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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/05/31 07:23:14 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@7131411f0a212cbcf8c16547e4b633001cba469e)
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 0163c5815a deploying docs (apache/tvm@7131411f0a212cbcf8c16547e4b633001cba469e)
0163c5815a is described below
commit 0163c5815a381a97210920e363d3d037c27bb4fd
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed May 31 07:23:03 2023 +0000
deploying docs (apache/tvm@7131411f0a212cbcf8c16547e4b633001cba469e)
---
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563 files changed, 156205 insertions(+), 149799 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 b59692e73e..904d9e9e12 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 33.519 seconds)
+ **Total running time of the script:** ( 1 minutes 31.214 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 4f8c1a3f02..b82fc72198 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.zipb22235ae-245f-4adc-965e-7d4358b48136 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipd2845028-9d8e-472d-8dd7-cdf1ab9efc6f 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 7d9b93e0ac..850a39ef7f 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -121,7 +121,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:01, 26.9MB/s]
21%|##1 | 8.90M/41.5M [00:00<00:01, 24.3MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 29.0MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 36.4MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 44.2MB/s]
92%|#########2| 38.3M/41.5M [00:01<00:00, 41.0MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 37.8MB/s]
+
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:00, 53.9MB/s]
28%|##7 | 11.5M/41.5M [00:00<00:00, 51.0MB/s]
39%|###9 | 16.3M/41.5M [00:00<00:00, 49.4MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 45.3MB/s]
79%|#######9 | 32.8M/41.5M [00:00<00:00, 58.5MB/s]
93%|#########3| 38.8M/41.5M [00:00<00:00, 55.7MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 52.7MB/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 3f65d1b24b..3000a2731f 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.593 seconds)
+ **Total running time of the script:** ( 1 minutes 1.131 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index de870ee227..33c31db920 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
0%| | 0.00/44.7M [00:00<?, ?B/s]
14%|#4 | 6.30M/44.7M [00:00<00:00, 54.8MB/s]
26%|##5 | 11.5M/44.7M [00:00<00:00, 44.4MB/s]
35%|###5 | 15.8M/44.7M [00:00<00:00, 43.0MB/s]
45%|####4 | 20.0M/44.7M [00:00<00:00, 41.6MB/s]
54%|#####3 | 24.0M/44.7M [00:00<00:00, 41.3MB/s]
70%|######9 | 31.1M/44.7M [00:00<00:00, 51.9MB/s]
81%|########1 | 36.2M/44.7M [00:00<00:00, 49.2MB/s]
92%|#########1| 40.9M/44.7M [00:01<00:00, 34.1MB/s]
100%|##########| 44.7M/44.7M [00:01<00:00, 43.6MB/s]
+
0%| | 0.00/44.7M [00:00<?, ?B/s]
18%|#7 | 7.99M/44.7M [00:00<00:00, 68.6MB/s]
36%|###5 | 16.0M/44.7M [00:00<00:00, 66.0MB/s]
54%|#####3 | 24.0M/44.7M [00:00<00:00, 71.8MB/s]
69%|######9 | 30.9M/44.7M [00:00<00:00, 67.8MB/s]
84%|########3 | 37.4M/44.7M [00:00<00:00, 63.8MB/s]
98%|#########7| 43.6M/44.7M [00:00<00:00, 52.8MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 60.8MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index c47b06c65b..36214e3b42 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 36.733 seconds)
+ **Total running time of the script:** ( 1 minutes 29.651 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 ca6ed863ad..9844d703fc 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**07:17.494** total execution time for **how_to_compile_models** files:
+**06:56.714** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:36.733 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:31.214 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:33.519 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:29.651 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:03.593 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:01.131 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:41.747 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:39.201 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.427 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:35.842 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:32.690 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:32.398 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.697 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.332 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.148 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.921 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.087 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.250 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.852 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.775 | 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 e01022c9ad..9ba5f63005 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
@@ -658,7 +658,7 @@ well as provides information about the model's performance
.. code-block:: none
- /workspace/python/tvm/runtime/ndarray.py:197: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
+ /workspace/python/tvm/runtime/ndarray.py:194: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
warnings.warn(
Top-1 id: 281, class name: tabby, tabby cat
@@ -673,8 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4223.3371 4222.8133 4227.2031 4220.7903 1.8714
-
+ 4222.9305 4221.7763 4228.6889 4219.1727 2.9875
@@ -682,7 +681,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.269 seconds)
+ **Total running time of the script:** ( 1 minutes 19.280 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 17cee83d7c..874e1460f3 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
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102967424/102967424 [==============================] - 3s 0us/step
+
8192/102967424 [..............................] - ETA: 0s
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] - ETA: 0s
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83877888/102967424 [=======================>......] - ETA: 0s
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98910208/102967424 [===========================>..] - ETA: 0s
102850560/102967424 [============================>.] - ETA: 0s
102967424/102967
424 [==============================] - 2s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index cc8cb80b67..6e492a4440 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
@@ -437,8 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.3276 16.3849 17.0408 15.5463 0.3819
-
+ 16.0645 16.1175 16.8762 15.1431 0.5888
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 58a5f18d9b..a6e44edee2 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
0%| | 0.00/170M [00:00<?, ?B/s]
4%|3 | 6.30M/170M [00:00<00:03, 48.6MB/s]
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s]
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+
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]
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 44.326 seconds)
+ **Total running time of the script:** ( 3 minutes 36.156 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 efad465e29..44e9c62bfc 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
-
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100%|##########| 13.6M/13.6M [00:00<00:00, 29.0MB/s]
+
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59%|#####8 | 7.99M/13.6M [00:00<00:00, 52.3MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 62.2MB/s]
@@ -409,8 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.1296 89.1136 92.8921 88.6593 0.4537
-
+ 88.8478 88.7596 92.1542 88.3577 0.4336
@@ -458,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.736 seconds)
+ **Total running time of the script:** ( 1 minutes 24.464 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 cfed7cbd6a..98d3c57723 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
@@ -423,8 +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)
- 109.1758 109.1228 111.5076 108.7437 0.3226
-
+ 109.1403 109.5202 110.9699 106.4726 1.1656
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 2e4242d75a..a2f586d708 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 18.952 seconds)
+ **Total running time of the script:** ( 2 minutes 11.564 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 97efe53418..a95cc1928f 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**12:20.825** total execution time for **how_to_deploy_models** files:
+**11:56.566** 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``) | 03:44.326 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:36.156 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:18.952 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:11.564 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:26.736 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:24.464 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.269 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.280 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.468 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:51.206 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:50.269 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:48.747 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:46.317 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.547 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:31.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.868 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.473 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.725 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 8f9c94327d..0681c3c267 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.zip8c007d3f-6ad4-47e1-a55b-d4e6c4e95ce7 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip1c008080-ce24-4fd0-87d3-2f5e7fa80e4b 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 0247af839c..181cca4804 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:57.543** total execution time for **how_to_extend_tvm** files:
+**00:56.671** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:53.643 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:52.831 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.728 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.709 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.164 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.123 | 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 8cc61c1b35..8b6b55b97e 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: 23859us [23859us] (48.63%; 48.63%)
- FoldScaleAxis: 25204us [9us] (51.37%; 51.37%)
- FoldConstant: 25195us [1816us] (51.35%; 99.97%)
- InferType: 23379us [23379us] (47.65%; 92.79%)
+ InferType: 23116us [23116us] (48.46%; 48.46%)
+ FoldScaleAxis: 24585us [7us] (51.54%; 51.54%)
+ FoldConstant: 24578us [1761us] (51.53%; 99.97%)
+ InferType: 22817us [22817us] (47.83%; 92.84%)
@@ -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: 23450us [23450us] (48.04%; 48.04%)
- FoldScaleAxis: 25364us [7us] (51.96%; 51.96%)
- FoldConstant: 25357us [1850us] (51.95%; 99.97%)
- InferType: 23507us [23507us] (48.16%; 92.70%)
+ InferType: 22627us [22627us] (48.71%; 48.71%)
+ FoldScaleAxis: 23829us [5us] (51.29%; 51.29%)
+ FoldConstant: 23824us [1676us] (51.28%; 99.98%)
+ InferType: 22148us [22148us] (47.67%; 92.96%)
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 f3eb12ab42..34e606995f 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: 38.808704 ms
+ Convolution: 43.546623 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 4b6b12a211..2a37e42e88 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: 12.274691 ms
+ conv2d with tensor core: 12.273254 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 f614d2a4b8..1ebc08bab2 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: 0.018137
- Baseline: 3.411660
+ Numpy running time: 0.017734
+ Baseline: 3.422762
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.300348
+ Opt1: 0.292154
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.282434
+ Opt2: 0.273640
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.117147
+ Opt3: 0.114720
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.105713
+ Opt4: 0.105986
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111915
+ Opt5: 0.111362
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.132885
+ Opt6: 0.132735
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 c3dfa38d63..4f013528a4 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.040** total execution time for **how_to_optimize_operators** files:
+**00:33.845** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.888 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.665 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.963 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.938 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.188 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.241 | 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 b83478154c..ae759a92dc 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
=================
-**03:35.328** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:31.301** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:30.177 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:28.263 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:15.381 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:14.541 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.568 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.258 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.223 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:15.824 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.875 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.312 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.105 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.102 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index 77cc0656cb..4cc0ca336b 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: 0.354 ms
+ Execution time of this operator: 0.348 ms
@@ -896,7 +896,7 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ extern "C" __global__ void __launch_bounds__(64) default_function_kernel(float* __restrict__ bias, float* __restrict__ compute, float* __restrict__ data, float* __restrict__ kernel) {
float conv2d_nchw[14];
__shared__ float pad_temp_shared[72];
__shared__ float kernel_shared[3072];
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 92b41b0758..f8b06d00eb 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
@@ -647,8 +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)
- 8.1314 8.1343 8.1352 8.1246 0.0048
-
+ 8.1506 8.1475 8.1607 8.1435 0.0073
@@ -675,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 15.381 seconds)
+ **Total running time of the script:** ( 1 minutes 14.541 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 b59048bbcd..8dcc6d9bac 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
@@ -666,8 +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)
- 755.8817 756.0270 757.4556 754.1624 1.3483
-
+ 761.5844 761.7077 761.9220 761.1234 0.3375
@@ -694,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 30.177 seconds)
+ **Total running time of the script:** ( 1 minutes 28.263 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 31b18b63fb..00d37d1884 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**00:23.554** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.665** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.515 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.629 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.020 | 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 |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
-+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
++--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index a9f64ade28..100d44cb41 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: 0.037162
+ Time cost of this operator: 0.037128
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 83666c8391..71061529bd 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 304.1 98.641 (1, 2, 10, 10, 3) 2 1 [304.1]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.215 1.043 (1, 6, 10, 10) 1 1 [3.215]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.973 0.316 (1, 1, 10, 10, 3) 1 1 [0.973]
- Total_time - 308.288 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.8 98.752 (1, 2, 10, 10, 3) 2 1 [304.8]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.897 0.939 (1, 6, 10, 10) 1 1 [2.897]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.309 (1, 1, 10, 10, 3) 1 1 [0.954]
+ Total_time - 308.651 - - - - -
@@ -428,10 +428,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.7 97.334 (1, 6, 10, 10, 1) 2 1 [100.7]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.78 1.72 (1, 6, 10, 10) 1 1 [1.78]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.946 (1, 1, 10, 10, 3) 1 1 [0.978]
- Total_time - 103.458 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.3 97.356 (1, 6, 10, 10, 1) 2 1 [100.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.754 1.702 (1, 6, 10, 10) 1 1 [1.754]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.97 0.942 (1, 1, 10, 10, 3) 1 1 [0.97]
+ Total_time - 103.024 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.162 seconds)
+ **Total running time of the script:** ( 1 minutes 25.753 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 489123c3f0..318c58f105 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
-
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 17.6MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 27.4MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 15.4MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 24.1MB/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 33.015 seconds)
+ **Total running time of the script:** ( 1 minutes 25.962 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 0f8851f773..7614cd674a 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/tmpp9jdv1s2/images/random'
+ '/tmp/tmpwn6bwf1b/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpp9jdv1s2/images/target contains 8144 images
- /tmp/tmpp9jdv1s2/images/random contains 5000 images
+ /tmp/tmpwn6bwf1b/images/target contains 8144 images
+ /tmp/tmpwn6bwf1b/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: 0.2127 - accuracy: 0.9264 - val_loss: 0.1109 - val_accuracy: 0.9569 - 40s/epoch - 121ms/step
+ 328/328 - 40s - loss: 0.2268 - accuracy: 0.9206 - val_loss: 0.1149 - val_accuracy: 0.9619 - 40s/epoch - 123ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.0970 - accuracy: 0.9641 - val_loss: 0.0892 - val_accuracy: 0.9698 - 35s/epoch - 108ms/step
+ 328/328 - 35s - loss: 0.1016 - accuracy: 0.9633 - val_loss: 0.0887 - val_accuracy: 0.9702 - 35s/epoch - 108ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0638 - accuracy: 0.9768 - val_loss: 0.0970 - val_accuracy: 0.9683 - 35s/epoch - 108ms/step
+ 328/328 - 35s - loss: 0.0654 - accuracy: 0.9759 - val_loss: 0.0884 - val_accuracy: 0.9683 - 35s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7f0e6c8fc400>
+ <keras.callbacks.History object at 0x7fe616947af0>
@@ -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 16.577 seconds)
+ **Total running time of the script:** ( 4 minutes 41.507 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 6b33a5299d..86d91332fa 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
=================
-**07:44.920** total execution time for **how_to_work_with_microtvm** files:
+**08:02.279** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:16.577 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:41.507 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:33.015 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:25.962 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:25.162 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:25.753 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.267 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.769 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.326 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.943 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.573 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.345 | 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 a84f826b75..69aa055208 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
=================
-**00:39.591** total execution time for **how_to_work_with_relay** files:
+**00:38.993** 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``) | 00:34.580 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.021 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.100 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.095 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.905 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.871 | 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 bfcd8a7e3c..9b07ba6705 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
@@ -108,7 +108,7 @@ In the following example, we use :any:`tvm.tir.call_pure_extern` to call
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+ extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = __expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -181,7 +181,7 @@ The following code use te.exp instead, which create an intrinsic call
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+ extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = __expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -217,8 +217,8 @@ The same te.exp can also be used for float64 data types.
.. code-block:: none
- // Function: myexp_kernel0
- __kernel void myexp_kernel0(__global float* restrict B, __global float* restrict A, int n, int stride, int stride_1) {
+ // Function: myexp_kernel
+ __kernel void myexp_kernel(__global float* restrict A, __global float* restrict B, int n, int stride, int stride_1) {
if (((int)get_group_id(0)) < (n >> 6)) {
B[(((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride)] = exp(A[(((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride_1)]);
} else {
@@ -278,7 +278,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f0cfc4538b0>
+ <function my_cuda_math_rule at 0x7fe2155b1e50>
@@ -326,7 +326,7 @@ fast math version :code:`__expf`.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+ extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -415,7 +415,7 @@ The following example add an intrinsic :code:`mylog` to the system.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) mylog_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+ extern "C" __global__ void __launch_bounds__(64) mylog_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = logf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
diff --git a/docs/_sources/how_to/work_with_schedules/reduction.rst.txt b/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
index 5718f6aad8..58eb02018d 100644
--- a/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/reduction.rst.txt
@@ -386,7 +386,7 @@ columns by threadIdx.x and finally do a cross thread reduction over threadIdx.x
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(512) default_function_kernel0(float* __restrict__ A, float* __restrict__ B, int m, int n, int stride, int stride_1, int stride_2) {
+ extern "C" __global__ void __launch_bounds__(512) default_function_kernel(float* __restrict__ A, float* __restrict__ B, int m, int n, int stride, int stride_1, int stride_2) {
float B_rf[1];
float red_buf0[1];
B_rf[0] = 0.000000e+00f;
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 10cd6719a5..51258cbd49 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:08.850** total execution time for **how_to_work_with_schedules** files:
+**00:06.368** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.640 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.282 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.483 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.402 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.746 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.721 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.726 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.712 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.119 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.112 | 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``) | 00:00.059 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.052 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.051 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.027 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.028 | 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 557e458db2..ee2bc364fa 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
=================
-**00:35.234** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:34.579** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.226 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:34.572 | 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 287a679182..f84087b684 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 38.02s!
+ resnet18_v1 inference graph built in 36.41s!
@@ -414,11 +414,6 @@ and an input test image.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 0.993 seconds)
-
-
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_classification.py:
.. only:: html
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 247152fd67..7ef473e584 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 25.64s!
