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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/07/19 07:34:06 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@d81e8809b81b0746d4a7230c10fde858f5a177ba)
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 83277c0ad7 deploying docs (apache/tvm@d81e8809b81b0746d4a7230c10fde858f5a177ba)
83277c0ad7 is described below
commit 83277c0ad73681cc48ef4abc1a126bcd73ea6318
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Jul 19 07:33:56 2023 +0000
deploying docs (apache/tvm@d81e8809b81b0746d4a7230c10fde858f5a177ba)
---
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472 files changed, 144899 insertions(+), 139482 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 2b4cb80055..17d5553e92 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 32.082 seconds)
+ **Total running time of the script:** ( 1 minutes 35.015 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 596cf077a2..f459f082de 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.zipf7fd45ce-e5e3-40f8-b941-09630fc23b8b from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip6a4eafe4-1cc7-4cd9-8ac0-0ac20e8dd14d 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 078ae1499c..94a13c8ac9 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -121,7 +121,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:00, 41.4MB/s]
25%|##4 | 10.3M/41.5M [00:00<00:00, 36.4MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 31.2MB/s]
54%|#####3 | 22.3M/41.5M [00:00<00:00, 40.2MB/s]
64%|######4 | 26.6M/41.5M [00:00<00:00, 35.1MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.7MB/s]
87%|########7 | 36.1M/41.5M [00:01<00:00, 27.9MB/s]
95%|#########4| 39.4M/41.5M [00:01<00:00, 25.5MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 29.3MB/s]
+
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:00, 51.5MB/s]
27%|##7 | 11.2M/41.5M [00:00<00:00, 39.7MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 28.0MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 27.7MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 36.9MB/s]
92%|#########2| 38.3M/41.5M [00:01<00:00, 41.6MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 36.5MB/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 bf8b89227a..a58d80b5ce 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 0.985 seconds)
+ **Total running time of the script:** ( 1 minutes 4.102 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 efac633614..1ae5098019 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, 61.1MB/s]
27%|##7 | 12.1M/44.7M [00:00<00:00, 43.5MB/s]
37%|###7 | 16.5M/44.7M [00:00<00:00, 31.9MB/s]
56%|#####6 | 25.1M/44.7M [00:00<00:00, 47.8MB/s]
68%|######8 | 30.6M/44.7M [00:00<00:00, 37.8MB/s]
78%|#######8 | 35.0M/44.7M [00:00<00:00, 36.5MB/s]
90%|########9 | 40.0M/44.7M [00:01<00:00, 31.7MB/s]
100%|##########| 44.7M/44.7M [00:01<00:00, 39.6MB/s]
+
0%| | 0.00/44.7M [00:00<?, ?B/s]
18%|#7 | 7.99M/44.7M [00:00<00:00, 57.8MB/s]
32%|###2 | 14.3M/44.7M [00:00<00:00, 40.4MB/s]
41%|####1 | 18.4M/44.7M [00:00<00:00, 39.8MB/s]
54%|#####3 | 24.0M/44.7M [00:00<00:00, 39.0MB/s]
68%|######7 | 30.3M/44.7M [00:00<00:00, 42.7MB/s]
77%|#######7 | 34.5M/44.7M [00:00<00:00, 36.3MB/s]
86%|########5 | 38.3M/44.7M [00:01<00:00, 31.1MB/s]
93%|#########2| 41.4M/44.7M [00:01<00:00, 20.7MB/s]
100%|##########| 44.7M/44.7M [00:01<00:00, 32.4MB/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 1ee8951a6f..51a61da156 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 36.155 seconds)
+ **Total running time of the script:** ( 1 minutes 42.133 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 139c2c61c2..4b6e4b3ab1 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
=================
-**07:14.676** total execution time for **how_to_compile_models** files:
+**07:32.525** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:36.155 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:42.133 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:32.082 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:35.015 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:00.985 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:04.102 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:41.856 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:43.854 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:36.623 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.259 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:34.792 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.110 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.630 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:29.512 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.961 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.911 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.749 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:13.753 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.844 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.876 | 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 9b6f24ccc2..986146f13c 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -673,7 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4226.8723 4225.5390 4232.2363 4223.2627 3.2770
