GFLOPS | Progress: (16/20) | 16.63 s
[Task 4/25] Current/Best: 20.25/ 20.50 GFLOPS | Progress: (20/20) | 18.33 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 23.21/ 23.21 GFLOPS | Progress: (4/20) | 4.47 s
[Task 5/25] Current/Best: 3.47/ 23.21 GFLOPS | Progress: (8/20) | 6.78 s
[Task 5/25] Current/Best: 14.63/ 23.21 GFLOPS | Progress: (12/20) | 8.59 s
[Task 5/25] Current/Best: 5.76/ 23.21 GFLOPS | Progress: (16/20) | 11.87 s
[Task 5/25] Current/Best: 14.44/ 23.21 GFLOPS | Progress: (20/20) | 14.98 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 9.29/ 10.79 GFLOPS | Progress: (4/20) | 8.37 s
[Task 6/25] Current/Best: 17.34/ 17.34 GFLOPS | Progress: (8/20) | 10.72 s
[Task 6/25] Current/Best: 19.79/ 19.79 GFLOPS | Progress: (12/20) | 13.27 s
[Task 6/25] Current/Best: 14.15/ 19.79 GFLOPS | Progress: (16/20) | 15.70 s
[Task 6/25] Current/Best: 13.55/ 19.79 GFLOPS | Progress: (20/20) | 18.46 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 14.51/ 15.33 GFLOPS | Progress: (4/20) | 5.39 s
[Task 7/25] Current/Best: 18.85/ 21.07 GFLOPS | Progress: (8/20) | 7.67 s
[Task 7/25] Current/Best: 9.34/ 21.07 GFLOPS | Progress: (12/20) | 10.47 s
[Task 7/25] Current/Best: 15.80/ 21.07 GFLOPS | Progress: (16/20) | 12.84 s
[Task 7/25] Current/Best: 7.30/ 21.07 GFLOPS | Progress: (20/20) | 15.60 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 16.83/ 17.35 GFLOPS | Progress: (4/20) | 4.89 s
[Task 8/25] Current/Best: 15.79/ 17.35 GFLOPS | Progress: (8/20) | 8.81 s
[Task 8/25] Current/Best: 10.36/ 17.35 GFLOPS | Progress: (12/20) | 12.11 s
[Task 8/25] Current/Best: 5.95/ 17.35 GFLOPS | Progress: (16/20) | 24.09 s
[Task 8/25] Current/Best: 14.12/ 17.35 GFLOPS | Progress: (20/20) | 26.92 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 15.86/ 20.20 GFLOPS | Progress: (4/20) | 4.69 s
[Task 9/25] Current/Best: 17.73/ 20.20 GFLOPS | Progress: (8/20) | 10.63 s
[Task 9/25] Current/Best: 12.74/ 20.20 GFLOPS | Progress: (12/20) | 13.53 s
[Task 9/25] Current/Best: 11.93/ 20.30 GFLOPS | Progress: (16/20) | 16.76 s
[Task 9/25] Current/Best: 22.89/ 22.89 GFLOPS | Progress: (20/20) | 23.16 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 10.67/ 10.67 GFLOPS | Progress: (4/20) | 4.77 s
[Task 10/25] Current/Best: 11.56/ 14.44 GFLOPS | Progress: (8/20) | 7.21 s
[Task 10/25] Current/Best: 3.22/ 14.44 GFLOPS | Progress: (12/20) | 9.38 s
[Task 10/25] Current/Best: 13.39/ 16.47 GFLOPS | Progress: (16/20) | 12.06 s
[Task 10/25] Current/Best: 14.56/ 16.47 GFLOPS | Progress: (20/20) | 14.51 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 20.44/ 23.34 GFLOPS | Progress: (4/20) | 4.63 s
[Task 11/25] Current/Best: 14.26/ 23.34 GFLOPS | Progress: (8/20) | 6.78 s
[Task 11/25] Current/Best: 20.10/ 23.34 GFLOPS | Progress: (12/20) | 9.75 s
[Task 11/25] Current/Best: 10.16/ 23.34 GFLOPS | Progress: (16/20) | 12.38 s
[Task 11/25] Current/Best: 21.52/ 23.34 GFLOPS | Progress: (20/20) | 14.29 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 13.66/ 18.18 GFLOPS | Progress: (4/20) | 6.48 s
[Task 12/25] Current/Best: 9.93/ 18.18 GFLOPS | Progress: (8/20) | 11.36 s
[Task 12/25] Current/Best: 14.45/ 18.18 GFLOPS | Progress: (12/20) | 15.21 s
[Task 12/25] Current/Best: 12.14/ 18.18 GFLOPS | Progress: (16/20) | 20.14 s
[Task 12/25] Current/Best: 10.93/ 18.18 GFLOPS | Progress: (20/20) | 23.59 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 19.59/ 20.38 GFLOPS | Progress: (4/20) | 6.20 s
[Task 13/25] Current/Best: 7.59/ 20.38 GFLOPS | Progress: (8/20) | 9.56 s
[Task 13/25] Current/Best: 5.13/ 20.38 GFLOPS | Progress: (12/20) | 13.40 s
[Task 13/25] Current/Best: 9.85/ 20.38 GFLOPS | Progress: (16/20) | 15.81 s
[Task 13/25] Current/Best: 8.76/ 21.94 GFLOPS | Progress: (20/20) | 19.24 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 3.49/ 15.78 GFLOPS | Progress: (4/20) | 6.91 s
[Task 14/25] Current/Best: 1.62/ 17.73 GFLOPS | Progress: (8/20) | 10.73 s
[Task 14/25] Current/Best: 1.60/ 18.42 GFLOPS | Progress: (12/20) | 14.59 s
[Task 14/25] Current/Best: 12.83/ 18.42 GFLOPS | Progress: (16/20) | 17.26 s
[Task 14/25] Current/Best: 8.63/ 18.98 GFLOPS | Progress: (20/20) | 22.91 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 19.28/ 21.23 GFLOPS | Progress: (4/20) | 4.39 s
[Task 15/25] Current/Best: 12.02/ 21.23 GFLOPS | Progress: (8/20) | 9.00 s
[Task 15/25] Current/Best: 14.70/ 21.23 GFLOPS | Progress: (12/20) | 11.47 s
[Task 15/25] Current/Best: 6.22/ 21.23 GFLOPS | Progress: (16/20) | 19.06 s
[Task 15/25] Current/Best: 8.93/ 21.23 GFLOPS | Progress: (20/20) | 22.82 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 15.21/ 17.58 GFLOPS | Progress: (4/20) | 4.41 s
[Task 16/25] Current/Best: 14.27/ 17.58 GFLOPS | Progress: (8/20) | 6.71 s
[Task 16/25] Current/Best: 8.97/ 17.58 GFLOPS | Progress: (12/20) | 9.98 s
[Task 16/25] Current/Best: 14.29/ 21.79 GFLOPS | Progress: (16/20) | 13.57 s
[Task 16/25] Current/Best: 6.55/ 21.79 GFLOPS | Progress: (20/20) | 16.21 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 10.01/ 19.44 GFLOPS | Progress: (4/20) | 5.21 s
[Task 17/25] Current/Best: 21.62/ 22.34 GFLOPS | Progress: (8/20) | 7.31 s
[Task 17/25] Current/Best: 3.07/ 22.34 GFLOPS | Progress: (12/20) | 10.62 s
[Task 17/25] Current/Best: 9.98/ 23.48 GFLOPS | Progress: (16/20) | 12.66 s
[Task 17/25] Current/Best: 10.81/ 23.48 GFLOPS | Progress: (20/20) | 17.36 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 21.20/ 21.20 GFLOPS | Progress: (4/20) | 8.91 s
[Task 18/25] Current/Best: 7.14/ 21.20 GFLOPS | Progress: (8/20) | 13.09 s
[Task 18/25] Current/Best: 14.49/ 21.20 GFLOPS | Progress: (12/20) | 15.17 s
[Task 18/25] Current/Best: 13.67/ 21.20 GFLOPS | Progress: (16/20) | 21.30 s
[Task 18/25] Current/Best: 14.88/ 21.20 GFLOPS | Progress: (20/20) | 24.63 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 11.84/ 22.16 GFLOPS | Progress: (4/20) | 6.21 s
[Task 19/25] Current/Best: 7.43/ 22.16 GFLOPS | Progress: (8/20) | 9.95 s
[Task 19/25] Current/Best: 5.38/ 22.16 GFLOPS | Progress: (12/20) | 14.06 s
[Task 19/25] Current/Best: 6.83/ 22.16 GFLOPS | Progress: (16/20) | 18.27 s
[Task 19/25] Current/Best: 12.14/ 22.16 GFLOPS | Progress: (20/20) | 22.32 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 18.75/ 18.75 GFLOPS | Progress: (4/20) | 5.16 s
[Task 20/25] Current/Best: 16.04/ 18.75 GFLOPS | Progress: (8/20) | 8.31 s
[Task 20/25] Current/Best: 12.07/ 18.75 GFLOPS | Progress: (12/20) | 12.11 s
[Task 20/25] Current/Best: 6.05/ 18.75 GFLOPS | Progress: (16/20) | 21.82 s
[Task 20/25] Current/Best: 13.62/ 18.75 GFLOPS | Progress: (20/20) | 34.65 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 10.37/ 16.94 GFLOPS | Progress: (4/20) | 5.93 s
[Task 21/25] Current/Best: 5.04/ 18.73 GFLOPS | Progress: (8/20) | 9.99 s
[Task 21/25] Current/Best: 15.41/ 18.73 GFLOPS | Progress: (12/20) | 11.95 s
[Task 21/25] Current/Best: 5.46/ 18.73 GFLOPS | Progress: (16/20) | 22.53 s
[Task 21/25] Current/Best: 11.50/ 20.36 GFLOPS | Progress: (20/20
) | 27.63 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 19.82/ 19.82 GFLOPS | Progress: (4/20) | 4.43 s
[Task 22/25] Current/Best: 17.68/ 19.82 GFLOPS | Progress: (8/20) | 6.34 s
[Task 22/25] Current/Best: 8.65/ 19.82 GFLOPS | Progress: (12/20) | 8.65 s
[Task 22/25] Current/Best: 18.60/ 19.82 GFLOPS | Progress: (16/20) | 12.31 s
[Task 22/25] Current/Best: 18.35/ 20.80 GFLOPS | Progress: (20/20) | 14.27 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 2.70/ 21.01 GFLOPS | Progress: (4/20) | 8.65 s
[Task 23/25] Current/Best: 23.62/ 23.62 GFLOPS | Progress: (8/20) | 12.00 s
[Task 23/25] Current/Best: 21.08/ 23.62 GFLOPS | Progress: (12/20) | 14.93 s
[Task 23/25] Current/Best: 3.13/ 23.62 GFLOPS | Progress: (16/20) | 19.89 s
[Task 23/25] Current/Best: 19.51/ 23.62 GFLOPS | Progress: (20/20) | 22.43 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 1.02/ 8.71 GFLOPS | Progress: (4/20) | 13.21 s
[Task 24/25] Current/Best: 3.56/ 8.71 GFLOPS | Progress: (8/20) | 19.16 s
[Task 24/25] Current/Best: 4.00/ 8.71 GFLOPS | Progress: (12/20) | 30.19 s
[Task 24/25] Current/Best: 3.85/ 8.71 GFLOPS | Progress: (16/20) | 41.19 s
[Task 24/25] Current/Best: 3.69/ 10.34 GFLOPS | Progress: (20/20) | 44.05 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 6.21/ 7.40 GFLOPS | Progress: (4/20) | 9.08 s
[Task 25/25] Current/Best: 6.03/ 8.75 GFLOPS | Progress: (8/20) | 16.85 s
[Task 25/25] Current/Best: 8.01/ 8.75 GFLOPS | Progress: (12/20) | 27.87 s
[Task 25/25] Current/Best: 2.88/ 8.75 GFLOPS | Progress: (16/20) | 31.10 s
[Task 25/25] Current/Best: 4.56/ 8.85 GFLOPS | Progress: (20
/20) | 32.76 s
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 18.72/ 22.10 GFLOPS | Progress: (4/20) | 9.16 s
[Task 1/25] Current/Best: 5.50/ 23.26 GFLOPS | Progress: (8/20) | 12.68 s
[Task 1/25] Current/Best: 12.68/ 23.38 GFLOPS | Progress: (12/20) | 15.13 s
[Task 1/25] Current/Best: 7.43/ 23.38 GFLOPS | Progress: (16/20) | 18.68 s
[Task 1/25] Current/Best: 9.30/ 23.98 GFLOPS | Progress: (20/20) | 26.66 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 4.76/ 17.87 GFLOPS | Progress: (4/20) | 4.49 s
[Task 2/25] Current/Best: 19.08/ 19.08 GFLOPS | Progress: (8/20) | 6.14 s
[Task 2/25] Current/Best: 7.38/ 19.08 GFLOPS | Progress: (12/20) | 7.84 s
[Task 2/25] Current/Best: 16.73/ 19.08 GFLOPS | Progress: (16/20) | 9.26 s
[Task 2/25] Current/Best: 13.01/ 19.08 GFLOPS | Progress: (20/20) | 11.98 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 22.77/ 22.77 GFLOPS | Progress: (4/20) | 5.41 s
[Task 3/25] Current/Best: 6.51/ 22.77 GFLOPS | Progress: (8/20) | 9.77 s
[Task 3/25] Current/Best: 5.72/ 22.77 GFLOPS | Progress: (12/20) | 12.64 s
[Task 3/25] Current/Best: 12.92/ 22.77 GFLOPS | Progress: (16/20) | 15.26 s
[Task 3/25] Current/Best: 20.37/ 22.77 GFLOPS | Progress: (20/20) | 17.80 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 6.43/ 16.53 GFLOPS | Progress: (4/20) | 4.97 s
[Task 4/25] Current/Best: 18.41/ 18.41 GFLOPS | Progress: (8/20) | 7.19 s
[Task 4/25] Current/Best: 8.28/ 18.41 GFLOPS | Progress: (12/20) | 9.16 s
[Task 4/25] Current/Best: 13.58/ 18.41 GFLOPS | Progress: (16/20) | 17.22 s
[Task 4/25] Current/Best: 13.92/ 18.41 GFLOPS | Progress: (20/20) | 21.21 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 14.55/ 21.99 GFLOPS | Progress: (4/20) | 4.76 s
[Task 5/25] Current/Best: 12.51/ 21.99 GFLOPS | Progress: (8/20) | 6.94 s
[Task 5/25] Current/Best: 3.60/ 21.99 GFLOPS | Progress: (12/20) | 9.16 s
[Task 5/25] Current/Best: 17.33/ 21.99 GFLOPS | Progress: (16/20) | 11.27 s
[Task 5/25] Current/Best: 3.45/ 21.99 GFLOPS | Progress: (20/20) | 13.27 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 6.37/ 12.27 GFLOPS | Progress: (4/20) | 5.65 s
[Task 6/25] Current/Best: 11.12/ 21.75 GFLOPS | Progress: (8/20) | 7.71 s
[Task 6/25] Current/Best: 11.74/ 21.75 GFLOPS | Progress: (12/20) | 10.19 s
[Task 6/25] Current/Best: 9.51/ 21.75 GFLOPS | Progress: (16/20) | 13.02 s
[Task 6/25] Current/Best: 12.24/ 21.75 GFLOPS | Progress: (20/20) | 15.80 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 5.73/ 22.17 GFLOPS | Progress: (4/20) | 5.07 s
[Task 7/25] Current/Best: 1.61/ 22.17 GFLOPS | Progress: (8/20) | 9.12 s
[Task 7/25] Current/Best: 6.08/ 22.36 GFLOPS | Progress: (12/20) | 11.63 s
[Task 7/25] Current/Best: 13.43/ 22.36 GFLOPS | Progress: (16/20) | 14.26 s
[Task 7/25] Current/Best: 20.23/ 22.36 GFLOPS | Progress: (20/20) | 17.59 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 7.63/ 15.25 GFLOPS | Progress: (4/20) | 8.99 s
[Task 8/25] Current/Best: 6.29/ 15.25 GFLOPS | Progress: (8/20) | 11.45 s
[Task 8/25] Current/Best: 17.82/ 17.82 GFLOPS | Progress: (12/20) | 17.31 s