+ yolov3-tiny inference graph built in 24.95s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.684 seconds)
+ **Total running time of the script:** ( 1 minutes 1.743 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index b05de9ad4e..bf7ae1384a 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**02:03.677** total execution time for **topic_vta_tutorials_frontend** files:
+**02:01.236** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:02.684 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:01.743 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:00.993 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:59.493 | 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 468ae26dee..37cf267f88 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
=================
-**00:03.322** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.310** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.815 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.797 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.507 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.514 | 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 177cc2686b..70db30e9ed 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
=================
-**00:00.858** total execution time for **topic_vta_tutorials** files:
+**00:00.857** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.440 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.447 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.417 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.410 | 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 3acb90cbac..b8d8443194 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 95.879 ms
+ Execution time of this operator: 96.816 ms
@@ -416,7 +416,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
- *E
+
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 58.644 seconds)
+ **Total running time of the script:** ( 1 minutes 32.412 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 113d1d789e..ec249e0be6 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: 9.19/9.19 result: MeasureResult(costs=(0.029224923999999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8213019371032715, timestamp=1685149229.7505598) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
- No: 2 GFLOPS: 0.51/9.19 result: MeasureResult(costs=(0.5279945328,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.705314636230469, timestamp=1685149238.453926) [('tile_y', [-1, 32]), ('tile_x', [-1, 1])],None,5
- No: 3 GFLOPS: 7.00/9.19 result: MeasureResult(costs=(0.038347444999999994,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8495769500732422, timestamp=1685149239.3285646) [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
- No: 4 GFLOPS: 3.40/9.19 result: MeasureResult(costs=(0.0788841794,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5267534255981445, timestamp=1685149240.8451707) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
- No: 5 GFLOPS: 11.25/11.25 result: MeasureResult(costs=(0.023854629600000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.677426815032959, timestamp=1685149241.6605406) [('tile_y', [-1, 4]), ('tile_x', [-1, 256])],None,82
- No: 6 GFLOPS: 13.70/13.70 result: MeasureResult(costs=(0.019594725,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6224403381347656, timestamp=1685149242.2370949) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
- No: 7 GFLOPS: 8.73/13.70 result: MeasureResult(costs=(0.0307319964,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7660579681396484, timestamp=1685149242.9885483) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
- No: 8 GFLOPS: 8.63/13.70 result: MeasureResult(costs=(0.031096800200000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7678494453430176, timestamp=1685149243.7452867) [('tile_y', [-1, 2]), ('tile_x', [-1, 64])],None,61
- No: 9 GFLOPS: 9.85/13.70 result: MeasureResult(costs=(0.0272455742,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7116098403930664, timestamp=1685149244.5665874) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
- No: 10 GFLOPS: 2.80/13.70 result: MeasureResult(costs=(0.0959320128,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7819972038269043, timestamp=1685149246.362564) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+ No: 1 GFLOPS: 14.14/14.14 result: MeasureResult(costs=(0.0189837902,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5748605728149414, timestamp=1685514993.924993) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 2 GFLOPS: 10.10/14.14 result: MeasureResult(costs=(0.0265651964,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6747539043426514, timestamp=1685514994.613086) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
+ No: 3 GFLOPS: 10.11/14.14 result: MeasureResult(costs=(0.0265496432,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6943986415863037, timestamp=1685514995.2973976) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
+ No: 4 GFLOPS: 8.81/14.14 result: MeasureResult(costs=(0.0304803488,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7656834125518799, timestamp=1685514996.057532) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
+ No: 5 GFLOPS: 8.68/14.14 result: MeasureResult(costs=(0.0309238314,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8046708106994629, timestamp=1685514996.9792159) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
+ No: 6 GFLOPS: 9.28/14.14 result: MeasureResult(costs=(0.0289293018,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7526719570159912, timestamp=1685514997.7026296) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
+ No: 7 GFLOPS: 0.51/14.14 result: MeasureResult(costs=(0.5296062902,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.743882417678833, timestamp=1685515006.4335506) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
+ No: 8 GFLOPS: 10.60/14.14 result: MeasureResult(costs=(0.0253185194,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6917047500610352, timestamp=1685515007.10611) [('tile_y', [-1, 32]), ('tile_x', [-1, 256])],None,85
+ No: 9 GFLOPS: 11.44/14.14 result: MeasureResult(costs=(0.0234725386,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6186003684997559, timestamp=1685515007.858741) [('tile_y', [-1, 4]), ('tile_x', [-1, 256])],None,82
+ No: 10 GFLOPS: 13.88/14.14 result: MeasureResult(costs=(0.019338623399999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5683112144470215, timestamp=1685515008.4284744) [('tile_y', [-1, 128]), ('tile_x', [-1, 64])],None,67
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 2fe3917814..b3573ff2bc 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': 491.4186656499999, 'median': 490.9893282499979, 'std': 2.3761424986359656}
+ {'mean': 492.11705968991737, 'median': 492.0835493998311, 'std': 1.9490640396338215}
@@ -582,32 +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: 10.78/ 13.82 GFLOPS | Progress: (4/20) | 11.26 s
[Task 1/25] Current/Best: 18.10/ 23.50 GFLOPS | Progress: (8/20) | 13.42 s
[Task 1/25] Current/Best: 12.79/ 23.50 GFLOPS | Progress: (12/20) | 15.86 s
[Task 1/25] Current/Best: 13.42/ 23.50 GFLOPS | Progress: (16/20) | 20.58 s
[Task 1/25] Current/Best: 8.50/ 23.50 GFLOPS | Progress: (20/20) | 23.26 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 19.16/ 19.16 GFLOPS | Progress: (4/20) | 4.57 s
[Task 2/25] Current/Best: 10.23/ 19.68 GFLOPS | Progress: (8/20) | 6.01 s
[Task 2/25] Current/Best: 7.77/ 19.68 GFLOPS | Progress: (12/20) | 7.53 s
[Task 2/25] Current/Best: 14.31/ 19.68 GFLOPS | Progress: (16/20) | 10.13 s
[Task 2/25] Current/Best: 11.64/ 20.81 GFLOPS | Progress: (20/20) | 11.82 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 8.41/ 22.33 GFLOPS | Progress: (4/20) | 5.43 s
[Task 3/25] Current/Best: 23.03/ 23.03 GFLOPS | Progress: (8/20) | 7.55 s
[Task 3/25] Current/Best: 6.15/ 23.03 GFLOPS | Progress: (12/20) | 9.95 s
[Task 3/25] Current/Best: 8.32/ 23.93 GFLOPS | Progress: (16/20) | 11.97 s
[Task 3/25] Current/Best: 15.34/ 23.93 GFLOPS | Progress: (20/20) | 14.04 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 16.90/ 16.90 GFLOPS | Progress: (4/20) | 4.92 s
[Task 4/25] Current/Best: 10.27/ 16.90 GFLOPS | Progress: (8/20) | 10.46 s
[Task 4/25] Current/Best: 14.84/ 20.95 GFLOPS | Progress: (12/20) | 12.17 s
[Task 4/25] Current/Best: 13.60/ 20.95 GFLOPS | Progress: (16/20) | 14.02 s
[Task 4/25] Current/Best: 19.67/ 20.95 GFLOPS | Progress: (20/20) | 16.75 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 8.66/ 14.63 GFLOPS | Progress: (4/20) | 4.87 s
[Task 5/25] Current/Best: 10.18/ 17.67 GFLOPS | Progress: (8/20) | 6.63 s
[Task 5/25] Current/Best: 14.47/ 17.67 GFLOPS | Progress: (12/20) | 8.77 s
[Task 5/25] Current/Best: 20.01/ 20.01 GFLOPS | Progress: (16/20) | 10.92 s
[Task 5/25] Current/Best: 7.53/ 20.01 GFLOPS | Progress: (20/20) | 12.80 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 5.80/ 14.21 GFLOPS | Progress: (4/20) | 5.65 s
[Task 6/25] Current/Best: 17.65/ 17.65 GFLOPS | Progress: (8/20) | 8.70 s
[Task 6/25] Current/Best: 6.59/ 17.65 GFLOPS | Progress: (12/20) | 12.19 s
[Task 6/25] Current/Best: 20.78/ 20.78 GFLOPS | Progress: (16/20) | 14.87 s
[Task 6/25] Current/Best: 11.25/ 20.78 GFLOPS | Progress: (20/20) | 18.42 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 21.33/ 21.33 GFLOPS | Progress: (4/20) | 6.46 s
[Task 7/25] Current/Best: 19.78/ 21.33 GFLOPS | Progress: (8/20) | 8.67 s
[Task 7/25] Current/Best: 18.12/ 21.33 GFLOPS | Progress: (12/20) | 11.05 s
[Task 7/25] Current/Best: 11.40/ 21.33 GFLOPS | Progress: (16/20) | 13.60 s
[Task 7/25] Current/Best: 9.20/ 21.33 GFLOPS | Progress: (20/20) | 16.89 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 13.22/ 13.22 GFLOPS | Progress: (4/20) | 6.63 s
[Task 8/25] Current/Best: 16.03/ 16.03 GFLOPS | Progress: (8/20) | 13.14 s
[Task 8/25] Current/Best: 2.55/ 19.27 GFLOPS | Progress: (12/20) | 17.43 s
[Task 8/25] Current/Best: 15.43/ 19.27 GFLOPS | Progress: (16/20) | 25.98 s
[Task 8/25] Current/Best: 5.97/ 19.27 GFLOPS | Progress: (20/20) | 29.23 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 9.86/ 18.72 GFLOPS | Progress: (4/20) | 5.84 s
[Task 9/25] Current/Best: 16.22/ 18.72 GFLOPS | Progress: (8/20) | 8.04 s
[Task 9/25] Current/Best: 18.24/ 21.49 GFLOPS | Progress: (12/20) | 10.05 s
[Task 9/25] Current/Best: 11.40/ 21.49 GFLOPS | Progress: (16/20) | 18.07 s
[Task 9/25] Current/Best: 6.92/ 21.49 GFLOPS | Progress: (20/20) | 29.46 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 13.84/ 20.58 GFLOPS | Progress: (4/20) | 5.78 s Done.
-
[Task 10/25] Current/Best: 4.20/ 20.58 GFLOPS | Progress: (8/20) | 8.04 s
[Task 10/25] Current/Best: 12.23/ 20.58 GFLOPS | Progress: (12/20) | 10.03 s
[Task 10/25] Current/Best: 16.30/ 20.58 GFLOPS | Progress: (16/20) | 11.66 s
[Task 10/25] Current/Best: 3.00/ 20.58 GFLOPS | Progress: (20/20) | 13.90 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 20.96/ 20.96 GFLOPS | Progress: (4/20) | 5.25 s
[Task 11/25] Current/Best: 11.56/ 20.96 GFLOPS | Progress: (8/20) | 8.15 s
[Task 11/25] Current/Best: 12.58/ 20.96 GFLOPS | Progress: (12/20) | 10.51 s
[Task 11/25] Current/Best: 12.74/ 20.96 GFLOPS | Progress: (16/20) | 12.84 s
[Task 11/25] Current/Best: 8.47/ 22.26 GFLOPS | Progress: (20/20) | 15.28 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 10.53/ 11.10 GFLOPS | Progress: (4/20) | 8.95 s
[Task 12/25] Current/Best: 15.88/ 15.88 GFLOPS | Progress: (8/20) | 13.19 s
[Task 12/25] Current/Best: 4.07/ 15.88 GFLOPS | Progress: (12/20) | 18.75 s
[Task 12/25] Current/Best: 12.21/ 15.88 GFLOPS | Progress: (16/20) | 22.76 s
[Task 12/25] Current/Best: 14.71/ 15.88 GFLOPS | Progress: (20/20) | 29.00 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 13.99/ 20.14 GFLOPS | Progress: (4/20) | 6.61 s
[Task 13/25] Current/Best: 19.12/ 20.14 GFLOPS | Progress: (8/20) | 9.95 s
[Task 13/25] Current/Best: 11.03/ 20.14 GFLOPS | Progress: (12/20) | 14.17 s
[Task 13/25] Current/Best: 9.43/ 20.14 GFLOPS | Progress: (16/20) | 16.73 s
[Task 13/25] Current/Best: 13.09/ 20.14 GFLOPS | Progress: (20/20) | 19.90 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 9.15/ 13.25 GFLOPS | Progress: (4/20) | 10.25 s
[Task 14/25] Current/Best: 18.75/ 18.75 GFLOPS | Progress: (8/20) | 14.25 s
[Task 14/25] Current/Best: 18.37/ 18.75 GFLOPS | Progress: (12/20) | 17.52 s
[Task 14/25] Current/Best: 12.92/ 19.37 GFLOPS | Progress: (16/20) | 19.55 s
[Task 14/25] Current/Best: 19.67/ 19.67 GFLOPS | Progress: (20/20) | 27.20 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 19.47/ 19.97 GFLOPS | Progress: (4/20) | 8.10 s
[Task 15/25] Current/Best: 11.02/ 19.97 GFLOPS | Progress: (8/20) | 13.69 s
[Task 15/25] Current/Best: 17.86/ 19.97 GFLOPS | Progress: (12/20) | 17.28 s
[Task 15/25] Current/Best: 13.54/ 19.97 GFLOPS | Progress: (16/20) | 28.05 s
[Task 15/25] Current/Best: 11.58/ 20.72 GFLOPS | Progress: (20/20) | 33.68 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 11.81/ 14.83 GFLOPS | Progress: (4/20) | 6.00 s
[Task 16/25] Current/Best: 6.50/ 17.66 GFLOPS | Progress: (8/20) | 9.85 s
[Task 16/25] Current/Best: 9.64/ 17.66 GFLOPS | Progress: (12/20) | 12.52 s
[Task 16/25] Current/Best: 15.41/ 17.66 GFLOPS | Progress: (16/20) | 15.04 s
[Task 16/25] Current/Best: 11.13/ 17.66 GFLOPS | Progress: (20/20) | 17.52 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 5.95/ 22.41 GFLOPS | Progress: (4/20) | 5.64 s
[Task 17/25] Current/Best: 9.85/ 22.41 GFLOPS | Progress: (8/20) | 8.44 s
[Task 17/25] Current/Best: 16.39/ 22.41 GFLOPS | Progress: (12/20) | 10.96 s
[Task 17/25] Current/Best: 7.28/ 22.64 GFLOPS | Progress: (16/20) | 14.08 s
[Task 17/25] Current/Best: 11.94/ 22.64 GFLOPS | Progress: (20/20) | 16.81 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 14.48/ 17.71 GFLOPS | Progress: (4/20) | 6.14 s
[Task 18/25] Current/Best: 10.99/ 18.39 GFLOPS | Progress: (8/20) | 8.25 s
[Task 18/25] Current/Best: 13.74/ 22.08 GFLOPS | Progress: (12/20) | 10.96 s
[Task 18/25] Current/Best: 3.06/ 22.08 GFLOPS | Progress: (16/20) | 15.28 s
[Task 18/25] Current/Best: 9.40/ 22.08 GFLOPS | Progress: (20/20) | 18.59 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 19.16/ 21.77 GFLOPS | Progress: (4/20) | 5.91 s
[Task 19/25] Current/Best: 10.66/ 21.77 GFLOPS | Progress: (8/20) | 8.67 s
[Task 19/25] Current/Best: 5.35/ 21.77 GFLOPS | Progress: (12/20) | 14.62 s
[Task 19/25] Current/Best: 21.01/ 22.41 GFLOPS | Progress: (16/20) | 18.27 s
[Task 19/25] Current/Best: 11.04/ 22.41 GFLOPS | Progress: (20/20) | 23.91 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 18.47/ 18.47 GFLOPS | Progress: (4/20) | 5.52 s
[Task 20/25] Current/Best: 2.68/ 19.70 GFLOPS | Progress: (8/20) | 8.16 s
[Task 20/25] Current/Best: 12.58/ 19.70 GFLOPS | Progress: (12/20) | 13.83 s
[Task 20/25] Current/Best: 4.51/ 19.70 GFLOPS | Progress: (16/20) | 17.10 s
[Task 20/25] Current/Best: 9.27/ 19.70 GFLOPS | Progress: (20/20) | 23.82 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 17.81/ 19.01 GFLOPS | Progress: (4/20) | 6.80 s
[Task 21/25] Current/Best: 6.37/ 19.01 GFLOPS | Progress: (8/20) | 9.10 s
[Task 21/25] Current/Best: 6.90/ 19.01 GFLOPS | Progress: (12/20) | 12.72 s
[Task 21/25] Current/Best: 10.93/ 19.01 GFLOPS | Progress: (16/20) | 14.55 s
[Task 21/25] Current/Best: 18.30/ 19.01 GFLOPS | Progress: (20/20) | 16.14 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 13.62/ 19.04 GFLOPS | Progress: (4/20) | 4.79 s
[Task 22/25] Current/Best: 13.08/ 19.04 GFLOPS | Progress: (8/20) | 7.92 s
[Task 22/25] Current/Best: 9.85/ 19.04 GFLOPS | Progress: (12/20) | 11.66 s
[Task 22/25] Current/Best: 10.95/ 21.44 GFLOPS | Progress: (16/20) | 14.05 s
[Task 22/25] Current/Best: 8.08/ 21.44 GFLOPS | Progress: (20/20) | 16.11 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 2.70/ 20.48 GFLOPS | Progress: (4/20) | 6.39 s
[Task 23/25] Current/Best: 23.22/ 23.22 GFLOPS | Progress: (8/20) | 8.59 s
[Task 23/25] Current/Best: 2.52/ 23.22 GFLOPS | Progress: (12/20) | 13.25 s
[Task 23/25] Current/Best: 5.36/ 23.22 GFLOPS | Progress: (16/20) | 16.71 s
[Task 23/25] Current/Best: 14.44/ 23.22 GFLOPS | Progress: (20/20) | 20.47 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 2.49/ 3.56 GFLOPS | Progress: (4/20) | 13.54 s
[Task 24/25] Current/Best: 3.91/ 8.27 GFLOPS | Progress: (8/20) | 18.43 s
[Task 24/25] Current/Best: 5.37/ 8.27 GFLOPS | Progress: (12/20) | 29.15 s
[Task 24/25] Current/Best: 5.47/ 8.27 GFLOPS | Progress: (16/20) | 41.89 s
[Task 24/25] Current/Best: 3.73/ 8.27 GFLOPS | Progress: (20/20) | 52.25 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 5.47/ 7.59 GFLOPS | Progress: (4/20) | 13.53 s Done.
-
[Task 25/25] Current/Best: 1.55/ 7.59 GFLOPS | Progress: (8/20) | 17.15 s
[Task 25/25] Current/Best: 2.89/ 8.18 GFLOPS | Progress: (12/20) | 18.64 s
[Task 25/25] Current/Best: 7.86/ 9.36 GFLOPS | Progress: (16/20) | 23.32 s
[Task 25/25] Current/Best: 5.53/ 9.36 GFLOPS | Progress: (20/20) | 26.35 s Done.