+ 4223.6853 4223.2203 4226.5434 4220.8838 1.9966
@@ -681,7 +681,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.846 seconds)
+ **Total running time of the script:** ( 1 minutes 20.941 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 d18118afc1..a0ee3299b2 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
8192/102967424 [..............................] - ETA: 0s
4366336/102967424 [>.............................] - ETA: 1s
13139968/102967424 [==>...........................] - ETA: 0s
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+
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diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index 1b45bd06e9..b6bdb9c9ab 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.5401 15.4043 16.2310 15.0227 0.4120
+ 15.5451 15.6149 16.1295 14.9890 0.4366
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 f7b44774d6..90d1f72e91 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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s]
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100%|##########| 170M/170M [00:04<00:00, 41.3MB/s]
+
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s]
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 32.834 seconds)
+ **Total running time of the script:** ( 3 minutes 34.647 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 1aac9f1b5a..896fe7a21e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -227,7 +227,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
34%|###3 | 4.60M/13.6M [00:00<00:00, 48.2MB/s]
68%|######7 | 9.20M/13.6M [00:00<00:00, 31.2MB/s]
92%|#########2| 12.5M/13.6M [00:00<00:00, 28.2MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 32.0MB/s]
+
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59%|#####8 | 7.99M/13.6M [00:00<00:00, 56.4MB/s]
99%|#########8| 13.4M/13.6M [00:00<00:00, 54.1MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 55.0MB/s]
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.2562 89.2117 89.9230 88.7683 0.2514
+ 88.7899 88.7312 91.4161 88.5616 0.3152
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 27.883 seconds)
+ **Total running time of the script:** ( 1 minutes 27.262 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 e42aee2638..c796c15740 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 110.1213 109.9974 117.4976 109.2398 1.0009
+ 109.8250 109.7896 110.8237 109.2390 0.2706
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 d0319f7687..31d02e8fbf 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 11.346 seconds)
+ **Total running time of the script:** ( 2 minutes 7.403 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 840cce17cc..1ab3d36d79 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
=================
-**12:07.853** total execution time for **how_to_deploy_models** files:
+**12:08.471** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:32.834 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:34.647 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:11.346 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:07.403 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:27.883 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:27.262 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:19.846 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.941 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:56.695 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:57.611 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:49.504 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:50.270 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.386 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:49.760 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.236 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.573 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.115 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:29.999 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 63f232e65e..b10b90f31c 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.zipcd940e61-6e7e-410a-af10-2fb3b9bce92e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip21af5192-da3f-460a-955e-e977a0d7d28e 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 d04bbf4728..0821a8ec04 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
=================
-**00:57.956** total execution time for **how_to_extend_tvm** files:
+**00:57.196** 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``) | 00:54.071 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:53.274 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.698 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.713 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.180 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.202 | 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 bf0a9bdec2..7239138c50 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: 23187us [23187us] (48.34%; 48.34%)
- FoldScaleAxis: 24779us [9us] (51.66%; 51.66%)
- FoldConstant: 24770us [1834us] (51.64%; 99.96%)
- InferType: 22936us [22936us] (47.82%; 92.60%)
+ InferType: 23327us [23327us] (48.32%; 48.32%)
+ FoldScaleAxis: 24954us [8us] (51.68%; 51.68%)
+ FoldConstant: 24945us [1838us] (51.67%; 99.97%)
+ InferType: 23108us [23108us] (47.86%; 92.63%)
@@ -262,10 +262,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 22874us [22874us] (48.36%; 48.36%)
- FoldScaleAxis: 24421us [7us] (51.64%; 51.64%)
- FoldConstant: 24415us [1819us] (51.62%; 99.97%)
- InferType: 22595us [22595us] (47.77%; 92.55%)
+ InferType: 23374us [23374us] (47.95%; 47.95%)
+ FoldScaleAxis: 25373us [8us] (52.05%; 52.05%)
+ FoldConstant: 25365us [2043us] (52.03%; 99.97%)
+ InferType: 23322us [23322us] (47.84%; 91.94%)
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 1a69a4b94b..d32df5c4c0 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: 50.781280 ms
+ Convolution: 34.940639 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 7d98f015f6..c2c55da3e2 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: 11.369632 ms
+ conv2d with tensor core: 12.270390 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 862a93c516..35ae893a0f 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: 0.018759
- Baseline: 3.230064
+ Numpy running time: 0.018685
+ Baseline: 3.342723
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.307181
+ Opt1: 0.295032
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.283013
+ Opt2: 0.274847
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.115540
+ Opt3: 0.113569
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.108210
+ Opt4: 0.110018
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111626
+ Opt5: 0.112191
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.132803
+ Opt6: 0.133091
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 21c5c00972..e7b8fd944b 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:33.800** total execution time for **how_to_optimize_operators** files:
+**00:34.240** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.527 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.639 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.045 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.185 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.227 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.415 | 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 58384eba4f..e5a7ee5d46 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
=================
-**03:47.740** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:50.301** 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``) | 01:39.235 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:40.103 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:18.625 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:19.023 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.124 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:18.347 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.479 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.437 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.173 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.285 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.105 | 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 a5a90fc7d8..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: 0.344 ms
+ Execution time of this operator: 0.348 ms
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 1a68bb0804..b5dc342051 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -647,7 +647,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 8.1135 8.1113 8.1203 8.1087 0.0050
+ 8.1215 8.1214 8.1248 8.1182 0.0027
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.625 seconds)
+ **Total running time of the script:** ( 1 minutes 19.023 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 babe64fbad..67a69b286f 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -666,7 +666,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 753.2856 753.3022 753.4253 753.1293 0.1214
+ 760.8718 760.9270 762.0119 759.6764 0.9543
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 39.235 seconds)
+ **Total running time of the script:** ( 1 minutes 40.103 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 24f7bcef15..73a22249a0 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
=================