[Task 8/25] Current/Best: 10.91/ 17.82 GFLOPS | Progress: (16/20) | 20.41 s
[Task 8/25] Current/Best: 12.61/ 17.82 GFLOPS | Progress: (20/20) | 22.95 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 10.49/ 14.95 GFLOPS | Progress: (4/20) | 8.14 s
[Task 9/25] Current/Best: 15.28/ 22.01 GFLOPS | Progress: (8/20) | 9.92 s
[Task 9/25] Current/Best: 12.83/ 22.01 GFLOPS | Progress: (12/20) | 11.66 s
[Task 9/25] Current/Best: 19.58/ 22.01 GFLOPS | Progress: (16/20) | 17.00 s
[Task 9/25] Current/Best: 18.89/ 22.01 GFLOPS | Progress: (20/20) | 18.60 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 7.05/ 15.54 GFLOPS | Progress: (4/20) | 4.99 s
[Task 10/25] Current/Best: 14.10/ 16.54 GFLOPS | Progress: (8/20) | 7.15 s
[Task 10/25] Current/Best: 6.51/ 16.54 GFLOPS | Progress: (12/20) | 9.35 s
[Task 10/25] Current/Best: 13.60/ 16.54 GFLOPS | Progress: (16/20) | 11.77 s
[Task 10/25] Current/Best: 12.17/ 16.54 GFLOPS | Progress: (20/20) | 13.37 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 17.12/ 22.80 GFLOPS | Progress: (4/20) | 5.13 s
[Task 11/25] Current/Best: 6.86/ 22.80 GFLOPS | Progress: (8/20) | 8.24 s
[Task 11/25] Current/Best: 16.47/ 23.28 GFLOPS | Progress: (12/20) | 10.61 s
[Task 11/25] Current/Best: 16.80/ 23.28 GFLOPS | Progress: (16/20) | 13.15 s
[Task 11/25] Current/Best: 16.82/ 23.28 GFLOPS | Progress: (20/20) | 16.04 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 21.21/ 21.21 GFLOPS | Progress: (4/20) | 6.15 s
[Task 12/25] Current/Best: 15.67/ 21.21 GFLOPS | Progress: (8/20) | 8.17 s
[Task 12/25] Current/Best: 13.37/ 21.21 GFLOPS | Progress: (12/20) | 16.82 s
[Task 12/25] Current/Best: 3.95/ 21.21 GFLOPS | Progress: (16/20) | 22.81 s
[Task 12/25] Current/Best: 12.10/ 21.21 GFLOPS | Progress: (20/20) | 27.67 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 21.38/ 21.38 GFLOPS | Progress: (4/20) | 6.13 s
[Task 13/25] Current/Best: 20.62/ 21.38 GFLOPS | Progress: (8/20) | 8.62 s
[Task 13/25] Current/Best: 19.71/ 21.38 GFLOPS | Progress: (12/20) | 11.67 s
[Task 13/25] Current/Best: 20.64/ 21.38 GFLOPS | Progress: (16/20) | 13.88 s
[Task 13/25] Current/Best: 13.57/ 22.18 GFLOPS | Progress: (20/20) | 17.17 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 19.65/ 19.65 GFLOPS | Progress: (4/20) | 9.48 s
[Task 14/25] Current/Best: 4.97/ 19.65 GFLOPS | Progress: (8/20) | 16.46 s
[Task 14/25] Current/Best: 3.16/ 19.65 GFLOPS | Progress: (12/20) | 19.03 s
[Task 14/25] Current/Best: 14.68/ 19.65 GFLOPS | Progress: (16/20) | 25.18 s
[Task 14/25] Current/Best: 20.08/ 20.08 GFLOPS | Progress: (20/20) | 36.91 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 15/25] Current/Best: 3.25/ 21.29 GFLOPS | Progress: (4/20) | 6.05 s
[Task 15/25] Current/Best: 9.66/ 21.29 GFLOPS | Progress: (8/20) | 17.22 s
[Task 15/25] Current/Best: 14.10/ 22.25 GFLOPS | Progress: (12/20) | 20.24 s
[Task 15/25] Current/Best: 13.57/ 22.25 GFLOPS | Progress: (16/20) | 22.64 s
[Task 15/25] Current/Best: 6.40/ 22.45 GFLOPS | Progress: (20/20) | 33.24 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 11.41/ 18.38 GFLOPS | Progress: (4/20) | 5.58 s
[Task 16/25] Current/Best: 15.30/ 18.38 GFLOPS | Progress: (8/20) | 7.31 s
[Task 16/25] Current/Best: 18.71/ 18.71 GFLOPS | Progress: (12/20) | 9.27 s
[Task 16/25] Current/Best: 14.80/ 18.71 GFLOPS | Progress: (16/20) | 10.98 s
[Task 16/25] Current/Best: 9.28/ 18.71 GFLOPS | Progress: (20/20) | 14.66 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 10.48/ 21.41 GFLOPS | Progress: (4/20) | 6.40 s
[Task 17/25] Current/Best: 6.28/ 21.95 GFLOPS | Progress: (8/20) | 8.94 s
[Task 17/25] Current/Best: 16.49/ 22.34 GFLOPS | Progress: (12/20) | 11.01 s
[Task 17/25] Current/Best: 8.81/ 22.39 GFLOPS | Progress: (16/20) | 13.83 s
[Task 17/25] Current/Best: 12.38/ 22.39 GFLOPS | Progress: (20/20) | 18.25 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 16.56/ 16.56 GFLOPS | Progress: (4/20) | 6.33 s
[Task 18/25] Current/Best: 5.97/ 16.56 GFLOPS | Progress: (8/20) | 10.34 s
[Task 18/25] Current/Best: 15.71/ 16.83 GFLOPS | Progress: (12/20) | 12.40 s
[Task 18/25] Current/Best: 17.73/ 17.79 GFLOPS | Progress: (16/20) | 16.33 s
[Task 18/25] Current/Best: 6.22/ 17.79 GFLOPS | Progress: (20/20) | 20.90 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 9.53/ 22.00 GFLOPS | Progress: (4/20) | 6.47 s
[Task 19/25] Current/Best: 17.28/ 22.00 GFLOPS | Progress: (8/20) | 9.84 s
[Task 19/25] Current/Best: 11.59/ 22.00 GFLOPS | Progress: (12/20) | 13.63 s
[Task 19/25] Current/Best: 18.21/ 22.00 GFLOPS | Progress: (16/20) | 18.31 s
[Task 19/25] Current/Best: 18.34/ 22.00 GFLOPS | Progress: (20/20) | 20.75 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 17.69/ 18.17 GFLOPS | Progress: (4/20) | 9.18 s
[Task 20/25] Current/Best: 8.61/ 18.17 GFLOPS | Progress: (8/20) | 11.60 s
[Task 20/25] Current/Best: 12.78/ 18.50 GFLOPS | Progress: (12/20) | 23.39 s
[Task 20/25] Current/Best: 8.18/ 18.50 GFLOPS | Progress: (16/20) | 30.22 s
[Task 20/25] Current/Best: 13.08/ 18.50 GFLOPS | Progress: (20/20) | 32.91 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 16.90/ 18.46 GFLOPS | Progress: (4/20) | 4.58 s
[Task 21/25] Current/Best: 19.33/ 22.87 GFLOPS | Progress: (8/20) | 6.34 s
[Task 21/25] Current/Best: 18.87/ 22.87 GFLOPS | Progress: (12/20) | 17.45 s
[Task 21/25] Current/Best: 23.11/ 23.11 GFLOPS | Progress: (16/20) | 20.38 s
[Task 21/25] Current/Best: 18.00/ 23.11 GFLOPS | Progress: (20/2
0) | 26.71 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 17.78/ 21.65 GFLOPS | Progress: (4/20) | 4.69 s
[Task 22/25] Current/Best: 8.83/ 21.65 GFLOPS | Progress: (8/20) | 7.20 s
[Task 22/25] Current/Best: 9.81/ 21.65 GFLOPS | Progress: (12/20) | 8.91 s
[Task 22/25] Current/Best: 7.31/ 21.65 GFLOPS | Progress: (16/20) | 14.25 s
[Task 22/25] Current/Best: 17.84/ 21.65 GFLOPS | Progress: (20/20) | 15.93 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 14.43/ 18.72 GFLOPS | Progress: (4/20) | 5.45 s
[Task 23/25] Current/Best: 3.12/ 18.72 GFLOPS | Progress: (8/20) | 9.51 s
[Task 23/25] Current/Best: 10.55/ 21.90 GFLOPS | Progress: (12/20) | 12.74 s
[Task 23/25] Current/Best: 5.43/ 21.90 GFLOPS | Progress: (16/20) | 16.26 s
[Task 23/25] Current/Best: 9.25/ 21.90 GFLOPS | Progress: (20/20) | 21.09 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 1.91/ 5.83 GFLOPS | Progress: (4/20) | 13.90 s Done.
+ Done.
+ Done.
+
[Task 24/25] Current/Best: 3.69/ 5.97 GFLOPS | Progress: (8/20) | 18.15 s
[Task 24/25] Current/Best: 8.45/ 8.45 GFLOPS | Progress: (12/20) | 28.87 s
[Task 24/25] Current/Best: 1.64/ 8.45 GFLOPS | Progress: (16/20) | 40.44 s
[Task 24/25] Current/Best: 7.93/ 8.45 GFLOPS | Progress: (20/20) | 51.44 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 5.77/ 8.81 GFLOPS | Progress: (4/20) | 9.62 s
[Task 25/25] Current/Best: 5.86/ 8.81 GFLOPS | Progress: (8/20) | 20.63 s
[Task 25/25] Current/Best: 4.97/ 9.27 GFLOPS | Progress: (12/20) | 31.62 s
[Task 25/25] Current/Best: 8.66/ 9.27 GFLOPS | Progress: (16/20) | 35.48 s
[Task 25/25] Current/Best: 4.71/ 9.27 GFLOPS | Progress: (20/20) | 46.47 s
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
Done.
Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 412.117700069939, 'median': 411.7156207499647, 'std': 2.200144495970549}
- unoptimized: {'mean': 490.87725040000805, 'median': 490.83310535006603, 'std': 2.1082866969456844}
+ optimized: {'mean': 418.74853045002965, 'median': 418.19785325005796, 'std': 2.134506473251905}
+ unoptimized: {'mean': 494.926992339997, 'median': 494.77054239996505, 'std': 1.3447543944605553}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 7.592 seconds)
+ **Total running time of the script:** ( 13 minutes 53.419 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 c45af1b545..da48c6518b 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
- 1.235e-07 secs/op
+ 1.105e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 789887068f..a1cabd9482 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -103,7 +103,7 @@ and to examine the IR code in human readable format, we can do
class Module:
@T.prim_func
def main(A: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B = T.allocate([n], "float32", "global")
@@ -147,7 +147,7 @@ Fortunately, we can replace those two lines with simple :code:`topi.sum` much li
class Module:
@T.prim_func
def main(A: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
A_red = T.allocate([n], "float32", "global")
@@ -229,7 +229,7 @@ we can schedule the following series of operations ending with :code:`topi.sum`
class Module:
@T.prim_func
def main(a: T.Buffer((100, 10, 10), "float32"), b: T.Buffer((10, 10), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
T_divide_red = T.allocate([1], "float32", "global")
threadIdx_x = T.launch_thread("threadIdx.x", 1024)
T_divide_red_rf = T.allocate([1], "float32", "local")
@@ -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, 0x111e4fe0)), stage(b, placeholder(b, 0x23dfd000)), 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, 0xc4dbb60)), stage(b, placeholder(b, 0x12276d80)), 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. [...]
@@ -332,7 +332,7 @@ TOPI also provides common neural nets operations such as _softmax_ with optimize
class Module:
@T.prim_func
def main(tarray: T.Buffer((512, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
T_softmax_norm = T.allocate([65536], "float32x4", "global")
blockIdx_x = T.launch_thread("blockIdx.x", 512)
normal_reduce_temp0 = T.allocate([1], "float32", "local")
@@ -414,7 +414,7 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
class Module:
@T.prim_func
def main(placeholder: T.Buffer((1, 3, 224, 224), "float32"), placeholder_1: T.Buffer((10, 3, 5, 5), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
compute = T.allocate([501760], "float32", "global")
blockIdx_z = T.launch_thread("blockIdx.z", 5)
conv2d_nchw = T.allocate([14], "float32", "local")
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 379fb7d984..80851a90aa 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**16:40.425** total execution time for **tutorial** files:
+**17:27.126** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:07.592 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:53.419 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:20.911 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:26.092 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.945 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.440 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.388 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.074 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:26.534 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:21.078 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.020 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.987 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.850 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.843 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.185 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.194 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index c5e514106d..5c3b58cb84 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,7 +285,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000006
+ Numpy running time: 0.000007
naive: 0.000007
@@ -350,7 +350,7 @@ return a readable C-style statement.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
A_1 = T.match_buffer(A, (n,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000007
+ parallel: 0.000008
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000039
+ vector: 0.000038
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -452,7 +452,7 @@ factor to be the number of threads on your CPU.