-
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 13.74/ 13.74 GFLOPS | Progress: (4/20) | 9.67 s
[Task 1/25] Current/Best: 11.73/ 20.33 GFLOPS | Progress: (8/20) | 13.73 s
[Task 1/25] Current/Best: 21.80/ 21.80 GFLOPS | Progress: (12/20) | 17.76 s
[Task 1/25] Current/Best: 3.45/ 21.80 GFLOPS | Progress: (16/20) | 21.94 s
[Task 1/25] Current/Best: 7.09/ 21.80 GFLOPS | Progress: (20/20) | 25.21 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 18.02/ 18.02 GFLOPS | Progress: (4/20) | 4.26 s
[Task 2/25] Current/Best: 15.84/ 18.02 GFLOPS | Progress: (8/20) | 6.02 s
[Task 2/25] Current/Best: 16.28/ 18.02 GFLOPS | Progress: (12/20) | 7.84 s
[Task 2/25] Current/Best: 7.70/ 18.02 GFLOPS | Progress: (16/20) | 9.54 s
[Task 2/25] Current/Best: 13.42/ 18.02 GFLOPS | Progress: (20/20) | 11.17 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 20.71/ 20.71 GFLOPS | Progress: (4/20) | 5.18 s
[Task 3/25] Current/Best: 22.37/ 22.37 GFLOPS | Progress: (8/20) | 7.49 s
[Task 3/25] Current/Best: 20.30/ 22.37 GFLOPS | Progress: (12/20) | 9.71 s
[Task 3/25] Current/Best: 21.07/ 22.37 GFLOPS | Progress: (16/20) | 12.57 s
[Task 3/25] Current/Best: 12.21/ 23.48 GFLOPS | Progress: (20/20) | 14.79 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 14.74/ 14.74 GFLOPS | Progress: (4/20) | 8.72 s
[Task 4/25] Current/Best: 6.59/ 16.23 GFLOPS | Progress: (8/20) | 11.05 s
[Task 4/25] Current/Best: 16.02/ 20.61 GFLOPS | Progress: (12/20) | 13.12 s
[Task 4/25] Current/Best: 6.33/ 20.61 GFLOPS | Progress: (16/20) | 17.32 s
[Task 4/25] Current/Best: 12.87/ 20.61 GFLOPS | Progress: (20/20) | 19.42 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 20.50/ 20.50 GFLOPS | Progress: (4/20) | 4.34 s
[Task 5/25] Current/Best: 9.71/ 20.50 GFLOPS | Progress: (8/20) | 6.49 s
[Task 5/25] Current/Best: 5.92/ 20.50 GFLOPS | Progress: (12/20) | 8.74 s
[Task 5/25] Current/Best: 15.70/ 20.50 GFLOPS | Progress: (16/20) | 10.57 s
[Task 5/25] Current/Best: 19.83/ 20.50 GFLOPS | Progress: (20/20) | 12.67 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 6.61/ 22.41 GFLOPS | Progress: (4/20) | 5.26 s
[Task 6/25] Current/Best: 13.50/ 22.41 GFLOPS | Progress: (8/20) | 8.23 s
[Task 6/25] Current/Best: 12.69/ 22.41 GFLOPS | Progress: (12/20) | 11.30 s
[Task 6/25] Current/Best: 11.99/ 22.41 GFLOPS | Progress: (16/20) | 15.04 s
[Task 6/25] Current/Best: 1.62/ 22.41 GFLOPS | Progress: (20/20) | 18.69 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 21.21/ 21.21 GFLOPS | Progress: (4/20) | 4.84 s
[Task 7/25] Current/Best: 12.33/ 21.21 GFLOPS | Progress: (8/20) | 7.56 s
[Task 7/25] Current/Best: 20.75/ 21.21 GFLOPS | Progress: (12/20) | 10.24 s
[Task 7/25] Current/Best: 6.11/ 21.21 GFLOPS | Progress: (16/20) | 12.52 s
[Task 7/25] Current/Best: 18.79/ 21.21 GFLOPS | Progress: (20/20) | 14.58 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 12.12/ 12.78 GFLOPS | Progress: (4/20) | 5.21 s
[Task 8/25] Current/Best: 13.30/ 13.30 GFLOPS | Progress: (8/20) | 10.02 s
[Task 8/25] Current/Best: 8.37/ 16.06 GFLOPS | Progress: (12/20) | 17.26 s
[Task 8/25] Current/Best: 14.07/ 16.06 GFLOPS | Progress: (16/20) | 23.71 s
[Task 8/25] Current/Best: 6.01/ 16.06 GFLOPS | Progress: (20/20) | 26.44 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 10.62/ 14.01 GFLOPS | Progress: (4/20) | 7.48 s
[Task 9/25] Current/Best: 6.52/ 17.96 GFLOPS | Progress: (8/20) | 11.53 s
[Task 9/25] Current/Best: 15.25/ 17.96 GFLOPS | Progress: (12/20) | 22.34 s
[Task 9/25] Current/Best: 15.43/ 17.96 GFLOPS | Progress: (16/20) | 24.04 s
[Task 9/25] Current/Best: 14.20/ 17.96 GFLOPS | Progress: (20/20) | 35.24 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 17.45/ 17.45 GFLOPS | Progress: (4/20) | 5.92 s Done.
+
[Task 10/25] Current/Best: 5.98/ 17.45 GFLOPS | Progress: (8/20) | 8.81 s
[Task 10/25] Current/Best: 5.93/ 17.45 GFLOPS | Progress: (12/20) | 11.20 s
[Task 10/25] Current/Best: 5.20/ 18.31 GFLOPS | Progress: (16/20) | 15.29 s
[Task 10/25] Current/Best: 4.36/ 18.31 GFLOPS | Progress: (20/20) | 17.74 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 9.24/ 20.03 GFLOPS | Progress: (4/20) | 5.17 s
[Task 11/25] Current/Best: 21.20/ 21.20 GFLOPS | Progress: (8/20) | 7.74 s
[Task 11/25] Current/Best: 16.12/ 21.20 GFLOPS | Progress: (12/20) | 9.91 s
[Task 11/25] Current/Best: 6.21/ 21.20 GFLOPS | Progress: (16/20) | 13.72 s
[Task 11/25] Current/Best: 21.36/ 23.15 GFLOPS | Progress: (20/20) | 15.91 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 13.66/ 15.18 GFLOPS | Progress: (4/20) | 6.90 s
[Task 12/25] Current/Best: 7.89/ 18.93 GFLOPS | Progress: (8/20) | 10.86 s
[Task 12/25] Current/Best: 12.70/ 18.93 GFLOPS | Progress: (12/20) | 13.66 s
[Task 12/25] Current/Best: 14.88/ 18.93 GFLOPS | Progress: (16/20) | 15.80 s
[Task 12/25] Current/Best: 17.45/ 18.93 GFLOPS | Progress: (20/20) | 18.89 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 18.38/ 19.98 GFLOPS | Progress: (4/20) | 5.16 s
[Task 13/25] Current/Best: 18.91/ 21.03 GFLOPS | Progress: (8/20) | 7.91 s
[Task 13/25] Current/Best: 8.60/ 21.03 GFLOPS | Progress: (12/20) | 10.93 s
[Task 13/25] Current/Best: 20.19/ 21.03 GFLOPS | Progress: (16/20) | 13.12 s
[Task 13/25] Current/Best: 19.24/ 21.03 GFLOPS | Progress: (20/20) | 16.11 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 6.76/ 18.63 GFLOPS | Progress: (4/20) | 11.34 s
[Task 14/25] Current/Best: 5.71/ 18.63 GFLOPS | Progress: (8/20) | 13.63 s
[Task 14/25] Current/Best: 9.57/ 18.63 GFLOPS | Progress: (12/20) | 19.07 s
[Task 14/25] Current/Best: 14.82/ 18.63 GFLOPS | Progress: (16/20) | 21.46 s
[Task 14/25] Current/Best: 20.82/ 20.82 GFLOPS | Progress: (20/20) | 23.69 s Done.
+
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 6.82/ 9.74 GFLOPS | Progress: (4/20) | 12.40 s
[Task 15/25] Current/Best: 15.66/ 18.62 GFLOPS | Progress: (8/20) | 16.04 s
[Task 15/25] Current/Best: 6.28/ 19.27 GFLOPS | Progress: (12/20) | 18.16 s
[Task 15/25] Current/Best: 21.51/ 21.51 GFLOPS | Progress: (16/20) | 19.73 s
[Task 15/25] Current/Best: 10.64/ 21.51 GFLOPS | Progress: (20/20) | 26.88 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 5.15/ 13.35 GFLOPS | Progress: (4/20) | 4.62 s
[Task 16/25] Current/Best: 19.29/ 20.44 GFLOPS | Progress: (8/20) | 8.36 s
[Task 16/25] Current/Best: 12.17/ 20.44 GFLOPS | Progress: (12/20) | 11.10 s
[Task 16/25] Current/Best: 13.26/ 20.44 GFLOPS | Progress: (16/20) | 12.86 s
[Task 16/25] Current/Best: 3.68/ 20.44 GFLOPS | Progress: (20/20) | 14.90 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.28/ 19.66 GFLOPS | Progress: (4/20) | 5.40 s
[Task 17/25] Current/Best: 16.00/ 20.54 GFLOPS | Progress: (8/20) | 7.75 s
[Task 17/25] Current/Best: 11.05/ 22.08 GFLOPS | Progress: (12/20) | 10.90 s
[Task 17/25] Current/Best: 17.56/ 22.08 GFLOPS | Progress: (16/20) | 13.25 s
[Task 17/25] Current/Best: 21.19/ 22.08 GFLOPS | Progress: (20/20) | 15.60 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 14.04/ 18.93 GFLOPS | Progress: (4/20) | 4.68 s
[Task 18/25] Current/Best: 13.42/ 21.17 GFLOPS | Progress: (8/20) | 10.04 s
[Task 18/25] Current/Best: 12.00/ 21.17 GFLOPS | Progress: (12/20) | 14.00 s
[Task 18/25] Current/Best: 12.98/ 21.17 GFLOPS | Progress: (16/20) | 17.81 s
[Task 18/25] Current/Best: 9.55/ 21.17 GFLOPS | Progress: (20/20) | 21.12 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 20.36/ 20.36 GFLOPS | Progress: (4/20) | 5.24 s
[Task 19/25] Current/Best: 22.21/ 22.21 GFLOPS | Progress: (8/20) | 10.85 s
[Task 19/25] Current/Best: 11.95/ 22.21 GFLOPS | Progress: (12/20) | 16.97 s
[Task 19/25] Current/Best: 18.30/ 22.21 GFLOPS | Progress: (16/20) | 21.10 s
[Task 19/25] Current/Best: 8.08/ 22.21 GFLOPS | Progress: (20/20) | 25.17 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.75/ 9.75 GFLOPS | Progress: (4/20) | 11.79 s
[Task 20/25] Current/Best: 14.90/ 18.14 GFLOPS | Progress: (8/20) | 21.10 s
[Task 20/25] Current/Best: 6.22/ 21.01 GFLOPS | Progress: (12/20) | 32.74 s
[Task 20/25] Current/Best: 18.47/ 21.01 GFLOPS | Progress: (16/20) | 44.53 s
[Task 20/25] Current/Best: 7.56/ 21.01 GFLOPS | Progress: (20/20) | 47.44 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 19.52/ 21.29 GFLOPS | Progress: (4/20) | 4.54 s
[Task 21/25] Current/Best: 18.70/ 21.29 GFLOPS | Progress: (8/20) | 6.85 s
[Task 21/25] Current/Best: 7.00/ 21.29 GFLOPS | Progress: (12/20) | 11.30 s
[Task 21/25] Current/Best: 18.65/ 21.29 GFLOPS | Progress: (16/20) | 14.61 s
[Task 21/25] Current/Best: 16.18/ 21.29 GFLOPS | Progress: (20/
20) | 16.77 s Done.
+
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 11.71/ 12.33 GFLOPS | Progress: (4/20) | 5.08 s
[Task 22/25] Current/Best: 10.64/ 21.39 GFLOPS | Progress: (8/20) | 6.80 s
[Task 22/25] Current/Best: 18.91/ 21.39 GFLOPS | Progress: (12/20) | 9.89 s
[Task 22/25] Current/Best: 22.74/ 22.74 GFLOPS | Progress: (16/20) | 12.36 s
[Task 22/25] Current/Best: 17.01/ 22.74 GFLOPS | Progress: (20/20) | 14.73 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 20.24/ 20.24 GFLOPS | Progress: (4/20) | 5.78 s
[Task 23/25] Current/Best: 23.15/ 23.15 GFLOPS | Progress: (8/20) | 8.40 s
[Task 23/25] Current/Best: 8.65/ 23.15 GFLOPS | Progress: (12/20) | 11.82 s
[Task 23/25] Current/Best: 9.01/ 23.15 GFLOPS | Progress: (16/20) | 14.47 s
[Task 23/25] Current/Best: 21.29/ 23.15 GFLOPS | Progress: (20/20) | 17.40 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 1.85/ 5.49 GFLOPS | Progress: (4/20) | 13.75 s
[Task 24/25] Current/Best: 3.87/ 9.61 GFLOPS | Progress: (8/20) | 21.07 s
[Task 24/25] Current/Best: 5.70/ 9.61 GFLOPS | Progress: (12/20) | 32.11 s
[Task 24/25] Current/Best: 5.84/ 9.63 GFLOPS | Progress: (16/20) | 34.75 s
[Task 24/25] Current/Best: 2.39/ 9.63 GFLOPS | Progress: (20/20) | 42.77 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 8.07/ 9.32 GFLOPS | Progress: (4/20) | 13.59 s
[Task 25/25] Current/Best: 5.32/ 9.32 GFLOPS | Progress: (8/20) | 16.73 s Done.
+ Done.
+
[Task 25/25] Current/Best: 1.54/ 9.32 GFLOPS | Progress: (12/20) | 27.74 s
[Task 25/25] Current/Best: 8.18/ 9.32 GFLOPS | Progress: (16/20) | 38.72 s
[Task 25/25] Current/Best: 8.03/ 9.32 GFLOPS | Progress: (20/20) | 41.34 s
@@ -671,6 +670,12 @@ model using optimized operators to speed up our computations.
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ Done.
+
@@ -761,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 409.5635175599955, 'median': 409.5282799499955, 'std': 1.088006998210618}
- unoptimized: {'mean': 491.4186656499999, 'median': 490.9893282499979, 'std': 2.3761424986359656}
+ optimized: {'mean': 414.9519407800108, 'median': 414.30271370008995, 'std': 2.133074809917747}
+ unoptimized: {'mean': 492.11705968991737, 'median': 492.0835493998311, 'std': 1.9490640396338215}
@@ -785,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 10.379 seconds)
+ **Total running time of the script:** ( 13 minutes 18.663 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 72a3813a3c..46299e5455 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
- 1.199e-07 secs/op
+ 1.14e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 96a5ee75d6..f26ebb1f16 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, 0xc4c2a50)), stage(b, placeholder(b, 0xb8feeb0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.R [...]
