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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/06/08 15:18:38 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@fa4d73ac51b03555e40b57e8f3ca17c250024bd6)
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 8794d8369d deploying docs (apache/tvm@fa4d73ac51b03555e40b57e8f3ca17c250024bd6)
8794d8369d is described below
commit 8794d8369da1d39fcb6f2de8a9b38504568a2025
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Thu Jun 8 15:18:27 2023 +0000
deploying docs (apache/tvm@fa4d73ac51b03555e40b57e8f3ca17c250024bd6)
---
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docs/tutorial/sg_execution_times.html | 18 +-
docs/tutorial/tensor_expr_get_started.html | 42 +-
335 files changed, 53314 insertions(+), 53791 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 b5c1b2ffa5..50b0488492 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 28.763 seconds)
+ **Total running time of the script:** ( 1 minutes 31.026 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 3148af06b0..ecc0dc70f3 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.zip332a8f24-1424-42f0-a1c2-f0f9620ec808 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip78dc07ef-b313-4dba-9e0b-26dea45313b6 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 e061d035f9..27b8fe13f7 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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+
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77%|#######7 | 32.0M/41.5M [00:00<00:00, 41.3MB/s]
92%|#########2| 38.3M/41.5M [00:00<00:00, 45.3MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 41.0MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 8df75c9e33..faa5d110e6 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -207,6 +207,11 @@ Look up prediction top 1 index in 1000 class synset.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 0.441 seconds)
+
+
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
.. only:: html
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 216b99da4d..7ddd5af4c0 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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81%|########1 | 36.2M/44.7M [00:00<00:00, 52.0MB/s]
92%|#########2| 41.2M/44.7M [00:01<00:00, 34.4MB/s]
100%|##########| 44.7M/44.7M [00:01<00:00, 43.7MB/s]
+
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86%|########5 | 38.3M/44.7M [00:01<00:00, 33.9MB/s]
93%|#########3| 41.6M/44.7M [00:01<00:00, 30.2MB/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 7aed900d9f..08421052e1 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 29.342 seconds)
+ **Total running time of the script:** ( 1 minutes 28.655 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 a3eb5f4314..42276570fa 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
=================
-**06:48.762** total execution time for **how_to_compile_models** files:
+**06:55.345** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:29.342 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:31.026 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:28.763 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:28.655 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:59.223 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:00.441 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:38.960 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:39.366 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:35.541 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:36.039 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:31.593 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:32.095 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:26.915 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.681 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:24.315 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.732 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.410 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.543 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.699 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.768 | 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 ed2c38e6ae..c6777d6a6b 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)
- 4162.8843 4160.5836 4178.1479 4157.3885 6.2815
+ 4222.6730 4222.1056 4226.4192 4220.2011 1.8073
@@ -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 17.987 seconds)
+ **Total running time of the script:** ( 1 minutes 18.928 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 d86d685733..ce81109ceb 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
8192/102967424 [..............................] - ETA: 0s
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+
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diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index 0114f4d25b..8d97763027 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 14.5436 14.3573 15.5172 14.0424 0.4604
+ 14.8199 14.7742 15.5662 14.4704 0.3144
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 4372e989c5..9aee63873f 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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100%|##########| 170M/170M [00:04<00:00, 39.4MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -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 30.315 seconds)
+ **Total running time of the script:** ( 3 minutes 33.319 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 dd0aebbd96..6ea010faff 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%|#####9 | 8.00M/13.6M [00:00<00:00, 36.0MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 43.6MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
47%|####6 | 6.30M/13.6M [00:00<00:00, 43.1MB/s]
77%|#######6 | 10.4M/13.6M [00:00<00:00, 32.0MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 34.1MB/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)
- 87.2185 87.1927 87.6484 86.8844 0.1895
+ 88.6973 88.6413 89.9433 88.2406 0.2612
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 23.540 seconds)
+ **Total running time of the script:** ( 1 minutes 23.701 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 cef59730e0..f22cd906f7 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)
- 106.8269 107.0837 110.9052 104.4513 1.2629
+ 108.4453 108.4047 112.4272 106.6496 0.8279
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 aef2a35550..c40d78608f 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 19.679 seconds)
+ **Total running time of the script:** ( 2 minutes 0.996 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 498e01655b..7cb7a26c07 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
=================
-**11:50.096** total execution time for **how_to_deploy_models** files:
+**11:39.739** 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``) | 03:30.315 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:33.319 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:19.679 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:00.996 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:23.540 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:23.701 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:17.987 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.928 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.409 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.912 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:47.580 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:48.106 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:42.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:45.292 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:28.954 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.428 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.751 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.050 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 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 537e6df088..d0c6c4db3c 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.zipf235fdcb-eec3-4451-8f47-43a53f968e01 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipdef6b1e0-795f-43e2-a772-f4888f0ae2e5 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index ea718de113..684c1ed9a0 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:55.073** total execution time for **how_to_extend_tvm** files:
+**00:55.265** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:51.345 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:51.447 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.608 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.660 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.113 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.151 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 6a194c07a8..add9588071 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: 23264us [23264us] (48.55%; 48.55%)
- FoldScaleAxis: 24652us [7us] (51.45%; 51.45%)
- FoldConstant: 24645us [1815us] (51.43%; 99.97%)
- InferType: 22830us [22830us] (47.64%; 92.63%)
+ InferType: 23278us [23278us] (48.30%; 48.30%)
+ FoldScaleAxis: 24920us [7us] (51.70%; 51.70%)
+ FoldConstant: 24912us [1837us] (51.69%; 99.97%)
+ InferType: 23075us [23075us] (47.88%; 92.63%)
@@ -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: 22559us [22559us] (48.22%; 48.22%)
- FoldScaleAxis: 24229us [5us] (51.78%; 51.78%)
- FoldConstant: 24223us [1753us] (51.77%; 99.98%)
- InferType: 22470us [22470us] (48.03%; 92.76%)
+ InferType: 23979us [23979us] (49.42%; 49.42%)
+ FoldScaleAxis: 24546us [6us] (50.58%; 50.58%)
+ FoldConstant: 24540us [1766us] (50.57%; 99.98%)
+ InferType: 22774us [22774us] (46.93%; 92.80%)
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 9795337200..7313e1d2a5 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: 38.520736 ms
+ Convolution: 34.863361 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 7a1bfdc673..abacc73d58 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.265840 ms
+ conv2d with tensor core: 12.089888 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 9bcd965874..4d400fb1c8 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: 0.015637
- Baseline: 3.335394
+ Numpy running time: 0.017764
+ Baseline: 3.258871
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.288612
+ Opt1: 0.301821
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.275176
+ Opt2: 0.282132
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.114469
+ Opt3: 0.110027
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.105877
+ Opt4: 0.104594
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.100043
+ Opt5: 0.111375
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.117888
+ Opt6: 0.132452
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 8cc7338ef0..97e6e9df26 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
=================
-**00:32.348** total execution time for **how_to_optimize_operators** files:
+**00:33.852** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:29.259 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.367 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.919 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.083 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.171 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.403 | 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 251896c80f..78882481f4 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
=================
-**03:27.279** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:29.616** 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``) | 01:26.546 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:27.828 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:13.058 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.199 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.479 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.901 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:15.756 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:15.581 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.339 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.003 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.100 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 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 eb0f6876b3..be8c033e2c 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
@@ -767,7 +767,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.337 ms
+ Execution time of this operator: 0.345 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 cac7056031..5b1bf30564 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)
- 8.1317 8.1322 8.1353 8.1276 0.0032
+ 8.0874 8.0876 8.0966 8.0778 0.0077
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 13.058 seconds)
+ **Total running time of the script:** ( 1 minutes 14.199 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 ab35ef2865..1408dba7c4 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)
- 722.8329 721.5569 726.9483 719.9934 2.9792
+ 753.1484 752.4428 754.8402 752.1624 1.2017
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.546 seconds)
+ **Total running time of the script:** ( 1 minutes 27.828 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 3610b9d9af..b8b7a1ec88 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:22.674** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.426** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.637 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.388 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 0e2fbe3a34..a6fede3976 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: 0.037097
+ Time cost of this operator: 0.037009
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 8707939ecb..13c412e61f 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 301.9 98.736 (1, 2, 10, 10, 3) 2 1 [301.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.943 0.962 (1, 6, 10, 10) 1 1 [2.943]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.302 (1, 1, 10, 10, 3) 1 1 [0.923]
- Total_time - 305.765 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 303.6 98.737 (1, 2, 10, 10, 3) 2 1 [303.6]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.924 0.951 (1, 6, 10, 10) 1 1 [2.924]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.96 0.312 (1, 1, 10, 10, 3) 1 1 [0.96]
+ Total_time - 307.484 - - - - -
@@ -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 104.7 97.615 (1, 6, 10, 10, 1) 2 1 [104.7]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.715 1.599 (1, 6, 10, 10) 1 1 [1.715]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.844 0.787 (1, 3, 10, 10, 1) 1 1 [0.844]
- Total_time - 107.258 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 102.4 97.405 (1, 6, 10, 10, 1) 2 1 [102.4]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.684 (1, 6, 10, 10) 1 1 [1.77]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.911 (1, 1, 10, 10, 3) 1 1 [0.958]
+ Total_time - 105.128 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 24.282 seconds)
+ **Total running time of the script:** ( 1 minutes 23.322 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 3df885ed9d..b4f6232913 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, 15.6MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 24.5MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 17.2MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 27.3MB/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 23.289 seconds)
+ **Total running time of the script:** ( 1 minutes 24.618 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 5ecc5dd182..c5432778f9 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/tmpr461q9cd/images/random'
+ '/tmp/tmp9f37tpeb/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpr461q9cd/images/target contains 8144 images
- /tmp/tmpr461q9cd/images/random contains 5000 images
+ /tmp/tmp9f37tpeb/images/target contains 8144 images
+ /tmp/tmp9f37tpeb/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 40s - loss: 0.2218 - accuracy: 0.9261 - val_loss: 0.1282 - val_accuracy: 0.9558 - 40s/epoch - 122ms/step
+ 328/328 - 41s - loss: 0.2150 - accuracy: 0.9256 - val_loss: 0.1165 - val_accuracy: 0.9603 - 41s/epoch - 125ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.1027 - accuracy: 0.9628 - val_loss: 0.0976 - val_accuracy: 0.9600 - 35s/epoch - 106ms/step
+ 328/328 - 35s - loss: 0.0956 - accuracy: 0.9645 - val_loss: 0.1144 - val_accuracy: 0.9585 - 35s/epoch - 108ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0623 - accuracy: 0.9764 - val_loss: 0.0992 - val_accuracy: 0.9687 - 35s/epoch - 106ms/step
+ 328/328 - 35s - loss: 0.0656 - accuracy: 0.9736 - val_loss: 0.1062 - val_accuracy: 0.9671 - 35s/epoch - 107ms/step
- <keras.callbacks.History object at 0x7fe48c8a2e80>
+ <keras.callbacks.History object at 0x7f24648816d0>
@@ -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 45.717 seconds)
+ **Total running time of the script:** ( 4 minutes 37.205 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 7e648fdfd4..b8e6523855 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
=================
-**08:01.660** total execution time for **how_to_work_with_microtvm** files:
+**07:53.574** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:45.717 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:37.205 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:24.282 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:24.618 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:23.289 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:23.322 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.420 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.445 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.603 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.734 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.349 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.250 | 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 b2851760d3..f94d894225 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
=================
-**00:39.022** total execution time for **how_to_work_with_relay** files:
+**00:39.253** 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``) | 00:34.062 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.297 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.086 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.079 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.869 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.872 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.006 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
index f6e44a0722..bb4ce36cce 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 0x7fe32c59c1f0>
+ <function my_cuda_math_rule at 0x7f2341952e50>
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 94a12266a9..33bb250844 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
=================
-**00:08.614** total execution time for **how_to_work_with_schedules** files:
+**00:06.058** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.549 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.230 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.361 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.134 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.731 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.726 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.722 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.719 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.114 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.113 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.058 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.057 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.052 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 01d500453a..ead37545db 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
=================
-**00:33.813** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:33.903** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:33.805 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:33.895 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 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 ab42450f8d..811ff26047 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 35.93s!