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
A_1 = T.match_buffer(A, (n,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 6.405450003512669e-06 1.0
- naive 6.6428999999999995e-06 1.0370699945135964
- parallel 6.663900000000001e-06 1.040348452699748
- vector 3.92357e-05 6.125361993065847
+ numpy 7.405910000670701e-06 1.0
+ naive 6.639499999999999e-06 0.8965137301693791
+ parallel 8.122e-06 1.0966916961270725
+ vector 3.8246000000000006e-05 5.164253953469099
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017626
+ Numpy running time: 0.018659
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.435826
+ none: 3.469232
@@ -1014,7 +1014,7 @@ nested loops over the indices of the A and B matrices.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for x, y in T.grid(1024, 1024):
C_1 = T.Buffer((1048576,), data=C.data)
C_1[x * 1024 + y] = T.float32(0)
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.285572
+ blocking: 0.317215
@@ -1113,7 +1113,7 @@ internal representation and compare it to the original:
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for x_outer, y_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for x_inner_init, y_inner_init in T.grid(32, 32):
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.268879
+ vectorization: 0.310761
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1172,7 +1172,7 @@ already cache friendly from our previous optimizations.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for x_outer, y_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for x_inner_init in range(32):
@@ -1230,7 +1230,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.112885
+ loop permutation: 0.114647
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1238,7 +1238,7 @@ more cache friendly.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for x_outer, y_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for x_inner_init in range(32):
@@ -1321,7 +1321,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.104595
+ array packing: 0.104076
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1329,7 +1329,7 @@ optimized schedule.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for x in T.parallel(32):
@@ -1404,7 +1404,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.108916
+ block caching: 0.109764
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1412,7 +1412,7 @@ to `C` when all the block results are ready.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
C_global = T.allocate([1024], "float32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
@@ -1478,7 +1478,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.129902
+ parallelization: 0.129648
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1486,7 +1486,7 @@ of thread-level parallelization.
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for x in T.parallel(32):
@@ -1548,13 +1548,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.4358257677000004 1.0
- blocking 0.2855715022 0.08311582760821029
- vectorization 0.2688787475 0.07825738721320318
- loop permutation 0.11288530199999999 0.03285536276642087
- array packing 0.1045945755 0.03044233979594878
- block caching 0.10891612359999998 0.03170013003101443
- parallelization 0.1299021283 0.03780812447511233
+ none 3.4692320232 1.0
+ blocking 0.3172145016 0.09143651951748188
+ vectorization 0.31076115410000005 0.08957635350470325
+ loop permutation 0.1146473691 0.0330469015428521
+ array packing 0.10407576710000002 0.02999965594806228
+ block caching 0.1097643211 0.03163937158597827
+ parallelization 0.12964787090000002 0.03737076967841821
@@ -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 1.440 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt b/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
index 68d74bbe29..2fb1f4c6f3 100644
--- a/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
+++ b/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
@@ -133,7 +133,7 @@ Following is a simple example for vector addition.
class Module:
@T.prim_func
def main(A: T.Buffer((8,), "float32"), B: T.Buffer((8,), "float32")):
- T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i in range(8):
with T.block("B"):
@@ -180,7 +180,7 @@ to an IRModule.
class Module:
@T.prim_func
def main(A: T.Buffer((8,), "float32"), B: T.Buffer((8,), "float32")):
- T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i0 in range(8):
with T.block("B"):
@@ -325,7 +325,7 @@ Tile the loop into 3 loops and print the result.
class Module:
@T.prim_func
def main(A: T.Buffer((8,), "float32"), B: T.Buffer((8,), "float32")):
- T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i_0, i_1, i_2 in T.grid(2, 2, 2):
with T.block("B"):
@@ -364,7 +364,7 @@ We can also reorder the loops. Now we move loop `i_2` to outside of `i_1`.
class Module:
@T.prim_func
def main(A: T.Buffer((8,), "float32"), B: T.Buffer((8,), "float32")):
- T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i_0, i_2, i_1 in T.grid(2, 2, 2):
with T.block("B"):
@@ -409,7 +409,7 @@ also use primitives and do incrementally transformation.
class Module:
@T.prim_func
def main(A: T.Buffer((8,), "float32"), B: T.Buffer((8,), "float32")):
- T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i_0 in T.thread_binding(2, thread="blockIdx.x"):
for i_2 in T.thread_binding(2, thread="threadIdx.x"):
diff --git a/docs/api/rust/help.html b/docs/api/rust/help.html
index 131b87bfc3..8d27f9b944 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 9987e08388..ab27dc3ccb 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 a64a8cbede..fe490125f4 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-991163c86c5f52a879eb11cdcb19d8bab952f209
+7e830e58708da700be8bbb849442180942b796ec
diff --git a/docs/genindex.html b/docs/genindex.html
index 99c71da9d8..21a54615c9 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -5199,17 +5199,19 @@
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.winograd_transform_matrices">winograd_transform_matrices() (in module tvm.topi.nn)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/ir.html#tvm.ir.BaseFunc.with_attr">with_attr() (tvm.ir.BaseFunc method)</a>
<ul>
<li><a href="reference/api/python/ir.html#tvm.ir.IRModule.with_attr">(tvm.ir.IRModule method)</a>
</li>
</ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/tir.html#tvm.tir.PrimFunc.with_body">with_body() (tvm.tir.PrimFunc method)</a>
</li>
<li><a href="reference/api/python/rpc.html#tvm.rpc.with_minrpc">with_minrpc() (in module tvm.rpc)</a>
+</li>
+ <li><a href="reference/api/python/ir.html#tvm.ir.BaseFunc.without_attr">without_attr() (tvm.ir.BaseFunc method)</a>
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Schedule.work_on">work_on() (tvm.tir.Schedule method)</a>
</li>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7f2c0788a8..ec0a8b0489 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -595,7 +595,7 @@ 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 33.684 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 32.546 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 0b3fb8cddd..5fd4f69053 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -449,7 +449,7 @@
<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.zip1fd12e26-640f-4b08-8eb5-65b65c6fd171 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.zip35390e19-423b-4b54-b5f0-a24f751a8f7f 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 6841f553fd..156a960b32 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,15 +459,16 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 25.8MB/s]
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+100%|##########| 41.5M/41.5M [00:01<00:00, 25.9MB/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 f139438524..89d41a581a 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -494,7 +494,7 @@ 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 2.203 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.353 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 d85f246e8f..13cb566db7 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,15 +442,14 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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</div>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 3bf2954e65..1705f03d2a 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -662,7 +662,7 @@ 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 39.681 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 35.586 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 43e18959ff..69243322c7 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>07:25.512</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:16.051</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><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>01:39.681</p></td>
+<td><p>01:35.586</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>01:33.684</p></td>
+<td><p>01:32.546</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>01:02.203</p></td>
+<td><p>01:02.353</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>00:42.008</p></td>
+<td><p>00:42.324</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:37.537</p></td>
+<td><p>00:36.930</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>
-<td><p>00:35.381</p></td>
+<td><p>00:34.875</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:29.228</p></td>
+<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>00:28.065</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:29.203</p></td>
+<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>00:27.877</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>00:13.728</p></td>
+<td><p>00:12.708</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>00:02.860</p></td>
+<td><p>00:02.787</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 b2f87ca283..a0145df9b3 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -840,10 +840,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4223.4739 4223.3115 4228.4866 4220.1845 2.2765
+ 4205.6209 4227.8086 4234.7956 4103.0899 49.1633
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.728 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.680 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 73933be41c..d9adfbdd96 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
@@ -448,36 +448,36 @@ 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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</pre></div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index d8490ba034..188a69af62 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -672,7 +672,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.1803 16.0044 17.8246 15.7566 0.5667
+ 15.5669 15.2494 17.4863 15.1335 0.7048
</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 c762c9317a..cb3da64f06 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,38 +464,35 @@ 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).
@@ -589,7 +586,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 32.229 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 41.737 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 1e4294a566..5a39f5da4b 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,9 +505,9 @@ 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>
</div>
</div>
@@ -598,7 +598,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.4155 89.4265 90.0712 88.8519 0.2779
+ 88.7830 88.7270 89.6273 88.5885 0.1656
</pre></div>
</div>
<div class="admonition note">
@@ -637,7 +637,7 @@ 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 28.343 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.577 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 039cb7521e..008fe2d182 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -590,7 +590,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 110.1314 110.0617 115.2141 109.6529 0.5545
+ 110.6743 110.6474 111.8093 109.9168 0.3296
</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 2d7e9c27b7..da711009f8 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -531,7 +531,7 @@ 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 13.454 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 8.235 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 30f18f3943..9eced3c4b3 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>12:14.488</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:22.948</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -359,39 +359,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:32.229</p></td>
+<td><p>03:41.737</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>02:13.454</p></td>
+<td><p>02:08.235</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>01:28.343</p></td>
+<td><p>01:29.577</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>01:19.728</p></td>
+<td><p>01:19.680</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>00:58.186</p></td>
+<td><p>00:58.591</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:51.321</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>00:51.669</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>00:50.403</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>00:51.472</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>00:30.519</p></td>
+<td><p>00:31.195</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>00:30.299</p></td>
+<td><p>00:30.786</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>
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 b2650a6f35..f090f0835b 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -629,7 +629,7 @@ 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.zip8ff35455-fc35-48fa-85e8-676b5e4ae3bf 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.zip9ec701a3-6083-49f5-87e5-076a5594399f 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/low_level_custom_pass.html b/docs/how_to/extend_tvm/low_level_custom_pass.html
index 012da918ad..ad1b11dde1 100644
--- a/docs/how_to/extend_tvm/low_level_custom_pass.html
+++ b/docs/how_to/extend_tvm/low_level_custom_pass.html
@@ -431,7 +431,7 @@ our customized lowering pass to manipulate the IR directly instead of using sche
class Module:
@T.prim_func
def main(a: T.Buffer((128,), "float32"), b: T.Buffer((128,), "float32"), c: T.Buffer((128,), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i in range(128):
c[i] = a[i] + b[i]
</pre></div>
@@ -539,7 +539,7 @@ called after each phase is done.</p>
class Module:
@T.prim_func
def main(a: T.Buffer((128,), "float32"), b: T.Buffer((128,), "float32"), c: T.Buffer((128,), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i_outer in range(16):
cse_var_1: T.int32 = i_outer * 8
c[cse_var_1:cse_var_1 + 8] = a[cse_var_1:cse_var_1 + 8] + b[cse_var_1:cse_var_1 + 8]
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 20ebb122e3..87eb1ec320 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:57.549</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:59.501</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,19 +359,19 @@
</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>00:53.672</p></td>
+<td><p>00:55.504</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>00:02.699</p></td>
+<td><p>00:02.781</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>00:01.171</p></td>
+<td><p>00:01.209</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>
-<td><p>00:00.007</p></td>
+<td><p>00:00.008</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 31ec897562..e9754fa271 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -536,10 +536,10 @@ 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: 23570us [23570us] (48.46%; 48.46%)
-FoldScaleAxis: 25069us [8us] (51.54%; 51.54%)
- FoldConstant: 25061us [1874us] (51.53%; 99.97%)
- InferType: 23187us [23187us] (47.67%; 92.52%)
+InferType: 23644us [23644us] (48.30%; 48.30%)
+FoldScaleAxis: 25312us [9us] (51.70%; 51.70%)
+ FoldConstant: 25304us [1889us] (51.69%; 99.97%)
+ InferType: 23415us [23415us] (47.83%; 92.54%)
</pre></div>
</div>
</div>
@@ -561,10 +561,10 @@ 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: 23113us [23113us] (48.31%; 48.31%)
-FoldScaleAxis: 24731us [6us] (51.69%; 51.69%)
- FoldConstant: 24725us [1814us] (51.68%; 99.98%)
- InferType: 22911us [22911us] (47.89%; 92.66%)
+InferType: 23499us [23499us] (47.97%; 47.97%)
+FoldScaleAxis: 25492us [9us] (52.03%; 52.03%)
+ FoldConstant: 25483us [1962us] (52.02%; 99.97%)
+ InferType: 23521us [23521us] (48.01%; 92.30%)
</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 6e4a025056..747464eeec 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -585,7 +585,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.544990 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 49.347137 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 cb1be10320..2d29f6f27a 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -740,7 +740,7 @@ The only thing we should do is to make sure all threads in a warp can call Tenso
class Module:
@T.prim_func
def main(A: T.Buffer((16, 14, 14, 16, 16, 16), "float16"), W: T.Buffer((3, 3, 16, 32, 16, 16), "float16"), Conv: T.Buffer((16, 14, 14, 32, 16, 16), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_z = T.launch_thread("blockIdx.z", 196)
Conv_wmma_accumulator = T.allocate([2048], "float32", "wmma.accumulator")
Apad_shared = T.allocate([12288], "float16", "shared")
@@ -809,7 +809,7 @@ by mapping the 2D convolution to tensor intrinsics</p>
class Module:
@T.prim_func
def main(A: T.Buffer((16, 14, 14, 16, 16, 16), "float16"), W: T.Buffer((3, 3, 16, 32, 16, 16), "float16"), Conv: T.Buffer((16, 14, 14, 32, 16, 16), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_z = T.launch_thread("blockIdx.z", 196)
Conv_wmma_accumulator = T.allocate([2048], "float32", "wmma.accumulator")
Apad_shared = T.allocate([12288], "float16", "shared")
@@ -867,7 +867,7 @@ 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: 12.276823 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.273459 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 7f055e2ddc..87e88b857b 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -482,8 +482,8 @@ 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: 0.018669
-Baseline: 3.446875
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019321
+Baseline: 3.462748
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -498,7 +498,7 @@ Here is the generated IR using our baseline schedule.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m, n in T.grid(1024, 1024):
C_1 = T.Buffer((1048576,), data=C.data)
C_1[m * 1024 + n] = T.float32(0)
@@ -542,7 +542,7 @@ 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: 0.310505
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.316440
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -556,7 +556,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init, n_inner_init in T.grid(32, 32):
@@ -599,7 +599,7 @@ 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: 0.289689
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.304398
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -613,7 +613,7 @@ vastly.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init in range(32):
@@ -654,7 +654,7 @@ 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: 0.114256
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117566
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -668,7 +668,7 @@ the access pattern for A matrix is more cache friendly.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for m_outer, n_outer in T.grid(32, 32):
C_1 = T.Buffer((1048576,), data=C.data)
for m_inner_init in range(32):
@@ -731,7 +731,7 @@ 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: 0.106624
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.105768
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -745,7 +745,7 @@ flattening.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for bigN in T.parallel(32):
@@ -809,7 +809,7 @@ 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: 0.112138
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111828
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -823,7 +823,7 @@ write to C when all the block results are ready.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
C_global = T.allocate([1024], "float32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
@@ -889,7 +889,7 @@ 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: 0.133126
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.132968
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
@@ -903,7 +903,7 @@ class Module:
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 1024), "float32"), B: T.Buffer((1024, 1024), "float32"), C: T.Buffer((1024, 1024), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
packedB = T.allocate([32768], "float32x32", "global")
packedB_1 = T.Buffer((32768,), "float32x32", data=packedB)
for bigN in T.parallel(32):
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 8cb5a6b1a9..92b1c8124e 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:34.765</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.165</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -359,15 +359,15 @@
</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>00:31.296</p></td>
+<td><p>00:31.681</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>00:02.076</p></td>
+<td><p>00:02.113</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>00:01.393</p></td>
+<td><p>00:01.372</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 f258d57c7e..0e3e3ecd1e 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>03:51.718</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:53.089</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -359,23 +359,23 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:41.549</p></td>
+<td><p>01:42.004</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>01:20.042</p></td>
+<td><p>01:20.111</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>00:17.121</p></td>
+<td><p>00:17.164</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:16.510</p></td>
+<td><p>00:16.956</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:16.393</p></td>
+<td><p>00:16.751</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index 1ddd121b61..c799c5051f 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
@@ -516,7 +516,7 @@ cooperative fetching, unrolling and operator fusion.</p>
class Module:
@T.prim_func
def main(data: T.Buffer((1, 512, 7, 7), "float32"), kernel: T.Buffer((512, 512, 3, 3), "float32"), bias: T.Buffer((1, 512, 1, 1), "float32"), compute: T.Buffer((1, 512, 7, 7), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
blockIdx_x = T.launch_thread("blockIdx.x", 28)
conv2d_nchw = T.allocate([14], "float32", "local")
pad_temp_shared = T.allocate([72], "float32", "shared")
@@ -1023,7 +1023,7 @@ 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: 0.347 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.351 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 9531506b81..ea7ddc0338 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -926,7 +926,7 @@ 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)
- 8.1031 8.1045 8.1048 8.1001 0.0021
+ 8.1523 8.1525 8.1545 8.1500 0.0019
</pre></div>
</div>
</div>
@@ -948,7 +948,7 @@ 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 20.042 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.111 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 48699ac63d..cc306d15e6 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -945,7 +945,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 766.0302 767.1045 767.1609 763.8254 1.5592
+ 766.1276 765.6038 770.8713 761.9077 3.6781
</pre></div>
</div>
</div>
@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 41.549 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.004 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 6f2d870710..5f80f5352b 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:23.501</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.563</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,7 +359,7 @@
</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>00:23.463</p></td>
+<td><p>00:23.526</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>
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 95c4bbab49..496100e433 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -620,7 +620,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.036955
+Time cost of this operator: 0.037149
</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 182ef60524..9fab110426 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -654,10 +654,10 @@ 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 306.2 98.736 (1, 2, 10, 10, 3) 2 1 [306.2]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.951 0.951 (1, 6, 10, 10) 1 1 [2.951]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.969 0.313 (1, 1, 10, 10, 3) 1 1 [0.969]
-Total_time - 310.12 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.4 98.757 (1, 2, 10, 10, 3) 2 1 [309.4]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.916 0.931 (1, 6, 10, 10) 1 1 [2.916]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.979 0.312 (1, 1, 10, 10, 3) 1 1 [0.979]
+Total_time - 313.295 - - - - -
</pre></div>
</div>
</div>
@@ -709,13 +709,13 @@ 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 177.3 98.385 (1, 1, 10, 10, 6) 2 1 [177.3]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.06 1.143 (1, 6, 10, 10) 1 1 [2.06]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.85 0.472 (1, 3, 10, 10, 1) 1 1 [0.85]
-Total_time - 180.21 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 101.4 97.334 (1, 6, 10, 10, 1) 2 1 [101.4]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.727 (1, 6, 10, 10) 1 1 [1.799]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.938 (1, 1, 10, 10, 3) 1 1 [0.978]
+Total_time - 104.177 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.530 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 26.822 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 9e81c778f9..9f945ca294 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -465,8 +465,7 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 17.5MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 27.5MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 55.6MB/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.