+ [stage(a, placeholder(a, 0x1c45eb20)), stage(b, placeholder(b, 0x61d3520)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T. [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index ac2ebd8639..47fc58cfbb 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
=================
-**17:16.239** total execution time for **tutorial** files:
+**16:55.484** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:10.379 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:18.663 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:58.644 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:32.412 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.221 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.301 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:40.345 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:40.504 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:24.113 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:21.598 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.365 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.961 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.977 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.866 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.194 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.179 | 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 0fda2e7e79..3db90b83fa 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,7 +285,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000007
+ Numpy running time: 0.000008
naive: 0.000007
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000039
+ vector: 0.000041
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 6.918149999819434e-06 1.0
- naive 6.8779000000000015e-06 0.9941819706394798
- parallel 7.170700000000001e-06 1.0365054241650091
- vector 3.94294e-05 5.699413860790691
+ numpy 8.297179992950988e-06 1.0
+ naive 7.011e-06 0.8449858874890404
+ parallel 7.2717e-06 0.8764062014055135
+ vector 4.05022e-05 4.881441650586023
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.019208
+ Numpy running time: 0.018463
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.397539
+ none: 3.423047
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.301170
+ blocking: 0.297312
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.277477
+ vectorization: 0.275760
# 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: 0.118050
+ loop permutation: 0.117089
# 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: 0.106599
+ array packing: 0.106695
# 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: 0.110945
+ block caching: 0.111114
# 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: 0.132121
+ parallelization: 0.132381
# 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 3.3975387295 1.0
- blocking 0.3011698601 0.08864353994996896
- vectorization 0.2774773899 0.08167011828025138
- loop permutation 0.1180503128 0.03474583285099826
- array packing 0.10659855879999999 0.031375229919950794
- block caching 0.11094496759999999 0.03265451152526736
- parallelization 0.13212091880000001 0.03888724436099177
+ none 3.4230465381000004 1.0
+ blocking 0.2973123233 0.08685605643709024
+ vectorization 0.2757600586 0.08055983333287185
+ loop permutation 0.11708906159999999 0.03420609690716959
+ array packing 0.1066951038 0.031169632843853268
+ block caching 0.11111383729999999 0.03246051026863191
+ parallelization 0.1323814996 0.03867359036067322
@@ -1596,7 +1596,7 @@ the computation for specific platforms.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 0.221 seconds)
+ **Total running time of the script:** ( 1 minutes 0.301 seconds)
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index 0d2086b2b5..8c9c38222e 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-6198c7fd8a75534d98efd0ef800b36fc4e3dc021
+7131411f0a212cbcf8c16547e4b633001cba469e
diff --git a/docs/genindex.html b/docs/genindex.html
index 8b9f826b52..2c42c052c6 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -472,6 +472,8 @@
<li><a href="reference/api/python/tir.html#tvm.tir.And">And (class in tvm.tir)</a>
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Schedule.annotate">annotate() (tvm.tir.Schedule method)</a>
+</li>
+ <li><a href="reference/api/python/tir.html#tvm.tir.transform.AnnotateDeviceRegions">AnnotateDeviceRegions() (in module tvm.tir.transform)</a>
</li>
<li><a href="reference/api/python/relay/analysis.html#tvm.relay.analysis.AnnotatedRegionSet">AnnotatedRegionSet (class in tvm.relay.analysis)</a>
</li>
@@ -2607,10 +2609,10 @@
<li><a href="reference/api/python/rpc.html#tvm.rpc.RPCSession.load_module">(tvm.rpc.RPCSession method)</a>
</li>
</ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/runtime.html#tvm.runtime.load_param_dict">load_param_dict() (in module tvm.runtime)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/runtime.html#tvm.runtime.load_param_dict_from_file">load_param_dict_from_file() (in module tvm.runtime)</a>
</li>
<li><a href="reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.load_params">load_params() (tvm.contrib.graph_executor.GraphModule method)</a>
@@ -2690,6 +2692,8 @@
<li><a href="reference/api/python/tir.html#tvm.tir.transform.LowerCrossThreadReduction">LowerCrossThreadReduction() (in module tvm.tir.transform)</a>
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.transform.LowerCustomDatatypes">LowerCustomDatatypes() (in module tvm.tir.transform)</a>
+</li>
+ <li><a href="reference/api/python/tir.html#tvm.tir.transform.LowerDeviceKernelLaunch">LowerDeviceKernelLaunch() (in module tvm.tir.transform)</a>
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.transform.LowerDeviceStorageAccessInfo">LowerDeviceStorageAccessInfo() (in module tvm.tir.transform)</a>
</li>
@@ -3861,6 +3865,8 @@
<li><a href="reference/api/python/tir.html#tvm.tir.SeqStmt">SeqStmt (class in tvm.tir)</a>
</li>
<li><a href="reference/api/python/ir.html#tvm.transform.Sequential">Sequential (class in tvm.transform)</a>
+</li>
+ <li><a href="reference/api/python/ir.html#tvm.ir.SequentialSpan">SequentialSpan (class in tvm.ir)</a>
</li>
<li><a href="reference/api/python/auto_scheduler.html#tvm.auto_scheduler.MeasureInput.serialize">serialize() (tvm.auto_scheduler.MeasureInput method)</a>
</li>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index b8d53874dd..20174b4236 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -595,7 +595,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 33.519 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 31.214 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 7a8408cf37..ebf6c168c1 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -449,7 +449,7 @@
<span class="nb">print</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipb22235ae-245f-4adc-965e-7d4358b48136 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipd2845028-9d8e-472d-8dd7-cdf1ab9efc6f 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 8aed8d9cb6..39c2ae566e 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,13 +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
0%| | 0.00/41.5M [00:00<?, ?B/s]
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- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 44.2MB/s]
- 92%|#########2| 38.3M/41.5M [00:01<00:00, 41.0MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 37.8MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 53.9MB/s]
+ 28%|##7 | 11.5M/41.5M [00:00<00:00, 51.0MB/s]
+ 39%|###9 | 16.3M/41.5M [00:00<00:00, 49.4MB/s]
+ 58%|#####7 | 24.0M/41.5M [00:00<00:00, 45.3MB/s]
+ 79%|#######9 | 32.8M/41.5M [00:00<00:00, 58.5MB/s]
+ 93%|#########3| 38.8M/41.5M [00:00<00:00, 55.7MB/s]
+100%|##########| 41.5M/41.5M [00:00<00:00, 52.7MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 907a61ca14..724b83e498 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -494,7 +494,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.593 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.131 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 5742451e07..79028c5382 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,15 +442,13 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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- 81%|########1 | 36.2M/44.7M [00:00<00:00, 49.2MB/s]
- 92%|#########1| 40.9M/44.7M [00:01<00:00, 34.1MB/s]
-100%|##########| 44.7M/44.7M [00:01<00:00, 43.6MB/s]
+ 18%|#7 | 7.99M/44.7M [00:00<00:00, 68.6MB/s]
+ 36%|###5 | 16.0M/44.7M [00:00<00:00, 66.0MB/s]
+ 54%|#####3 | 24.0M/44.7M [00:00<00:00, 71.8MB/s]
+ 69%|######9 | 30.9M/44.7M [00:00<00:00, 67.8MB/s]
+ 84%|########3 | 37.4M/44.7M [00:00<00:00, 63.8MB/s]
+ 98%|#########7| 43.6M/44.7M [00:00<00:00, 52.8MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 60.8MB/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 1538a20b18..c23ce68f24 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 36.733 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.651 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 453068dadf..4f12fc60b8 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>07:17.494</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:56.714</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>01:36.733</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>01:31.214</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:33.519</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>01:29.651</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:03.593</p></td>
+<td><p>01:01.131</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:41.747</p></td>
+<td><p>00:39.201</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:37.427</p></td>
+<td><p>00:35.842</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>00:32.690</p></td>
+<td><p>00:32.398</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>00:29.697</p></td>
+<td><p>00:27.332</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:27.148</p></td>
+<td><p>00:25.921</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>00:12.087</p></td>
+<td><p>00:11.250</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>00:02.852</p></td>
+<td><p>00:02.775</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 1218807ad1..1be902ffa2 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -825,7 +825,7 @@ well as provides information about the model’s performance</p>
<span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.benchmark" title="tvm.contrib.graph_executor.GraphModule.benchmark" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">m</span><span class="o">.</span><span class="n">benchmark</span></a><span class="p">(</span><span class="n">dev</span><span class="p">,</span> <span class="n">number</sp [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/runtime/ndarray.py:197: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/runtime/ndarray.py:194: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
warnings.warn(
Top-1 id: 281, class name: tabby, tabby cat
@@ -840,10 +840,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4223.3371 4222.8133 4227.2031 4220.7903 1.8714
+ 4222.9305 4221.7763 4228.6889 4219.1727 2.9875
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.269 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.280 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 43ee38e9b2..4963aad937 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
@@ -448,33 +448,29 @@ 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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+ 87261184/102967424 [========================>.....] - ETA: 0s
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98910208/102967424 [===========================>..] - ETA: 0s
-100646912/102967424 [============================>.] - ETA: 0s
102850560/102967424 [============================>.] - ETA: 0s
-102967424/102967424 [==============================] - 3s 0us/step
+102967424/102967424 [==============================] - 2s 0us/step
</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 14b2dbf86d..8b50c7a5ce 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -672,7 +672,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.3276 16.3849 17.0408 15.5463 0.3819
+ 16.0645 16.1175 16.8762 15.1431 0.5888
</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 62eb444e92..5c2f367140 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,35 +464,34 @@ 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).
@@ -586,7 +585,7 @@ 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 44.326 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 36.156 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 514e7f7198..4754d0a625 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,9 +505,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
</div>
</div>
@@ -598,7 +597,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.1296 89.1136 92.8921 88.6593 0.4537
+ 88.8478 88.7596 92.1542 88.3577 0.4336
</pre></div>
</div>
<div class="admonition note">
@@ -637,7 +636,7 @@ 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 26.736 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 24.464 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 f41167e6b1..3de13cf240 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)
- 109.1758 109.1228 111.5076 108.7437 0.3226
+ 109.1403 109.5202 110.9699 106.4726 1.1656
</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 4b19e07ff3..77bdcca246 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 18.952 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.564 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 585f3df19b..4fba81f3f6 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>12:20.825</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:56.566</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>03:44.326</p></td>
+<td><p>03:36.156</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>02:18.952</p></td>
+<td><p>02:11.564</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>01:26.736</p></td>
+<td><p>01:24.464</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>01:20.269</p></td>
+<td><p>01:19.280</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>00:52.468</p></td>
+<td><p>00:51.206</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>00:50.269</p></td>
+<td><p>00:48.747</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>00:46.317</p></td>
+<td><p>00:44.547</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>00:31.008</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
+<td><p>00:30.868</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:30.473</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:29.725</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 4fbcd56237..609eb1f2ec 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.zip8c007d3f-6ad4-47e1-a55b-d4e6c4e95ce7 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.zip1c008080-ce24-4fd0-87d3-2f5e7fa80e4b 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 42eeb471de..e855d00616 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:57.543</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:56.671</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>00:53.643</p></td>
+<td><p>00:52.831</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>00:02.728</p></td>
+<td><p>00:02.709</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>00:01.164</p></td>
+<td><p>00:01.123</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 4700f74bc4..0c6c3529a3 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: 23859us [23859us] (48.63%; 48.63%)
-FoldScaleAxis: 25204us [9us] (51.37%; 51.37%)
- FoldConstant: 25195us [1816us] (51.35%; 99.97%)
- InferType: 23379us [23379us] (47.65%; 92.79%)
+InferType: 23116us [23116us] (48.46%; 48.46%)
+FoldScaleAxis: 24585us [7us] (51.54%; 51.54%)
+ FoldConstant: 24578us [1761us] (51.53%; 99.97%)
+ InferType: 22817us [22817us] (47.83%; 92.84%)
</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: 23450us [23450us] (48.04%; 48.04%)
-FoldScaleAxis: 25364us [7us] (51.96%; 51.96%)
- FoldConstant: 25357us [1850us] (51.95%; 99.97%)
- InferType: 23507us [23507us] (48.16%; 92.70%)
+InferType: 22627us [22627us] (48.71%; 48.71%)
+FoldScaleAxis: 23829us [5us] (51.29%; 51.29%)
+ FoldConstant: 23824us [1676us] (51.28%; 99.98%)
+ InferType: 22148us [22148us] (47.67%; 92.96%)
</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 2292e9e804..5190a9fed1 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: 38.808704 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 43.546623 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 86b1b89e56..d91a828aae 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: 12.274691 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.273254 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 bbbf476037..7129d7494e 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: 0.018137
-Baseline: 3.411660
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.017734
+Baseline: 3.422762
</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: 0.300348
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.292154
</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: 0.282434
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.273640
</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: 0.117147
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.114720
</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: 0.105713
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.105986
</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: 0.111915
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111362
</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: 0.132885
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.132735
</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 6be4ff43a3..b31a7545f0 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.040</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:33.845</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>00:30.888</p></td>
+<td><p>00:30.665</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>00:01.963</p></td>
+<td><p>00:01.938</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>00:01.188</p></td>
+<td><p>00:01.241</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 1d779a9b33..bdaf905820 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>03:35.328</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:31.301</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>01:30.177</p></td>
+<td><p>01:28.263</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:15.381</p></td>
+<td><p>01:14.541</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:17.568</p></td>
+<td><p>00:17.258</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:16.223</p></td>
+<td><p>00:15.824</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>
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<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>
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diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index a3e3bf09e3..9b60e2cb42 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: 0.354 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.348 ms
</pre></div>
</div>
</div>
@@ -1133,7 +1133,7 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+extern "C" __global__ void __launch_bounds__(64) default_function_kernel(float* __restrict__ bias, float* __restrict__ compute, float* __restrict__ data, float* __restrict__ kernel) {
float conv2d_nchw[14];
__shared__ float pad_temp_shared[72];
__shared__ float kernel_shared[3072];
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 171e0b5209..11aa08eefa 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)
- 8.1314 8.1343 8.1352 8.1246 0.0048
+ 8.1506 8.1475 8.1607 8.1435 0.0073
</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 15.381 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 14.541 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 0621792dbf..72d3ecd164 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)
- 755.8817 756.0270 757.4556 754.1624 1.3483
+ 761.5844 761.7077 761.9220 761.1234 0.3375
</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 class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.263 seconds)</p>
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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
index ebac5040dc..92c6479e38 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:23.554</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.665</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,22 +359,22 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:23.515</p></td>
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<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
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+<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<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 7ec5f9d4b9..8ccf2a6299 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -620,7 +620,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.037162
+Time cost of this operator: 0.037128
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
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index a25475ab33..a23c8086c3 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -654,10 +654,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.1 98.641 (1, 2, 10, 10, 3) 2 1 [304.1]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.215 1.043 (1, 6, 10, 10) 1 1 [3.215]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.973 0.316 (1, 1, 10, 10, 3) 1 1 [0.973]
-Total_time - 308.288 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.8 98.752 (1, 2, 10, 10, 3) 2 1 [304.8]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.897 0.939 (1, 6, 10, 10) 1 1 [2.897]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.309 (1, 1, 10, 10, 3) 1 1 [0.954]
+Total_time - 308.651 - - - - -
</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 100.7 97.334 (1, 6, 10, 10, 1) 2 1 [100.7]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.78 1.72 (1, 6, 10, 10) 1 1 [1.78]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.946 (1, 1, 10, 10, 3) 1 1 [0.978]
-Total_time - 103.458 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.3 97.356 (1, 6, 10, 10, 1) 2 1 [100.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.754 1.702 (1, 6, 10, 10) 1 1 [1.754]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.97 0.942 (1, 1, 10, 10, 3) 1 1 [0.97]
+Total_time - 103.024 - - - - -
</pre></div>
</div>
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 42197eb7ee..e782623b0b 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, 17.6MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 27.4MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 15.4MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 24.1MB/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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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.962 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
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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 bb3b466740..18b450fa08 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/tmpp9jdv1s2/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpwn6bwf1b/images/random'
</pre></div>
</div>
</div>
@@ -593,8 +593,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpp9jdv1s2/images/target contains 8144 images
-/tmp/tmpp9jdv1s2/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/tmpwn6bwf1b/images/target contains 8144 images
+/tmp/tmpwn6bwf1b/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -706,13 +706,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 40s - loss: 0.2127 - accuracy: 0.9264 - val_loss: 0.1109 - val_accuracy: 0.9569 - 40s/epoch - 121ms/step
+328/328 - 40s - loss: 0.2268 - accuracy: 0.9206 - val_loss: 0.1149 - val_accuracy: 0.9619 - 40s/epoch - 123ms/step
Epoch 2/3
-328/328 - 35s - loss: 0.0970 - accuracy: 0.9641 - val_loss: 0.0892 - val_accuracy: 0.9698 - 35s/epoch - 108ms/step
+328/328 - 35s - loss: 0.1016 - accuracy: 0.9633 - val_loss: 0.0887 - val_accuracy: 0.9702 - 35s/epoch - 108ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0638 - accuracy: 0.9768 - val_loss: 0.0970 - val_accuracy: 0.9683 - 35s/epoch - 108ms/step
+328/328 - 35s - loss: 0.0654 - accuracy: 0.9759 - val_loss: 0.0884 - val_accuracy: 0.9683 - 35s/epoch - 108ms/step
-<keras.callbacks.History object at 0x7f0e6c8fc400>
+<keras.callbacks.History object at 0x7fe616947af0>
</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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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 41.507 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index df24b5ca6e..1a41c7dbb8 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>07:44.920</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>08:02.279</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -359,27 +359,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
-<td><p>04:16.577</p></td>
+<td><p>04:41.507</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_pytorch.html#sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">4. microTVM PyTorch Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_pytorch.py</span></code>)</p></td>
-<td><p>01:33.015</p></td>
+<td><p>01:25.962</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:25.162</p></td>
+<td><p>01:25.753</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:12.267</p></td>
+<td><p>00:11.769</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:09.326</p></td>
+<td><p>00:08.943</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
-<td><p>00:08.573</p></td>
+<td><p>00:08.345</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 921d072487..1ef31995aa 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:39.591</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:38.993</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,15 +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>00:34.580</p></td>
+<td><p>00:34.021</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>00:03.100</p></td>
+<td><p>00:03.095</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>00:01.905</p></td>
+<td><p>00:01.871</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 ee5df27b6d..d8d199c749 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -448,7 +448,7 @@ In the following example, we use <a class="reference internal" href="../../refer
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = __expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -502,7 +502,7 @@ The following code use te.exp instead, which create an intrinsic call
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = __expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -519,8 +519,8 @@ The same te.exp can also be used for float64 data types.</p>
<span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/runtime.html#tvm.runtime.Module.imported_modules" title="tvm.runtime.Module.imported_modules" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-property"><span class="n">fopencl</span><span class="o">.</span><span class="n">imported_modules</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_source</span><spa [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>// Function: myexp_kernel0
-__kernel void myexp_kernel0(__global float* restrict B, __global float* restrict A, int n, int stride, int stride_1) {
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>// Function: myexp_kernel
+__kernel void myexp_kernel(__global float* restrict A, __global float* restrict B, int n, int stride, int stride_1) {
if (((int)get_group_id(0)) < (n >> 6)) {
B[(((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride)] = exp(A[(((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride_1)]);
} else {
@@ -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 0x7f0cfc4538b0>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fe2155b1e50>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
@@ -590,7 +590,7 @@ fast math version <code class="code docutils literal notranslate"><span class="p
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+extern "C" __global__ void __launch_bounds__(64) myexp_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = expf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
@@ -658,7 +658,7 @@ The following example add an intrinsic <code class="code docutils literal notran
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) mylog_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride_1) {
+extern "C" __global__ void __launch_bounds__(64) mylog_kernel(float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride_1) {
if (((int)blockIdx.x) < (n >> 6)) {
B[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride)] = logf(A[(((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride_1)]);
} else {
diff --git a/docs/how_to/work_with_schedules/reduction.html b/docs/how_to/work_with_schedules/reduction.html
index 308d67544c..3f4537734f 100644
--- a/docs/how_to/work_with_schedules/reduction.html
+++ b/docs/how_to/work_with_schedules/reduction.html
@@ -627,7 +627,7 @@ columns by threadIdx.x and finally do a cross thread reduction over threadIdx.x<
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(512) default_function_kernel0(float* __restrict__ A, float* __restrict__ B, int m, int n, int stride, int stride_1, int stride_2) {
+extern "C" __global__ void __launch_bounds__(512) default_function_kernel(float* __restrict__ A, float* __restrict__ B, int m, int n, int stride, int stride_1, int stride_2) {
float B_rf[1];
float red_buf0[1];
B_rf[0] = 0.000000e+00f;