-**00:23.563** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.877** 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``) | 00:23.526 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.839 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :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 |
+--------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.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 1ad38e3fbf..69f440e2ef 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: 0.037153
+ Time cost of this operator: 0.037136
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 6a757e8cd4..a1ad27ed00 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 304.7 98.728 (1, 2, 10, 10, 3) 2 1 [304.7]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.951 0.956 (1, 6, 10, 10) 1 1 [2.951]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.975 0.316 (1, 1, 10, 10, 3) 1 1 [0.975]
- Total_time - 308.625 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.0 98.727 (1, 2, 10, 10, 3) 2 1 [304.0]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.953 0.959 (1, 6, 10, 10) 1 1 [2.953]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.967 0.314 (1, 1, 10, 10, 3) 1 1 [0.967]
+ Total_time - 307.92 - - - - -
@@ -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 135.9 98.11 (1, 6, 10, 10, 1) 2 1 [135.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.278 (1, 6, 10, 10) 1 1 [1.77]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.848 0.612 (1, 3, 10, 10, 1) 1 1 [0.848]
- Total_time - 138.518 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 101.2 97.339 (1, 6, 10, 10, 1) 2 1 [101.2]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.73 (1, 6, 10, 10) 1 1 [1.799]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.967 0.931 (1, 1, 10, 10, 3) 1 1 [0.967]
+ Total_time - 103.966 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.351 seconds)
+ **Total running time of the script:** ( 1 minutes 26.260 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 c66a5e64f8..1d3c0c9ffa 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, 16.3MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 21.7MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 60.5MB/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 29.204 seconds)
+ **Total running time of the script:** ( 1 minutes 27.856 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 5eb1310a5f..e4cff91bde 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/tmpku3g7t96/images/random'
+ '/tmp/tmp2p1svjhr/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpku3g7t96/images/target contains 8144 images
- /tmp/tmpku3g7t96/images/random contains 5000 images
+ /tmp/tmp2p1svjhr/images/target contains 8144 images
+ /tmp/tmp2p1svjhr/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 41s - loss: 0.2102 - accuracy: 0.9254 - val_loss: 0.1317 - val_accuracy: 0.9524 - 41s/epoch - 125ms/step
+ 328/328 - 41s - loss: 0.2334 - accuracy: 0.9233 - val_loss: 0.1257 - val_accuracy: 0.9547 - 41s/epoch - 124ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.1003 - accuracy: 0.9630 - val_loss: 0.0929 - val_accuracy: 0.9690 - 35s/epoch - 108ms/step
+ 328/328 - 36s - loss: 0.1016 - accuracy: 0.9634 - val_loss: 0.1121 - val_accuracy: 0.9611 - 36s/epoch - 108ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0679 - accuracy: 0.9757 - val_loss: 0.0970 - val_accuracy: 0.9656 - 35s/epoch - 108ms/step
+ 328/328 - 35s - loss: 0.0665 - accuracy: 0.9754 - val_loss: 0.1127 - val_accuracy: 0.9641 - 35s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7f6dd453b580>
+ <keras.callbacks.History object at 0x7fa9ac664cd0>
@@ -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 32.285 seconds)
+ **Total running time of the script:** ( 4 minutes 46.485 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 36b690af2e..dc1ffcf376 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
=================
-**07:57.441** total execution time for **how_to_work_with_microtvm** files:
+**08:10.982** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:32.285 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:46.485 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:29.204 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:27.856 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.351 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.260 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.447 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.467 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.610 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.296 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.544 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.617 | 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 01dc05a05a..662bb848c5 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
=================
-**00:39.913** total execution time for **how_to_work_with_relay** files:
+**00:41.063** 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``) | 00:34.740 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.469 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.296 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.612 | 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_build_gcn.py` (``build_gcn.py``) | 00:01.976 | 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 048d3067fb..9af2c1999f 100644
--- a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
@@ -278,7 +278,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f6b349d2ee0>
+ <function my_cuda_math_rule at 0x7fa5a33dd160>
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 0fa00367e1..6d66f48917 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:06.410** total execution time for **how_to_work_with_schedules** files:
+**00:09.132** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.309 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.733 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.296 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.560 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.760 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.783 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.754 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.762 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.118 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.120 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.082 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.060 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.061 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 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 684c3386b7..cb55fcc503 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
=================
-**00:35.355** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:35.393** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.348 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.385 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index cfef8930a7..4791aa714b 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -293,7 +293,7 @@ The compilation steps are:
warnings.warn(
/workspace/vta/tutorials/frontend/deploy_classification.py:212: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
graph, lib, params = relay.build(
- resnet18_v1 inference graph built in 37.94s!
+ resnet18_v1 inference graph built in 37.47s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.212 seconds)
+ **Total running time of the script:** ( 1 minutes 4.845 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_classification.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index b745013ce6..c78421346d 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 26.10s!
+ yolov3-tiny inference graph built in 26.05s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.519 seconds)
+ **Total running time of the script:** ( 1 minutes 9.910 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 09825c58e1..897c109fd4 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**02:14.731** total execution time for **topic_vta_tutorials_frontend** files:
+**02:14.755** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:09.519 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:09.910 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:05.212 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:04.845 | 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 b5660616ce..b5b1d46300 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
=================
-**00:03.446** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.468** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.893 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.901 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.552 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.566 | 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 ecd47a5675..fb3116c36a 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
=================
-**00:00.931** total execution time for **topic_vta_tutorials** files:
+**00:00.958** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.480 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.493 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.452 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.465 | 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 5dc6f96a88..2fc010ab68 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 97.521 ms
+ Execution time of this operator: 93.120 ms
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 35.220 seconds)
+ **Total running time of the script:** ( 1 minutes 23.834 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 0be128ffc8..94c404be7a 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 1.55/1.55 result: MeasureResult(costs=(0.172932057,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.015423536300659, timestamp=1689744138.3580832) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