@@ -594,7 +593,7 @@ 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 28.047 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.190 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 949d980735..067fb88d4e 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -533,7 +533,7 @@ 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/tmpf1dqv31q/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpy3urn_f8/images/random'
</pre></div>
</div>
</div>
@@ -593,8 +593,8 @@ 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/tmpf1dqv31q/images/target contains 8144 images
-/tmp/tmpf1dqv31q/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/tmpy3urn_f8/images/target contains 8144 images
+/tmp/tmpy3urn_f8/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -706,13 +706,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 42s - loss: 0.2119 - accuracy: 0.9288 - val_loss: 0.1281 - val_accuracy: 0.9547 - 42s/epoch - 128ms/step
+328/328 - 40s - loss: 0.2804 - accuracy: 0.9116 - val_loss: 0.1295 - val_accuracy: 0.9551 - 40s/epoch - 123ms/step
Epoch 2/3
-328/328 - 36s - loss: 0.1014 - accuracy: 0.9640 - val_loss: 0.1191 - val_accuracy: 0.9543 - 36s/epoch - 108ms/step
+328/328 - 36s - loss: 0.1107 - accuracy: 0.9601 - val_loss: 0.1012 - val_accuracy: 0.9637 - 36s/epoch - 109ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0667 - accuracy: 0.9748 - val_loss: 0.1169 - val_accuracy: 0.9611 - 35s/epoch - 108ms/step
+328/328 - 36s - loss: 0.0733 - accuracy: 0.9733 - val_loss: 0.0910 - val_accuracy: 0.9694 - 36s/epoch - 108ms/step
-<keras.callbacks.History object at 0x7f1e1253fcd0>
+<keras.callbacks.History object at 0x7f301c6f68b0>
</pre></div>
</div>
</div>
@@ -976,7 +976,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 43.330 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 51.463 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 1d7d5d12e6..bff6d6c8ca 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:06.980</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>08:18.874</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -359,27 +359,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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+<td><p>04:51.463</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_pytorch.html#sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">4. microTVM PyTorch Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_pytorch.py</span></code>)</p></td>
-<td><p>01:28.047</p></td>
+<td><p>01:30.190</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:25.530</p></td>
+<td><p>01:26.822</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_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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+<td><p>00:12.694</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_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<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>00:08.494</p></td>
+<td><p>00:08.690</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 508103b82f..cd39bf3a0f 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:40.906</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:41.363</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,19 +359,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:35.594</p></td>
+<td><p>00:36.061</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
-<td><p>00:03.385</p></td>
+<td><p>00:03.364</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
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+<td><p>00:01.931</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
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diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 6bf50dd741..746a922cdb 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -559,7 +559,7 @@ 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 0x7f1b66552160>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f2ea47173a0>
</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/reduction.html b/docs/how_to/work_with_schedules/reduction.html
index db95d2fbd6..59dd3a364c 100644
--- a/docs/how_to/work_with_schedules/reduction.html
+++ b/docs/how_to/work_with_schedules/reduction.html
@@ -450,7 +450,7 @@ Before doing anything, let us print out the IR code of default schedule.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -478,7 +478,7 @@ axis by different factors. The result is a nested reduction.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -507,7 +507,7 @@ class Module:
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -546,7 +546,7 @@ result B.rf. The factored dimension becomes the first dimension of B.rf.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n, m = T.int32(), T.int32()
A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -697,7 +697,7 @@ Here is an example for 2D convolution with filter size = [3, 3] and strides = [1
class Module:
@T.prim_func
def main(Input: T.handle, Filter: T.Buffer((3, 3), "float32"), Output: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
Input_1 = T.match_buffer(Input, (n, n), strides=("stride", "stride"), buffer_type="auto")
Output_1 = T.match_buffer(Output, (n - 2, n - 2))
diff --git a/docs/how_to/work_with_schedules/scan.html b/docs/how_to/work_with_schedules/scan.html
index e589c5104a..3bfe13fa82 100644
--- a/docs/how_to/work_with_schedules/scan.html
+++ b/docs/how_to/work_with_schedules/scan.html
@@ -454,7 +454,7 @@ To split on the time iteration, user can schedule on scan_op.scan_axis instead.<
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_1 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -524,7 +524,7 @@ the body of scan to be compute_at locations outside the scan loop.</p>
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_1 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -575,7 +575,7 @@ The following example demonstrates how we can build recurrence with two states.<
class Module:
@T.prim_func
def main(X: T.handle, scan: T.handle, scan_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
X_1 = T.match_buffer(X, (m, n), strides=("stride", "stride"), buffer_type="auto")
scan_2 = T.match_buffer(scan, (m, n), strides=("stride", "stride"), buffer_type="auto")
diff --git a/docs/how_to/work_with_schedules/schedule_primitives.html b/docs/how_to/work_with_schedules/schedule_primitives.html
index c9a99f5a2c..dbf539f20e 100644
--- a/docs/how_to/work_with_schedules/schedule_primitives.html
+++ b/docs/how_to/work_with_schedules/schedule_primitives.html
@@ -442,7 +442,7 @@ schedule computes tensor in a serial manner in a row-major order.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -476,7 +476,7 @@ methods to schedule every stage.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -505,7 +505,7 @@ contrary with <code class="code docutils literal notranslate"><span class="pre">
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -536,7 +536,7 @@ axes.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -573,7 +573,7 @@ class Module:
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -609,7 +609,7 @@ class Module:
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A_1 = T.match_buffer(A, (m, n), strides=("stride", "stride"), buffer_type="auto")
B_1 = T.match_buffer(B, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -646,7 +646,7 @@ in gpu programming.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
n = T.int32()
A_1 = T.match_buffer(A, (n,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (n,), strides=("stride",), buffer_type="auto")
@@ -678,7 +678,7 @@ tensors at the root separately by default.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -710,7 +710,7 @@ of computation of <cite>C</cite>.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -745,7 +745,7 @@ tensor is required.</p>
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
@@ -777,7 +777,7 @@ class Module:
class Module:
@T.prim_func
def main(A: T.handle, B: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m = T.int32()
A_1 = T.match_buffer(A, (m,), strides=("stride",), buffer_type="auto")
B_1 = T.match_buffer(B, (m,), strides=("stride",), buffer_type="auto")
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 bd07916641..0b65770994 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -350,7 +350,7 @@
<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>00:07.935</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:07.738</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -359,19 +359,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
-<td><p>00:04.676</p></td>
+<td><p>00:04.491</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
-<td><p>00:01.428</p></td>
+<td><p>00:01.405</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
-<td><p>00:00.775</p></td>
+<td><p>00:00.776</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
-<td><p>00:00.759</p></td>
+<td><p>00:00.763</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
@@ -379,7 +379,7 @@
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
-<td><p>00:00.083</p></td>
+<td><p>00:00.089</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 92f8133345..ccc1749a97 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -432,7 +432,7 @@ The following lines describe the computation <code class="code docutils literal
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j in T.grid(1024, 512):
C_1 = T.Buffer((524288,), data=C.data)
C_1[i * 512 + j] = T.float32(0)
@@ -466,7 +466,7 @@ Thus we break down the matmul loops to make the innermost loops a (16x64) GEMV.<
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j_outer, j_inner in T.grid(1024, 32, 16):
C_1 = T.Buffer((524288,), data=C.data)
C_1[i * 512 + j_outer * 16 + j_inner] = T.float32(0)
@@ -552,7 +552,7 @@ such placeholder can be put to let TVM automatically bind the inferred value for
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
for i, j_outer in T.grid(1024, 32):
T.call_extern("int32", "gemv_update", T.tvm_access_ptr(T.type_annotation("float32"), C.data, i * 512 + j_outer * 16, 16, 2), T.tvm_access_ptr(T.type_annotation("float32"), A.data, i * 64, 64, 1), T.tvm_access_ptr(T.type_annotation("float32"), B.data, j_outer * 1024, 1024, 1), 16, 64, 64)
</pre></div>
@@ -594,7 +594,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
class Module:
@T.prim_func
def main(A: T.Buffer((1024, 64), "float32"), B: T.Buffer((512, 64), "float32"), C: T.Buffer((1024, 512), "float32")):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
i = T.int32()
T.attr(T.iter_var(i, None, "DataPar", ""), "pragma_import_llvm", metadata["tir.StringImm"][0])
for i, j_outer in T.grid(1024, 32):
diff --git a/docs/how_to/work_with_schedules/tuple_inputs.html b/docs/how_to/work_with_schedules/tuple_inputs.html
index 62532563e6..2f23e9b5c9 100644
--- a/docs/how_to/work_with_schedules/tuple_inputs.html
+++ b/docs/how_to/work_with_schedules/tuple_inputs.html
@@ -428,7 +428,7 @@ together in the next schedule procedure.</p>
class Module:
@T.prim_func
def main(A0: T.handle, A1: T.handle, B: T.handle, B_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A0_1 = T.match_buffer(A0, (m, n), strides=("stride", "stride"), buffer_type="auto")
A1_1 = T.match_buffer(A1, (m, n), strides=("stride", "stride"), buffer_type="auto")
@@ -487,7 +487,7 @@ with <code class="xref py py-func docutils literal notranslate"><span class="pre
class Module:
@T.prim_func
def main(idx: T.handle, val: T.handle, T: T.handle, T_1: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
idx_1 = T.match_buffer(idx, (m, n), "int32", strides=("stride", "stride"), buffer_type="auto")
val_1 = T.match_buffer(val, (m, n), "int32", strides=("stride", "stride"), buffer_type="auto")
@@ -536,7 +536,7 @@ in terms of operation.</p>
class Module:
@T.prim_func
def main(A0: T.handle, A1: T.handle, C: T.handle):
- T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
+ T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)})
m, n = T.int32(), T.int32()
A0_1 = T.match_buffer(A0, (m, n), strides=("stride", "stride"), buffer_type="auto")
A1_1 = T.match_buffer(A1, (m, n), strides=("stride", "stride"), buffer_type="auto")
diff --git a/docs/install/nnpack.html b/docs/install/nnpack.html
index 6f2eee3512..2d646d6fd1 100644
--- a/docs/install/nnpack.html
+++ b/docs/install/nnpack.html
@@ -239,7 +239,17 @@
<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">
-<li class="toctree-l2"><a class="reference internal" href="from_source.html">Install from Source</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="from_source.html">Install from Source</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#developers-get-source-from-github">Developers: Get Source from Github</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#build-the-shared-library">Build the Shared Library</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#python-package-installation">Python Package Installation</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="from_source.html#install-contrib-libraries">Install Contrib Libraries</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#enable-c-tests">Enable C++ Tests</a></li>
+</ul>
+</li>
<li class="toctree-l2"><a class="reference internal" href="docker.html">Docker Images</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#conditions">Conditions</a></li>
diff --git a/docs/objects.inv b/docs/objects.inv
index bfd12205a8..73f0e79c54 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg
index 0bc682e967..06d0780bd2 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg
@@ -450,30 +450,31 @@
<g id="node17" class="node">
<title>Node17</title>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html
index c13122b733..534a57a3fe 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html
@@ -210,7 +210,7 @@ Additional Inherited Members</h2></td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>A dialect-agnostic <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilder.html" title="Managed reference to an IRBuilderNode.">IRBuilder</a> that constructs any IR of TVM. An idiomatic use of this class is to put this inside the RAII with-scope, call dialect-specific methods accordingly. Upon exiting the scope. </p>
-<div class="fragment"><div class="line"><a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">PrimFunc</a> ConstructPrimFunc() {</div>
+<div class="fragment"><div class="line"><a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">PrimFunc</a> ConstructPrimFunc() {</div>
<div class="line"> <span class="keyword">using</span> <a class="code" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilder.html">tvm::script::ir_builder::IRBuilder</a>;</div>
<div class="line"> <span class="keyword">using</span> T = <a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html">tvm::script::ir_builder::tir</a>;</div>
<div class="line"> IRBuilder builder;</div>
@@ -222,11 +222,11 @@ Additional Inherited Members</h2></td></tr>
<div class="line"> <a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aeed7efeb604129dd83dc6862fd857992">T::MatchBuffer</a>(...);</div>
<div class="line"> }</div>
<div class="line"> <span class="comment">// Step 3. Return the constructed PrimFunc.</span></div>
-<div class="line"> <span class="keywordflow">return</span> builder->Get<<a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">PrimFunc</a>>();</div>
+<div class="line"> <span class="keywordflow">return</span> builder->Get<<a class="code" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">PrimFunc</a>>();</div>
<div class="line">}</div>
<div class="ttc" id="aclasstvm_1_1script_1_1ir__builder_1_1IRBuilder_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1IRBuilder.html">tvm::script::ir_builder::IRBuilder</a></div><div class="ttdoc">Managed reference to an IRBuilderNode.</div><div class="ttdef"><b>Definition:</b> base.h:200</div></div>
<div class="ttc" id="anamespacetvm_1_1script_1_1ir__builder_1_1tir_html"><div class="ttname"><a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html">tvm::script::ir_builder::tir</a></div><div class="ttdef"><b>Definition:</b> frame.h:29</div></div>
-<div class="ttc" id="anamespacetvm_1_1script_1_1ir__builder_1_1tir_html_a713fe9c5ed9b44e16b7e5f9a23622543"><div class="ttname"><a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">tvm::script::ir_builder::tir::PrimFunc</a></div><div class="ttdeci">PrimFuncFrame PrimFunc()</div><div class="ttdoc">The primitive function statement.</div></div>
+<div class="ttc" id="anamespacetvm_1_1script_1_1ir__builder_1_1tir_html_a223f44b71840a89ddd7921d0b48cd4d0"><div class="ttname"><a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">tvm::script::ir_builder::tir::PrimFunc</a></div><div class="ttdeci">PrimFuncFrame PrimFunc(bool is_private)</div><div class="ttdoc">The primitive function statement.</div></div>
<div class="ttc" id="anamespacetvm_1_1script_1_1ir__builder_1_1tir_html_aeed7efeb604129dd83dc6862fd857992"><div class="ttname"><a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aeed7efeb604129dd83dc6862fd857992">tvm::script::ir_builder::tir::MatchBuffer</a></div><div class="ttdeci">Buffer MatchBuffer(ObjectRef param, Array< PrimExpr > shape, DataType dtype=DataType::Float(32), Optional< Var > data=NullOpt, Array< PrimExpr > strides={}, PrimExpr elem_offset=Prim [...]