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 c73ed1d192..b071083b51 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>00:08.850</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:06.368</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -359,35 +359,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
-<td><p>00:05.640</p></td>
+<td><p>00:03.282</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>00:01.483</p></td>
+<td><p>00:01.402</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>00:00.746</p></td>
+<td><p>00:00.721</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>00:00.726</p></td>
+<td><p>00:00.712</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>00:00.119</p></td>
+<td><p>00:00.112</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>00:00.059</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>
-<td><p>00:00.052</p></td>
+<td><p>00:00.051</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
-<td><p>00:00.027</p></td>
+<td><p>00:00.028</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/install/nnpack.html b/docs/install/nnpack.html
index affe6b3a11..2ebf51b511 100644
--- a/docs/install/nnpack.html
+++ b/docs/install/nnpack.html
@@ -239,7 +239,17 @@
<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Installing TVM</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="from_source.html">Install from Source</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="from_source.html">Install from Source</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#developers-get-source-from-github">Developers: Get Source from Github</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#build-the-shared-library">Build the Shared Library</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#python-package-installation">Python Package Installation</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="from_source.html#install-contrib-libraries">Install Contrib Libraries</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#enable-c-tests">Enable C++ Tests</a></li>
+</ul>
+</li>
<li class="toctree-l2"><a class="reference internal" href="docker.html">Docker Images</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#conditions">Conditions</a></li>
diff --git a/docs/objects.inv b/docs/objects.inv
index 61630d5214..4a612a63e4 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 d084943e25..cbb6cd4827 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -1103,109 +1103,112 @@ $(function() {
<tr id="row_1_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpNode.html" target="_self">OpNode</a></td><td class="desc">Primitive Op(builtin intrinsics) </td></tr>
<tr id="row_1_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics)">OpNode</a> </td></tr>
<tr id="row_1_83_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpRegEntry.html" target="_self">OpRegEntry</a></td><td class="desc">Helper structure to register operators </td></tr>
-<tr id="row_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNameNode.html" target="_self">SourceNameNode</a></td><td class="desc">The name of a source fragment </td></tr>
-<tr id="row_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">SourceName</a></td><td class="desc">The source name of a file span </td></tr>
-<tr id="row_1_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SpanNode.html" target="_self">SpanNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
-<tr id="row_1_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">Span</a></td><td class="desc"></td></tr>
-<tr id="row_1_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNode.html" target="_self">SourceNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Source.html" target="_self">Source</a></td><td class="desc"></td></tr>
-<tr id="row_1_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceMapNode.html" target="_self">SourceMapNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
-<tr id="row_1_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceMap.html" target="_self">SourceMap</a></td><td class="desc"></td></tr>
-<tr id="row_1_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorTypeNode.html" target="_self">BaseTensorTypeNode</a></td><td class="desc">Base of all Tensor types This container can hold <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode.">TensorType</a> or GenericTensorType </td></tr>
-<tr id="row_1_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType.">BaseTensorTypeNode</a> </td></tr>
-<tr id="row_1_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode.">TensorType</a> have a fixed dimension, data type </td></tr>
-<tr id="row_1_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type.">TensorTypeNode</a> </td></tr>
-<tr id="row_1_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> is the base type of all types </td></tr>
-<tr id="row_1_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types.">TypeNode</a> </td></tr>
-<tr id="row_1_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimTypeNode.html" target="_self">PrimTypeNode</a></td><td class="desc">Primitive data types used in the low-level IR </td></tr>
-<tr id="row_1_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">PrimType</a></td><td class="desc"></td></tr>
-<tr id="row_1_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerTypeNode.html" target="_self">PointerTypeNode</a></td><td class="desc">Low-level raw pointer type </td></tr>
-<tr id="row_1_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">PointerType</a></td><td class="desc"></td></tr>
-<tr id="row_1_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> parameter in functions </td></tr>
-<tr id="row_1_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions.">TypeVarNode</a> </td></tr>
-<tr id="row_1_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
-<tr id="row_1_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases.">GlobalTypeVarNode</a> </td></tr>
-<tr id="row_1_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
-<tr id="row_1_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values.">TupleTypeNode</a> </td></tr>
-<tr id="row_1_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
-<tr id="row_1_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function.">TypeConstraintNode</a> </td></tr>
-<tr id="row_1_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
-<tr id="row_1_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type.">FuncTypeNode</a> </td></tr>
-<tr id="row_1_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
-<tr id="row_1_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference.">IncompleteTypeNode</a> </td></tr>
-<tr id="row_1_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefTypeNode.html" target="_self">RelayRefTypeNode</a></td><td class="desc">Reference <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> High-level Relay IR </td></tr>
-<tr id="row_1_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR.">RelayRefTypeNode</a> </td></tr>
-<tr id="row_1_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
-<tr id="row_1_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
-<tr id="row_1_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions.">TypeMutator</a> that mutates expressions </td></tr>
-<tr id="row_1_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> function application </td></tr>
-<tr id="row_1_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application.">TypeCallNode</a> </td></tr>
-<tr id="row_1_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
-<tr id="row_1_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter.">TypeReporter</a> </td></tr>
-<tr id="row_1_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
-<tr id="row_1_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types.">TypeRelationNode</a> </td></tr>
-<tr id="row_1_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html" target="_self">AttrRegistryMapContainerMap</a></td><td class="desc">Generic attribute map </td></tr>
-<tr id="row_1_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistryMap.html" target="_self">AttrRegistryMap</a></td><td class="desc">Map<Key, ValueType> used to store meta-data </td></tr>
-<tr id="row_1_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
-<tr id="row_1_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor< R(const ObjectRef &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathNode.html" target="_self">ObjectPathNode</a></td><td class="desc">Path to an object from some root object </td></tr>
-<tr id="row_1_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPath.html" target="_self">ObjectPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RootPathNode.html" target="_self">RootPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RootPath.html" target="_self">RootPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttributeAccessPathNode.html" target="_self">AttributeAccessPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttributeAccessPath.html" target="_self">AttributeAccessPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1UnknownAttributeAccessPathNode.html" target="_self">UnknownAttributeAccessPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1UnknownAttributeAccessPath.html" target="_self">UnknownAttributeAccessPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ArrayIndexPathNode.html" target="_self">ArrayIndexPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ArrayIndexPath.html" target="_self">ArrayIndexPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingArrayElementPathNode.html" target="_self">MissingArrayElementPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingArrayElementPath.html" target="_self">MissingArrayElementPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapValuePathNode.html" target="_self">MapValuePathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapValuePath.html" target="_self">MapValuePath</a></td><td class="desc"></td></tr>
-<tr id="row_1_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingMapEntryPathNode.html" target="_self">MissingMapEntryPathNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingMapEntryPath.html" target="_self">MissingMapEntryPath</a></td><td class="desc"></td></tr>
-<tr id="row_1_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrVisitor.html" target="_self">AttrVisitor</a></td><td class="desc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each field </td></tr>
-<tr id="row_1_147_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_147_" class="arrow" onclick="toggleFolder('1_147_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
-<tr id="row_1_147_0_" 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_1ReflectionVTable_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table.">Registry</a> of a reflection table </td></tr>
-<tr id="row_1_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
-<tr id="row_1_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprLegacyPrinter.html" target="_self">ReprLegacyPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
-<tr id="row_1_150_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrinterConfigNode.html" target="_self">PrinterConfigNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrinterConfig.html" target="_self">PrinterConfig</a></td><td class="desc"></td></tr>
-<tr id="row_1_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TVMScriptPrinter.html" target="_self">TVMScriptPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
-<tr id="row_1_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueEqual.html" target="_self">BaseValueEqual</a></td><td class="desc">Equality definition of base value class </td></tr>
-<tr id="row_1_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathPairNode.html" target="_self">ObjectPathPairNode</a></td><td class="desc">Pair of <code><a class="el" href="classtvm_1_1ObjectPath.html">ObjectPath</a></code>s, one for each object being tested for structural equality </td></tr>
-<tr id="row_1_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathPair.html" target="_self">ObjectPathPair</a></td><td class="desc"></td></tr>
-<tr id="row_1_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
-<tr id="row_1_157_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_157_" class="arrow" onclick="toggleFolder('1_157_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer.html" target="_self">SEqualReducer</a></td><td class="desc">A Reducer class to reduce the structural equality result of two objects </td></tr>
-<tr id="row_1_157_0_" 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_1SEqualReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors. </td></tr>
-<tr id="row_1_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualHandlerDefault.html" target="_self">SEqualHandlerDefault</a></td><td class="desc">The default handler for equality testing </td></tr>
-<tr id="row_1_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueHash.html" target="_self">BaseValueHash</a></td><td class="desc">Hash definition of base value classes </td></tr>
-<tr id="row_1_160_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hashing </td></tr>
-<tr id="row_1_161_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_161_" class="arrow" onclick="toggleFolder('1_161_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer.html" target="_self">SHashReducer</a></td><td class="desc">A Reducer class to reduce the structural hash value </td></tr>
-<tr id="row_1_161_0_" 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_1SHashReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors </td></tr>
-<tr id="row_1_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashHandlerDefault.html" target="_self">SHashHandlerDefault</a></td><td class="desc">The default handler for hash key computation </td></tr>
-<tr id="row_1_163_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1NDArrayContainerTrait.html" target="_self">NDArrayContainerTrait</a></td><td class="desc"></td></tr>
-<tr id="row_1_164_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistry.html" target="_self">AttrRegistry</a></td><td class="desc"></td></tr>
-<tr id="row_1_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
-<tr id="row_1_166_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Ce [...]
-<tr id="row_1_167_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig.">CompilationConfig</a></code> </td></tr>
-<tr id="row_1_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_1_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFuncNode.html" target="_self">GenericFuncNode</a></td><td class="desc">Represents a generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_1_170_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagNode.html" target="_self">TargetTagNode</a></td><td class="desc">A target tag </td></tr>
-<tr id="row_1_171_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag.">TargetTagNode</a> </td></tr>
-<tr id="row_1_172_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagRegEntry.html" target="_self">TargetTagRegEntry</a></td><td class="desc"></td></tr>
-<tr id="row_1_173_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
-<tr id="row_1_174_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target.">TargetNode</a> </td></tr>
-<tr id="row_1_175_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfoNode.html" target="_self">MemoryInfoNode</a></td><td class="desc">Memory information of special memory region. Use <a class="el" href="classtvm_1_1MemoryInfo.html" title="Defines memory info.">MemoryInfo</a> as its container type </td></tr>
-<tr id="row_1_176_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
-<tr id="row_1_177_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindAttrMap.html" target="_self">TargetKindAttrMap</a></td><td class="desc">Map<TargetKind, ValueType> used to store meta-information about <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode.">TargetKind</a> </td></tr>
-<tr id="row_1_178_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindNode.html" target="_self">TargetKindNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode.">Target</a> kind, specifies the kind of the target </td></tr>
-<tr id="row_1_179_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target.">TargetKindNode</a> </td></tr>
-<tr id="row_1_180_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindRegEntry.html" target="_self">TargetKindRegEntry</a></td><td class="desc">Helper structure to register <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode.">TargetKind</a> </td></tr>
-<tr id="row_1_181_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDeviceNode.html" target="_self">VirtualDeviceNode</a></td><td class="desc">Describes at compile time the constraints on where data is to be stored at runtime down to the (virtual) device and memory scope level, and how to compile code to compute that data. Used by t [...]
-<tr id="row_1_182_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDevice.html" target="_self">VirtualDevice</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1VirtualDeviceNode.html" title="Describes at compile time the constraints on where data is to be stored at runtime down to the (virtu.. [...]
-<tr id="row_1_183_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDeviceCache.html" target="_self">VirtualDeviceCache</a></td><td class="desc">A cache of <code>VirtualDevices</code>. This can be used: </td></tr>
+<tr id="row_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SIBuilder.html" target="_self">SIBuilder</a></td><td class="desc"><a class="el" href="classtvm_1_1Source.html">Source</a> Information Builder, <a class="el" href="classtvm_1_1SIBuilder.html" title="Source Information Builder, SIBuilder provides helper APIs for filling spans [...]
+<tr id="row_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNameNode.html" target="_self">SourceNameNode</a></td><td class="desc">The name of a source fragment </td></tr>
+<tr id="row_1_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">SourceName</a></td><td class="desc">The source name of a file span </td></tr>
+<tr id="row_1_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SpanNode.html" target="_self">SpanNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
+<tr id="row_1_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">Span</a></td><td class="desc"></td></tr>
+<tr id="row_1_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SequentialSpanNode.html" target="_self">SequentialSpanNode</a></td><td class="desc">Store a list of spans for an expr generated from mulitple source exprs </td></tr>
+<tr id="row_1_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SequentialSpan.html" target="_self">SequentialSpan</a></td><td class="desc">Reference class of <a class="el" href="classtvm_1_1SequentialSpanNode.html" title="Store a list of spans for an expr generated from mulitple source exprs.">SequentialSpanNode</a> </td></tr>
+<tr id="row_1_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNode.html" target="_self">SourceNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Source.html" target="_self">Source</a></td><td class="desc"></td></tr>
+<tr id="row_1_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceMapNode.html" target="_self">SourceMapNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
+<tr id="row_1_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceMap.html" target="_self">SourceMap</a></td><td class="desc"></td></tr>
+<tr id="row_1_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorTypeNode.html" target="_self">BaseTensorTypeNode</a></td><td class="desc">Base of all Tensor types This container can hold <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode.">TensorType</a> or GenericTensorType </td></tr>
+<tr id="row_1_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType.">BaseTensorTypeNode</a> </td></tr>
+<tr id="row_1_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode.">TensorType</a> have a fixed dimension, data type </td></tr>
+<tr id="row_1_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type.">TensorTypeNode</a> </td></tr>
+<tr id="row_1_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> is the base type of all types </td></tr>
+<tr id="row_1_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types.">TypeNode</a> </td></tr>
+<tr id="row_1_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimTypeNode.html" target="_self">PrimTypeNode</a></td><td class="desc">Primitive data types used in the low-level IR </td></tr>
+<tr id="row_1_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">PrimType</a></td><td class="desc"></td></tr>
+<tr id="row_1_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerTypeNode.html" target="_self">PointerTypeNode</a></td><td class="desc">Low-level raw pointer type </td></tr>
+<tr id="row_1_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">PointerType</a></td><td class="desc"></td></tr>
+<tr id="row_1_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> parameter in functions </td></tr>
+<tr id="row_1_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions.">TypeVarNode</a> </td></tr>
+<tr id="row_1_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
+<tr id="row_1_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases.">GlobalTypeVarNode</a> </td></tr>
+<tr id="row_1_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
+<tr id="row_1_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values.">TupleTypeNode</a> </td></tr>
+<tr id="row_1_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
+<tr id="row_1_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function.">TypeConstraintNode</a> </td></tr>
+<tr id="row_1_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
+<tr id="row_1_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type.">FuncTypeNode</a> </td></tr>
+<tr id="row_1_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
+<tr id="row_1_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference.">IncompleteTypeNode</a> </td></tr>
+<tr id="row_1_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefTypeNode.html" target="_self">RelayRefTypeNode</a></td><td class="desc">Reference <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> High-level Relay IR </td></tr>
+<tr id="row_1_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR.">RelayRefTypeNode</a> </td></tr>
+<tr id="row_1_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
+<tr id="row_1_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
+<tr id="row_1_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions.">TypeMutator</a> that mutates expressions </td></tr>
+<tr id="row_1_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a> function application </td></tr>
+<tr id="row_1_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application.">TypeCallNode</a> </td></tr>
+<tr id="row_1_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
+<tr id="row_1_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter.">TypeReporter</a> </td></tr>
+<tr id="row_1_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
+<tr id="row_1_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types.">TypeRelationNode</a> </td></tr>
+<tr id="row_1_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html" target="_self">AttrRegistryMapContainerMap</a></td><td class="desc">Generic attribute map </td></tr>
+<tr id="row_1_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistryMap.html" target="_self">AttrRegistryMap</a></td><td class="desc">Map<Key, ValueType> used to store meta-data </td></tr>
+<tr id="row_1_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
+<tr id="row_1_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor< R(const ObjectRef &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathNode.html" target="_self">ObjectPathNode</a></td><td class="desc">Path to an object from some root object </td></tr>
+<tr id="row_1_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPath.html" target="_self">ObjectPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RootPathNode.html" target="_self">RootPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RootPath.html" target="_self">RootPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttributeAccessPathNode.html" target="_self">AttributeAccessPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttributeAccessPath.html" target="_self">AttributeAccessPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1UnknownAttributeAccessPathNode.html" target="_self">UnknownAttributeAccessPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1UnknownAttributeAccessPath.html" target="_self">UnknownAttributeAccessPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ArrayIndexPathNode.html" target="_self">ArrayIndexPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ArrayIndexPath.html" target="_self">ArrayIndexPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingArrayElementPathNode.html" target="_self">MissingArrayElementPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingArrayElementPath.html" target="_self">MissingArrayElementPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapValuePathNode.html" target="_self">MapValuePathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapValuePath.html" target="_self">MapValuePath</a></td><td class="desc"></td></tr>
+<tr id="row_1_147_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingMapEntryPathNode.html" target="_self">MissingMapEntryPathNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MissingMapEntryPath.html" target="_self">MissingMapEntryPath</a></td><td class="desc"></td></tr>
+<tr id="row_1_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrVisitor.html" target="_self">AttrVisitor</a></td><td class="desc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each field </td></tr>
+<tr id="row_1_150_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_150_" class="arrow" onclick="toggleFolder('1_150_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
+<tr id="row_1_150_0_" 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_1ReflectionVTable_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table.">Registry</a> of a reflection table </td></tr>
+<tr id="row_1_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
+<tr id="row_1_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprLegacyPrinter.html" target="_self">ReprLegacyPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
+<tr id="row_1_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrinterConfigNode.html" target="_self">PrinterConfigNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrinterConfig.html" target="_self">PrinterConfig</a></td><td class="desc"></td></tr>
+<tr id="row_1_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TVMScriptPrinter.html" target="_self">TVMScriptPrinter</a></td><td class="desc">Legacy behavior of <a class="el" href="classtvm_1_1ReprPrinter.html" title="A printer class to print the AST/IR nodes.">ReprPrinter</a> </td></tr>
+<tr id="row_1_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueEqual.html" target="_self">BaseValueEqual</a></td><td class="desc">Equality definition of base value class </td></tr>
+<tr id="row_1_157_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathPairNode.html" target="_self">ObjectPathPairNode</a></td><td class="desc">Pair of <code><a class="el" href="classtvm_1_1ObjectPath.html">ObjectPath</a></code>s, one for each object being tested for structural equality </td></tr>
+<tr id="row_1_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ObjectPathPair.html" target="_self">ObjectPathPair</a></td><td class="desc"></td></tr>
+<tr id="row_1_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
+<tr id="row_1_160_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_160_" class="arrow" onclick="toggleFolder('1_160_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer.html" target="_self">SEqualReducer</a></td><td class="desc">A Reducer class to reduce the structural equality result of two objects </td></tr>
+<tr id="row_1_160_0_" 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_1SEqualReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors. </td></tr>
+<tr id="row_1_161_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualHandlerDefault.html" target="_self">SEqualHandlerDefault</a></td><td class="desc">The default handler for equality testing </td></tr>
+<tr id="row_1_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueHash.html" target="_self">BaseValueHash</a></td><td class="desc">Hash definition of base value classes </td></tr>
+<tr id="row_1_163_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hashing </td></tr>
+<tr id="row_1_164_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_164_" class="arrow" onclick="toggleFolder('1_164_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer.html" target="_self">SHashReducer</a></td><td class="desc">A Reducer class to reduce the structural hash value </td></tr>
+<tr id="row_1_164_0_" 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_1SHashReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors </td></tr>
+<tr id="row_1_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashHandlerDefault.html" target="_self">SHashHandlerDefault</a></td><td class="desc">The default handler for hash key computation </td></tr>
+<tr id="row_1_166_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1NDArrayContainerTrait.html" target="_self">NDArrayContainerTrait</a></td><td class="desc"></td></tr>
+<tr id="row_1_167_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrRegistry.html" target="_self">AttrRegistry</a></td><td class="desc"></td></tr>
+<tr id="row_1_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
+<tr id="row_1_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>VirtualDevices</code> in canonical form needed to compile a Relay module for execution over possibly heterogeneous devices. Ce [...]