- No: 2 GFLOPS: 7.45/7.45 result: MeasureResult(costs=(0.0360528846,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8443689346313477, timestamp=1689744139.1961207) [('tile_y', [-1, 512]), ('tile_x', [-1, 16])],None,49
- No: 3 GFLOPS: 12.65/12.65 result: MeasureResult(costs=(0.0212223058,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.687690019607544, timestamp=1689744139.7997057) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
- No: 4 GFLOPS: 11.83/12.65 result: MeasureResult(costs=(0.0226985016,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6689817905426025, timestamp=1689744140.421761) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
- No: 5 GFLOPS: 5.81/12.65 result: MeasureResult(costs=(0.046212209399999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9923748970031738, timestamp=1689744141.66569) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 6 GFLOPS: 0.51/12.65 result: MeasureResult(costs=(0.5271544946,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.692301988601685, timestamp=1689744150.3586473) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
- No: 7 GFLOPS: 9.33/12.65 result: MeasureResult(costs=(0.0287655558,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8496115207672119, timestamp=1689744151.083799) [('tile_y', [-1, 2]), ('tile_x', [-1, 128])],None,71
- No: 8 GFLOPS: 8.85/12.65 result: MeasureResult(costs=(0.030329974800000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7640454769134521, timestamp=1689744151.8265688) [('tile_y', [-1, 2]), ('tile_x', [-1, 16])],None,41
- No: 9 GFLOPS: 11.69/12.65 result: MeasureResult(costs=(0.022958162400000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5910384654998779, timestamp=1689744152.5971482) [('tile_y', [-1, 128]), ('tile_x', [-1, 512])],None,97
- No: 10 GFLOPS: 11.89/12.65 result: MeasureResult(costs=(0.022584913399999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6533718109130859, timestamp=1689744153.2188451) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+ No: 1 GFLOPS: 9.75/9.75 result: MeasureResult(costs=(0.027532022,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8355751037597656, timestamp=1689747907.2207866) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
+ No: 2 GFLOPS: 4.56/9.75 result: MeasureResult(costs=(0.058886225199999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2264795303344727, timestamp=1689747908.4312394) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 3 GFLOPS: 8.96/9.75 result: MeasureResult(costs=(0.029949520400000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7626183032989502, timestamp=1689747909.1794956) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
+ No: 4 GFLOPS: 0.87/9.75 result: MeasureResult(costs=(0.3073426348,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.101713418960571, timestamp=1689747914.316367) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 5 GFLOPS: 7.09/9.75 result: MeasureResult(costs=(0.0378628396,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8616349697113037, timestamp=1689747915.3136322) [('tile_y', [-1, 1]), ('tile_x', [-1, 8])],None,30
+ No: 6 GFLOPS: 9.93/9.93 result: MeasureResult(costs=(0.0270325494,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7143855094909668, timestamp=1689747916.0125077) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
+ No: 7 GFLOPS: 2.04/9.93 result: MeasureResult(costs=(0.131777551,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.368669271469116, timestamp=1689747918.3915312) [('tile_y', [-1, 1]), ('tile_x', [-1, 2])],None,10
+ No: 8 GFLOPS: 13.62/13.62 result: MeasureResult(costs=(0.019703800200000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5916750431060791, timestamp=1689747918.9779162) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 9 GFLOPS: 14.75/14.75 result: MeasureResult(costs=(0.0182014388,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7868077754974365, timestamp=1689747919.8748004) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+ No: 10 GFLOPS: 11.21/14.75 result: MeasureResult(costs=(0.023952648,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6269969940185547, timestamp=1689747920.5306573) [('tile_y', [-1, 8]), ('tile_x', [-1, 512])],None,93
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 5b2d0cb402..39d1132313 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': 489.89261000002443, 'median': 489.3413210998915, 'std': 2.9164566676576857}
+ {'mean': 496.82192496000425, 'median': 496.40536309998424, 'std': 1.9683461116534566}
@@ -582,30 +582,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.79/ 17.79 GFLOPS | Progress: (4/20) | 9.69 s
[Task 1/25] Current/Best: 20.68/ 20.68 GFLOPS | Progress: (8/20) | 12.15 s
[Task 1/25] Current/Best: 17.79/ 20.68 GFLOPS | Progress: (12/20) | 19.97 s
[Task 1/25] Current/Best: 12.81/ 20.68 GFLOPS | Progress: (16/20) | 23.69 s