</div><!-- fragment --> </div><h2 class="groupheader">Member Function Documentation</h2>
<a id="ab982b59b7e5a37ba3c16d69a9e576208"></a>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode-members.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode-members.html
index 0dd1dc73d6..34a78e95be 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode-members.html
@@ -96,30 +96,31 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">GetTypeKey</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a5693cbadcc1168b96db7b1cc5c200b86">GetTypeKeyHash</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ac9e5eed7719e322117bde996a171e33a">IncRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a4925b38d836fe1fa7ed1c223a125343f">name</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">Object</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ab7968feb6ad38ecaffc320e13819d826">Object</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#aa1612f69ea5b4225d4cda759cd517323">Object</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">operator=</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a55549a6c23987890246248682560a03d">RefCounterType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a716400f5d324044ca6b73fe7650eb395">ret_type</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a3f9592997636143b3ad1114d8f9070cc">root_alloc_buffers</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">stmts</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a4caa65dc8a549ed58fcb1d47964734d8">TVM_DECLARE_BASE_OBJECT_INFO</a>(TIRFrameNode, IRBuilderFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a290e180b9b57d3e133874fc0343a0453">tvm::script::ir_builder::IRBuilderFrameNode::TVM_DECLARE_BASE_OBJECT_INFO</a>(IRBuilderFrameNode, runtime::Object)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a6320bba232e0f1ee474e0d0a998c47b6">TVM_DECLARE_FINAL_OBJECT_INFO</a>(PrimFuncFrameNode, TIRFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4bfc2586cb55f2af47728187b3256255">type_index_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a10591ade2f115bc06a98a57652ff589c">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a8e61799eac4e806aef5a87d07943883d">~IRBuilderFrameNode</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#aa344de104b0ce9962fdcf2a80e55254c">is_private</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a4925b38d836fe1fa7ed1c223a125343f">name</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">Object</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ab7968feb6ad38ecaffc320e13819d826">Object</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#aa1612f69ea5b4225d4cda759cd517323">Object</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">operator=</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a55549a6c23987890246248682560a03d">RefCounterType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a716400f5d324044ca6b73fe7650eb395">ret_type</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a3f9592997636143b3ad1114d8f9070cc">root_alloc_buffers</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">stmts</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a4caa65dc8a549ed58fcb1d47964734d8">TVM_DECLARE_BASE_OBJECT_INFO</a>(TIRFrameNode, IRBuilderFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a290e180b9b57d3e133874fc0343a0453">tvm::script::ir_builder::IRBuilderFrameNode::TVM_DECLARE_BASE_OBJECT_INFO</a>(IRBuilderFrameNode, runtime::Object)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a6320bba232e0f1ee474e0d0a998c47b6">TVM_DECLARE_FINAL_OBJECT_INFO</a>(PrimFuncFrameNode, TIRFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4bfc2586cb55f2af47728187b3256255">type_index_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a10591ade2f115bc06a98a57652ff589c">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html">tvm::script::ir_builder::tir::PrimFuncFrameNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a8e61799eac4e806aef5a87d07943883d">~IRBuilderFrameNode</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html
index 2485dfedb0..db56474d42 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html
@@ -82,13 +82,13 @@ $(function() {
<div class="dynheader">
Inheritance diagram for tvm::script::ir_builder::tir::PrimFuncFrameNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__inherit__graph.svg" width="256" height="1256"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__inherit__graph.svg" width="256" height="1271"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<div class="dynheader">
Collaboration diagram for tvm::script::ir_builder::tir::PrimFuncFrameNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg" width="1404" height="2076"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg" width="1402" height="2091"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
@@ -151,6 +151,9 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a6b883f39efc67336b4f84e831046488d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a6b883f39efc67336b4f84e831046488d">args</a></td></tr>
<tr class="memdesc:a6b883f39efc67336b4f84e831046488d"><td class="mdescLeft"> </td><td class="mdescRight">Function parameters. <a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a6b883f39efc67336b4f84e831046488d">More...</a><br /></td></tr>
<tr class="separator:a6b883f39efc67336b4f84e831046488d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa344de104b0ce9962fdcf2a80e55254c"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#aa344de104b0ce9962fdcf2a80e55254c">is_private</a></td></tr>
+<tr class="memdesc:aa344de104b0ce9962fdcf2a80e55254c"><td class="mdescLeft"> </td><td class="mdescRight">Whether the PrimFunc is annotated as private. <a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#aa344de104b0ce9962fdcf2a80e55254c">More...</a><br /></td></tr>
+<tr class="separator:aa344de104b0ce9962fdcf2a80e55254c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a716400f5d324044ca6b73fe7650eb395"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1Type.html">Type</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a716400f5d324044ca6b73fe7650eb395">ret_type</a></td></tr>
<tr class="memdesc:a716400f5d324044ca6b73fe7650eb395"><td class="mdescLeft"> </td><td class="mdescRight">The return type of the function. <a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#a716400f5d324044ca6b73fe7650eb395">More...</a><br /></td></tr>
<tr class="separator:a716400f5d324044ca6b73fe7650eb395"><td class="memSeparator" colspan="2"> </td></tr>
@@ -420,6 +423,22 @@ Additional Inherited Members</h2></td></tr>
<p>The variable map bound to thread env. </p>
+</div>
+</div>
+<a id="aa344de104b0ce9962fdcf2a80e55254c"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aa344de104b0ce9962fdcf2a80e55254c">◆ </a></span>is_private</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">bool tvm::script::ir_builder::tir::PrimFuncFrameNode::is_private</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Whether the PrimFunc is annotated as private. </p>
+
</div>
</div>
<a id="a4925b38d836fe1fa7ed1c223a125343f"></a>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg
index 64ac9b792d..4495b1ddcf 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__coll__graph.svg
@@ -4,19 +4,20 @@
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+<text text-anchor="start" x="467" y="-100.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="547" y="-89.5" font-family="Helvetica,sans-Serif" font-size="10.00">::tir::PrimFuncFrameNode</text>
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-<text text-anchor="start" x="13.5" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
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+<text text-anchor="middle" x="92" y="-282.5" font-family="Helvetica,sans-Serif" font-size="10.00">::tir::TIRFrameNode</text>
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+<text text-anchor="start" x="13.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
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<title>Node3</title>
<g id="a_node3"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html" target="_top" xlink:title=" ">
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-<text text-anchor="start" x="13.5" y="-830.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="92" y="-819.5" font-family="Helvetica,sans-Serif" font-size="10.00">::IRBuilderFrameNode</text>
-<polyline fill="none" stroke="black" points="5.5,-812.5 178.5,-812.5 "/>
-<text text-anchor="start" x="13.5" y="-800.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ callbacks</text>
-<text text-anchor="start" x="13.5" y="-789.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
-<polyline fill="none" stroke="black" points="5.5,-782.5 178.5,-782.5 "/>
-<text text-anchor="start" x="13.5" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
-<text text-anchor="start" x="13.5" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_DECLARE_BASE_OBJECT</text>
-<text text-anchor="start" x="13.5" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00">_INFO()</text>
-<text text-anchor="start" x="13.5" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ~IRBuilderFrameNode()</text>
-<text text-anchor="start" x="13.5" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ EnterWithScope()</text>
-<text text-anchor="start" x="13.5" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ExitWithScope()</text>
-<text text-anchor="start" x="13.5" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AddCallback()</text>
+<polygon fill="white" stroke="black" points="5.5,-708.5 5.5,-853.5 178.5,-853.5 178.5,-708.5 5.5,-708.5"/>
+<text text-anchor="start" x="13.5" y="-841.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="92" y="-830.5" font-family="Helvetica,sans-Serif" font-size="10.00">::IRBuilderFrameNode</text>
+<polyline fill="none" stroke="black" points="5.5,-823.5 178.5,-823.5 "/>
+<text text-anchor="start" x="13.5" y="-811.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ callbacks</text>
+<text text-anchor="start" x="13.5" y="-800.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
+<polyline fill="none" stroke="black" points="5.5,-793.5 178.5,-793.5 "/>
+<text text-anchor="start" x="13.5" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
+<text text-anchor="start" x="13.5" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_DECLARE_BASE_OBJECT</text>
+<text text-anchor="start" x="13.5" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00">_INFO()</text>
+<text text-anchor="start" x="13.5" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ~IRBuilderFrameNode()</text>
+<text text-anchor="start" x="13.5" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ EnterWithScope()</text>
+<text text-anchor="start" x="13.5" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ExitWithScope()</text>
+<text text-anchor="start" x="13.5" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AddCallback()</text>
</a>
</g>
</g>
<!-- Node3->Node2 -->
<g id="edge2" class="edge">
<title>Node3->Node2</title>
-<path fill="none" stroke="midnightblue" d="M92,-687.3C92,-576.29 92,-381.69 92,-294.7"/>
-<polygon fill="none" stroke="midnightblue" points="88.5,-687.45 92,-697.45 95.5,-687.45 88.5,-687.45"/>
+<path fill="none" stroke="midnightblue" d="M92,-698.3C92,-587.29 92,-392.69 92,-305.7"/>
+<polygon fill="none" stroke="midnightblue" points="88.5,-698.45 92,-708.45 95.5,-698.45 88.5,-698.45"/>
</g>
<!-- Node4 -->
<g id="node4" class="node">
<title>Node4</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1Object.html" target="_top" xlink:title="base class of all object containers.">
-<polygon fill="white" stroke="black" points="0,-935.5 0,-1322.5 184,-1322.5 184,-935.5 0,-935.5"/>
-<text text-anchor="middle" x="92" y="-1310.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Object</text>
-<polyline fill="none" stroke="black" points="0,-1303.5 184,-1303.5 "/>
-<text text-anchor="start" x="8" y="-1291.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
-<text text-anchor="start" x="8" y="-1280.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_final</text>
-<text text-anchor="start" x="8" y="-1269.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots</text>
-<text text-anchor="start" x="8" y="-1258.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots_can</text>
-<text text-anchor="start" x="8" y="-1247.5" font-family="Helvetica,sans-Serif" font-size="10.00">_overflow</text>
-<text text-anchor="start" x="8" y="-1236.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_visit</text>
-<text text-anchor="start" x="8" y="-1225.5" font-family="Helvetica,sans-Serif" font-size="10.00">_attrs</text>
-<text text-anchor="start" x="8" y="-1214.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_sequal</text>
-<text text-anchor="start" x="8" y="-1203.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
-<text text-anchor="start" x="8" y="-1192.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_shash</text>
-<text text-anchor="start" x="8" y="-1181.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
-<text text-anchor="start" x="8" y="-1170.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_index</text>
-<text text-anchor="start" x="8" y="-1159.5" font-family="Helvetica,sans-Serif" font-size="10.00"># type_index_</text>
-<text text-anchor="start" x="8" y="-1148.5" font-family="Helvetica,sans-Serif" font-size="10.00"># ref_counter_</text>
-<polyline fill="none" stroke="black" points="0,-1141.5 184,-1141.5 "/>
-<text text-anchor="start" x="8" y="-1129.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ type_index()</text>
-<text text-anchor="start" x="8" y="-1118.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKey()</text>
-<text text-anchor="start" x="8" y="-1107.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKeyHash()</text>
-<text text-anchor="start" x="8" y="-1096.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ IsInstance()</text>
-<text text-anchor="start" x="8" y="-1085.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
+<polygon fill="white" stroke="black" points="0,-946.5 0,-1333.5 184,-1333.5 184,-946.5 0,-946.5"/>
+<text text-anchor="middle" x="92" y="-1321.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Object</text>
+<polyline fill="none" stroke="black" points="0,-1314.5 184,-1314.5 "/>
+<text text-anchor="start" x="8" y="-1302.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
+<text text-anchor="start" x="8" y="-1291.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_final</text>
+<text text-anchor="start" x="8" y="-1280.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots</text>
+<text text-anchor="start" x="8" y="-1269.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots_can</text>
+<text text-anchor="start" x="8" y="-1258.5" font-family="Helvetica,sans-Serif" font-size="10.00">_overflow</text>
+<text text-anchor="start" x="8" y="-1247.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_visit</text>
+<text text-anchor="start" x="8" y="-1236.5" font-family="Helvetica,sans-Serif" font-size="10.00">_attrs</text>
+<text text-anchor="start" x="8" y="-1225.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="8" y="-1214.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
+<text text-anchor="start" x="8" y="-1203.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_shash</text>
+<text text-anchor="start" x="8" y="-1192.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
+<text text-anchor="start" x="8" y="-1181.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_index</text>
+<text text-anchor="start" x="8" y="-1170.5" font-family="Helvetica,sans-Serif" font-size="10.00"># type_index_</text>
+<text text-anchor="start" x="8" y="-1159.5" font-family="Helvetica,sans-Serif" font-size="10.00"># ref_counter_</text>
+<polyline fill="none" stroke="black" points="0,-1152.5 184,-1152.5 "/>
+<text text-anchor="start" x="8" y="-1140.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ type_index()</text>
+<text text-anchor="start" x="8" y="-1129.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKey()</text>
+<text text-anchor="start" x="8" y="-1118.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="8" y="-1107.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ IsInstance()</text>
+<text text-anchor="start" x="8" y="-1096.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
+<text text-anchor="start" x="8" y="-1085.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