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-<path fill="none" stroke="midnightblue" d="M3423.23,-406.44C3329.27,-393.64 3168.72,-371.76 3081.25,-359.84"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3422.84,-409.92 3433.22,-407.8 3423.79,-402.98 3422.84,-409.92"/>
+<!-- Node103->Node40 -->
+<g id="edge175" class="edge">
+<title>Node103->Node40</title>
+<path fill="none" stroke="midnightblue" d="M2125.3,-402.93C2123.18,-402.61 2121.07,-402.3 2119,-402 1967.41,-380.22 1673.92,-444.49 1781,-335 1843.94,-270.64 3315.81,-328.09 3401,-299 3422.96,-291.5 3421.14,-275.78 3443,-268 3598.51,-212.61 4021.2,-252.58 4185,-232 4188.25,-231.59 4191.56,-231.12 4194.91,-230.59"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2124.89,-406.41 2135.31,-404.52 2125.99,-399.5 2124.89,-406.41"/>
</g>
-<!-- Node103->Node43 -->
-<g id="edge188" class="edge">
-<title>Node103->Node43</title>
-<path fill="none" stroke="midnightblue" d="M3484.53,-393.59C3473.21,-375.04 3454.85,-349.65 3432.06,-335 3390.09,-308.03 3334.67,-295.46 3292.5,-289.6"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3481.58,-395.47 3489.66,-402.33 3487.62,-391.93 3481.58,-395.47"/>
+<!-- Node103->Node41 -->
+<g id="edge177" class="edge">
+<title>Node103->Node41</title>
+<path fill="none" stroke="midnightblue" d="M2125.3,-402.91C2123.18,-402.6 2121.07,-402.29 2119,-402 2078.97,-396.39 1784.13,-395.96 1757,-366 1747.75,-355.79 1748.57,-345.89 1757,-335 1909.08,-138.57 1708.48,-310.46 3234,-232 3634.05,-211.42 4114.03,-170.79 4282.72,-156.03"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2124.9,-406.39 2135.32,-404.48 2125.98,-399.48 2124.9,-406.39"/>
</g>
<!-- Node103->Node44 -->
-<g id="edge182" class="edge">
+<g id="edge176" class="edge">
<title>Node103->Node44</title>
-<path fill="none" stroke="midnightblue" d="M3570.75,-402.05C3864.15,-365.83 3941.98,-393.14 4232.06,-366 4247.64,-364.54 4264.43,-362.53 4280.05,-360.47"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3570.16,-398.6 3560.67,-403.31 3571.03,-405.55 3570.16,-398.6"/>
+<path fill="none" stroke="midnightblue" d="M2124.95,-402.55C2123.62,-402.36 2122.31,-402.18 2121,-402 1939.32,-377.21 1891.59,-391.44 1710,-366 1705.53,-365.37 1700.93,-364.66 1696.31,-363.89"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2124.82,-406.07 2135.23,-404.13 2125.87,-399.16 2124.82,-406.07"/>
</g>
<!-- Node103->Node45 -->
-<g id="edge189" class="edge">
+<g id="edge178" class="edge">
<title>Node103->Node45</title>
-<path fill="none" stroke="midnightblue" d="M3570.75,-400.68C3629.19,-387.95 3703.26,-371.34 3717.06,-366 3743.23,-355.87 3745.18,-343.06 3772.06,-335 3991.83,-269.05 4059.68,-329.71 4287.06,-299 4290.22,-298.57 4293.45,-298.09 4296.7,-297.57"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3569.81,-397.3 3560.78,-402.85 3571.3,-404.14 3569.81,-397.3"/>
+<path fill="none" stroke="midnightblue" d="M2124.98,-409.02C2100.77,-406.66 2073.75,-404.12 2049,-402 1995.27,-397.41 1605.97,-401.07 1565,-366 1545.57,-349.37 1543.88,-317.09 1544.72,-298.53"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2124.87,-412.52 2135.16,-410.01 2125.55,-405.56 2124.87,-412.52"/>
</g>
-<!-- Node103->Node46 -->
+<!-- Node104->Node42 -->
<g id="edge183" class="edge">
-<title>Node103->Node46</title>
-<path fill="none" stroke="midnightblue" d="M3570.84,-401.92C3675.5,-384.01 3785.31,-368.56 3851.88,-359.57"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3569.99,-398.51 3560.72,-403.66 3571.17,-405.41 3569.99,-398.51"/>
+<title>Node104->Node42</title>
+<path fill="none" stroke="midnightblue" d="M2525.23,-392.99C2521.42,-383.8 2517.17,-373.58 2513.85,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2522.07,-394.5 2529.14,-402.4 2528.54,-391.82 2522.07,-394.5"/>
</g>
-<!-- Node103->Node47 -->
+<!-- Node104->Node43 -->
<g id="edge190" class="edge">
-<title>Node103->Node47</title>
-<path fill="none" stroke="midnightblue" d="M3564.6,-399.85C3597.21,-391.07 3636.64,-379.34 3671.06,-366 3700.15,-354.72 3705.42,-347.41 3734.06,-335 3765.08,-321.56 3800.96,-308.15 3827.78,-298.53"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3563.47,-396.53 3554.71,-402.48 3565.27,-403.29 3563.47,-396.53"/>
+<title>Node104->Node43</title>
+<path fill="none" stroke="midnightblue" d="M2466.18,-399.93C2422.62,-388.74 2373.34,-374.52 2367,-366 2349.69,-342.73 2379.65,-315.14 2402.57,-298.76"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2465.39,-403.34 2475.95,-402.41 2467.12,-396.55 2465.39,-403.34"/>
</g>
-<!-- Node103->Node48 -->
+<!-- Node104->Node44 -->
<g id="edge184" class="edge">
-<title>Node103->Node48</title>
-<path fill="none" stroke="midnightblue" d="M3523.96,-396.17C3537.11,-386.23 3552.61,-374.51 3564.43,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3521.59,-393.57 3515.72,-402.4 3525.81,-399.16 3521.59,-393.57"/>
+<title>Node104->Node44</title>
+<path fill="none" stroke="midnightblue" d="M2461.17,-412.34C2326.09,-404.57 2030.12,-386.71 1781,-366 1753.08,-363.68 1722.19,-360.68 1696.05,-358"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2461.21,-415.85 2471.4,-412.93 2461.61,-408.86 2461.21,-415.85"/>
</g>
-<!-- Node103->Node49 -->
+<!-- Node104->Node45 -->
<g id="edge191" class="edge">
-<title>Node103->Node49</title>
-<path fill="none" stroke="midnightblue" d="M3570.56,-401.42C3605.84,-392.54 3642.69,-380.26 3653.06,-366 3677.08,-332.97 3626.95,-310.67 3581.31,-297.86"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3569.55,-398.06 3560.67,-403.83 3571.22,-404.86 3569.55,-398.06"/>
+<title>Node104->Node45</title>
+<path fill="none" stroke="midnightblue" d="M2461.44,-410.01C2347.94,-399.66 2139.82,-379.25 2108,-366 2087.56,-357.49 2089.53,-343.29 2069,-335 1986.85,-301.81 1728.25,-289.91 1608.27,-286.1"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2461.18,-413.5 2471.45,-410.92 2461.81,-406.53 2461.18,-413.5"/>
</g>
-<!-- Node103->Node50 -->
+<!-- Node104->Node46 -->
<g id="edge185" class="edge">
-<title>Node103->Node50</title>
-<path fill="none" stroke="midnightblue" d="M3570.83,-402.07C4043.62,-348.34 4167.96,-403.23 4638.06,-366 4653.74,-364.76 4670.56,-362.94 4686.39,-361.01"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3570.2,-398.62 3560.66,-403.23 3571,-405.57 3570.2,-398.62"/>
+<title>Node104->Node46</title>
+<path fill="none" stroke="midnightblue" d="M2608.82,-405.13C2690.3,-392.58 2819.95,-372.61 2895.94,-360.91"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2607.99,-401.72 2598.63,-406.7 2609.05,-408.64 2607.99,-401.72"/>
+</g>
+<!-- Node104->Node47 -->
+<g id="edge192" class="edge">
+<title>Node104->Node47</title>
+<path fill="none" stroke="midnightblue" d="M2466.86,-399.53C2423.28,-385.51 2379.35,-363.33 2405,-335 2434.35,-302.58 2720.88,-290.11 2849.76,-286.12"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2465.83,-402.87 2476.42,-402.48 2467.9,-396.18 2465.83,-402.87"/>
</g>
-<!-- Node103->Node51 -->
+<!-- Node104->Node48 -->
<g id="edge186" class="edge">
-<title>Node103->Node51</title>
-<path fill="none" stroke="midnightblue" d="M3570.59,-401.93C3655.71,-385.87 3679.64,-388.05 3759.06,-366 3797.4,-355.35 3804.04,-342.83 3843.06,-335 3951.78,-313.17 4719.85,-292.14 4954.28,-286.18"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3569.92,-398.5 3560.75,-403.82 3571.23,-405.37 3569.92,-398.5"/>
+<title>Node104->Node48</title>
+<path fill="none" stroke="midnightblue" d="M2461.41,-403.61C2404.47,-393.56 2323.69,-379.16 2253,-366 2248.57,-365.17 2243.99,-364.31 2239.38,-363.44"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2460.94,-407.08 2471.4,-405.37 2462.15,-400.18 2460.94,-407.08"/>
+</g>
+<!-- Node104->Node49 -->
+<g id="edge193" class="edge">
+<title>Node104->Node49</title>
+<path fill="none" stroke="midnightblue" d="M2475.1,-399.64C2440.79,-389.92 2396.94,-377.41 2358,-366 2278.79,-342.79 2186.83,-314.94 2132.97,-298.54"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2474.4,-403.08 2484.97,-402.43 2476.3,-396.34 2474.4,-403.08"/>
</g>
-<!-- Node103->Node52 -->
+<!-- Node104->Node50 -->
<g id="edge187" class="edge">
-<title>Node103->Node52</title>
-<path fill="none" stroke="midnightblue" d="M3570.98,-402.06C4128.53,-339.84 4273.83,-391.47 4830.06,-366 4884.71,-363.5 4946.61,-359.53 4992.41,-356.38"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3570.21,-398.62 3560.66,-403.21 3570.99,-405.58 3570.21,-398.62"/>
+<title>Node104->Node50</title>
+<path fill="none" stroke="midnightblue" d="M2461.48,-411.29C2324.26,-401.41 2034,-379.7 1934,-366 1931.34,-365.64 1928.63,-365.24 1925.89,-364.82"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2461.27,-414.79 2471.49,-412.01 2461.77,-407.8 2461.27,-414.79"/>
</g>
-<!-- Node103->Node53 -->
-<g id="edge192" class="edge">
-<title>Node103->Node53</title>
-<path fill="none" stroke="midnightblue" d="M3423.59,-400.66C3377.49,-390.71 3316.8,-377.61 3263.06,-366 3260.38,-365.42 3257.65,-364.83 3254.88,-364.23"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3422.98,-404.11 3433.49,-402.79 3424.46,-397.26 3422.98,-404.11"/>
+<!-- Node104->Node51 -->
+<g id="edge188" class="edge">
+<title>Node104->Node51</title>
+<path fill="none" stroke="midnightblue" d="M2487.15,-398.39C2452.36,-382.66 2415.06,-358.74 2438,-335 2441.06,-331.84 3539.9,-295.98 3826.37,-286.69"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2485.81,-401.62 2496.37,-402.4 2488.6,-395.2 2485.81,-401.62"/>
</g>
-<!-- Node103->Node54 -->
-<g id="edge193" class="edge">
-<title>Node103->Node54</title>
-<path fill="none" stroke="midnightblue" d="M3570.67,-402.23C3639.77,-394.71 3970.41,-367.45 4106.49,-356.29"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3570.2,-398.76 3560.69,-403.43 3571.03,-405.71 3570.2,-398.76"/>
+<!-- Node104->Node52 -->
+<g id="edge189" class="edge">
+<title>Node104->Node52</title>
+<path fill="none" stroke="midnightblue" d="M2608.79,-412.12C2810.39,-400.15 3368.32,-367.03 3562.4,-355.51"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2608.38,-408.64 2598.6,-412.72 2608.79,-415.63 2608.38,-408.64"/>
</g>
-<!-- Node103->Node55 -->
+<!-- Node104->Node53 -->
<g id="edge194" class="edge">
-<title>Node103->Node55</title>
-<path fill="none" stroke="midnightblue" d="M3451.55,-398.68C3424.93,-388.26 3391.79,-375.28 3367.01,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3450.45,-402.01 3461.04,-402.4 3453,-395.49 3450.45,-402.01"/>
-</g>
-<!-- Node104->Node80 -->
-<g id="edge196" class="edge">
-<title>Node104->Node80</title>
-<path fill="none" stroke="midnightblue" d="M1348.18,-409.59C1311.96,-403.08 1269.48,-390.46 1238.06,-366 1131.61,-283.15 1166.54,-85.43 1178.66,-30.74"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1347.78,-413.07 1358.22,-411.27 1348.94,-406.17 1347.78,-413.07"/>
+<title>Node104->Node53</title>
+<path fill="none" stroke="midnightblue" d="M2597.76,-399.92C2637.9,-389.31 2689.42,-375.7 2727.6,-365.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2596.85,-396.54 2588.08,-402.48 2598.64,-403.31 2596.85,-396.54"/>
</g>
-<!-- Node105->Node79 -->
-<g id="edge199" class="edge">
-<title>Node105->Node79</title>
-<path fill="none" stroke="midnightblue" d="M2369.44,-392.11C2369.52,-375.9 2369.32,-354.16 2368.06,-335 2364.13,-275.15 2363.66,-259.85 2352.06,-201 2349.14,-186.18 2344.12,-169.35 2340.95,-159.34"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2365.93,-392.32 2369.34,-402.35 2372.93,-392.38 2365.93,-392.32"/>
+<!-- Node104->Node54 -->
+<g id="edge195" class="edge">
+<title>Node104->Node54</title>
+<path fill="none" stroke="midnightblue" d="M2460.92,-410.36C2369.8,-402.35 2210.52,-386.86 2075,-366 2070.4,-365.29 2065.65,-364.5 2060.89,-363.65"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2460.88,-413.87 2471.15,-411.25 2461.49,-406.9 2460.88,-413.87"/>
</g>
-<!-- Node105->Node85 -->
-<g id="edge200" class="edge">
-<title>Node105->Node85</title>
-<path fill="none" stroke="midnightblue" d="M2368.14,-392.34C2366.1,-373.93 2360.25,-349.37 2344.06,-335 2276.85,-275.33 2227.78,-334.15 2145.06,-299 2123.38,-289.79 2123.66,-277.39 2102.06,-268 2045.67,-243.49 1977.07,-230.76 1925.23,-224.22"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2364.65,-392.67 2368.95,-402.35 2371.63,-392.1 2364.65,-392.67"/>
+<!-- Node104->Node55 -->
+<g id="edge196" class="edge">
+<title>Node104->Node55</title>
+<path fill="none" stroke="midnightblue" d="M2567.8,-397.3C2585.16,-387.12 2606.12,-374.85 2621.96,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2565.95,-394.32 2559.09,-402.4 2569.48,-400.36 2565.95,-394.32"/>
</g>
-<!-- Node105->Node88 -->
+<!-- Node105->Node81 -->
<g id="edge198" class="edge">
-<title>Node105->Node88</title>
-<path fill="none" stroke="midnightblue" d="M2333.08,-397.64C2309.35,-385.2 2279.42,-369.51 2261.29,-360.01"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2331.67,-400.85 2342.15,-402.4 2334.92,-394.65 2331.67,-400.85"/>
+<title>Node105->Node81</title>
+<path fill="none" stroke="midnightblue" d="M180.33,-409.37C105.32,-397.57 0,-366.42 0,-284.5 0,-284.5 0,-284.5 0,-148.5 0,-100.46 27.4,-87.16 71,-67 169.71,-21.36 3729.65,-16.93 4219.37,-16.54"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="179.83,-412.84 190.23,-410.85 180.86,-405.91 179.83,-412.84"/>
</g>
-<!-- Node106->Node85 -->
-<g id="edge203" class="edge">
-<title>Node106->Node85</title>
-<path fill="none" stroke="midnightblue" d="M2275.58,-398.55C2310.81,-383.5 2347.56,-360.42 2326.06,-335 2263.77,-261.36 2201.98,-333.88 2112.06,-299 2088.38,-289.82 2087.44,-277.91 2064.06,-268 2013.85,-246.71 1953.12,-233.55 1908.69,-226.03"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2274.14,-395.36 2266.21,-402.39 2276.79,-401.84 2274.14,-395.36"/>
+<!-- Node106->Node80 -->
+<g id="edge201" class="edge">
+<title>Node106->Node80</title>
+<path fill="none" stroke="midnightblue" d="M1072,-395.14C1064.41,-386.88 1056.98,-376.81 1053,-366 1037.13,-322.87 1047.54,-293.17 1086,-268 1295.13,-131.14 3083.44,-178.59 3333,-165 3383.94,-162.23 3441.19,-158.62 3485.83,-155.7"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1069.7,-397.8 1079.2,-402.47 1074.69,-392.89 1069.7,-397.8"/>
</g>
-<!-- Node106->Node88 -->
+<!-- Node106->Node86 -->
<g id="edge202" class="edge">
-<title>Node106->Node88</title>
-<path fill="none" stroke="midnightblue" d="M2231.78,-392.6C2235.44,-381.28 2239.58,-368.46 2242.25,-360.19"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2228.36,-391.8 2228.62,-402.4 2235.02,-393.96 2228.36,-391.8"/>