+ resnet18_v1 inference graph built in 36.04s!
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 43bf659458..3b035ded7a 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 24.63s!
+ yolov3-tiny inference graph built in 24.75s!
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 82b041db44..83c47c988f 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
=================
-**01:54.312** total execution time for **topic_vta_tutorials_frontend** files:
+**01:55.211** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:57.668 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:58.234 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:56.644 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:56.977 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index 535a7ee3b5..f62e4b94fa 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
=================
-**00:03.241** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.285** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.732 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.779 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.509 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.506 | 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 d824cb030b..4ad360b1b5 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
=================
-**00:00.856** total execution time for **topic_vta_tutorials** files:
+**00:00.854** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.441 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.439 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.416 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.415 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 326812aeea..4bdfd61a0e 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,13 +207,6 @@ trials, we can load the best schedule from the log file and apply it.
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- .T
-
-
@@ -325,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 96.962 ms
+ Execution time of this operator: 95.739 ms
@@ -441,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.380 seconds)
+ **Total running time of the script:** ( 1 minutes 31.151 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 628e98d778..943e29e32d 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: 1.35/1.35 result: MeasureResult(costs=(0.198581842,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.4274001121520996, timestamp=1686231758.3812604) [('tile_y', [-1, 2]), ('tile_x', [-1, 1])],None,1
- No: 2 GFLOPS: 14.51/14.51 result: MeasureResult(costs=(0.0185003446,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6087970733642578, timestamp=1686231758.9356291) [('tile_y', [-1, 32]), ('tile_x', [-1, 64])],None,65
- No: 3 GFLOPS: 9.62/14.51 result: MeasureResult(costs=(0.0279183026,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7027921676635742, timestamp=1686231759.639009) [('tile_y', [-1, 2]), ('tile_x', [-1, 64])],None,61
- No: 4 GFLOPS: 11.18/14.51 result: MeasureResult(costs=(0.024013211,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6388506889343262, timestamp=1686231760.2788773) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
- No: 5 GFLOPS: 10.64/14.51 result: MeasureResult(costs=(0.0252390528,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7116949558258057, timestamp=1686231761.1160207) [('tile_y', [-1, 4]), ('tile_x', [-1, 128])],None,72
- No: 6 GFLOPS: 7.15/14.51 result: MeasureResult(costs=(0.0375530822,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8909363746643066, timestamp=1686231761.9706888) [('tile_y', [-1, 64]), ('tile_x', [-1, 16])],None,46
- No: 7 GFLOPS: 11.22/14.51 result: MeasureResult(costs=(0.0239264412,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6510071754455566, timestamp=1686231762.6114988) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
- No: 8 GFLOPS: 13.81/14.51 result: MeasureResult(costs=(0.0194328088,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6231801509857178, timestamp=1686231763.1820245) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
- No: 9 GFLOPS: 11.16/14.51 result: MeasureResult(costs=(0.0240477396,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6226780414581299, timestamp=1686231763.9148998) [('tile_y', [-1, 256]), ('tile_x', [-1, 256])],None,88
- No: 10 GFLOPS: 9.95/14.51 result: MeasureResult(costs=(0.026974993200000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6543610095977783, timestamp=1686231764.6041312) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
+ No: 1 GFLOPS: 10.94/10.94 result: MeasureResult(costs=(0.0245306796,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.66025710105896, timestamp=1686234542.039556) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 2 GFLOPS: 9.90/10.94 result: MeasureResult(costs=(0.027101661,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6918718814849854, timestamp=1686234542.7315114) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
+ No: 3 GFLOPS: 4.45/10.94 result: MeasureResult(costs=(0.0603336682,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2169880867004395, timestamp=1686234543.9503102) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
+ No: 4 GFLOPS: 12.72/12.72 result: MeasureResult(costs=(0.0210963826,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6064317226409912, timestamp=1686234544.5493762) [('tile_y', [-1, 32]), ('tile_x', [-1, 128])],None,75
+ No: 5 GFLOPS: 13.69/13.69 result: MeasureResult(costs=(0.019603665799999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5935487747192383, timestamp=1686234545.2811615) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 6 GFLOPS: 1.05/13.69 result: MeasureResult(costs=(0.2558564402,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.357801675796509, timestamp=1686234549.639376) [('tile_y', [-1, 32]), ('tile_x', [-1, 2])],None,15
+ No: 7 GFLOPS: 7.18/13.69 result: MeasureResult(costs=(0.0373630356,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.894737958908081, timestamp=1686234550.498396) [('tile_y', [-1, 64]), ('tile_x', [-1, 16])],None,46
+ No: 8 GFLOPS: 11.35/13.69 result: MeasureResult(costs=(0.0236492144,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6389908790588379, timestamp=1686234551.1431663) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+ No: 9 GFLOPS: 11.40/13.69 result: MeasureResult(costs=(0.023548297,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6381542682647705, timestamp=1686234551.8915417) [('tile_y', [-1, 128]), ('tile_x', [-1, 256])],None,87
+ No: 10 GFLOPS: 0.50/13.69 result: MeasureResult(costs=(0.5394803368,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.874113082885742, timestamp=1686234560.7933185) [('tile_y', [-1, 32]), ('tile_x', [-1, 1])],None,5
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index b23875e261..55e01be1ca 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': 475.8450447699943, 'median': 475.32472699995196, 'std': 1.3308575341288986}
+ {'mean': 487.61344594993716, 'median': 487.8080582999246, 'std': 1.429295624857209}
@@ -582,30 +582,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 21.17/ 21.17 GFLOPS | Progress: (4/20) | 8.62 s
[Task 1/25] Current/Best: 17.02/ 21.17 GFLOPS | Progress: (8/20) | 11.76 s
[Task 1/25] Current/Best: 15.68/ 21.17 GFLOPS | Progress: (12/20) | 15.27 s
[Task 1/25] Current/Best: 10.00/ 21.17 GFLOPS | Progress: (16/20) | 19.21 s
[Task 1/25] Current/Best: 17.42/ 21.17 GFLOPS | Progress: (20/20) | 22.62 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 5.74/ 14.71 GFLOPS | Progress: (4/20) | 4.21 s
[Task 2/25] Current/Best: 13.86/ 20.67 GFLOPS | Progress: (8/20) | 5.73 s
[Task 2/25] Current/Best: 22.43/ 22.43 GFLOPS | Progress: (12/20) | 7.15 s
[Task 2/25] Current/Best: 7.72/ 22.43 GFLOPS | Progress: (16/20) | 8.72 s
[Task 2/25] Current/Best: 7.82/ 22.43 GFLOPS | Progress: (20/20) | 10.14 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 8.39/ 21.04 GFLOPS | Progress: (4/20) | 5.11 s
[Task 3/25] Current/Best: 21.50/ 21.50 GFLOPS | Progress: (8/20) | 7.54 s
[Task 3/25] Current/Best: 16.62/ 21.50 GFLOPS | Progress: (12/20) | 11.23 s
[Task 3/25] Current/Best: 18.00/ 24.31 GFLOPS | Progress: (16/20) | 13.17 s
[Task 3/25] Current/Best: 9.56/ 24.31 GFLOPS | Progress: (20/20) | 15.75 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 10.13/ 14.93 GFLOPS | Progress: (4/20) | 5.84 s
[Task 4/25] Current/Best: 6.41/ 14.93 GFLOPS | Progress: (8/20) | 8.29 s