<text text-anchor="start" x="8" y="-1074.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
<text text-anchor="start" x="8" y="-1063.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
-<text text-anchor="start" x="8" y="-1052.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
+<text text-anchor="start" x="8" y="-1052.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="8" y="-1041.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="8" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="8" y="-1019.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="8" y="-1008.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="8" y="-997.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeKey2Index()</text>
-<text text-anchor="start" x="8" y="-986.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-975.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-964.5" font-family="Helvetica,sans-Serif" font-size="10.00"># IncRef()</text>
-<text text-anchor="start" x="8" y="-953.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DecRef()</text>
-<text text-anchor="start" x="8" y="-942.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="8" y="-1019.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="8" y="-1008.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeKey2Index()</text>
+<text text-anchor="start" x="8" y="-997.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-986.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-975.5" font-family="Helvetica,sans-Serif" font-size="10.00"># IncRef()</text>
+<text text-anchor="start" x="8" y="-964.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DecRef()</text>
+<text text-anchor="start" x="8" y="-953.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node4->Node3 -->
<g id="edge3" class="edge">
<title>Node4->Node3</title>
-<path fill="none" stroke="midnightblue" d="M92,-925.33C92,-895.76 92,-867.09 92,-842.64"/>
-<polygon fill="none" stroke="midnightblue" points="88.5,-925.39 92,-935.39 95.5,-925.39 88.5,-925.39"/>
+<path fill="none" stroke="midnightblue" d="M92,-936.33C92,-906.76 92,-878.09 92,-853.64"/>
+<polygon fill="none" stroke="midnightblue" points="88.5,-936.39 92,-946.39 95.5,-936.39 88.5,-936.39"/>
</g>
<!-- Node4->Node4 -->
<g id="edge4" class="edge">
<title>Node4->Node4</title>
-<path fill="none" stroke="#404040" d="M184.28,-1162.92C195.02,-1156.66 202,-1145.36 202,-1129 202,-1118.01 198.85,-1109.3 193.52,-1102.87"/>
-<polygon fill="none" stroke="#404040" points="193.46,-1102.81 186.29,-1102 184.28,-1095.08 191.45,-1095.89 193.46,-1102.81"/>
-<text text-anchor="middle" x="228" y="-1126.5" font-family="Helvetica,sans-Serif" font-size="10.00"> #deleter_</text>
+<path fill="none" stroke="#404040" d="M184.28,-1173.92C195.02,-1167.66 202,-1156.36 202,-1140 202,-1129.01 198.85,-1120.3 193.52,-1113.87"/>
+<polygon fill="none" stroke="#404040" points="193.46,-1113.81 186.29,-1113 184.28,-1106.08 191.45,-1106.89 193.46,-1113.81"/>
+<text text-anchor="middle" x="228" y="-1137.5" font-family="Helvetica,sans-Serif" font-size="10.00"> #deleter_</text>
</g>
<!-- Node5 -->
<g id="node5" class="node">
<title>Node5</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title=" ">
-<polygon fill="white" stroke="black" points="210.5,-642.5 210.5,-897.5 331.5,-897.5 331.5,-642.5 210.5,-642.5"/>
-<text text-anchor="start" x="218.5" y="-885.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
-<text text-anchor="middle" x="271" y="-874.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Stmt ></text>
-<polyline fill="none" stroke="black" points="210.5,-867.5 331.5,-867.5 "/>
-<text text-anchor="middle" x="271" y="-855.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
-<polyline fill="none" stroke="black" points="210.5,-848.5 331.5,-848.5 "/>
+<polygon fill="white" stroke="black" points="210.5,-653.5 210.5,-908.5 331.5,-908.5 331.5,-653.5 210.5,-653.5"/>
+<text text-anchor="start" x="218.5" y="-896.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
+<text text-anchor="middle" x="271" y="-885.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Stmt ></text>
+<polyline fill="none" stroke="black" points="210.5,-878.5 331.5,-878.5 "/>
+<text text-anchor="middle" x="271" y="-866.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
+<polyline fill="none" stroke="black" points="210.5,-859.5 331.5,-859.5 "/>
+<text text-anchor="start" x="218.5" y="-847.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="218.5" y="-836.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="218.5" y="-825.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="218.5" y="-814.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
@@ -148,72 +150,72 @@
<text text-anchor="start" x="218.5" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="218.5" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="218.5" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
-<text text-anchor="start" x="218.5" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
+<text text-anchor="start" x="218.5" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="218.5" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="218.5" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="218.5" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="218.5" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="218.5" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="218.5" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="218.5" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
-<text text-anchor="start" x="218.5" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
+<text text-anchor="start" x="218.5" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="218.5" y="-682.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="218.5" y="-671.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="218.5" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="218.5" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
+<text text-anchor="start" x="218.5" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
</a>
</g>
</g>
<!-- Node5->Node2 -->
<g id="edge5" class="edge">
<title>Node5->Node2</title>
-<path fill="none" stroke="#404040" d="M210.28,-666.77C203.01,-657.89 195.22,-649.46 187,-642 174.34,-630.51 162.97,-637.89 153,-624 143.81,-611.21 114.16,-408.03 99.86,-306.85"/>
-<polygon fill="none" stroke="#404040" points="99.83,-306.63 95.03,-301.24 98.15,-294.74 102.95,-300.13 99.83,-306.63"/>
-<text text-anchor="middle" x="173" y="-493.5" font-family="Helvetica,sans-Serif" font-size="10.00"> +stmts</text>
+<path fill="none" stroke="#404040" d="M210.28,-677.77C203.01,-668.89 195.22,-660.46 187,-653 174.34,-641.51 162.97,-648.89 153,-635 143.81,-622.21 114.16,-419.03 99.86,-317.85"/>
+<polygon fill="none" stroke="#404040" points="99.83,-317.63 95.03,-312.24 98.15,-305.74 102.95,-311.13 99.83,-317.63"/>
+<text text-anchor="middle" x="173" y="-504.5" font-family="Helvetica,sans-Serif" font-size="10.00"> +stmts</text>
</g>
<!-- Node6 -->
<g id="node6" class="node">
<title>Node6</title>
<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference.">
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-<text text-anchor="middle" x="498" y="-1233.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::ObjectRef</text>
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-<text text-anchor="start" x="439" y="-1214.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_is_nullable</text>
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+<polygon fill="white" stroke="black" points="431,-1023.5 431,-1256.5 565,-1256.5 565,-1023.5 431,-1023.5"/>
+<text text-anchor="middle" x="498" y="-1244.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="black" points="431,-1237.5 565,-1237.5 "/>
+<text text-anchor="start" x="439" y="-1225.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_is_nullable</text>
+<polyline fill="none" stroke="black" points="431,-1218.5 565,-1218.5 "/>
+<text text-anchor="start" x="439" y="-1206.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectRef()</text>
<text text-anchor="start" x="439" y="-1195.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectRef()</text>
-<text text-anchor="start" x="439" y="-1184.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectRef()</text>
-<text text-anchor="start" x="439" y="-1173.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ same_as()</text>
-<text text-anchor="start" x="439" y="-1162.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator==()</text>
-<text text-anchor="start" x="439" y="-1151.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator!=()</text>
-<text text-anchor="start" x="439" y="-1140.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator<()</text>
-<text text-anchor="start" x="439" y="-1129.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ defined()</text>
-<text text-anchor="start" x="439" y="-1118.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ get()</text>
-<text text-anchor="start" x="439" y="-1107.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator->()</text>
-<text text-anchor="start" x="439" y="-1096.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
-<text text-anchor="start" x="439" y="-1085.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ use_count()</text>
+<text text-anchor="start" x="439" y="-1184.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ same_as()</text>
+<text text-anchor="start" x="439" y="-1173.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator==()</text>
+<text text-anchor="start" x="439" y="-1162.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator!=()</text>
+<text text-anchor="start" x="439" y="-1151.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator<()</text>
+<text text-anchor="start" x="439" y="-1140.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ defined()</text>
+<text text-anchor="start" x="439" y="-1129.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ get()</text>
+<text text-anchor="start" x="439" y="-1118.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator->()</text>
+<text text-anchor="start" x="439" y="-1107.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
+<text text-anchor="start" x="439" y="-1096.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ use_count()</text>
+<text text-anchor="start" x="439" y="-1085.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ as()</text>
<text text-anchor="start" x="439" y="-1074.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ as()</text>
-<text text-anchor="start" x="439" y="-1063.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ as()</text>
-<text text-anchor="start" x="439" y="-1052.5" font-family="Helvetica,sans-Serif" font-size="10.00"># get_mutable()</text>
-<text text-anchor="start" x="439" y="-1041.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DowncastNoCheck()</text>
-<text text-anchor="start" x="439" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="439" y="-1019.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetDataPtr()</text>
+<text text-anchor="start" x="439" y="-1063.5" font-family="Helvetica,sans-Serif" font-size="10.00"># get_mutable()</text>
+<text text-anchor="start" x="439" y="-1052.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DowncastNoCheck()</text>
+<text text-anchor="start" x="439" y="-1041.5" font-family="Helvetica,sans-Serif" font-size="10.00"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="439" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetDataPtr()</text>
</a>
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</g>
<!-- Node6->Node5 -->
<g id="edge6" class="edge">
<title>Node6->Node5</title>
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-<polygon fill="none" stroke="midnightblue" points="422.49,-1015.78 430.81,-1022.34 428.4,-1012.02 422.49,-1015.78"/>
+<path fill="none" stroke="midnightblue" d="M425.42,-1024.85C395.18,-977.3 360.47,-922.72 331.63,-877.35"/>
+<polygon fill="none" stroke="midnightblue" points="422.49,-1026.78 430.81,-1033.34 428.4,-1023.02 422.49,-1026.78"/>
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<!-- Node8 -->
<g id="node8" class="node">
<title>Node8</title>
<g id="a_node8"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title=" ">
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-<text text-anchor="start" x="209.5" y="-611.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
-<text text-anchor="middle" x="262" y="-600.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Var ></text>
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-<text text-anchor="middle" x="262" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
-<polyline fill="none" stroke="black" points="201.5,-574.5 322.5,-574.5 "/>
+<polygon fill="white" stroke="black" points="201.5,-379.5 201.5,-634.5 322.5,-634.5 322.5,-379.5 201.5,-379.5"/>
+<text text-anchor="start" x="209.5" y="-622.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
+<text text-anchor="middle" x="262" y="-611.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Var ></text>
+<polyline fill="none" stroke="black" points="201.5,-604.5 322.5,-604.5 "/>
+<text text-anchor="middle" x="262" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
+<polyline fill="none" stroke="black" points="201.5,-585.5 322.5,-585.5 "/>
+<text text-anchor="start" x="209.5" y="-573.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="209.5" y="-562.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="209.5" y="-551.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="209.5" y="-540.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
@@ -221,36 +223,36 @@
<text text-anchor="start" x="209.5" y="-518.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="209.5" y="-507.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="209.5" y="-496.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
-<text text-anchor="start" x="209.5" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
+<text text-anchor="start" x="209.5" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="209.5" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="209.5" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="209.5" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="209.5" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="209.5" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="209.5" y="-441.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="209.5" y="-430.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
-<text text-anchor="start" x="209.5" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
+<text text-anchor="start" x="209.5" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="209.5" y="-408.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="209.5" y="-397.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="209.5" y="-386.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="209.5" y="-375.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
+<text text-anchor="start" x="209.5" y="-386.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
</a>
</g>
</g>
<!-- Node6->Node8 -->
<g id="edge9" class="edge">
<title>Node6->Node8</title>
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-<polygon fill="none" stroke="midnightblue" points="448.63,-1004.08 455.3,-1012.32 455.23,-1001.73 448.63,-1004.08"/>
+<path fill="none" stroke="midnightblue" d="M451.91,-1013.84C439.83,-979.95 427.09,-943.19 416,-909 379.41,-796.23 393.13,-759.49 341,-653 336.66,-644.14 333.18,-643.4 328,-635 326.17,-632.04 324.35,-629.02 322.54,-625.96"/>
+<polygon fill="none" stroke="midnightblue" points="448.63,-1015.08 455.3,-1023.32 455.23,-1012.73 448.63,-1015.08"/>
</g>
<!-- Node9 -->
<g id="node9" class="node">
<title>Node9</title>
<g id="a_node9"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title=" ">
-<polygon fill="white" stroke="black" points="336.5,-368.5 336.5,-623.5 457.5,-623.5 457.5,-368.5 336.5,-368.5"/>
-<text text-anchor="start" x="344.5" y="-611.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
-<text text-anchor="middle" x="397" y="-600.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Buffer ></text>
-<polyline fill="none" stroke="black" points="336.5,-593.5 457.5,-593.5 "/>
-<text text-anchor="middle" x="397" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
-<polyline fill="none" stroke="black" points="336.5,-574.5 457.5,-574.5 "/>
+<polygon fill="white" stroke="black" points="336.5,-379.5 336.5,-634.5 457.5,-634.5 457.5,-379.5 336.5,-379.5"/>