+<title>Node106->Node86</title>
+<path fill="none" stroke="midnightblue" d="M1075.37,-394.18C1062.55,-377.48 1050.37,-354.3 1061,-335 1087.89,-286.19 1113.67,-284.18 1167,-268 1259.37,-239.98 2713.25,-222.26 3068.93,-218.33"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1072.96,-396.76 1082.01,-402.26 1078.37,-392.31 1072.96,-396.76"/>
</g>
-<!-- Node107->Node40 -->
-<g id="edge211" class="edge">
-<title>Node107->Node40</title>
-<path fill="none" stroke="midnightblue" d="M4288.77,-268C4429.56,-244.67 4583,-229.71 4666.06,-222.48"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4287.94,-264.59 4278.65,-269.7 4289.09,-271.5 4287.94,-264.59"/>
+<!-- Node106->Node89 -->
+<g id="edge200" class="edge">
+<title>Node106->Node89</title>
+<path fill="none" stroke="midnightblue" d="M1044.63,-399.11C1007.27,-386.46 958.33,-369.89 929.13,-360.01"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1043.74,-402.5 1054.34,-402.4 1045.99,-395.87 1043.74,-402.5"/>
</g>
-<!-- Node107->Node41 -->
-<g id="edge212" class="edge">
-<title>Node107->Node41</title>
-<path fill="none" stroke="midnightblue" d="M4258.39,-263.47C4308.18,-236.64 4397.86,-188.32 4442.03,-164.52"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4256.42,-260.55 4249.28,-268.37 4259.74,-266.71 4256.42,-260.55"/>
+<!-- Node107->Node86 -->
+<g id="edge205" class="edge">
+<title>Node107->Node86</title>
+<path fill="none" stroke="midnightblue" d="M858.52,-397.82C844.5,-390.21 831.07,-379.85 823,-366 816.07,-354.09 814.55,-345.88 823,-335 897.02,-239.76 966.51,-284.47 1086,-268 1479.79,-213.72 2741.86,-215.62 3068.97,-217.07"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="857.26,-401.1 867.77,-402.44 860.39,-394.84 857.26,-401.1"/>
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-<!-- Node111->Node110 -->
-<g id="edge218" class="edge">
-<title>Node111->Node110</title>
-<path fill="none" stroke="midnightblue" d="M2683.13,-393.87C2677.34,-384.46 2670.8,-373.83 2665.72,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2680.16,-395.71 2688.38,-402.4 2686.12,-392.05 2680.16,-395.71"/>
+<!-- Node107->Node89 -->
+<g id="edge204" class="edge">
+<title>Node107->Node89</title>
+<path fill="none" stroke="midnightblue" d="M909.02,-392.25C907.63,-381.02 906.08,-368.38 905.07,-360.19"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="905.57,-392.9 910.26,-402.4 912.52,-392.04 905.57,-392.9"/>
</g>
-<!-- Node114->Node36 -->
-<g id="edge235" class="edge">
-<title>Node114->Node36</title>
-<path fill="none" stroke="midnightblue" d="M3675.68,-674.2C3666.82,-672.65 3657.69,-671.18 3649.06,-670 3474.85,-646.1 3420.09,-694.7 3255.06,-634 3232.61,-625.74 3233.16,-612.16 3211.06,-603 3124.8,-567.24 3093.13,-595.05 3004.06,-567 2948.38,-549.46 2887.51,-517.82 2854.57,-499.55"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3675.07,-677.65 3685.53,-675.97 3676.31,-670.76 3675.07,-677.65"/>
+<!-- Node108->Node40 -->
+<g id="edge213" class="edge">
+<title>Node108->Node40</title>
+<path fill="none" stroke="midnightblue" d="M4411.63,-265.44C4377.43,-254.91 4334.07,-241.55 4301.81,-231.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="4410.89,-268.88 4421.48,-268.48 4412.95,-262.19 4410.89,-268.88"/>
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-<!-- Node114->Node37 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="5421.42,-464.4 5412.21,-469.64 5422.67,-471.29 5421.42,-464.4"/>
+<!-- Node118->Node17 -->
+<g id="edge244" class="edge">
+<title>Node118->Node17</title>
+<path fill="none" stroke="midnightblue" d="M506.97,-325.06C501.68,-292.49 487.77,-235 453,-201 435.15,-183.55 410.86,-172.09 387.47,-164.57"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="503.53,-325.71 508.45,-335.09 510.45,-324.68 503.53,-325.71"/>
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-<!-- Node119->Node14 -->
-<g id="edge247" class="edge">
-<title>Node119->Node14</title>
-<path fill="none" stroke="midnightblue" d="M5697.64,-392.2C5699.64,-375.29 5699.83,-352.68 5691.06,-335 5679.68,-312.05 5657.15,-322.08 5646.06,-299 5640.09,-286.58 5641.02,-280.82 5646.06,-268 5651.76,-253.51 5663.79,-240.71 5674.25,-231.63"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="5694.14,-391.98 5696.15,-402.39 5701.06,-393 5694.14,-391.98"/>
+<!-- Node120 -->
+<g id="node120" class="node">
+<title>Node120</title>
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+<text text-anchor="middle" x="488" y="-409.5" font-family="Helvetica,sans-Serif" font-size="10.00">/loop_state.h</text>
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-<!-- Node119->Node117 -->
-<g id="edge246" class="edge">
-<title>Node119->Node117</title>
-<path fill="none" stroke="midnightblue" d="M5665.84,-396.17C5652.54,-386.23 5636.86,-374.51 5624.9,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="5664.07,-399.21 5674.18,-402.4 5668.26,-393.61 5664.07,-399.21"/>
</g>
-<!-- Node120->Node4 -->
-<g id="edge252" class="edge">
-<title>Node120->Node4</title>
-<path fill="none" stroke="midnightblue" d="M5001.01,-406.44C5102.86,-393.12 5279.82,-369.97 5367.98,-358.44"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="5000.55,-402.97 4991.09,-407.73 5001.46,-409.91 5000.55,-402.97"/>
+<!-- Node119->Node120 -->
+<g id="edge247" class="edge">
+<title>Node119->Node120</title>
+<path fill="none" stroke="midnightblue" d="M883.86,-469.56C882.56,-469.37 881.28,-469.19 880,-469 770.5,-453.13 643.42,-437.22 564.35,-427.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="883.51,-473.05 893.92,-471.07 884.55,-466.13 883.51,-473.05"/>
</g>
-<!-- Node120->Node40 -->
-<g id="edge253" class="edge">
-<title>Node120->Node40</title>
-<path fill="none" stroke="midnightblue" d="M4909.4,-393.8C4897.97,-376.83 4881.2,-353.47 4864.06,-335 4825.9,-293.89 4774.12,-252.83 4746.43,-231.84"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4906.73,-396.1 4915.17,-402.5 4912.56,-392.23 4906.73,-396.1"/>
+<!-- Node120->Node14 -->
+<g id="edge249" class="edge">
+<title>Node120->Node14</title>
+<path fill="none" stroke="midnightblue" d="M452.43,-396.69C441.57,-388.69 430.95,-378.39 425,-366 419.04,-353.58 420.53,-348.03 425,-335 431.58,-315.83 446.42,-318.17 453,-299 457.47,-285.97 459.81,-279.97 453,-268 443.49,-251.29 426.03,-239.54 409.4,-231.6"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="450.47,-399.59 460.7,-402.36 454.43,-393.82 450.47,-399.59"/>
+</g>
+<!-- Node120->Node118 -->
+<g id="edge248" class="edge">
+<title>Node120->Node118</title>
+<path fill="none" stroke="midnightblue" d="M496.06,-392.7C499.14,-383.59 502.56,-373.49 505.24,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="492.66,-391.8 492.77,-402.4 499.29,-394.05 492.66,-391.8"/>
</g>
-<!-- Node120->Node42 -->
+<!-- Node121->Node4 -->
<g id="edge254" class="edge">
-<title>Node120->Node42</title>
-<path fill="none" stroke="midnightblue" d="M4846.84,-402.14C4085.24,-323.92 3886.6,-424.68 3128.06,-366 3112.87,-364.83 3096.56,-362.96 3081.33,-360.94"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4846.66,-405.64 4856.96,-403.19 4847.38,-398.68 4846.66,-405.64"/>
+<title>Node121->Node4</title>
+<path fill="none" stroke="midnightblue" d="M2951.28,-413.53C3038.43,-410.28 3184.37,-405.1 3310,-402 3943.87,-386.34 6023.99,-357.22 6383.75,-352.23"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2951.07,-410.04 2941.21,-413.91 2951.34,-417.03 2951.07,-410.04"/>
</g>
-<!-- Node120->Node44 -->
+<!-- Node121->Node40 -->
<g id="edge255" class="edge">
-<title>Node120->Node44</title>
-<path fill="none" stroke="midnightblue" d="M4846.71,-402.33C4790.37,-396.23 4524.21,-369.73 4402.21,-357.59"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4846.59,-405.84 4856.92,-403.5 4847.39,-398.88 4846.59,-405.84"/>
+<title>Node121->Node40</title>
+<path fill="none" stroke="midnightblue" d="M2929.86,-399.43C2959.04,-390.18 2995.19,-378.16 3027,-366 3059.16,-353.7 3064.66,-343.62 3098,-335 3156.73,-319.81 4125,-240.94 4185,-232 4188.24,-231.52 4191.55,-230.98 4194.88,-230.41"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2928.74,-396.12 2920.25,-402.46 2930.84,-402.8 2928.74,-396.12"/>
</g>
-<!-- Node120->Node46 -->
+<!-- Node121->Node42 -->
<g id="edge256" class="edge">
-<title>Node120->Node46</title>
-<path fill="none" stroke="midnightblue" d="M4846.53,-402.08C4513.84,-363.52 4426.69,-386.89 4097.06,-366 4056.13,-363.41 4010.13,-359.78 3974.36,-356.8"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4846.61,-405.61 4856.95,-403.3 4847.42,-398.66 4846.61,-405.61"/>
+<title>Node121->Node42</title>
+<path fill="none" stroke="midnightblue" d="M2796.75,-404.09C2736.94,-394.29 2652.09,-379.99 2578,-366 2575.07,-365.45 2572.08,-364.87 2569.06,-364.28"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2796.44,-407.58 2806.87,-405.74 2797.57,-400.67 2796.44,-407.58"/>
</g>
-<!-- Node120->Node48 -->
+<!-- Node121->Node44 -->
<g id="edge257" class="edge">
-<title>Node120->Node48</title>
-<path fill="none" stroke="midnightblue" d="M4846.71,-402.11C4401.55,-353.05 4285.72,-386.28 3843.06,-366 3774.98,-362.88 3696.92,-358.41 3644.24,-355.26"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4846.63,-405.62 4856.96,-403.25 4847.4,-398.67 4846.63,-405.62"/>
+<title>Node121->Node44</title>
+<path fill="none" stroke="midnightblue" d="M2796.74,-412.09C2743.43,-409.09 2670.92,-405.13 2607,-402 2239.98,-384.04 2147.53,-392.17 1781,-366 1753.05,-364 1722.17,-361.05 1696.03,-358.32"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2796.62,-415.58 2806.8,-412.65 2797.01,-408.59 2796.62,-415.58"/>
</g>
-<!-- Node120->Node50 -->
+<!-- Node121->Node46 -->
<g id="edge258" class="edge">
-<title>Node120->Node50</title>
-<path fill="none" stroke="midnightblue" d="M4877.81,-398.82C4850.49,-388.37 4816.37,-375.32 4790.87,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4876.58,-402.09 4887.17,-402.4 4879.08,-395.56 4876.58,-402.09"/>
+<title>Node121->Node46</title>
+<path fill="none" stroke="midnightblue" d="M2899.96,-396.17C2912.65,-386.23 2927.61,-374.51 2939.03,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2897.72,-393.47 2892.01,-402.4 2902.04,-398.99 2897.72,-393.47"/>
</g>
-<!-- Node120->Node51 -->
+<!-- Node121->Node48 -->
<g id="edge259" class="edge">
-<title>Node120->Node51</title>
-<path fill="none" stroke="midnightblue" d="M4940.17,-393.84C4951.93,-377.41 4968.32,-354.71 4983.06,-335 4992.35,-322.58 5003.14,-308.64 5011.01,-298.56"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4937.12,-392.1 4934.16,-402.27 4942.82,-396.16 4937.12,-392.1"/>
+<title>Node121->Node48</title>
+<path fill="none" stroke="midnightblue" d="M2796.77,-409.29C2657.37,-396.27 2366.4,-369.09 2239.45,-357.24"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2796.67,-412.79 2806.95,-410.24 2797.32,-405.82 2796.67,-412.79"/>
</g>
-<!-- Node120->Node52 -->
+<!-- Node121->Node50 -->
<g id="edge260" class="edge">
-<title>Node120->Node52</title>
-<path fill="none" stroke="midnightblue" d="M4962.78,-397.99C4984.34,-387.69 5010.75,-375.07 5030.61,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4961.08,-394.93 4953.57,-402.4 4964.1,-401.24 4961.08,-394.93"/>
+<title>Node121->Node50</title>
+<path fill="none" stroke="midnightblue" d="M2796.73,-412.29C2743.42,-409.38 2670.91,-405.44 2607,-402 2307.9,-385.88 2231.35,-402.15 1934,-366 1931.33,-365.68 1928.62,-365.31 1925.88,-364.92"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2796.62,-415.79 2806.79,-412.83 2797,-408.8 2796.62,-415.79"/>
</g>
-<!-- Node120->Node54 -->
+<!-- Node121->Node51 -->
<g id="edge261" class="edge">
-<title>Node120->Node54</title>
-<path fill="none" stroke="midnightblue" d="M4846.97,-402.12C4590.82,-370.74 4523.06,-391.08 4271.06,-366 4255.63,-364.46 4238.98,-362.39 4223.56,-360.29"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="4846.59,-405.6 4856.94,-403.36 4847.45,-398.65 4846.59,-405.6"/>
+<title>Node121->Node51</title>
+<path fill="none" stroke="midnightblue" d="M2948.78,-400.29C2992.39,-390.67 3048.4,-378.05 3098,-366 3151.11,-353.1 3163.09,-343.96 3217,-335 3332.9,-315.74 3678.9,-295.85 3826.3,-287.99"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2947.84,-396.91 2938.83,-402.48 2949.35,-403.75 2947.84,-396.91"/>
+</g>
+<!-- Node121->Node52 -->
+<g id="edge262" class="edge">
+<title>Node121->Node52</title>
+<path fill="none" stroke="midnightblue" d="M2951.47,-409.84C3099.84,-397.08 3421.83,-369.4 3562.25,-357.33"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2950.75,-406.39 2941.09,-410.73 2951.35,-413.36 2950.75,-406.39"/>
</g>
-<!-- Node121->Node116 -->
+<!-- Node121->Node54 -->
<g id="edge263" class="edge">
-<title>Node121->Node116</title>
-<path fill="none" stroke="midnightblue" d="M3916.76,-607.89C3930.76,-606.14 3945.36,-604.42 3959.06,-603 4161.27,-582.06 4213.29,-591.92 4415.06,-567 4428.13,-565.39 4442.25,-563.19 4455.2,-561"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3916.17,-604.44 3906.69,-609.17 3917.05,-611.38 3916.17,-604.44"/>
+<title>Node121->Node54</title>
+<path fill="none" stroke="midnightblue" d="M2796.81,-412.1C2629.94,-402.48 2239.99,-379.36 2108,-366 2092.57,-364.44 2075.93,-362.35 2060.51,-360.25"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2796.81,-415.61 2807,-412.69 2797.22,-408.62 2796.81,-415.61"/>
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-<!-- Node122->Node36 -->
+<!-- Node122->Node117 -->
<g id="edge265" class="edge">
-<title>Node122->Node36</title>
-<path fill="none" stroke="midnightblue" d="M2711.74,-532.68C2737.88,-522.26 2770.42,-509.28 2794.76,-499.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2710.42,-529.44 2702.43,-536.4 2713.01,-535.94 2710.42,-529.44"/>
+<title>Node122->Node117</title>
+<path fill="none" stroke="midnightblue" d="M2544.58,-602.71C2629.03,-586.18 2716.32,-570.45 2769.25,-561.07"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2543.7,-599.32 2534.56,-604.68 2545.05,-606.19 2543.7,-599.32"/>
</g>
-<!-- Node122->Node85 -->
+<!-- Node123->Node36 -->
<g id="edge267" class="edge">
-<title>Node122->Node85</title>
-<path fill="none" stroke="midnightblue" d="M2622.41,-532.85C2571.1,-513.44 2483.94,-483.15 2406.06,-469 2128.37,-418.56 2050.42,-479.55 1772.06,-433 1721.96,-424.62 1711.82,-412.19 1662.06,-402 1601.25,-389.55 1426.02,-412.64 1385.06,-366 1275.17,-240.91 1610.27,-220.46 1768.81,-217.63"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2621.28,-536.16 2631.87,-536.46 2623.77,-529.62 2621.28,-536.16"/>