[Task 4/25] Current/Best: 8.98/ 14.93 GFLOPS | Progress: (12/20) | 10.47 s
[Task 4/25] Current/Best: 15.28/ 15.28 GFLOPS | Progress: (16/20) | 12.66 s
[Task 4/25] Current/Best: 6.54/ 15.28 GFLOPS | Progress: (20/20) | 15.99 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 17.46/ 17.46 GFLOPS | Progress: (4/20) | 4.48 s
[Task 5/25] Current/Best: 4.18/ 18.04 GFLOPS | Progress: (8/20) | 6.71 s
[Task 5/25] Current/Best: 12.76/ 18.04 GFLOPS | Progress: (12/20) | 9.29 s
[Task 5/25] Current/Best: 14.12/ 18.04 GFLOPS | Progress: (16/20) | 11.12 s
[Task 5/25] Current/Best: 17.80/ 18.04 GFLOPS | Progress: (20/20) | 13.78 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.50/ 13.68 GFLOPS | Progress: (4/20) | 6.09 s
[Task 6/25] Current/Best: 13.40/ 13.68 GFLOPS | Progress: (8/20) | 8.51 s
[Task 6/25] Current/Best: 7.92/ 16.28 GFLOPS | Progress: (12/20) | 12.61 s
[Task 6/25] Current/Best: 17.02/ 17.02 GFLOPS | Progress: (16/20) | 15.52 s
[Task 6/25] Current/Best: 16.41/ 17.02 GFLOPS | Progress: (20/20) | 18.04 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 13.92/ 13.92 GFLOPS | Progress: (4/20) | 5.38 s
[Task 7/25] Current/Best: 12.59/ 13.92 GFLOPS | Progress: (8/20) | 8.09 s
[Task 7/25] Current/Best: 20.89/ 20.89 GFLOPS | Progress: (12/20) | 10.49 s
[Task 7/25] Current/Best: 5.70/ 20.89 GFLOPS | Progress: (16/20) | 12.97 s
[Task 7/25] Current/Best: 22.56/ 22.56 GFLOPS | Progress: (20/20) | 15.64 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 13.29/ 13.29 GFLOPS | Progress: (4/20) | 6.32 s
[Task 8/25] Current/Best: 7.68/ 15.42 GFLOPS | Progress: (8/20) | 12.44 s
[Task 8/25] Current/Best: 11.05/ 15.42 GFLOPS | Progress: (12/20) | 24.16 s
[Task 8/25] Current/Best: 4.69/ 19.17 GFLOPS | Progress: (16/20) | 33.14 s
[Task 8/25] Current/Best: 7.62/ 19.17 GFLOPS | Progress: (20/20) | 41.70 s
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
-
[Task 9/25] Current/Best: 6.27/ 19.72 GFLOPS | Progress: (4/20) | 7.90 s
[Task 9/25] Current/Best: 8.50/ 19.72 GFLOPS | Progress: (8/20) | 9.95 s
[Task 9/25] Current/Best: 7.44/ 19.72 GFLOPS | Progress: (12/20) | 17.84 s
[Task 9/25] Current/Best: 6.82/ 19.72 GFLOPS | Progress: (16/20) | 28.35 s
[Task 9/25] Current/Best: 20.24/ 20.24 GFLOPS | Progress: (20/20) | 30.42 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 11.63/ 16.42 GFLOPS | Progress: (4/20) | 5.27 s
[Task 10/25] Current/Best: 12.27/ 16.42 GFLOPS | Progress: (8/20) | 8.16 s
[Task 10/25] Current/Best: 4.12/ 19.97 GFLOPS | Progress: (12/20) | 11.63 s
[Task 10/25] Current/Best: 13.53/ 19.97 GFLOPS | Progress: (16/20) | 13.52 s
[Task 10/25] Current/Best: 3.97/ 19.97 GFLOPS | Progress: (20/20) | 15.63 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 6.94/ 12.46 GFLOPS | Progress: (4/20) | 5.59 s
[Task 11/25] Current/Best: 11.83/ 16.19 GFLOPS | Progress: (8/20) | 9.03 s
[Task 11/25] Current/Best: 10.97/ 21.64 GFLOPS | Progress: (12/20) | 11.03 s
[Task 11/25] Current/Best: 14.90/ 21.64 GFLOPS | Progress: (16/20) | 13.52 s
[Task 11/25] Current/Best: 1.59/ 21.64 GFLOPS | Progress: (20/20) | 17.22 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 4.51/ 16.96 GFLOPS | Progress: (4/20) | 7.23 s
[Task 12/25] Current/Best: 13.20/ 22.17 GFLOPS | Progress: (8/20) | 10.33 s
[Task 12/25] Current/Best: 7.73/ 22.17 GFLOPS | Progress: (12/20) | 13.89 s
[Task 12/25] Current/Best: 4.20/ 22.17 GFLOPS | Progress: (16/20) | 16.13 s
[Task 12/25] Current/Best: 8.44/ 22.17 GFLOPS | Progress: (20/20) | 18.98 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 12.36/ 19.79 GFLOPS | Progress: (4/20) | 5.93 s
[Task 13/25] Current/Best: 9.75/ 19.79 GFLOPS | Progress: (8/20) | 9.15 s
[Task 13/25] Current/Best: 9.36/ 20.01 GFLOPS | Progress: (12/20) | 11.88 s
[Task 13/25] Current/Best: 6.12/ 22.19 GFLOPS | Progress: (16/20) | 14.64 s
[Task 13/25] Current/Best: 9.35/ 22.19 GFLOPS | Progress: (20/20) | 16.88 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 7.77/ 15.25 GFLOPS | Progress: (4/20) | 6.05 s
[Task 14/25] Current/Best: 10.84/ 15.25 GFLOPS | Progress: (8/20) | 9.96 s
[Task 14/25] Current/Best: 18.77/ 18.77 GFLOPS | Progress: (12/20) | 14.32 s
[Task 14/25] Current/Best: 6.74/ 18.77 GFLOPS | Progress: (16/20) | 23.58 s
[Task 14/25] Current/Best: 9.53/ 18.77 GFLOPS | Progress: (20/20) | 35.28 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 15.34/ 19.67 GFLOPS | Progress: (4/20) | 13.32 s
[Task 15/25] Current/Best: 18.14/ 19.67 GFLOPS | Progress: (8/20) | 18.90 s
[Task 15/25] Current/Best: 21.77/ 21.77 GFLOPS | Progress: (12/20) | 24.07 s
[Task 15/25] Current/Best: 20.53/ 21.77 GFLOPS | Progress: (16/20) | 26.39 s
[Task 15/25] Current/Best: 14.04/ 21.77 GFLOPS | Progress: (20/
20) | 33.24 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 6.22/ 18.90 GFLOPS | Progress: (4/20) | 4.54 s
[Task 16/25] Current/Best: 17.13/ 18.90 GFLOPS | Progress: (8/20) | 6.76 s
[Task 16/25] Current/Best: 14.14/ 18.90 GFLOPS | Progress: (12/20) | 8.68 s
[Task 16/25] Current/Best: 6.55/ 18.90 GFLOPS | Progress: (16/20) | 12.11 s
[Task 16/25] Current/Best: 6.59/ 18.90 GFLOPS | Progress: (20/20) | 14.11 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 9.53/ 18.56 GFLOPS | Progress: (4/20) | 5.76 s
[Task 17/25] Current/Best: 2.94/ 22.09 GFLOPS | Progress: (8/20) | 8.75 s
[Task 17/25] Current/Best: 11.83/ 22.09 GFLOPS | Progress: (12/20) | 11.39 s
[Task 17/25] Current/Best: 18.84/ 22.09 GFLOPS | Progress: (16/20) | 13.99 s
[Task 17/25] Current/Best: 13.11/ 22.09 GFLOPS | Progress: (20/20) | 16.88 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 17.34/ 17.34 GFLOPS | Progress: (4/20) | 4.86 s
[Task 18/25] Current/Best: 13.47/ 19.03 GFLOPS | Progress: (8/20) | 6.76 s
[Task 18/25] Current/Best: 7.01/ 19.03 GFLOPS | Progress: (12/20) | 12.71 s
[Task 18/25] Current/Best: 13.84/ 19.03 GFLOPS | Progress: (16/20) | 15.57 s
[Task 18/25] Current/Best: 19.16/ 19.16 GFLOPS | Progress: (20/20) | 17.56 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 8.00/ 18.62 GFLOPS | Progress: (4/20) | 7.18 s
[Task 19/25] Current/Best: 2.70/ 19.45 GFLOPS | Progress: (8/20) | 10.17 s
[Task 19/25] Current/Best: 15.05/ 20.91 GFLOPS | Progress: (12/20) | 12.50 s
[Task 19/25] Current/Best: 9.62/ 20.91 GFLOPS | Progress: (16/20) | 18.90 s
[Task 19/25] Current/Best: 7.53/ 20.91 GFLOPS | Progress: (20/20) | 23.71 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 10.31/ 15.09 GFLOPS | Progress: (4/20) | 10.88 s
[Task 20/25] Current/Best: 9.54/ 15.09 GFLOPS | Progress: (8/20) | 13.77 s
[Task 20/25] Current/Best: 16.85/ 16.85 GFLOPS | Progress: (12/20) | 25.60 s
[Task 20/25] Current/Best: 19.84/ 19.84 GFLOPS | Progress: (16/20) | 29.24 s
[Task 20/25] Current/Best: 20.03/ 20.03 GFLOPS | Progress: (20/20) | 32.99 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 20.26/ 20.26 GFLOPS | Progress: (4/20) | 11.60 s
[Task 21/25] Current/Best: 9.53/ 20.26 GFLOPS | Progress: (8/20) | 22.75 s
[Task 21/25] Current/Best: 8.70/ 20.26 GFLOPS | Progress: (12/20) | 27.96 s
[Task 21/25] Current/Best: 13.45/ 20.26 GFLOPS | Progress: (16/20) | 33.10 s
[Task 21/25] Current/Best: 13.44/ 22.85 GFLOPS | Progress: (2
0/20) | 34.82 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 21.10/ 21.10 GFLOPS | Progress: (4/20) | 4.89 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 19.43/ 19.43 GFLOPS | Progress: (4/20) | 9.35 s
[Task 1/25] Current/Best: 5.71/ 20.99 GFLOPS | Progress: (8/20) | 12.03 s
[Task 1/25] Current/Best: 13.28/ 21.42 GFLOPS | Progress: (12/20) | 16.81 s
[Task 1/25] Current/Best: 12.01/ 21.42 GFLOPS | Progress: (16/20) | 21.82 s
[Task 1/25] Current/Best: 9.58/ 21.42 GFLOPS | Progress: (20/20) | 25.23 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 16.51/ 18.26 GFLOPS | Progress: (4/20) | 4.23 s
[Task 2/25] Current/Best: 11.18/ 18.26 GFLOPS | Progress: (8/20) | 7.12 s
[Task 2/25] Current/Best: 15.49/ 18.26 GFLOPS | Progress: (12/20) | 8.50 s
[Task 2/25] Current/Best: 19.22/ 19.22 GFLOPS | Progress: (16/20) | 10.31 s
[Task 2/25] Current/Best: 14.77/ 19.22 GFLOPS | Progress: (20/20) | 12.05 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 19.02/ 21.34 GFLOPS | Progress: (4/20) | 5.13 s
[Task 3/25] Current/Best: 8.02/ 21.34 GFLOPS | Progress: (8/20) | 7.35 s
[Task 3/25] Current/Best: 8.85/ 21.34 GFLOPS | Progress: (12/20) | 9.73 s
[Task 3/25] Current/Best: 12.41/ 21.34 GFLOPS | Progress: (16/20) | 11.89 s
[Task 3/25] Current/Best: 23.61/ 23.61 GFLOPS | Progress: (20/20) | 15.59 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 16.89/ 22.35 GFLOPS | Progress: (4/20) | 4.40 s