+<text text-anchor="start" x="344.5" y="-622.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Array</text>
+<text text-anchor="middle" x="397" y="-611.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::tir::Buffer ></text>
+<polyline fill="none" stroke="black" points="336.5,-604.5 457.5,-604.5 "/>
+<text text-anchor="middle" x="397" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
+<polyline fill="none" stroke="black" points="336.5,-585.5 457.5,-585.5 "/>
+<text text-anchor="start" x="344.5" y="-573.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="344.5" y="-562.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="344.5" y="-551.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="344.5" y="-540.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
@@ -258,36 +260,36 @@
<text text-anchor="start" x="344.5" y="-518.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="344.5" y="-507.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
<text text-anchor="start" x="344.5" y="-496.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
-<text text-anchor="start" x="344.5" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Array()</text>
+<text text-anchor="start" x="344.5" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="344.5" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="344.5" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="344.5" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="344.5" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 25 more...</text>
+<text text-anchor="start" x="344.5" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="344.5" y="-441.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
<text text-anchor="start" x="344.5" y="-430.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
-<text text-anchor="start" x="344.5" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CalcCapacityImpl()</text>
+<text text-anchor="start" x="344.5" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="344.5" y="-408.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
<text text-anchor="start" x="344.5" y="-397.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="344.5" y="-386.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ AgregateImpl()</text>
-<text text-anchor="start" x="344.5" y="-375.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
+<text text-anchor="start" x="344.5" y="-386.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Agregate()</text>
</a>
</g>
</g>
<!-- Node6->Node9 -->
<g id="edge11" class="edge">
<title>Node6->Node9</title>
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-<polygon fill="none" stroke="midnightblue" points="474.3,-1003.16 479.35,-1012.47 481.22,-1002.04 474.3,-1003.16"/>
+<path fill="none" stroke="midnightblue" d="M477.76,-1013.58C473.22,-985.43 468.44,-955.69 464,-928 448.22,-829.57 430.45,-718.16 417.18,-634.82"/>
+<polygon fill="none" stroke="midnightblue" points="474.3,-1014.16 479.35,-1023.47 481.22,-1013.04 474.3,-1014.16"/>
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<!-- Node10 -->
<g id="node10" class="node">
<title>Node10</title>
<g id="a_node10"><a xlink:href="classtvm_1_1runtime_1_1Optional.html" target="_top" xlink:title=" ">
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-<text text-anchor="start" x="409.5" y="-326.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Optional</text>
-<text text-anchor="middle" x="470" y="-315.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::runtime::String ></text>
-<polyline fill="none" stroke="black" points="401.5,-308.5 538.5,-308.5 "/>
-<text text-anchor="start" x="409.5" y="-296.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_is_nullable</text>
-<polyline fill="none" stroke="black" points="401.5,-289.5 538.5,-289.5 "/>
+<polygon fill="white" stroke="black" points="401.5,-171.5 401.5,-349.5 538.5,-349.5 538.5,-171.5 401.5,-171.5"/>
+<text text-anchor="start" x="409.5" y="-337.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Optional</text>
+<text text-anchor="middle" x="470" y="-326.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::runtime::String ></text>
+<polyline fill="none" stroke="black" points="401.5,-319.5 538.5,-319.5 "/>
+<text text-anchor="start" x="409.5" y="-307.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_is_nullable</text>
+<polyline fill="none" stroke="black" points="401.5,-300.5 538.5,-300.5 "/>
+<text text-anchor="start" x="409.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
<text text-anchor="start" x="409.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
<text text-anchor="start" x="409.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
<text text-anchor="start" x="409.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
@@ -295,62 +297,62 @@
<text text-anchor="start" x="409.5" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
<text text-anchor="start" x="409.5" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
<text text-anchor="start" x="409.5" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
-<text text-anchor="start" x="409.5" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Optional()</text>
+<text text-anchor="start" x="409.5" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="409.5" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="409.5" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="409.5" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 15 more...</text>
+<text text-anchor="start" x="409.5" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 15 more...</text>
</a>
</g>
</g>
<!-- Node6->Node10 -->
<g id="edge13" class="edge">
<title>Node6->Node10</title>
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-<polygon fill="none" stroke="midnightblue" points="490.49,-1002.5 494.31,-1012.38 497.49,-1002.27 490.49,-1002.5"/>
+<path fill="none" stroke="midnightblue" d="M493.99,-1013.32C488.24,-833.17 477.8,-506.04 472.81,-349.53"/>
+<polygon fill="none" stroke="midnightblue" points="490.49,-1013.5 494.31,-1023.38 497.49,-1013.27 490.49,-1013.5"/>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__inherit__graph.svg
index 0dc5602a25..233d8f76ea 100644
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+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode__inherit__graph.svg
@@ -4,21 +4,22 @@
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<title>tvm::script::ir_builder::tir::PrimFuncFrameNode</title>
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+<polygon fill="white" stroke="transparent" points="-4,4 -4,-949 188,-949 188,4 -4,4"/>
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<g id="node1" class="node">
<title>Node1</title>
<g id="a_node1"><a xlink:title="A frame that represents the PrimFunc containing TIR statements.">
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+<text text-anchor="start" x="12" y="-177.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="92" y="-166.5" font-family="Helvetica,sans-Serif" font-size="10.00">::tir::PrimFuncFrameNode</text>
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<title>Node2</title>
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-<text text-anchor="start" x="8" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
-<text text-anchor="start" x="8" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_index</text>
-<text text-anchor="start" x="8" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00"># type_index_</text>
-<text text-anchor="start" x="8" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00"># ref_counter_</text>
-<text text-anchor="start" x="8" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00"># deleter_</text>
-<polyline fill="none" stroke="black" points="0,-741.5 184,-741.5 "/>
-<text text-anchor="start" x="8" y="-729.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ type_index()</text>
-<text text-anchor="start" x="8" y="-718.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKey()</text>
-<text text-anchor="start" x="8" y="-707.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKeyHash()</text>
-<text text-anchor="start" x="8" y="-696.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ IsInstance()</text>
-<text text-anchor="start" x="8" y="-685.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
+<polygon fill="white" stroke="black" points="0,-546.5 0,-944.5 184,-944.5 184,-546.5 0,-546.5"/>
+<text text-anchor="middle" x="92" y="-932.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::Object</text>
+<polyline fill="none" stroke="black" points="0,-925.5 184,-925.5 "/>
+<text text-anchor="start" x="8" y="-913.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
+<text text-anchor="start" x="8" y="-902.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_final</text>
+<text text-anchor="start" x="8" y="-891.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots</text>
+<text text-anchor="start" x="8" y="-880.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_child_slots_can</text>
+<text text-anchor="start" x="8" y="-869.5" font-family="Helvetica,sans-Serif" font-size="10.00">_overflow</text>
+<text text-anchor="start" x="8" y="-858.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_visit</text>
+<text text-anchor="start" x="8" y="-847.5" font-family="Helvetica,sans-Serif" font-size="10.00">_attrs</text>
+<text text-anchor="start" x="8" y="-836.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="8" y="-825.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
+<text text-anchor="start" x="8" y="-814.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_has_method_shash</text>
+<text text-anchor="start" x="8" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00">_reduce</text>
+<text text-anchor="start" x="8" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_index</text>
+<text text-anchor="start" x="8" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00"># type_index_</text>
+<text text-anchor="start" x="8" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00"># ref_counter_</text>
+<text text-anchor="start" x="8" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00"># deleter_</text>
+<polyline fill="none" stroke="black" points="0,-752.5 184,-752.5 "/>
+<text text-anchor="start" x="8" y="-740.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ type_index()</text>
+<text text-anchor="start" x="8" y="-729.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKey()</text>
+<text text-anchor="start" x="8" y="-718.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="8" y="-707.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ IsInstance()</text>
+<text text-anchor="start" x="8" y="-696.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ unique()</text>
+<text text-anchor="start" x="8" y="-685.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
<text text-anchor="start" x="8" y="-674.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
<text text-anchor="start" x="8" y="-663.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
-<text text-anchor="start" x="8" y="-652.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Object()</text>
+<text text-anchor="start" x="8" y="-652.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
<text text-anchor="start" x="8" y="-641.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="8" y="-630.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator=()</text>
-<text text-anchor="start" x="8" y="-619.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="8" y="-608.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="8" y="-597.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeKey2Index()</text>
-<text text-anchor="start" x="8" y="-586.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-575.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="8" y="-564.5" font-family="Helvetica,sans-Serif" font-size="10.00"># IncRef()</text>
-<text text-anchor="start" x="8" y="-553.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DecRef()</text>
-<text text-anchor="start" x="8" y="-542.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-630.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="8" y="-619.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="8" y="-608.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TypeKey2Index()</text>
+<text text-anchor="start" x="8" y="-597.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-586.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="8" y="-575.5" font-family="Helvetica,sans-Serif" font-size="10.00"># IncRef()</text>
+<text text-anchor="start" x="8" y="-564.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DecRef()</text>
+<text text-anchor="start" x="8" y="-553.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetOrAllocRuntimeTypeIndex()</text>
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</g>
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<g id="edge3" class="edge">
<title>Node4->Node3</title>
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-<polygon fill="none" stroke="midnightblue" points="88.5,-525.49 92,-535.49 95.5,-525.49 88.5,-525.49"/>
+<path fill="none" stroke="midnightblue" d="M92,-536.29C92,-527 92,-518.02 92,-509.51"/>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
index 1bdc3240dc..29776409cc 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
@@ -347,30 +347,31 @@
<g id="node16" class="node">
<title>Node16</title>
<g id="a_node16"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html" target="_top" xlink:title="A frame that represents the PrimFunc containing TIR statements.">
-<polygon fill="white" stroke="black" points="2328,-22.5 2328,-200.5 2504,-200.5 2504,-22.5 2328,-22.5"/>
-<text text-anchor="start" x="2336" y="-188.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="2416" y="-177.5" font-family="Helvetica,sans-Serif" font-size="10.00">::tir::PrimFuncFrameNode</text>
-<polyline fill="none" stroke="black" points="2328,-170.5 2504,-170.5 "/>
-<text text-anchor="start" x="2336" y="-158.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ name</text>
-<text text-anchor="start" x="2336" y="-147.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ args</text>
-<text text-anchor="start" x="2336" y="-136.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ret_type</text>
-<text text-anchor="start" x="2336" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ buffer_map</text>
-<text text-anchor="start" x="2336" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ attrs</text>
-<text text-anchor="start" x="2336" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ env_threads</text>
-<text text-anchor="start" x="2336" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ root_alloc_buffers</text>
-<text text-anchor="start" x="2336" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
-<polyline fill="none" stroke="black" points="2328,-74.5 2504,-74.5 "/>
-<text text-anchor="start" x="2336" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
-<text text-anchor="start" x="2336" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_DECLARE_FINAL_OBJECT</text>
-<text text-anchor="start" x="2336" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00">_INFO()</text>
-<text text-anchor="start" x="2336" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ExitWithScope()</text>
+<polygon fill="white" stroke="black" points="2328,-17 2328,-206 2504,-206 2504,-17 2328,-17"/>
+<text text-anchor="start" x="2336" y="-194" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="2416" y="-183" font-family="Helvetica,sans-Serif" font-size="10.00">::tir::PrimFuncFrameNode</text>
+<polyline fill="none" stroke="black" points="2328,-176 2504,-176 "/>
+<text text-anchor="start" x="2336" y="-164" font-family="Helvetica,sans-Serif" font-size="10.00">+ name</text>
+<text text-anchor="start" x="2336" y="-153" font-family="Helvetica,sans-Serif" font-size="10.00">+ args</text>
+<text text-anchor="start" x="2336" y="-142" font-family="Helvetica,sans-Serif" font-size="10.00">+ is_private</text>
+<text text-anchor="start" x="2336" y="-131" font-family="Helvetica,sans-Serif" font-size="10.00">+ ret_type</text>
+<text text-anchor="start" x="2336" y="-120" font-family="Helvetica,sans-Serif" font-size="10.00">+ buffer_map</text>
+<text text-anchor="start" x="2336" y="-109" font-family="Helvetica,sans-Serif" font-size="10.00">+ attrs</text>
+<text text-anchor="start" x="2336" y="-98" font-family="Helvetica,sans-Serif" font-size="10.00">+ env_threads</text>