[Task 1/25] Current/Best: 13.74/ 22.39 GFLOPS | Progress: (20/20) | 26.09 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 7.68/ 16.88 GFLOPS | Progress: (4/20) | 4.19 s
[Task 2/25] Current/Best: 9.36/ 20.36 GFLOPS | Progress: (8/20) | 5.66 s
[Task 2/25] Current/Best: 15.07/ 21.00 GFLOPS | Progress: (12/20) | 7.04 s
[Task 2/25] Current/Best: 8.51/ 21.00 GFLOPS | Progress: (16/20) | 8.73 s
[Task 2/25] Current/Best: 17.03/ 21.00 GFLOPS | Progress: (20/20) | 10.24 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 16.08/ 16.08 GFLOPS | Progress: (4/20) | 5.71 s
[Task 3/25] Current/Best: 7.07/ 20.50 GFLOPS | Progress: (8/20) | 8.28 s
[Task 3/25] Current/Best: 19.35/ 23.44 GFLOPS | Progress: (12/20) | 10.48 s
[Task 3/25] Current/Best: 1.63/ 23.44 GFLOPS | Progress: (16/20) | 14.35 s
[Task 3/25] Current/Best: 15.72/ 23.44 GFLOPS | Progress: (20/20) | 16.88 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 12.42/ 17.96 GFLOPS | Progress: (4/20) | 4.74 s
[Task 4/25] Current/Best: 11.98/ 19.30 GFLOPS | Progress: (8/20) | 7.54 s
[Task 4/25] Current/Best: 13.15/ 19.30 GFLOPS | Progress: (12/20) | 10.76 s
[Task 4/25] Current/Best: 16.29/ 19.30 GFLOPS | Progress: (16/20) | 14.97 s
[Task 4/25] Current/Best: 16.25/ 19.30 GFLOPS | Progress: (20/20) | 17.04 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 8.41/ 13.66 GFLOPS | Progress: (4/20) | 4.87 s
[Task 5/25] Current/Best: 5.94/ 13.66 GFLOPS | Progress: (8/20) | 6.77 s
[Task 5/25] Current/Best: 12.50/ 14.31 GFLOPS | Progress: (12/20) | 8.81 s
[Task 5/25] Current/Best: 6.45/ 15.56 GFLOPS | Progress: (16/20) | 11.11 s
[Task 5/25] Current/Best: 14.49/ 17.93 GFLOPS | Progress: (20/20) | 13.23 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.44/ 12.44 GFLOPS | Progress: (4/20) | 6.64 s
[Task 6/25] Current/Best: 15.96/ 20.77 GFLOPS | Progress: (8/20) | 8.87 s
[Task 6/25] Current/Best: 15.74/ 20.77 GFLOPS | Progress: (12/20) | 12.97 s
[Task 6/25] Current/Best: 14.45/ 20.77 GFLOPS | Progress: (16/20) | 15.06 s
[Task 6/25] Current/Best: 14.45/ 20.77 GFLOPS | Progress: (20/20) | 19.77 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 8.46/ 23.17 GFLOPS | Progress: (4/20) | 5.13 s
[Task 7/25] Current/Best: 20.79/ 23.17 GFLOPS | Progress: (8/20) | 7.88 s
[Task 7/25] Current/Best: 9.19/ 23.17 GFLOPS | Progress: (12/20) | 10.30 s
[Task 7/25] Current/Best: 19.41/ 23.17 GFLOPS | Progress: (16/20) | 12.68 s
[Task 7/25] Current/Best: 14.55/ 23.17 GFLOPS | Progress: (20/20) | 15.53 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 8.67/ 13.92 GFLOPS | Progress: (4/20) | 7.80 s
[Task 8/25] Current/Best: 2.84/ 13.92 GFLOPS | Progress: (8/20) | 12.97 s
[Task 8/25] Current/Best: 9.44/ 22.98 GFLOPS | Progress: (12/20) | 15.24 s
[Task 8/25] Current/Best: 18.19/ 22.98 GFLOPS | Progress: (16/20) | 18.85 s
[Task 8/25] Current/Best: 11.09/ 22.98 GFLOPS | Progress: (20/20) | 22.63 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 23.37/ 23.37 GFLOPS | Progress: (4/20) | 5.18 s
[Task 9/25] Current/Best: 13.89/ 23.37 GFLOPS | Progress: (8/20) | 7.61 s
[Task 9/25] Current/Best: 3.49/ 23.37 GFLOPS | Progress: (12/20) | 9.51 s
[Task 9/25] Current/Best: 16.69/ 23.37 GFLOPS | Progress: (16/20) | 20.75 s
[Task 9/25] Current/Best: 13.69/ 23.37 GFLOPS | Progress: (20/20) | 23.84 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
-
[Task 10/25] Current/Best: 14.15/ 16.53 GFLOPS | Progress: (4/20) | 4.78 s
[Task 10/25] Current/Best: 12.81/ 16.53 GFLOPS | Progress: (8/20) | 6.87 s
[Task 10/25] Current/Best: 19.00/ 19.00 GFLOPS | Progress: (12/20) | 8.70 s
[Task 10/25] Current/Best: 19.43/ 19.43 GFLOPS | Progress: (16/20) | 10.53 s
[Task 10/25] Current/Best: 11.88/ 19.43 GFLOPS | Progress: (20/20) | 12.67 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 9.58/ 18.84 GFLOPS | Progress: (4/20) | 5.15 s
[Task 11/25] Current/Best: 16.46/ 18.84 GFLOPS | Progress: (8/20) | 7.47 s
[Task 11/25] Current/Best: 11.59/ 20.45 GFLOPS | Progress: (12/20) | 10.11 s
[Task 11/25] Current/Best: 3.13/ 20.45 GFLOPS | Progress: (16/20) | 13.52 s
[Task 11/25] Current/Best: 17.70/ 20.49 GFLOPS | Progress: (20/20) | 15.79 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 5.37/ 15.57 GFLOPS | Progress: (4/20) | 6.36 s
[Task 12/25] Current/Best: 10.75/ 19.56 GFLOPS | Progress: (8/20) | 8.75 s
[Task 12/25] Current/Best: 5.23/ 19.56 GFLOPS | Progress: (12/20) | 11.21 s
[Task 12/25] Current/Best: 18.76/ 19.56 GFLOPS | Progress: (16/20) | 13.97 s
[Task 12/25] Current/Best: 14.02/ 19.56 GFLOPS | Progress: (20/20) | 16.70 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 7.99/ 19.99 GFLOPS | Progress: (4/20) | 6.20 s
[Task 13/25] Current/Best: 13.13/ 19.99 GFLOPS | Progress: (8/20) | 9.50 s
[Task 13/25] Current/Best: 6.06/ 19.99 GFLOPS | Progress: (12/20) | 14.06 s
[Task 13/25] Current/Best: 18.23/ 19.99 GFLOPS | Progress: (16/20) | 17.45 s