[Task 4/25] Current/Best: 15.60/ 22.35 GFLOPS | Progress: (8/20) | 7.45 s
[Task 4/25] Current/Best: 17.39/ 22.35 GFLOPS | Progress: (12/20) | 9.23 s
[Task 4/25] Current/Best: 10.71/ 22.35 GFLOPS | Progress: (16/20) | 12.78 s
[Task 4/25] Current/Best: 13.05/ 22.35 GFLOPS | Progress: (20/20) | 14.75 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 14.09/ 16.29 GFLOPS | Progress: (4/20) | 5.07 s
[Task 5/25] Current/Best: 15.01/ 16.29 GFLOPS | Progress: (8/20) | 7.78 s
[Task 5/25] Current/Best: 5.96/ 19.91 GFLOPS | Progress: (12/20) | 9.62 s
[Task 5/25] Current/Best: 3.29/ 22.08 GFLOPS | Progress: (16/20) | 11.54 s
[Task 5/25] Current/Best: 12.65/ 22.08 GFLOPS | Progress: (20/20) | 13.43 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 4.72/ 15.26 GFLOPS | Progress: (4/20) | 5.45 s
[Task 6/25] Current/Best: 14.61/ 19.31 GFLOPS | Progress: (8/20) | 7.34 s
[Task 6/25] Current/Best: 11.74/ 19.31 GFLOPS | Progress: (12/20) | 9.67 s
[Task 6/25] Current/Best: 15.48/ 19.31 GFLOPS | Progress: (16/20) | 12.31 s
[Task 6/25] Current/Best: 4.58/ 19.31 GFLOPS | Progress: (20/20) | 15.02 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 9.38/ 12.94 GFLOPS | Progress: (4/20) | 5.52 s
[Task 7/25] Current/Best: 14.55/ 14.55 GFLOPS | Progress: (8/20) | 8.83 s
[Task 7/25] Current/Best: 19.43/ 19.43 GFLOPS | Progress: (12/20) | 11.18 s
[Task 7/25] Current/Best: 7.82/ 19.59 GFLOPS | Progress: (16/20) | 13.86 s
[Task 7/25] Current/Best: 8.94/ 19.59 GFLOPS | Progress: (20/20) | 16.58 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 14.27/ 14.27 GFLOPS | Progress: (4/20) | 8.16 s
[Task 8/25] Current/Best: 11.43/ 14.27 GFLOPS | Progress: (8/20) | 13.99 s
[Task 8/25] Current/Best: 9.74/ 14.27 GFLOPS | Progress: (12/20) | 20.42 s
[Task 8/25] Current/Best: 7.62/ 22.59 GFLOPS | Progress: (16/20) | 25.94 s
[Task 8/25] Current/Best: 13.19/ 22.59 GFLOPS | Progress: (20/20) | 29.38 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.57/ 16.01 GFLOPS | Progress: (4/20) | 5.55 s
[Task 9/25] Current/Best: 13.78/ 18.52 GFLOPS | Progress: (8/20) | 7.67 s
[Task 9/25] Current/Best: 16.33/ 21.84 GFLOPS | Progress: (12/20) | 9.34 s
[Task 9/25] Current/Best: 11.87/ 21.84 GFLOPS | Progress: (16/20) | 14.95 s
[Task 9/25] Current/Best: 17.33/ 21.84 GFLOPS | Progress: (20/20) | 17.76 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 5.45/ 17.79 GFLOPS | Progress: (4/20) | 4.66 s
[Task 10/25] Current/Best: 6.08/ 17.79 GFLOPS | Progress: (8/20) | 6.71 s
[Task 10/25] Current/Best: 10.12/ 18.22 GFLOPS | Progress: (12/20) | 8.71 s
[Task 10/25] Current/Best: 13.66/ 18.22 GFLOPS | Progress: (16/20) | 10.41 s
[Task 10/25] Current/Best: 4.97/ 18.22 GFLOPS | Progress: (20/20) | 12.42 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 19.91/ 21.38 GFLOPS | Progress: (4/20) | 4.65 s
[Task 11/25] Current/Best: 23.32/ 23.32 GFLOPS | Progress: (8/20) | 6.67 s
[Task 11/25] Current/Best: 8.54/ 23.32 GFLOPS | Progress: (12/20) | 8.99 s
[Task 11/25] Current/Best: 21.64/ 23.32 GFLOPS | Progress: (16/20) | 12.01 s
[Task 11/25] Current/Best: 9.65/ 23.32 GFLOPS | Progress: (20/20) | 14.37 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 8.51/ 15.13 GFLOPS | Progress: (4/20) | 5.33 s
[Task 12/25] Current/Best: 12.68/ 19.45 GFLOPS | Progress: (8/20) | 9.10 s
[Task 12/25] Current/Best: 15.87/ 19.45 GFLOPS | Progress: (12/20) | 11.78 s
[Task 12/25] Current/Best: 3.69/ 19.45 GFLOPS | Progress: (16/20) | 20.53 s
[Task 12/25] Current/Best: 8.72/ 19.45 GFLOPS | Progress: (20/20) | 22.96 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 21.40/ 21.40 GFLOPS | Progress: (4/20) | 4.76 s
[Task 13/25] Current/Best: 9.21/ 21.40 GFLOPS | Progress: (8/20) | 7.75 s
[Task 13/25] Current/Best: 19.21/ 21.40 GFLOPS | Progress: (12/20) | 10.16 s
[Task 13/25] Current/Best: 13.17/ 22.07 GFLOPS | Progress: (16/20) | 12.80 s
[Task 13/25] Current/Best: 5.75/ 22.07 GFLOPS | Progress: (20/20) | 16.29 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.36/ 14.18 GFLOPS | Progress: (4/20) | 6.19 s
[Task 14/25] Current/Best: 6.82/ 14.18 GFLOPS | Progress: (8/20) | 14.64 s
[Task 14/25] Current/Best: 16.79/ 16.79 GFLOPS | Progress: (12/20) | 23.83 s
[Task 14/25] Current/Best: 11.82/ 16.79 GFLOPS | Progress: (16/20) | 35.37 s
[Task 14/25] Current/Best: 7.70/ 16.79 GFLOPS | Progress: (20/20) | 43.93 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 15/25] Current/Best: 14.55/ 18.50 GFLOPS | Progress: (4/20) | 4.45 s
[Task 15/25] Current/Best: 12.75/ 18.50 GFLOPS | Progress: (8/20) | 6.61 s
[Task 15/25] Current/Best: 13.78/ 18.50 GFLOPS | Progress: (12/20) | 14.31 s
[Task 15/25] Current/Best: 10.93/ 18.50 GFLOPS | Progress: (16/20) | 19.72 s
[Task 15/25] Current/Best: 8.40/ 18.50 GFLOPS | Progress: (20/20) | 22.25 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 15.07/ 17.89 GFLOPS | Progress: (4/20) | 5.86 s
[Task 16/25] Current/Best: 11.68/ 17.89 GFLOPS | Progress: (8/20) | 9.10 s
[Task 16/25] Current/Best: 3.07/ 17.89 GFLOPS | Progress: (12/20) | 11.09 s
[Task 16/25] Current/Best: 3.09/ 17.89 GFLOPS | Progress: (16/20) | 13.23 s
[Task 16/25] Current/Best: 6.48/ 19.17 GFLOPS | Progress: (20/20) | 14.94 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 20.15/ 20.15 GFLOPS | Progress: (4/20) | 5.05 s
[Task 17/25] Current/Best: 22.18/ 23.01 GFLOPS | Progress: (8/20) | 7.35 s
[Task 17/25] Current/Best: 3.10/ 23.01 GFLOPS | Progress: (12/20) | 10.55 s
[Task 17/25] Current/Best: 5.37/ 23.01 GFLOPS | Progress: (16/20) | 13.52 s
[Task 17/25] Current/Best: 8.99/ 23.01 GFLOPS | Progress: (20/20) | 16.38 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 13.83/ 17.33 GFLOPS | Progress: (4/20) | 4.74 s
[Task 18/25] Current/Best: 18.23/ 21.71 GFLOPS | Progress: (8/20) | 7.13 s
[Task 18/25] Current/Best: 8.07/ 21.71 GFLOPS | Progress: (12/20) | 11.53 s
[Task 18/25] Current/Best: 9.95/ 21.71 GFLOPS | Progress: (16/20) | 17.53 s
[Task 18/25] Current/Best: 17.62/ 21.71 GFLOPS | Progress: (20/20) | 19.31 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 5.38/ 10.99 GFLOPS | Progress: (4/20) | 8.24 s
[Task 19/25] Current/Best: 20.72/ 20.72 GFLOPS | Progress: (8/20) | 11.36 s
[Task 19/25] Current/Best: 16.72/ 20.72 GFLOPS | Progress: (12/20) | 13.65 s
[Task 19/25] Current/Best: 17.98/ 20.72 GFLOPS | Progress: (16/20) | 16.82 s
[Task 19/25] Current/Best: 10.39/ 20.72 GFLOPS | Progress: (20/20) | 21.19 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.86/ 20.81 GFLOPS | Progress: (4/20) | 5.91 s
[Task 20/25] Current/Best: 17.69/ 20.81 GFLOPS | Progress: (8/20) | 8.25 s
[Task 20/25] Current/Best: 10.62/ 20.81 GFLOPS | Progress: (12/20) | 12.68 s
[Task 20/25] Current/Best: 19.52/ 20.81 GFLOPS | Progress: (16/20) | 15.19 s
[Task 20/25] Current/Best: 10.64/ 20.81 GFLOPS | Progress: (20/20) | 19.74 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 7.72/ 17.64 GFLOPS | Progress: (4/20) | 6.65 s
[Task 21/25] Current/Best: 17.05/ 17.64 GFLOPS | Progress: (8/20) | 17.83 s
[Task 21/25] Current/Best: 10.52/ 19.45 GFLOPS | Progress: (12/20) | 20.85 s
[Task 21/25] Current/Best: 5.30/ 19.45 GFLOPS | Progress: (16/20) | 25.38 s
[Task 21/25] Current/Best: 14.21/ 19.45 GFLOPS | Progress: (20/20) | 27.88 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.75/ 13.84 GFLOPS | Progress: (4/20) | 5.21 s
[Task 22/25] Current/Best: 6.44/ 17.41 GFLOPS | Progress: (8/20) | 7.17 s
[Task 22/25] Current/Best: 7.35/ 21.01 GFLOPS | Progress: (12/20) | 10.14 s
[Task 22/25] Current/Best: 10.42/ 21.01 GFLOPS | Progress: (16/20) | 13.35 s
[Task 22/25] Current/Best: 19.61/ 21.01 GFLOPS | Progress: (20/2
0) | 15.22 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 22.62/ 22.62 GFLOPS | Progress: (4/20) | 8.68 s
[Task 23/25] Current/Best: 19.46/ 22.64 GFLOPS | Progress: (8/20) | 11.27 s
[Task 23/25] Current/Best: 9.94/ 22.64 GFLOPS | Progress: (12/20) | 14.84 s
[Task 23/25] Current/Best: 2.70/ 22.64 GFLOPS | Progress: (16/20) | 19.48 s
[Task 23/25] Current/Best: 6.16/ 22.64 GFLOPS | Progress: (20/20) | 22.58 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 6.90/ 6.90 GFLOPS | Progress: (4/20) | 13.84 s
[Task 24/25] Current/Best: 3.22/ 6.90 GFLOPS | Progress: (8/20) | 26.41 s
[Task 24/25] Current/Best: 3.60/ 10.36 GFLOPS | Progress: (12/20) | 31.77 s
[Task 24/25] Current/Best: 0.61/ 10.36 GFLOPS | Progress: (16/20) | 42.82 s
[Task 24/25] Current/Best: 7.25/ 10.36 GFLOPS | Progress: (20/20) | 53.80 s Done.
Done.
- Done.