+<text text-anchor="start" x="2336" y="-87" font-family="Helvetica,sans-Serif" font-size="10.00">+ root_alloc_buffers</text>
+<text text-anchor="start" x="2336" y="-76" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
+<polyline fill="none" stroke="black" points="2328,-69 2504,-69 "/>
+<text text-anchor="start" x="2336" y="-57" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
+<text text-anchor="start" x="2336" y="-46" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_DECLARE_FINAL_OBJECT</text>
+<text text-anchor="start" x="2336" y="-35" font-family="Helvetica,sans-Serif" font-size="10.00">_INFO()</text>
+<text text-anchor="start" x="2336" y="-24" font-family="Helvetica,sans-Serif" font-size="10.00">+ ExitWithScope()</text>
</a>
</g>
</g>
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<g id="edge15" class="edge">
<title>Node1->Node16</title>
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+<path fill="none" stroke="midnightblue" d="M1639.85,-306.92C1820.94,-301.63 2203.01,-283.44 2319,-223 2327.83,-218.4 2336.22,-212.64 2344.1,-206.17"/>
<polygon fill="none" stroke="midnightblue" points="1639.69,-303.42 1629.8,-307.21 1639.89,-310.42 1639.69,-303.42"/>
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diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index 3dc5601ccd..07364b946f 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -395,7 +395,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a360b264ed892e620935b648e5a91a5ea">tvm::runtime::vm::Instruction</a>
</li>
<li>Array()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a7c03ae71aa89ec2591ae9ba7b3acc9d1">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#af8b7450aea8633a51d3e8c75ed9fe2be">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a23070656da6784a7e4c33c4b0ea9de35">tvm::runtime::ArrayNode</a>
</li>
<li>array
diff --git a/docs/reference/api/doxygen/functions_b.html b/docs/reference/api/doxygen/functions_b.html
index d09dc0c43b..0105f65f7f 100644
--- a/docs/reference/api/doxygen/functions_b.html
+++ b/docs/reference/api/doxygen/functions_b.html
@@ -133,7 +133,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html#a39f754ef4c60c084fb1d0379fbc264c6">tvm::tir::BijectiveLayout</a>
</li>
<li>Bind()
-: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c">tvm::arith::Analyzer</a>
+: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a675ba44dc7d70ef5606e4147919da9f2">tvm::arith::Analyzer</a>
, <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html#a7107c1d182a3ac72336d83ab50d9d1c3">tvm::arith::ConstIntBoundAnalyzer</a>
, <a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d">tvm::arith::IntSetAnalyzer</a>
, <a class="el" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779">tvm::arith::TransitiveComparisonAnalyzer</a>
@@ -191,7 +191,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BlockRV.html#a16d0fddd0faa2b62fea6c22d2895bd3e">tvm::tir::BlockRV</a>
</li>
<li>BlockScope()
-: <a class="el" href="classtvm_1_1tir_1_1BlockScope.html#a6178e756a3b76b6bb8d124f58846da72">tvm::tir::BlockScope</a>
+: <a class="el" href="classtvm_1_1tir_1_1BlockScope.html#a6f82fccdfd633aafbdf9604c6d31c62e">tvm::tir::BlockScope</a>
</li>
<li>body
: <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html#a32687067886a6d94e798431a01d04949">tvm::relay::FunctionNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_b.html b/docs/reference/api/doxygen/functions_func_b.html
index dd357b2e0d..d42d88ce98 100644
--- a/docs/reference/api/doxygen/functions_func_b.html
+++ b/docs/reference/api/doxygen/functions_func_b.html
@@ -93,10 +93,10 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html#a39f754ef4c60c084fb1d0379fbc264c6">tvm::tir::BijectiveLayout</a>
</li>
<li>Bind()
-: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a1165f8727b0b51cf2ea246b93634711c">tvm::arith::Analyzer</a>
+: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a20c4bae46db46362ba6bca4c1d368345">tvm::arith::Analyzer</a>
, <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html#a7107c1d182a3ac72336d83ab50d9d1c3">tvm::arith::ConstIntBoundAnalyzer</a>
, <a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d">tvm::arith::IntSetAnalyzer</a>
-, <a class="el" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779">tvm::arith::TransitiveComparisonAnalyzer</a>
+, <a class="el" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00">tvm::arith::TransitiveComparisonAnalyzer</a>
</li>
<li>bind()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1State.html#afe4bbe8e149a2ac418b3be72054b0649">tvm::auto_scheduler::State</a>
@@ -115,7 +115,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BlockDependenceInfo.html#a51aa1c7985e83c71f57a5a03aa64d423">tvm::tir::BlockDependenceInfo</a>
</li>
<li>BlockInfo()
-: <a class="el" href="structtvm_1_1tir_1_1BlockInfo.html#a2cfc1a31fe21b77d5b14907d6e645212">tvm::tir::BlockInfo</a>
+: <a class="el" href="structtvm_1_1tir_1_1BlockInfo.html#a41dd8c062eb43ab169ed59d27c37741e">tvm::tir::BlockInfo</a>
</li>
<li>Blockize()
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#ad519f290c705afd441eaf683c4e24693">tvm::tir::ScheduleNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_c.html b/docs/reference/api/doxygen/functions_func_c.html
index 7b87c18e1f..a7eccdcf98 100644
--- a/docs/reference/api/doxygen/functions_func_c.html
+++ b/docs/reference/api/doxygen/functions_func_c.html
@@ -266,7 +266,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#af901a7fa1336ee2e6668a2e44a8a2efd">tvm::tir::ScheduleNode</a>
</li>
<li>ComputeAtStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html#a52e1ff3450cea4694bab7fb8696a28fc">tvm::auto_scheduler::ComputeAtStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html#ae65a1fe0eeb84df13d81e8d2651c8e8a">tvm::auto_scheduler::ComputeAtStep</a>
</li>
<li>ComputeDAG()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html#a98edfb8259ecefff7c7c87d38692c316">tvm::auto_scheduler::ComputeDAG</a>
@@ -284,7 +284,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html#a45e42c63bd2ba62b6f85b9b79e0b9361">tvm::te::ComputeOpNode</a>
</li>
<li>ComputeRootStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html#ab2e65ee558a69c55141b4319a81cb2cc">tvm::auto_scheduler::ComputeRootStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html#a26f5d934c644d211cd5bb51c5abb8734">tvm::auto_scheduler::ComputeRootStep</a>
</li>
<li>Concat()
: <a class="el" href="classtvm_1_1tir_1_1Shuffle.html#a0a46caa8627735586e7dc67fa9321c59">tvm::tir::Shuffle</a>
@@ -302,11 +302,11 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataNode.html#a9f73424531616f0500209ccf35676db0">tvm::runtime::metadata::MetadataNode</a>
</li>
<li>ConstantAllocationSize()
-: <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#ad540975cdb5def27f3aa3564b2f427db">tvm::tir::AllocateConstNode</a>
-, <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html#a99fdb058ac50da40885f5261f476b07d">tvm::tir::AllocateNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#a3abf125e0a61e818a152e273ed8a63db">tvm::tir::AllocateConstNode</a>
+, <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html#a395d7a1e6dc5e461259c3a107b950c6e">tvm::tir::AllocateNode</a>
</li>
<li>ConstantInfo()
-: <a class="el" href="classtvm_1_1ConstantInfo.html#aefce632d230aa4ef80e762eeeeea77a7">tvm::ConstantInfo</a>
+: <a class="el" href="classtvm_1_1ConstantInfo.html#ad16639db7fedf1dfacc0eadb75ac0bb1">tvm::ConstantInfo</a>
</li>
<li>ConstantInfoMetadata()
: <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadata.html#a5ecad1efb7d2f1abbd56ae9d0c0d83a3">tvm::runtime::metadata::ConstantInfoMetadata</a>
@@ -429,7 +429,7 @@ $(function() {
: <a class="el" href="classtvm_1_1ReflectionVTable.html#a4a07073de645894b19522d7da1ac708c">tvm::ReflectionVTable</a>
</li>
<li>CreateObject()
-: <a class="el" href="classtvm_1_1ReflectionVTable.html#a7e9282bf41a0b789833f63fd93f4d181">tvm::ReflectionVTable</a>
+: <a class="el" href="classtvm_1_1ReflectionVTable.html#a64c45110f5eb7dbb7976a35f58a3e20c">tvm::ReflectionVTable</a>
</li>
<li>CreateRepeated()
: <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#aae210dbc3667622a873a27b822462ace">tvm::runtime::ArrayNode</a>
diff --git a/docs/reference/api/doxygen/functions_i.html b/docs/reference/api/doxygen/functions_i.html
index 9b0712ff64..3864849d56 100644
--- a/docs/reference/api/doxygen/functions_i.html
+++ b/docs/reference/api/doxygen/functions_i.html
@@ -394,7 +394,7 @@ $(function() {
</li>
<li>Invoke()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#acb19406a24fa95bf39a29d15ad6be256">tvm::runtime::vm::Instruction</a>
-, <a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html#a1094291352e07e4c827a88b1167b89ad">tvm::runtime::vm::VirtualMachine</a>
+, <a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html#aa5f4724e2e702ef9d5c34e85dec53b02">tvm::runtime::vm::VirtualMachine</a>
</li>
<li>invoke_args_registers
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a6fc678bca0e215303087981a79f23b7f">tvm::runtime::vm::Instruction</a>
@@ -431,7 +431,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#a13983d7ae23271c7fed4794de0110c96">tvm::tir::AllocateConstNode</a>
</li>
<li>IRModule()
-: <a class="el" href="classtvm_1_1IRModule.html#a69a94bf1f2e54191598b2271443d2696">tvm::IRModule</a>
+: <a class="el" href="classtvm_1_1IRModule.html#a808e55df1297fae159c8b26f37a44d42">tvm::IRModule</a>
, <a class="el" href="classtvm_1_1IRModuleNode.html#a2b30d3e406c01bb6287d1d7514ecaf46">tvm::IRModuleNode</a>
</li>
<li>IRModuleNode()
@@ -491,6 +491,9 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html#a99f270b8b0d0beb3367ea53215cc7440">tvm::auto_scheduler::AccessAnalyzerNode</a>
, <a class="el" href="classtvm_1_1te_1_1StageNode.html#af5cc9e6c2276cf8abde8f437f8bdbda4">tvm::te::StageNode</a>
</li>
+<li>is_private
+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#aa344de104b0ce9962fdcf2a80e55254c">tvm::script::ir_builder::tir::PrimFuncFrameNode</a>
+</li>
<li>is_pure
: <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html#ae4f8ea92043029f1eb1956df091f91d6">tvm::tir::InstructionKindNode</a>
</li>
@@ -691,7 +694,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html#a65304957db6f84d8d7c90ad553453bb9">tvm::auto_scheduler::StageNode</a>
</li>
<li>IterSplitExpr()
-: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#a754a9d8338aa2d2b5fac9e10c95c9128">tvm::arith::IterSplitExpr</a>
+: <a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html#af919631fd9bfb7726d0a867ee9f0e6f5">tvm::arith::IterSplitExpr</a>
</li>
<li>IterSumExpr()
: <a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html#a1b9f8013f3978bafe4da3a6cad65fb36">tvm::arith::IterSumExpr</a>
diff --git a/docs/reference/api/doxygen/functions_vars_i.html b/docs/reference/api/doxygen/functions_vars_i.html
index 34caac6079..f8ae319163 100644
--- a/docs/reference/api/doxygen/functions_vars_i.html
+++ b/docs/reference/api/doxygen/functions_vars_i.html
@@ -248,6 +248,9 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html#a99f270b8b0d0beb3367ea53215cc7440">tvm::auto_scheduler::AccessAnalyzerNode</a>
, <a class="el" href="classtvm_1_1te_1_1StageNode.html#af5cc9e6c2276cf8abde8f437f8bdbda4">tvm::te::StageNode</a>
</li>
+<li>is_private
+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrameNode.html#aa344de104b0ce9962fdcf2a80e55254c">tvm::script::ir_builder::tir::PrimFuncFrameNode</a>
+</li>
<li>is_pure
: <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html#ae4f8ea92043029f1eb1956df091f91d6">tvm::tir::InstructionKindNode</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_p.html b/docs/reference/api/doxygen/namespacemembers_func_p.html
index 086af205a8..2aaed586b5 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_p.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_p.html
@@ -163,7 +163,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1relay.html#a3b98dc5e89534ce68b4be3b9c7ceafaf">tvm::relay</a>
</li>
<li>PrimFunc()
-: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">tvm::script::ir_builder::tir</a>
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">tvm::script::ir_builder::tir</a>
</li>
<li>PrintIR()
: <a class="el" href="namespacetvm_1_1transform.html#a8cca5b98fb5d34e66444343786d85529">tvm::transform</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_p.html b/docs/reference/api/doxygen/namespacemembers_p.html
index e6aba9f2e2..b6d1cc3075 100644
--- a/docs/reference/api/doxygen/namespacemembers_p.html
+++ b/docs/reference/api/doxygen/namespacemembers_p.html
@@ -221,7 +221,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1relay.html#a3b98dc5e89534ce68b4be3b9c7ceafaf">tvm::relay</a>
</li>
<li>PrimFunc()
-: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">tvm::script::ir_builder::tir</a>
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">tvm::script::ir_builder::tir</a>
</li>
<li>PrintIR()
: <a class="el" href="namespacetvm_1_1transform.html#a8cca5b98fb5d34e66444343786d85529">tvm::transform</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html b/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
index 97f77ff1f7..4e9ef756b8 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
@@ -187,9 +187,9 @@ Functions</h2></td></tr>
<tr class="memitem:a3a79fe9790be33b4a309756b29a6e7f8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3a79fe9790be33b4a309756b29a6e7f8">BufferDecl</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > shape, <a class="e [...]
<tr class="memdesc:a3a79fe9790be33b4a309756b29a6e7f8"><td class="mdescLeft"> </td><td class="mdescRight">The buffer declaration function. <a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3a79fe9790be33b4a309756b29a6e7f8">More...</a><br /></td></tr>
<tr class="separator:a3a79fe9790be33b4a309756b29a6e7f8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a713fe9c5ed9b44e16b7e5f9a23622543"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrame.html">PrimFuncFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">PrimFunc</a> ()</td></tr>
-<tr class="memdesc:a713fe9c5ed9b44e16b7e5f9a23622543"><td class="mdescLeft"> </td><td class="mdescRight">The primitive function statement. <a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a713fe9c5ed9b44e16b7e5f9a23622543">More...</a><br /></td></tr>
-<tr class="separator:a713fe9c5ed9b44e16b7e5f9a23622543"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a223f44b71840a89ddd7921d0b48cd4d0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrame.html">PrimFuncFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">PrimFunc</a> (bool is_private)</td></tr>
+<tr class="memdesc:a223f44b71840a89ddd7921d0b48cd4d0"><td class="mdescLeft"> </td><td class="mdescRight">The primitive function statement. <a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a223f44b71840a89ddd7921d0b48cd4d0">More...</a><br /></td></tr>
+<tr class="separator:a223f44b71840a89ddd7921d0b48cd4d0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab08a03e66424220556b00a6b43650ee2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab08a03e66424220556b00a6b43650ee2">Arg</a> (<a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> name, <a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> var)</td></tr>
<tr class="memdesc:ab08a03e66424220556b00a6b43650ee2"><td class="mdescLeft"> </td><td class="mdescRight">The PrimFunc variable arguments adding function. <a href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab08a03e66424220556b00a6b43650ee2">More...</a><br /></td></tr>
<tr class="separator:ab08a03e66424220556b00a6b43650ee2"><td class="memSeparator" colspan="2"> </td></tr>
@@ -3481,8 +3481,8 @@ Functions</h2></td></tr>
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-<a id="a713fe9c5ed9b44e16b7e5f9a23622543"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a713fe9c5ed9b44e16b7e5f9a23622543">◆ </a></span>PrimFunc()</h2>
+<a id="a223f44b71840a89ddd7921d0b48cd4d0"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a223f44b71840a89ddd7921d0b48cd4d0">◆ </a></span>PrimFunc()</h2>
<div class="memitem">
<div class="memproto">
@@ -3490,7 +3490,8 @@ Functions</h2></td></tr>
<tr>
<td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1PrimFuncFrame.html">PrimFuncFrame</a> tvm::script::ir_builder::tir::PrimFunc </td>
<td>(</td>
- <td class="paramname"></td><td>)</td>
+ <td class="paramtype">bool </td>
+ <td class="paramname"><em>is_private</em></td><td>)</td>
<td></td>
</tr>
</table>
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