-</g>
-<!-- Node122->Node88 -->
-<g id="edge266" class="edge">
-<title>Node122->Node88</title>
-<path fill="none" stroke="midnightblue" d="M2642.62,-529.84C2618.15,-510.45 2578.54,-482.46 2539.06,-469 2455,-440.34 2200.96,-498.56 2141.06,-433 2112.74,-402.01 2178.05,-373.68 2217.6,-360.04"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2640.64,-532.74 2650.62,-536.31 2645.04,-527.3 2640.64,-532.74"/>
+<title>Node123->Node36</title>
+<path fill="none" stroke="midnightblue" d="M2139.06,-548.98C2259.01,-546.29 2518.24,-540.51 2737,-536 3561.42,-519.01 3769.59,-560.11 4592,-500 4610.01,-498.68 4629.53,-496.49 4647.25,-494.19"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2138.85,-545.49 2128.93,-549.21 2139.01,-552.49 2138.85,-545.49"/>
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-<!-- Node126->Node36 -->
-<g id="edge280" class="edge">
-<title>Node126->Node36</title>
-<path fill="none" stroke="midnightblue" d="M516.42,-815.44C729.15,-806.38 1334.93,-775.5 1833.06,-701 1880.59,-693.89 2564.69,-543.82 2771.36,-498.41"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="516.13,-811.95 506.28,-815.87 516.42,-818.95 516.13,-811.95"/>
+<!-- Node123->Node86 -->
+<g id="edge269" class="edge">
+<title>Node123->Node86</title>
+<path fill="none" stroke="midnightblue" d="M2005.46,-537.92C2000.58,-537.2 1995.72,-536.55 1991,-536 1845.32,-519.05 1469.52,-550.98 1332,-500 1276.28,-479.34 1226,-477.93 1226,-418.5 1226,-418.5 1226,-418.5 1226,-349.5 1226,-233.06 1360.3,-288.07 1475,-268 1632.74,-240.39 2759.81,-222.87 3068.82,-218.55"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2005.06,-541.4 2015.48,-539.47 2006.14,-534.48 2005.06,-541.4"/>
</g>
-<!-- Node126->Node61 -->
-<g id="edge278" class="edge">
-<title>Node126->Node61</title>
-<path fill="none" stroke="midnightblue" d="M491.32,-807.5C520.94,-799.69 557.47,-787.13 586.06,-768 634.22,-735.78 620.55,-696.54 672.06,-670 745.45,-632.19 984.22,-622.68 1102.46,-620.3"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="490.4,-804.12 481.56,-809.97 492.12,-810.91 490.4,-804.12"/>
+<!-- Node123->Node89 -->
+<g id="edge268" class="edge">
+<title>Node123->Node89</title>
+<path fill="none" stroke="midnightblue" d="M2005.42,-546C1961.33,-543.05 1902.22,-539.18 1850,-536 1566.52,-518.74 1492.79,-542.63 1212,-500 1155.84,-491.47 1143.74,-479.97 1088,-469 1033.14,-458.21 875.6,-475.27 839,-433 816.61,-407.14 862.71,-375.04 888.37,-360.01"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2005.23,-549.49 2015.44,-546.67 2005.69,-542.51 2005.23,-549.49"/>
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-<!-- Node126->Node62 -->
-<g id="edge275" class="edge">
-<title>Node126->Node62</title>
-<path fill="none" stroke="midnightblue" d="M438.77,-799.69C440.75,-746.87 446.33,-598.56 447.73,-561.15"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="435.26,-799.83 438.38,-809.95 442.25,-800.09 435.26,-799.83"/>
+<!-- Node127->Node36 -->
+<g id="edge282" class="edge">
+<title>Node127->Node36</title>
+<path fill="none" stroke="midnightblue" d="M5089.01,-804.54C5115.35,-787.96 5160.95,-759.69 5201,-737 5348.11,-653.65 5640.33,-658.71 5524,-536 5498.2,-508.79 4887.39,-502.88 4850,-500 4821.94,-497.84 4790.87,-494.79 4764.8,-492.05"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="5087.12,-801.59 5080.53,-809.89 5090.85,-807.51 5087.12,-801.59"/>
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-<g id="edge276" class="edge">
-<title>Node126->Node63</title>
-<path fill="none" stroke="midnightblue" d="M426.29,-801.08C396.82,-755.11 325.6,-627.74 374.06,-536 384.81,-515.65 407.86,-501.95 425.4,-494.01"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="423.48,-803.17 431.89,-809.61 429.33,-799.33 423.48,-803.17"/>
+<!-- Node127->Node61 -->
+<g id="edge280" class="edge">
+<title>Node127->Node61</title>
+<path fill="none" stroke="midnightblue" d="M5044.06,-804.43C5030.39,-795.26 5013.36,-782.35 5001,-768 4961.02,-721.59 4931.87,-652.56 4922.35,-628.31"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="5042.18,-807.38 5052.47,-809.9 5046,-801.52 5042.18,-807.38"/>
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-<!-- Node126->Node68 -->
+<!-- Node127->Node63 -->
<g id="edge277" class="edge">
-<title>Node126->Node68</title>
-<path fill="none" stroke="midnightblue" d="M455.91,-803.06C496.2,-768.51 593.75,-686.7 633.06,-670 696.01,-643.25 1143.68,-584.45 1322.52,-561.72"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="453.52,-800.49 448.22,-809.67 458.09,-805.8 453.52,-800.49"/>
+<title>Node127->Node63</title>
+<path fill="none" stroke="midnightblue" d="M4988.58,-811.89C4922.81,-804.99 4826.92,-791.76 4746,-768 4675.16,-747.2 4660.94,-732.14 4594,-701 4504.25,-659.25 4489.39,-629.44 4394,-603 4212.6,-552.73 4158.41,-593.14 3972,-567 3960.72,-565.42 3948.56,-563.24 3937.42,-561.05"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="4988.56,-815.41 4998.87,-812.94 4989.28,-808.44 4988.56,-815.41"/>
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+<!-- Node127->Node64 -->
+<g id="edge278" class="edge">
+<title>Node127->Node64</title>
+<path fill="none" stroke="midnightblue" d="M5032.41,-806.58C5003.58,-796.55 4961.48,-781.7 4925,-768 4738.41,-697.93 4701.6,-650.36 4508,-603 4432.48,-584.52 4219.49,-617.05 4160,-567 4138.06,-548.54 4136.2,-510.92 4136.56,-494.03"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="5031.43,-809.94 5042.02,-809.91 5033.72,-803.33 5031.43,-809.94"/>
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-<!-- Node126->Node73 -->
+<!-- Node127->Node69 -->
<g id="edge279" class="edge">
-<title>Node126->Node73</title>
-<path fill="none" stroke="midnightblue" d="M447.34,-801.17C464.82,-770.16 505.65,-705.16 558.06,-670 629.03,-622.39 877.38,-578.89 990.34,-561.05"/>
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+<title>Node127->Node69</title>
+<path fill="none" stroke="midnightblue" d="M5063.35,-799.85C5053.5,-749.43 5026.47,-611.19 5017.75,-566.58"/>
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-<!-- Node127->Node4 -->
-<g id="edge282" class="edge">
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+<title>Node127->Node74</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6476.76,-739.66 6467.15,-744.11 6477.44,-746.62 6476.76,-739.66"/>
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+<title>Node128->Node4</title>
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+<!-- Node129->Node30 -->
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+<title>Node129->Node30</title>
+<path fill="none" stroke="midnightblue" d="M8691.13,-732.85C8670.26,-722.58 8644.77,-710.03 8625.59,-700.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="8689.84,-736.12 8700.36,-737.4 8692.93,-729.84 8689.84,-736.12"/>
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+<!-- Node129->Node31 -->
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+<title>Node129->Node31</title>
+<path fill="none" stroke="midnightblue" d="M8781.44,-733.94C8803.91,-725.42 8829.99,-714.17 8852,-701 8910.79,-665.82 8915.48,-643.14 8971,-603 8989.23,-589.82 9010.86,-576.28 9027.03,-566.54"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6373.01,-731.08 6381.51,-737.4 6378.8,-727.15 6373.01,-731.08"/>
+<!-- Node129->Node130 -->
+<g id="edge288" class="edge">
+<title>Node129->Node130</title>
+<path fill="none" stroke="midnightblue" d="M8783.91,-734.48C8868.94,-708.14 9030.83,-657.98 9109.64,-633.56"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="8782.7,-731.2 8774.19,-737.5 8784.77,-737.88 8782.7,-731.2"/>
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-<title>Node128->Node132</title>
-<path fill="none" stroke="midnightblue" d="M6304.68,-737.6C6232.96,-726.06 6131.93,-709.81 6063.21,-698.75"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="6304.17,-741.06 6314.59,-739.2 6305.28,-734.15 6304.17,-741.06"/>
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-<title>Node129->Node28</title>
-<path fill="none" stroke="midnightblue" d="M6226.5,-595.87C6260.74,-562.73 6331.36,-499.97 6404.06,-469 6620.42,-376.82 6902.61,-356.69 7035.95,-352.48"/>
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+<!-- Node129->Node131 -->
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+<title>Node129->Node131</title>
+<path fill="none" stroke="midnightblue" d="M8742.4,-728.58C8747.86,-719.24 8753.99,-708.75 8758.77,-700.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="8739.27,-727 8737.25,-737.4 8745.31,-730.53 8739.27,-727"/>
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+<title>Node129->Node133</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="8814.71,-737.85 8805.19,-742.5 8815.53,-744.8 8814.71,-737.85"/>
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-<title>Node130->Node27</title>
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+<!-- Node130->Node28 -->
+<g id="edge290" class="edge">
+<title>Node130->Node28</title>
+<path fill="none" stroke="midnightblue" d="M9165.94,-594.24C9188.31,-549.4 9243.74,-451.11 9321,-402 9366.42,-373.13 9426.04,-360.62 9472.93,-355.27"/>
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<!-- Node130->Node31 -->
-<g id="edge294" class="edge">
+<g id="edge289" class="edge">
<title>Node130->Node31</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6427.8,-665.14 6418.66,-670.5 6429.14,-672.01 6427.8,-665.14"/>
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-<title>Node130->Node129</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6305.7,-669.42 6316.25,-670.4 6308.61,-663.05 6305.7,-669.42"/>
+<!-- Node131->Node27 -->
+<g id="edge295" class="edge">
+<title>Node131->Node27</title>
+<path fill="none" stroke="midnightblue" d="M8777,-661.16C8785.65,-643.28 8799.66,-619.04 8818,-603 8872.36,-555.48 8897.85,-559.86 8966,-536 9123.28,-480.93 9316.1,-445.94 9420.68,-429.52"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="8773.77,-659.8 8772.75,-670.35 8780.13,-662.74 8773.77,-659.8"/>
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-<title>Node130->Node131</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6432.67,-670.02 6423.12,-674.61 6433.44,-676.98 6432.67,-670.02"/>
+<!-- Node131->Node31 -->
+<g id="edge296" class="edge">
+<title>Node131->Node31</title>
+<path fill="none" stroke="midnightblue" d="M8787.77,-663.13C8806.8,-644.72 8836.55,-618.69 8867,-603 8900.57,-585.7 8940.73,-573.79 8974.95,-565.91"/>
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-<title>Node132->Node27</title>
-<path fill="none" stroke="midnightblue" d="M5996.89,-661.1C6005.58,-642.98 6019.86,-618.44 6039.06,-603 6173.72,-494.69 6235.2,-505.76 6404.06,-469 6610.26,-424.11 6860.5,-417.71 6983.82,-417.63"/>
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+<!-- Node131->Node130 -->
+<g id="edge293" class="edge">
+<title>Node131->Node130</title>
+<path fill="none" stroke="midnightblue" d="M8853.4,-670.03C8921.03,-658.7 9014.17,-643.09 9078.98,-632.24"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="8852.48,-666.63 8843.19,-671.74 8853.63,-673.54 8852.48,-666.63"/>
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+<!-- Node131->Node132 -->
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+<title>Node131->Node132</title>
+<path fill="none" stroke="midnightblue" d="M8680.33,-670C8078.91,-621.04 4203.13,-641.37 3647,-634 3400.88,-630.74 3109.32,-624.37 2974.01,-621.28"/>
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-<!-- Node132->Node28 -->
+<!-- Node133->Node27 -->
<g id="edge299" class="edge">
-<title>Node132->Node28</title>
-<path fill="none" stroke="midnightblue" d="M5975.9,-660.86C5964.69,-634.89 5949.06,-591.66 5949.06,-552.5 5949.06,-552.5 5949.06,-552.5 5949.06,-483.5 5949.06,-373.77 6777.01,-355.24 7035.75,-352.12"/>
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+<title>Node133->Node27</title>
+<path fill="none" stroke="midnightblue" d="M9311.06,-664.81C9323.66,-656.22 9337.77,-645.5 9349,-634 9414.18,-567.29 9470.09,-468.82 9489.6,-432.56"/>
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+<title>Node133->Node28</title>
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+<title>Node133->Node29</title>
+<path fill="none" stroke="midnightblue" d="M9280.3,-660.36C9280.04,-642.88 9277.06,-619.51 9265,-603 9226.89,-550.83 9197.86,-554.64 9136,-536 9000.39,-495.13 8834.2,-486.36 8739.26,-485.02"/>
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+<!-- Node133->Node31 -->
<g id="edge300" class="edge">
-<title>Node132->Node29</title>
-<path fill="none" stroke="midnightblue" d="M6008.86,-663.45C6032.82,-640.52 6068.99,-606.62 6077.06,-603 6258.57,-521.55 6871.67,-494.71 7087.84,-487.65"/>
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+<title>Node133->Node31</title>
+<path fill="none" stroke="midnightblue" d="M9273.46,-660.76C9267.96,-642.4 9257.74,-617.72 9240,-603 9208.51,-576.86 9164.79,-564.14 9127.33,-558"/>
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+<!-- Node133->Node130 -->
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-<title>Node132->Node31</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6042.68,-664.26 6034.09,-670.48 6044.68,-670.97 6042.68,-664.26"/>
+<title>Node133->Node130</title>
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-<title>Node133->Node64</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1154.62,-542.63 1165.19,-541.87 1156.45,-535.88 1154.62,-542.63"/>
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+<g id="edge312" class="edge">
+<title>Node134->Node65</title>
+<path fill="none" stroke="midnightblue" d="M4314.59,-539.34C4356.58,-528.33 4420.9,-511.47 4466.36,-499.56"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="4313.46,-536.01 4304.68,-541.94 4315.24,-542.79 4313.46,-536.01"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1181.44,-535.94 1190.32,-541.73 1186.99,-531.67 1181.44,-535.94"/>
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+<title>Node134->Node66</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="4284.3,-532.01 4280.09,-541.73 4289.45,-536.76 4284.3,-532.01"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="6742.64,-735.32 6733.34,-740.4 6743.78,-742.23 6742.64,-735.32"/>
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