-
[Task 22/25] Current/Best: 18.57/ 21.10 GFLOPS | Progress: (8/20) | 7.29 s
[Task 22/25] Current/Best: 18.24/ 21.10 GFLOPS | Progress: (12/20) | 9.66 s
[Task 22/25] Current/Best: 6.12/ 21.10 GFLOPS | Progress: (16/20) | 12.00 s
[Task 22/25] Current/Best: 8.79/ 21.10 GFLOPS | Progress: (20/20) | 15.54 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 11.12/ 22.60 GFLOPS | Progress: (4/20) | 6.54 s
[Task 23/25] Current/Best: 22.00/ 22.60 GFLOPS | Progress: (8/20) | 9.67 s
[Task 23/25] Current/Best: 20.28/ 22.60 GFLOPS | Progress: (12/20) | 12.63 s
[Task 23/25] Current/Best: 20.33/ 22.60 GFLOPS | Progress: (16/20) | 19.01 s
[Task 23/25] Current/Best: 12.17/ 22.60 GFLOPS | Progress: (20/20) | 22.09 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 6.33/ 6.33 GFLOPS | Progress: (4/20) | 13.78 s
[Task 24/25] Current/Best: 1.68/ 7.75 GFLOPS | Progress: (8/20) | 22.78 s
[Task 24/25] Current/Best: 2.93/ 8.73 GFLOPS | Progress: (12/20) | 33.78 s
[Task 24/25] Current/Best: 2.17/ 8.73 GFLOPS | Progress: (16/20) | 41.99 s
[Task 24/25] Current/Best: 0.64/ 9.87 GFLOPS | Progress: (20/20) | 48.03 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 9.11/ 9.11 GFLOPS | Progress: (4/20) | 7.77 s
[Task 25/25] Current/Best: 9.91/ 9.91 GFLOPS | Progress: (8/20) | 9.12 s
[Task 25/25] Current/Best: 1.55/ 9.91 GFLOPS | Progress: (12/20) | 14.23 s
[Task 25/25] Current/Best: 7.51/ 9.91 GFLOPS | Progress: (16/20) | 25.19 s
[Task 25/25] Current/Best: 8.71/ 9.91 GFLOPS | Progress: (20/
20) | 28.15 s
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 2.65/ 10.05 GFLOPS | Progress: (4/20) | 13.70 s
[Task 25/25] Current/Best: 1.51/ 10.05 GFLOPS | Progress: (8/20) | 16.64 s
[Task 25/25] Current/Best: 9.47/ 10.05 GFLOPS | Progress: (12/20) | 27.59 s
[Task 25/25] Current/Best: 5.57/ 10.05 GFLOPS | Progress: (16/20) | 32.68 s
[Task 25/25] Current/Best: 3.02/ 10.05 GFLOPS | Progress: (20/20) | 34.19 s
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 415.54578162000325, 'median': 415.5448007999894, 'std': 0.7973872856655191}
- unoptimized: {'mean': 475.8450447699943, 'median': 475.32472699995196, 'std': 1.3308575341288986}
+ optimized: {'mean': 413.43917781992786, 'median': 413.2477134999135, 'std': 0.8035540102112394}
+ unoptimized: {'mean': 487.61344594993716, 'median': 487.8080582999246, 'std': 1.429295624857209}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 35.251 seconds)
+ **Total running time of the script:** ( 12 minutes 59.287 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 8d2dc4ca86..434fd0d4ae 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
- 1.287e-07 secs/op
+ 1.186e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 4dcbf57fcb..d282103fd2 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -270,7 +270,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x123729f0)), stage(b, placeholder(b, 0xbc5fbc0)), 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, 0xd9fd630)), stage(b, placeholder(b, 0xbc9f5a0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.R [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 2c782b9114..74f3bb1652 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
=================
-**17:06.367** total execution time for **tutorial** files:
+**16:35.142** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:35.251 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:59.287 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:34.380 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:31.151 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.866 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.163 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:39.613 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:39.728 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:16.275 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:25.829 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.952 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.962 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.845 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.853 | 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``) | 00:00.169 | 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 91a7e7f6d6..94b2711f1b 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000008
- naive: 0.000008
+ Numpy running time: 0.000007
+ naive: 0.000007
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.84421999924234e-06 1.0
- naive 7.9445e-06 1.0127839352755716
- parallel 7.3729e-06 0.9399149948257616
- vector 3.91285e-05 4.988195130144152
+ numpy 7.152530015446246e-06 1.0
+ naive 6.6715e-06 0.932746872168668
+ parallel 7.2587000000000005e-06 1.0148436964716647
+ vector 3.92361e-05 5.485625354282706
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018793
+ Numpy running time: 0.017845
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.344315
+ none: 3.199074
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.280329
+ blocking: 0.299416
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.260366
+ vectorization: 0.275349
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1230,7 +1230,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.114737
+ loop permutation: 0.113558
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1321,7 +1321,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.107461
+ array packing: 0.106133
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1404,7 +1404,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.111248
+ block caching: 0.110452
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1478,7 +1478,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.132252
+ parallelization: 0.131667
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1548,13 +1548,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.3443153451999996 1.0
- blocking 0.2803293332 0.08382263759975526
- vectorization 0.2603664509 0.07785343905253926
- loop permutation 0.11473654039999999 0.03430793108810091
- array packing 0.10746134389999999 0.03213253919198624
- block caching 0.1112478325 0.033264755567883525
- parallelization 0.1322520151 0.039545318383273145
+ none 3.1990735863 1.0
+ blocking 0.2994157182 0.09359450794825251
+ vectorization 0.2753485178 0.08607132983097895
+ loop permutation 0.1135576833 0.03549705257994365
+ array packing 0.1061330758 0.03317619083678282
+ block caching 0.1104523874 0.03452636659344481
+ parallelization 0.1316670366 0.04115786431542642
diff --git a/docs/commit_hash b/docs/commit_hash
index 192157b022..022489d838 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-e5c1a24bdacff2ba15098e9b38fbf39fa66b878c
+fa4d73ac51b03555e40b57e8f3ca17c250024bd6
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 43e6a8bf62..88a7b2bdf0 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 28.763 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.026 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 4915275427..99dfa28466 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>
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+<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.zip78dc07ef-b313-4dba-9e0b-26dea45313b6 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 b83534fb45..7f94914ad0 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,12 +459,13 @@ 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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+ 19%|#9 | 7.99M/41.5M [00:00<00:01, 34.6MB/s]
+ 39%|###8 | 16.0M/41.5M [00:00<00:00, 39.5MB/s]
+ 54%|#####3 | 22.3M/41.5M [00:00<00:00, 44.5MB/s]
+ 65%|######4 | 26.8M/41.5M [00:00<00:00, 42.6MB/s]
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+ 92%|#########2| 38.3M/41.5M [00:00<00:00, 45.3MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 41.0MB/s]
</pre></div>
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index cc0004df51..aa03195b20 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -494,6 +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 0.441 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 5fd867f6e1..020748ae4f 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,14 +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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- 68%|######7 | 30.4M/44.7M [00:00<00:00, 55.0MB/s]
- 81%|########1 | 36.2M/44.7M [00:00<00:00, 52.0MB/s]
- 92%|#########2| 41.2M/44.7M [00:01<00:00, 34.4MB/s]
-100%|##########| 44.7M/44.7M [00:01<00:00, 43.7MB/s]
+ 18%|#7 | 7.99M/44.7M [00:00<00:01, 32.5MB/s]
+ 36%|###5 | 16.0M/44.7M [00:00<00:00, 37.1MB/s]
+ 54%|#####3 | 24.0M/44.7M [00:00<00:00, 43.1MB/s]
+ 63%|######3 | 28.3M/44.7M [00:00<00:00, 38.3MB/s]
+ 72%|#######1 | 32.0M/44.7M [00:00<00:00, 30.6MB/s]
+ 86%|########5 | 38.3M/44.7M [00:01<00:00, 33.9MB/s]
+ 93%|#########3| 41.6M/44.7M [00:01<00:00, 30.2MB/s]
+100%|##########| 44.7M/44.7M [00:01<00:00, 34.8MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 97305600cc..06b127e50f 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 29.342 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.655 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 21d09bc805..a0b9ce1ad6 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>06:48.762</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:55.345</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -358,44 +358,44 @@
<col style="width: 8%" />
</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>01:29.342</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
+<td><p>01:31.026</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>01:28.763</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
+<td><p>01:28.655</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>00:59.223</p></td>
+<td><p>01:00.441</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>00:38.960</p></td>
+<td><p>00:39.366</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>00:35.541</p></td>
+<td><p>00:36.039</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>00:31.593</p></td>
+<td><p>00:32.095</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:26.915</p></td>
+<td><p>00:27.681</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:24.315</p></td>
+<td><p>00:25.732</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>00:11.410</p></td>
+<td><p>00:11.543</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>00:02.699</p></td>
+<td><p>00:02.768</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 5b0027638e..f78445740e 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)
- 4162.8843 4160.5836 4178.1479 4157.3885 6.2815
+ 4222.6730 4222.1056 4226.4192 4220.2011 1.8073
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 17.987 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.928 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 c0f5dce6f5..8a413c4048 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
@@ -448,24 +448,32 @@ 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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+102850560/102967424 [============================>.] - ETA: 0s
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</pre></div>
</div>
</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 a348186443..ce42d6264f 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)
- 14.5436 14.3573 15.5172 14.0424 0.4604
+ 14.8199 14.7742 15.5662 14.4704 0.3144
</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 1517718bd8..32aa9b194f 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,36 +464,37 @@ 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).
@@ -587,7 +588,7 @@ 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 30.315 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 33.319 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 f129b2234f..f9e64dded8 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,8 +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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+100%|##########| 13.6M/13.6M [00:00<00:00, 34.1MB/s]
</pre></div>
</div>
</div>
@@ -597,7 +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)
- 87.2185 87.1927 87.6484 86.8844 0.1895
+ 88.6973 88.6413 89.9433 88.2406 0.2612
</pre></div>
</div>
<div class="admonition note">
@@ -636,7 +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 23.540 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.701 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 c77767c4c0..75fb7df0c6 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)
- 106.8269 107.0837 110.9052 104.4513 1.2629
+ 108.4453 108.4047 112.4272 106.6496 0.8279
</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 ea78520772..82a3e81c8d 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 19.679 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 0.996 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 d30a702e86..53944395c2 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>11:50.096</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:39.739</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>03:30.315</p></td>
+<td><p>03:33.319</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>02:19.679</p></td>
+<td><p>02:00.996</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>01:23.540</p></td>
+<td><p>01:23.701</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>01:17.987</p></td>
+<td><p>01:18.928</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:50.409</p></td>
+<td><p>00:50.912</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:47.580</p></td>
+<td><p>00:48.106</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno_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>00:42.874</p></td>
+<td><p>00:45.292</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>00:28.954</p></td>
+<td><p>00:29.428</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>00:28.751</p></td>
+<td><p>00:29.050</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 b42b0f9334..dce71a3271 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.zipf235fdcb-eec3-4451-8f47-43a53f968e01 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.zipdef6b1e0-795f-43e2-a772-f4888f0ae2e5 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index d6432345e0..6598a5503a 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>00:55.073</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:55.265</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,15 +359,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:51.345</p></td>
+<td><p>00:51.447</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>00:02.608</p></td>
+<td><p>00:02.660</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>00:01.113</p></td>
+<td><p>00:01.151</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 728e9140cc..00d5c159ed 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: 23264us [23264us] (48.55%; 48.55%)
-FoldScaleAxis: 24652us [7us] (51.45%; 51.45%)
- FoldConstant: 24645us [1815us] (51.43%; 99.97%)
- InferType: 22830us [22830us] (47.64%; 92.63%)
+InferType: 23278us [23278us] (48.30%; 48.30%)
+FoldScaleAxis: 24920us [7us] (51.70%; 51.70%)
+ FoldConstant: 24912us [1837us] (51.69%; 99.97%)
+ InferType: 23075us [23075us] (47.88%; 92.63%)
</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: 22559us [22559us] (48.22%; 48.22%)
-FoldScaleAxis: 24229us [5us] (51.78%; 51.78%)
- FoldConstant: 24223us [1753us] (51.77%; 99.98%)
- InferType: 22470us [22470us] (48.03%; 92.76%)
+InferType: 23979us [23979us] (49.42%; 49.42%)
+FoldScaleAxis: 24546us [6us] (50.58%; 50.58%)
+ FoldConstant: 24540us [1766us] (50.57%; 99.98%)
+ InferType: 22774us [22774us] (46.93%; 92.80%)
</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 67aa85d309..17b7b012b2 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: 38.520736 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 34.863361 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 cbe9e265d3..6bef4b79be 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -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: 12.265840 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.089888 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 c2c60c8581..f9c7c2d9c9 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: 0.015637
-Baseline: 3.335394
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.017764
+Baseline: 3.258871
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -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: 0.288612
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.301821
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -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: 0.275176
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.282132
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -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: 0.114469
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.110027
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -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: 0.105877
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.104594
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -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: 0.100043
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111375
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -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: 0.117888
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.132452
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 48b3a1ec99..6fb706110c 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>00:32.348</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:33.852</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>00:29.259</p></td>
+<td><p>00:30.367</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>00:01.919</p></td>
+<td><p>00:02.083</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>00:01.171</p></td>
+<td><p>00:01.403</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 e1676c7388..c8ee85a426 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>03:27.279</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:29.616</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -359,27 +359,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:26.546</p></td>
+<td><p>01:27.828</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>01:13.058</p></td>
+<td><p>01:14.199</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:16.479</p></td>
+<td><p>00:16.901</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:15.756</p></td>
+<td><p>00:15.581</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:15.339</p></td>
+<td><p>00:15.003</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.100</p></td>
+<td><p>00:00.103</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index cda695b826..e1f1638e5a 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
@@ -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: 0.337 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.345 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 923d385ae2..b9ae1b90fa 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)
- 8.1317 8.1322 8.1353 8.1276 0.0032
+ 8.0874 8.0876 8.0966 8.0778 0.0077
</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 13.058 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.199 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 9273343bb6..7df2895dea 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)
- 722.8329 721.5569 726.9483 719.9934 2.9792
+ 753.1484 752.4428 754.8402 752.1624 1.2017
</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>
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<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
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+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -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>00:22.674</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.426</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,11 +359,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index fa5e8f8f86..1fe6960edb 100644
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+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -620,7 +620,7 @@ and measure running time.</p>
Best config:
,None
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
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index 3e17fdd21e..1799b73240 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)
--------- --- -------- ------- ----- ------ ------- ----------------
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-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.923 0.302 (1, 1, 10, 10, 3) 1 1 [0.923]
-Total_time - 305.765 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 303.6 98.737 (1, 2, 10, 10, 3) 2 1 [303.6]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.924 0.951 (1, 6, 10, 10) 1 1 [2.924]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.96 0.312 (1, 1, 10, 10, 3) 1 1 [0.96]
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</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 104.7 97.615 (1, 6, 10, 10, 1) 2 1 [104.7]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.715 1.599 (1, 6, 10, 10) 1 1 [1.715]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.844 0.787 (1, 3, 10, 10, 1) 1 1 [0.844]
-Total_time - 107.258 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 102.4 97.405 (1, 6, 10, 10, 1) 2 1 [102.4]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.684 (1, 6, 10, 10) 1 1 [1.77]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.911 (1, 1, 10, 10, 3) 1 1 [0.958]
+Total_time - 105.128 - - - - -
</pre></div>
</div>
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<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index f6813397ee..c07cd522cb 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -465,8 +465,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 15.6MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 24.5MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 17.2MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 27.3MB/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 +594,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
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<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 5c3cd83e8a..77d76fd43e 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/tmpr461q9cd/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp9f37tpeb/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>
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-/tmp/tmpr461q9cd/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/tmp9f37tpeb/images/target contains 8144 images
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</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 - 40s - loss: 0.2218 - accuracy: 0.9261 - val_loss: 0.1282 - val_accuracy: 0.9558 - 40s/epoch - 122ms/step
+328/328 - 41s - loss: 0.2150 - accuracy: 0.9256 - val_loss: 0.1165 - val_accuracy: 0.9603 - 41s/epoch - 125ms/step
Epoch 2/3
-328/328 - 35s - loss: 0.1027 - accuracy: 0.9628 - val_loss: 0.0976 - val_accuracy: 0.9600 - 35s/epoch - 106ms/step
+328/328 - 35s - loss: 0.0956 - accuracy: 0.9645 - val_loss: 0.1144 - val_accuracy: 0.9585 - 35s/epoch - 108ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0623 - accuracy: 0.9764 - val_loss: 0.0992 - val_accuracy: 0.9687 - 35s/epoch - 106ms/step
+328/328 - 35s - loss: 0.0656 - accuracy: 0.9736 - val_loss: 0.1062 - val_accuracy: 0.9671 - 35s/epoch - 107ms/step
-<keras.callbacks.History object at 0x7fe48c8a2e80>
+<keras.callbacks.History object at 0x7f24648816d0>
</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>
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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 c328830069..c2b241d556 100644
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+++ 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>08:01.660</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
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<table class="docutils align-default">
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 124381c230..e717796643 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>
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@@ -359,15 +359,15 @@
</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>00:34.062</p></td>
+<td><p>00:34.297</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>00:03.086</p></td>
+<td><p>00:03.079</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>
-<td><p>00:01.869</p></td>
+<td><p>00:01.872</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 74712c396b..4a3b992979 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 0x7fe32c59c1f0>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f2341952e50>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index d20323ffaa..d8032172da 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>00:08.614</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:06.058</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -359,27 +359,27 @@
</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>00:05.549</p></td>
+<td><p>00:03.230</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>00:01.361</p></td>
+<td><p>00:01.134</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="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>00:00.731</p></td>
+<td><p>00:00.726</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>00:00.722</p></td>
+<td><p>00:00.719</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
-<td><p>00:00.114</p></td>
+<td><p>00:00.113</p></td>
<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>00:00.058</p></td>
+<td><p>00:00.057</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/objects.inv b/docs/objects.inv
index bfa89c3368..bc370ef7d3 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/reference/api/doxygen/annotated.html b/docs/reference/api/doxygen/annotated.html
index de3f4fc662..cbb6cd4827 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -848,107 +848,107 @@ $(function() {
<tr id="row_1_10_0_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfo.html" target="_self">AllocatedPoolInfo</a></td><td class="desc"></td></tr>
<tr id="row_1_10_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html" target="_self">ExprDeepEqual</a></td><td class="desc">Compare two expressions recursively and check if they are equal to each other without var remapping </td></tr>
<tr id="row_1_10_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1MemCpyDetails.html" target="_self">MemCpyDetails</a></td><td class="desc">Helper struct for return value of IdentifyMemCpy </td></tr>
-<tr id="row_1_10_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" target="_self">StmtSRefNode</a></td><td class="desc">An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref" </td></tr>
-<tr id="row_1_10_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref".">StmtSRefNode</a> [...]
-<tr id="row_1_10_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" target="_self">DependencyNode</a></td><td class="desc">A tuple (src, dst, kind) representing certain types of dependency. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, (A, B, kRAW) means [...]
-<tr id="row_1_10_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A,...">DependencyNode</a> </ [...]
-<tr id="row_1_10_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" target="_self">BlockScopeNode</a></td><td class="desc">An object with 1-to-1 correspondence with each block reference in the sref tree. This data structure is used to track the producer-consumer dependencies between blocks. <a class="el" href=" [...]
-<tr id="row_1_10_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structure ...">Blo [...]
-<tr id="row_1_10_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferNode.html" target="_self">BufferNode</a></td><td class="desc">Node to represent a buffer </td></tr>
-<tr id="row_1_10_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types,...">Buffer</a> is a symbolic n-darray structure. It is [...]
-<tr id="row_1_10_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" target="_self">DataProducerNode</a></td><td class="desc">Base node for data producers </td></tr>
-<tr id="row_1_10_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base node for data producers.">DataProducerNode</a> </td></tr>
-<tr id="row_1_10_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html" target="_self">LayoutAxis</a></td><td class="desc"></td></tr>
-<tr id="row_1_10_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" target="_self">LayoutNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode.">Layout</a> is to describe how data is organized within an N-dimention tensor. It is composed of upper c [...]
-<tr id="row_1_10_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">LayoutNode</a> [...]
-<tr id="row_1_10_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html" target="_self">BijectiveLayoutNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_10_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode.">Layout</a>, <a class="el [...]
-<tr id="row_1_10_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataTypeLegalizer.html" target="_self">DataTypeLegalizer</a></td><td class="desc">Legalize the data types of expressions to make sure they are consistent with other parts of the program </td></tr>
-<tr id="row_1_10_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexDataTypeRewriter.html" target="_self">IndexDataTypeRewriter</a></td><td class="desc">Data type rewriter for buffer indices </td></tr>
-<tr id="row_1_10_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexDataTypeNormalizer.html" target="_self">IndexDataTypeNormalizer</a></td><td class="desc">Normalize the data types of buffer shapes and indices to the same data type </td></tr>
-<tr id="row_1_10_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" target="_self">StringImmNode</a></td><td class="desc">String constants, only used in asserts </td></tr>
-<tr id="row_1_10_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts.">StringImmNode</a> </td></tr>
-<tr id="row_1_10_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CastNode.html" target="_self">CastNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Cast.html" title="Managed reference to CastNode.">Cast</a> value from one data type to another </td></tr>
-<tr id="row_1_10_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another.">CastNode</a> </td></tr>
-<tr id="row_1_10_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html" target="_self">BinaryOpNode</a></td><td class="desc">Base template to implement binary ops </td></tr>
-<tr id="row_1_10_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AddNode.html" target="_self">AddNode</a></td><td class="desc"><ul>
+<tr id="row_1_10_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferNode.html" target="_self">BufferNode</a></td><td class="desc">Node to represent a buffer </td></tr>
+<tr id="row_1_10_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types,...">Buffer</a> is a symbolic n-darray structure. It is a [...]
+<tr id="row_1_10_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" target="_self">DataProducerNode</a></td><td class="desc">Base node for data producers </td></tr>
+<tr id="row_1_10_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base node for data producers.">DataProducerNode</a> </td></tr>
+<tr id="row_1_10_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html" target="_self">LayoutAxis</a></td><td class="desc"></td></tr>
+<tr id="row_1_10_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" target="_self">LayoutNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode.">Layout</a> is to describe how data is organized within an N-dimention tensor. It is composed of upper ca [...]
+<tr id="row_1_10_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">LayoutNode</a> [...]
+<tr id="row_1_10_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html" target="_self">BijectiveLayoutNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_10_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode.">Layout</a>, <a class="el [...]
+<tr id="row_1_10_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataTypeLegalizer.html" target="_self">DataTypeLegalizer</a></td><td class="desc">Legalize the data types of expressions to make sure they are consistent with other parts of the program </td></tr>
+<tr id="row_1_10_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexDataTypeRewriter.html" target="_self">IndexDataTypeRewriter</a></td><td class="desc">Data type rewriter for buffer indices </td></tr>
+<tr id="row_1_10_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexDataTypeNormalizer.html" target="_self">IndexDataTypeNormalizer</a></td><td class="desc">Normalize the data types of buffer shapes and indices to the same data type </td></tr>
+<tr id="row_1_10_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" target="_self">StringImmNode</a></td><td class="desc">String constants, only used in asserts </td></tr>
+<tr id="row_1_10_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts.">StringImmNode</a> </td></tr>
+<tr id="row_1_10_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CastNode.html" target="_self">CastNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Cast.html" title="Managed reference to CastNode.">Cast</a> value from one data type to another </td></tr>
+<tr id="row_1_10_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another.">CastNode</a> </td></tr>
+<tr id="row_1_10_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html" target="_self">BinaryOpNode</a></td><td class="desc">Base template to implement binary ops </td></tr>
+<tr id="row_1_10_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AddNode.html" target="_self">AddNode</a></td><td class="desc"><ul>
<li>b </li>
</ul>
</td></tr>
-<tr id="row_1_10_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b">AddNode</a> </td></tr>
-<tr id="row_1_10_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SubNode.html" target="_self">SubNode</a></td><td class="desc"><ul>
+<tr id="row_1_10_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b">AddNode</a> </td></tr>
+<tr id="row_1_10_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SubNode.html" target="_self">SubNode</a></td><td class="desc"><ul>
<li>b </li>
</ul>
</td></tr>
-<tr id="row_1_10_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b">SubNode</a> </td></tr>
-<tr id="row_1_10_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MulNode.html" target="_self">MulNode</a></td><td class="desc"><ul>
+<tr id="row_1_10_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b">SubNode</a> </td></tr>
+<tr id="row_1_10_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MulNode.html" target="_self">MulNode</a></td><td class="desc"><ul>
<li>b </li>
</ul>
</td></tr>
-<tr id="row_1_10_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b">MulNode</a> </td></tr>
-<tr id="row_1_10_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DivNode.html" target="_self">DivNode</a></td><td class="desc">/ b in the C semnatics </td></tr>
-<tr id="row_1_10_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics.">DivNode</a> </td></tr>
-<tr id="row_1_10_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ModNode.html" target="_self">ModNode</a></td><td class="desc">% b in the C semnatics </td></tr>
-<tr id="row_1_10_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics.">ModNode</a> </td></tr>
-<tr id="row_1_10_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" target="_self">FloorDivNode</a></td><td class="desc">Floor division, floor(a/b) </td></tr>
-<tr id="row_1_10_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b)">FloorDivNode</a> </td></tr>
-<tr id="row_1_10_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" target="_self">FloorModNode</a></td><td class="desc">The remainder of the floordiv </td></tr>
-<tr id="row_1_10_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv.">FloorModNode</a> </td></tr>
-<tr id="row_1_10_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MinNode.html" target="_self">MinNode</a></td><td class="desc">Min(a, b) </td></tr>
-<tr id="row_1_10_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b)">MinNode</a> </td></tr>
-<tr id="row_1_10_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MaxNode.html" target="_self">MaxNode</a></td><td class="desc">Max(a, b) </td></tr>
-<tr id="row_1_10_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b)">MaxNode</a> </td></tr>
-<tr id="row_1_10_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html" target="_self">CmpOpNode</a></td><td class="desc">Base template to implement comparison ops </td></tr>
-<tr id="row_1_10_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQNode.html" target="_self">EQNode</a></td><td class="desc">== b </td></tr>
-<tr id="row_1_10_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b">EQNode</a> </td></tr>
-<tr id="row_1_10_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NENode.html" target="_self">NENode</a></td><td class="desc">!= b </td></tr>
-<tr id="row_1_10_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b">NENode</a> </td></tr>
-<tr id="row_1_10_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LTNode.html" target="_self">LTNode</a></td><td class="desc">< b </td></tr>
-<tr id="row_1_10_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b">LTNode</a> </td></tr>
-<tr id="row_1_10_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1LENode.html" target="_self">LENode</a></td><td class="desc"><= b </td></tr>
-<tr id="row_1_10_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b">LENode</a> </td></tr>
-<tr id="row_1_10_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GTNode.html" target="_self">GTNode</a></td><td class="desc">> b </td></tr>
-<tr id="row_1_10_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b">GTNode</a> </td></tr>
-<tr id="row_1_10_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GENode.html" target="_self">GENode</a></td><td class="desc">>= b </td></tr>
-<tr id="row_1_10_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b">GENode</a> </td></tr>
-<tr id="row_1_10_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AndNode.html" target="_self">AndNode</a></td><td class="desc">&& b </td></tr>
-<tr id="row_1_10_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b">AndNode</a> </td></tr>
-<tr id="row_1_10_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1OrNode.html" target="_self">OrNode</a></td><td class="desc">|| b </td></tr>
-<tr id="row_1_10_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b">OrNode</a> </td></tr>
-<tr id="row_1_10_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NotNode.html" target="_self">NotNode</a></td><td class="desc">!a </td></tr>
-<tr id="row_1_10_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a">NotNode</a> </td></tr>
-<tr id="row_1_10_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SelectNode.html" target="_self">SelectNode</a></td><td class="desc">Return true_value if condition is true, otherwise return false_value </td></tr>
-<tr id="row_1_10_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value.">SelectNode</a> </td></tr>
-<tr id="row_1_10_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" target="_self">BufferLoadNode</a></td><td class="desc">Load value from the high dimension buffer </td></tr>
-<tr id="row_1_10_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer.">BufferLoadNode</a> </td></tr>
-<tr id="row_1_10_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" target="_self">ProducerLoadNode</a></td><td class="desc">Load value from the result produced by the producer </td></tr>
-<tr id="row_1_10_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer.">ProducerLoadNode</a> </td></tr>
-<tr id="row_1_10_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1RampNode.html" target="_self">RampNode</a></td><td class="desc">Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to construct a index for a continuous vector load </td></tr>
-<tr id="row_1_10_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride....">RampNode</a> </td></tr>
-<tr id="row_1_10_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" target="_self">BroadcastNode</a></td><td class="desc">Create a vector where all the elements are value </td></tr>
-<tr id="row_1_10_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value.">BroadcastNode</a> </td></tr>
-<tr id="row_1_10_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Let.html" title="Managed reference to LetNode.">Let</a> binding. Bind var to value then evaluate body </td></tr>
-<tr id="row_1_10_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body.">LetNode</a> </td></tr>
-<tr id="row_1_10_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Call.html" title="Managed reference to CallNode.">Call</a> node </td></tr>
-<tr id="row_1_10_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node.">CallNode</a> </td></tr>
-<tr id="row_1_10_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" target="_self">ShuffleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" title="Managed reference to ShuffleNode.">Shuffle</a> instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...) </td></tr>
-<tr id="row_1_10_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ....">ShuffleNode</a> </td></tr>
-<tr id="row_1_10_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" target="_self">CommReducerNode</a></td><td class="desc">A commutative reducer node to represent a commutative binary operator with identity element </td></tr>
-<tr id="row_1_10_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element.">CommReducer [...]
-<tr id="row_1_10_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" target="_self">ReduceNode</a></td><td class="desc">Reduction operator operator </td></tr>
-<tr id="row_1_10_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator.">ReduceNode</a> </td></tr>
-<tr id="row_1_10_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AnyNode.html" target="_self">AnyNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Any.html" title="Managed reference to AnyNode.">Any</a> shape </td></tr>
-<tr id="row_1_10_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape.">AnyNode</a> </td></tr>
-<tr id="row_1_10_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit Function </td></tr>
-<tr id="row_1_10_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor< R(const PrimExpr &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_10_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" title="ExprVisitor.">ExprVisitor</a> </td></tr>
-<tr id="row_1_10_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" title="ExprMutator that mutates expressions.">ExprMutator</a> that mutates expressions </td></tr>
-<tr id="row_1_10_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" target="_self">PrimFuncNode</a></td><td class="desc">Primitive functions that contains TIR statements </td></tr>
-<tr id="row_1_10_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements.">PrimFuncNode</a> </td></tr>
-<tr id="row_1_10_91_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" target="_self">TensorIntrinNode</a></td><td class="desc">Tensor intrinsics for tensorization </td></tr>
-<tr id="row_1_10_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization.">TensorIntrinNode</a> </td></tr>
-<tr id="row_1_10_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html" target="_self">IndexMapNode</a></td><td class="desc">Defines a mapping between two representations of indices into a buffer </td></tr>
-<tr id="row_1_10_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">IndexMap</a></td><td class="desc"></td></tr>
+<tr id="row_1_10_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b">MulNode</a> </td></tr>
+<tr id="row_1_10_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DivNode.html" target="_self">DivNode</a></td><td class="desc">/ b in the C semnatics </td></tr>
+<tr id="row_1_10_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics.">DivNode</a> </td></tr>
+<tr id="row_1_10_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ModNode.html" target="_self">ModNode</a></td><td class="desc">% b in the C semnatics </td></tr>
+<tr id="row_1_10_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics.">ModNode</a> </td></tr>
+<tr id="row_1_10_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" target="_self">FloorDivNode</a></td><td class="desc">Floor division, floor(a/b) </td></tr>
+<tr id="row_1_10_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b)">FloorDivNode</a> </td></tr>
+<tr id="row_1_10_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" target="_self">FloorModNode</a></td><td class="desc">The remainder of the floordiv </td></tr>
+<tr id="row_1_10_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv.">FloorModNode</a> </td></tr>
+<tr id="row_1_10_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MinNode.html" target="_self">MinNode</a></td><td class="desc">Min(a, b) </td></tr>
+<tr id="row_1_10_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b)">MinNode</a> </td></tr>
+<tr id="row_1_10_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MaxNode.html" target="_self">MaxNode</a></td><td class="desc">Max(a, b) </td></tr>
+<tr id="row_1_10_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b)">MaxNode</a> </td></tr>
+<tr id="row_1_10_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html" target="_self">CmpOpNode</a></td><td class="desc">Base template to implement comparison ops </td></tr>
+<tr id="row_1_10_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQNode.html" target="_self">EQNode</a></td><td class="desc">== b </td></tr>
+<tr id="row_1_10_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b">EQNode</a> </td></tr>
+<tr id="row_1_10_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NENode.html" target="_self">NENode</a></td><td class="desc">!= b </td></tr>
+<tr id="row_1_10_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b">NENode</a> </td></tr>
+<tr id="row_1_10_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LTNode.html" target="_self">LTNode</a></td><td class="desc">< b </td></tr>
+<tr id="row_1_10_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b">LTNode</a> </td></tr>
+<tr id="row_1_10_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1LENode.html" target="_self">LENode</a></td><td class="desc"><= b </td></tr>
+<tr id="row_1_10_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b">LENode</a> </td></tr>
+<tr id="row_1_10_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GTNode.html" target="_self">GTNode</a></td><td class="desc">> b </td></tr>
+<tr id="row_1_10_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b">GTNode</a> </td></tr>
+<tr id="row_1_10_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GENode.html" target="_self">GENode</a></td><td class="desc">>= b </td></tr>
+<tr id="row_1_10_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b">GENode</a> </td></tr>
+<tr id="row_1_10_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AndNode.html" target="_self">AndNode</a></td><td class="desc">&& b </td></tr>
+<tr id="row_1_10_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b">AndNode</a> </td></tr>
+<tr id="row_1_10_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1OrNode.html" target="_self">OrNode</a></td><td class="desc">|| b </td></tr>
+<tr id="row_1_10_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b">OrNode</a> </td></tr>
+<tr id="row_1_10_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NotNode.html" target="_self">NotNode</a></td><td class="desc">!a </td></tr>
+<tr id="row_1_10_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a">NotNode</a> </td></tr>
+<tr id="row_1_10_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SelectNode.html" target="_self">SelectNode</a></td><td class="desc">Return true_value if condition is true, otherwise return false_value </td></tr>
+<tr id="row_1_10_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value.">SelectNode</a> </td></tr>
+<tr id="row_1_10_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" target="_self">BufferLoadNode</a></td><td class="desc">Load value from the high dimension buffer </td></tr>
+<tr id="row_1_10_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer.">BufferLoadNode</a> </td></tr>
+<tr id="row_1_10_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" target="_self">ProducerLoadNode</a></td><td class="desc">Load value from the result produced by the producer </td></tr>
+<tr id="row_1_10_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer.">ProducerLoadNode</a> </td></tr>
+<tr id="row_1_10_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1RampNode.html" target="_self">RampNode</a></td><td class="desc">Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to construct a index for a continuous vector load </td></tr>
+<tr id="row_1_10_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride....">RampNode</a> </td></tr>
+<tr id="row_1_10_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" target="_self">BroadcastNode</a></td><td class="desc">Create a vector where all the elements are value </td></tr>
+<tr id="row_1_10_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value.">BroadcastNode</a> </td></tr>
+<tr id="row_1_10_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Let.html" title="Managed reference to LetNode.">Let</a> binding. Bind var to value then evaluate body </td></tr>
+<tr id="row_1_10_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body.">LetNode</a> </td></tr>
+<tr id="row_1_10_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Call.html" title="Managed reference to CallNode.">Call</a> node </td></tr>
+<tr id="row_1_10_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node.">CallNode</a> </td></tr>
+<tr id="row_1_10_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" target="_self">ShuffleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" title="Managed reference to ShuffleNode.">Shuffle</a> instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...) </td></tr>
+<tr id="row_1_10_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ....">ShuffleNode</a> </td></tr>
+<tr id="row_1_10_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" target="_self">CommReducerNode</a></td><td class="desc">A commutative reducer node to represent a commutative binary operator with identity element </td></tr>
+<tr id="row_1_10_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element.">CommReducer [...]
+<tr id="row_1_10_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" target="_self">ReduceNode</a></td><td class="desc">Reduction operator operator </td></tr>
+<tr id="row_1_10_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator.">ReduceNode</a> </td></tr>
+<tr id="row_1_10_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AnyNode.html" target="_self">AnyNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Any.html" title="Managed reference to AnyNode.">Any</a> shape </td></tr>
+<tr id="row_1_10_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape.">AnyNode</a> </td></tr>
+<tr id="row_1_10_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit Function </td></tr>
+<tr id="row_1_10_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor< R(const PrimExpr &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_10_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" title="ExprVisitor.">ExprVisitor</a> </td></tr>
+<tr id="row_1_10_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" title="ExprMutator that mutates expressions.">ExprMutator</a> that mutates expressions </td></tr>
+<tr id="row_1_10_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" target="_self">PrimFuncNode</a></td><td class="desc">Primitive functions that contains TIR statements </td></tr>
+<tr id="row_1_10_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements.">PrimFuncNode</a> </td></tr>
+<tr id="row_1_10_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" target="_self">TensorIntrinNode</a></td><td class="desc">Tensor intrinsics for tensorization </td></tr>
+<tr id="row_1_10_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization.">TensorIntrinNode</a> </td></tr>
+<tr id="row_1_10_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html" target="_self">IndexMapNode</a></td><td class="desc">Defines a mapping between two representations of indices into a buffer </td></tr>
+<tr id="row_1_10_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">IndexMap</a></td><td class="desc"></td></tr>
+<tr id="row_1_10_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" target="_self">StmtSRefNode</a></td><td class="desc">An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref" </td></tr>
+<tr id="row_1_10_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref".">StmtSRefNode</a [...]
+<tr id="row_1_10_91_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" target="_self">DependencyNode</a></td><td class="desc">A tuple (src, dst, kind) representing certain types of dependency. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, (A, B, kRAW) mean [...]
+<tr id="row_1_10_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A,...">DependencyNode</a> < [...]
+<tr id="row_1_10_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" target="_self">BlockScopeNode</a></td><td class="desc">An object with 1-to-1 correspondence with each block reference in the sref tree. This data structure is used to track the producer-consumer dependencies between blocks. <a class="el" href= [...]
+<tr id="row_1_10_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structure ...">Bl [...]
<tr id="row_1_10_95_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" target="_self">InstructionKindNode</a></td><td class="desc">Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of instruction has its own properties, including: 1) A boolean indicating if the instruction is pur [...]
<tr id="row_1_10_96_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html" target="_self">InstructionKind</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" title="Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of instruction [...]
<tr id="row_1_10_97_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionNode.html" target="_self">InstructionNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Schedule.html" title="Managed reference to ScheduleNode.">Schedule</a> instructions each corresponds to a schedule primitive </td></tr>
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<g id="node36" class="node">
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<g id="node56" class="node">
<title>Node56</title>
<g id="a_node56"><a xlink:href="tag_8h.html" target="_top" xlink:title="Target tag registry.">
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<g id="edge117" class="edge">
<title>Node12->Node76</title>
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<g id="node14" class="node">
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<title>Node154->Node81</title>
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<title>Node154->Node83</title>
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<title>Node154->Node90</title>
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<!-- Node154->Node100 -->
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<title>Node154->Node100</title>
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<!-- Node154->Node102 -->
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<title>Node154->Node102</title>
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<!-- Node154->Node109 -->
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<title>Node154->Node109</title>
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<!-- Node155->Node115 -->
<g id="edge380" class="edge">
<title>Node155->Node115</title>
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<!-- Node156->Node154 -->
<g id="edge382" class="edge">
<title>Node156->Node154</title>
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<!-- Node156->Node155 -->
<g id="edge383" class="edge">
<title>Node156->Node155</title>
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<!-- Node158->Node58 -->
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<title>Node158->Node58</title>
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<!-- Node158->Node59 -->
<g id="edge401" class="edge">
<title>Node158->Node59</title>
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<!-- Node158->Node60 -->
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<title>Node158->Node60</title>
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<!-- Node158->Node61 -->
<g id="edge410" class="edge">
<title>Node158->Node61</title>
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<!-- Node158->Node63 -->
<g id="edge393" class="edge">
<title>Node158->Node63</title>
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<!-- Node158->Node67 -->
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<!-- Node158->Node68 -->
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<title>Node158->Node68</title>
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<!-- Node158->Node93 -->
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<!-- Node158->Node94 -->
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<title>Node158->Node94</title>
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<title>Node158->Node125</title>
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<g id="a_node187"><a xlink:href="ir__docsifier__functor_8h.html" target="_top" xlink:title=" ">
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index 2a5e15df71..9b73fa3b67 100644
--- a/docs/reference/api/doxygen/block__scope_8h_source.html
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+<title>tvm: include/tvm/tir/schedule/block_scope.h Source File</title>
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@@ -61,7 +61,7 @@ $(function() {
<div id="nav-path" class="navpath">
<ul>
-<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_b4c7d8e826c599ba55146c099a14beb5.html">tvm</a></li><li class="navelem"><a class="el" href="dir_72c2f11201cd7636dc7624de0754daa5.html">tir</a></li> </ul>
+<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_b4c7d8e826c599ba55146c099a14beb5.html">tvm</a></li><li class="navelem"><a class="el" href="dir_72c2f11201cd7636dc7624de0754daa5.html">tir</a></li><li class="navelem"><a class="el" href="dir_006b1f4ac353a18abb55f74cc4796db6.html">schedule</a></li> </ul>
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@@ -87,8 +87,8 @@ $(function() {
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * specific language governing permissions and limitations</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * under the License.</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#ifndef TVM_TIR_BLOCK_SCOPE_H_</span></div>
-<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#define TVM_TIR_BLOCK_SCOPE_H_</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#ifndef TVM_TIR_SCHEDULE_BLOCK_SCOPE_H_</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#define TVM_TIR_SCHEDULE_BLOCK_SCOPE_H_</span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  </div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <<a class="code" href="stmt_8h.html">tvm/tir/stmt.h</a>></span></div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  </div>
@@ -175,30 +175,31 @@ $(function() {
<div class="line"><a name="l00214"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#ab53bbd3de33008374182579b2a5923e4"> 214</a></span>  std::unordered_map<StmtSRef, Array<Dependency>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a>> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#ab53bbd3de330083741 [...]
<div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#aa7f2ecb9b79e88b9060ba23af522abe7"> 216</a></span>  std::unordered_map<StmtSRef, Array<Dependency>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a>> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#aa7f2ecb9b79e88b906 [...]
<div class="line"><a name="l00218"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#af5aafa70b0f0625c5f6d8556c799ed90"> 218</a></span>  std::unordered_map<Buffer, Array<StmtSRef>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a>, <a class="code" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a>> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#af5aafa70b0f0625c5f6d85 [...]
-<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  </div>
-<div class="line"><a name="l00220"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a460aa1dc8f4f608db1b7f529d51b9d29"> 220</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a460aa1dc8f4f608db1b7f529d51b9d29">VisitAttrs</a>(<a class="code" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a>* v) {}</div>
-<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  </div>
-<div class="line"><a name="l00222"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#aff26a836ffc2147f95487d228632c678"> 222</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#aff26a836ffc2147f95487d228632c678">_type_key</a> = <span class="stringliteral">"tir.BlockScope"</span>;</div>
-<div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#afbf297b8f7bb0aa9a23bfd2706481554"> 223</a></span>  <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#afbf297b8f7bb0aa9a23bfd2706481554">TVM_DECLARE_FINAL_OBJECT_INFO</a>(<a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html">BlockScopeNode</a>, <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>);</div>
-<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  </div>
-<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="comment"> /******** Dependency ********/</span></div>
-<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a9a884e740029ad0976dcc22dca8ed25f"> 232</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Dependency></a> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a9a884e740029ad0976dcc22dca8ed25f">GetDepsBySrc</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1StmtSRef.html">StmtSRef</a>&am [...]
-<div class="line"><a name="l00238"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a312bcbd592ee4f234f82cce920af7fab"> 238</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Dependency></a> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a312bcbd592ee4f234f82cce920af7fab">GetDepsByDst</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1StmtSRef.html">StmtSRef</a>&am [...]
-<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> };</div>
-<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  </div>
-<div class="line"><a name="l00245"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html"> 245</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> {</div>
-<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#a6178e756a3b76b6bb8d124f58846da72"> 248</a></span>  TVM_DLL <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#a6178e756a3b76b6bb8d124f58846da72">BlockScope</a>();</div>
-<div class="line"><a name="l00255"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#a6f82fccdfd633aafbdf9604c6d31c62e"> 255</a></span>  TVM_DLL <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#a6f82fccdfd633aafbdf9604c6d31c62e">BlockScope</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<StmtSRef></a>& child_block_srefs);</div>
-<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  </div>
-<div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#aa01b50d8de4bd42abacf55d33f4fca25"> 257</a></span>  <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#aa01b50d8de4bd42abacf55d33f4fca25">TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS</a>(<a class="code" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a>, <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a class="code" href [...]
-<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> };</div>
-<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  </div>
-<div class="line"><a name="l00260"></a><span class="lineno"> 260</span> } <span class="comment">// namespace tir</span></div>
-<div class="line"><a name="l00261"></a><span class="lineno"> 261</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  </div>
-<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TIR_BLOCK_SCOPE_H_</span></div>
+<div class="line"><a name="l00228"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#ace2ff75b4be37feeccbc5e12b1c8d30e"> 228</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#ace2ff75b4be37feeccbc5e12b1c8d30e">stage_pipeline</a>{<span class="keyword">false</span>};</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  </div>
+<div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a460aa1dc8f4f608db1b7f529d51b9d29"> 230</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a460aa1dc8f4f608db1b7f529d51b9d29">VisitAttrs</a>(<a class="code" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a>* v) {}</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  </div>
+<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#aff26a836ffc2147f95487d228632c678"> 232</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#aff26a836ffc2147f95487d228632c678">_type_key</a> = <span class="stringliteral">"tir.BlockScope"</span>;</div>
+<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#afbf297b8f7bb0aa9a23bfd2706481554"> 233</a></span>  <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#afbf297b8f7bb0aa9a23bfd2706481554">TVM_DECLARE_FINAL_OBJECT_INFO</a>(<a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html">BlockScopeNode</a>, <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>);</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  </div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment"> /******** Dependency ********/</span></div>
+<div class="line"><a name="l00242"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a9a884e740029ad0976dcc22dca8ed25f"> 242</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Dependency></a> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a9a884e740029ad0976dcc22dca8ed25f">GetDepsBySrc</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1StmtSRef.html">StmtSRef</a>&am [...]
+<div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScopeNode.html#a312bcbd592ee4f234f82cce920af7fab"> 248</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<Dependency></a> <a class="code" href="classtvm_1_1tir_1_1BlockScopeNode.html#a312bcbd592ee4f234f82cce920af7fab">GetDepsByDst</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tir_1_1StmtSRef.html">StmtSRef</a>&am [...]
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span> };</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  </div>
+<div class="line"><a name="l00255"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html"> 255</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> {</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00258"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#a6178e756a3b76b6bb8d124f58846da72"> 258</a></span>  TVM_DLL <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#a6178e756a3b76b6bb8d124f58846da72">BlockScope</a>();</div>
+<div class="line"><a name="l00265"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#a6f82fccdfd633aafbdf9604c6d31c62e"> 265</a></span>  TVM_DLL <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#a6f82fccdfd633aafbdf9604c6d31c62e">BlockScope</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Array.html">Array<StmtSRef></a>& child_block_srefs);</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  </div>
+<div class="line"><a name="l00267"></a><span class="lineno"><a class="line" href="classtvm_1_1tir_1_1BlockScope.html#aa01b50d8de4bd42abacf55d33f4fca25"> 267</a></span>  <a class="code" href="classtvm_1_1tir_1_1BlockScope.html#aa01b50d8de4bd42abacf55d33f4fca25">TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS</a>(<a class="code" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a>, <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a class="code" href [...]
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span> };</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  </div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> } <span class="comment">// namespace tir</span></div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  </div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TIR_SCHEDULE_BLOCK_SCOPE_H_</span></div>
<div class="ttc" id="aclasstvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs.</div><div class="ttdef"><b>Definition:</b> array.h:289</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference.</div><div class="ttdef"><b>Definition:</b> object.h:515</div></div>
@@ -207,14 +208,15 @@ $(function() {
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<div class="ttc" id="aclasstvm_1_1tir_1_1BlockScopeNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1BlockScopeNode.html">tvm::tir::BlockScopeNode</a></div><div class="ttdoc">An object with 1-to-1 correspondence with each block reference in the sref tree. This data structure ...</div><div class="ttdef"><b>Definition:</b> block_scope.h:207</div></div>
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