[Task 13/25] Current/Best: 19.33/ 20.27 GFLOPS | Progress: (20/20) | 20.54 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 14.74/ 22.81 GFLOPS | Progress: (4/20) | 14.07 s
[Task 14/25] Current/Best: 15.86/ 22.81 GFLOPS | Progress: (8/20) | 17.58 s
[Task 14/25] Current/Best: 2.95/ 22.81 GFLOPS | Progress: (12/20) | 20.88 s
[Task 14/25] Current/Best: 14.66/ 22.81 GFLOPS | Progress: (16/20) | 24.90 s
[Task 14/25] Current/Best: 5.30/ 22.81 GFLOPS | Progress: (20/20) | 33.72 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 20.19/ 20.98 GFLOPS | Progress: (4/20) | 8.74 s
[Task 15/25] Current/Best: 20.41/ 20.98 GFLOPS | Progress: (8/20) | 12.17 s
[Task 15/25] Current/Best: 18.58/ 20.98 GFLOPS | Progress: (12/20) | 15.99 s
[Task 15/25] Current/Best: 3.23/ 20.98 GFLOPS | Progress: (16/20) | 17.86 s
[Task 15/25] Current/Best: 14.72/ 20.98 GFLOPS | Progress: (20
/20) | 19.83 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 11.68/ 18.91 GFLOPS | Progress: (4/20) | 4.72 s
[Task 16/25] Current/Best: 5.05/ 18.91 GFLOPS | Progress: (8/20) | 6.74 s
[Task 16/25] Current/Best: 7.67/ 18.91 GFLOPS | Progress: (12/20) | 8.59 s
[Task 16/25] Current/Best: 4.45/ 22.87 GFLOPS | Progress: (16/20) | 10.59 s
[Task 16/25] Current/Best: 17.26/ 22.87 GFLOPS | Progress: (20/20) | 13.54 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 16.73/ 19.90 GFLOPS | Progress: (4/20) | 5.41 s
[Task 17/25] Current/Best: 8.73/ 22.00 GFLOPS | Progress: (8/20) | 8.06 s
[Task 17/25] Current/Best: 11.27/ 22.00 GFLOPS | Progress: (12/20) | 10.67 s
[Task 17/25] Current/Best: 20.28/ 22.00 GFLOPS | Progress: (16/20) | 14.97 s
[Task 17/25] Current/Best: 19.40/ 22.00 GFLOPS | Progress: (20/20) | 17.67 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 18.73/ 18.73 GFLOPS | Progress: (4/20) | 4.85 s
[Task 18/25] Current/Best: 4.06/ 18.73 GFLOPS | Progress: (8/20) | 7.63 s
[Task 18/25] Current/Best: 14.07/ 18.73 GFLOPS | Progress: (12/20) | 10.01 s
[Task 18/25] Current/Best: 8.78/ 18.73 GFLOPS | Progress: (16/20) | 18.38 s
[Task 18/25] Current/Best: 17.43/ 18.73 GFLOPS | Progress: (20/20) | 20.70 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 9.22/ 13.64 GFLOPS | Progress: (4/20) | 6.96 s
[Task 19/25] Current/Best: 10.61/ 18.76 GFLOPS | Progress: (8/20) | 11.92 s
[Task 19/25] Current/Best: 7.86/ 19.23 GFLOPS | Progress: (12/20) | 15.04 s
[Task 19/25] Current/Best: 8.30/ 20.36 GFLOPS | Progress: (16/20) | 20.99 s
[Task 19/25] Current/Best: 7.27/ 20.36 GFLOPS | Progress: (20/20) | 25.53 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.53/ 18.11 GFLOPS | Progress: (4/20) | 5.01 s
[Task 20/25] Current/Best: 12.12/ 18.54 GFLOPS | Progress: (8/20) | 14.98 s
[Task 20/25] Current/Best: 3.10/ 18.54 GFLOPS | Progress: (12/20) | 27.00 s
[Task 20/25] Current/Best: 7.34/ 18.54 GFLOPS | Progress: (16/20) | 34.95 s
[Task 20/25] Current/Best: 10.85/ 18.54 GFLOPS | Progress: (20/20) | 38.39 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 8.20/ 12.87 GFLOPS | Progress: (4/20) | 7.29 s
[Task 21/25] Current/Best: 18.12/ 18.57 GFLOPS | Progress: (8/20) | 9.69 s
[Task 21/25] Current/Best: 17.18/ 18.57 GFLOPS | Progress: (12/20) | 13.92 s
[Task 21/25] Current/Best: 1.62/ 19.08 GFLOPS | Progress: (16/20) | 17.11 s
[Task 21/25] Current/Best: 20.39/ 20.67 GFLOPS | Progress: (20/2
0) | 25.82 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 8.08/ 16.88 GFLOPS | Progress: (4/20) | 4.86 s
[Task 22/25] Current/Best: 10.00/ 18.38 GFLOPS | Progress: (8/20) | 6.55 s
[Task 22/25] Current/Best: 11.52/ 18.91 GFLOPS | Progress: (12/20) | 8.59 s
[Task 22/25] Current/Best: 5.17/ 18.91 GFLOPS | Progress: (16/20) | 10.54 s
[Task 22/25] Current/Best: 13.07/ 20.42 GFLOPS | Progress: (20/20) | 12.48 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 20.20/ 20.20 GFLOPS | Progress: (4/20) | 5.50 s
[Task 23/25] Current/Best: 11.05/ 21.95 GFLOPS | Progress: (8/20) | 8.05 s
[Task 23/25] Current/Best: 5.35/ 21.95 GFLOPS | Progress: (12/20) | 11.47 s
[Task 23/25] Current/Best: 6.13/ 22.03 GFLOPS | Progress: (16/20) | 14.73 s
[Task 23/25] Current/Best: 2.70/ 22.03 GFLOPS | Progress: (20/20) | 18.44 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 1.55/ 5.86 GFLOPS | Progress: (4/20) | 13.82 s
[Task 24/25] Current/Best: 8.95/ 8.95 GFLOPS | Progress: (8/20) | 24.77 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 9.40/ 19.03 GFLOPS | Progress: (4/20) | 9.94 s
[Task 1/25] Current/Best: 6.48/ 22.56 GFLOPS | Progress: (8/20) | 12.67 s
[Task 1/25] Current/Best: 14.63/ 23.59 GFLOPS | Progress: (12/20) | 14.84 s
[Task 1/25] Current/Best: 21.90/ 23.59 GFLOPS | Progress: (16/20) | 17.58 s
[Task 1/25] Current/Best: 11.32/ 23.59 GFLOPS | Progress: (20/20) | 20.68 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 7.38/ 17.16 GFLOPS | Progress: (4/20) | 4.49 s
[Task 2/25] Current/Best: 13.51/ 17.16 GFLOPS | Progress: (8/20) | 6.43 s
[Task 2/25] Current/Best: 6.64/ 17.18 GFLOPS | Progress: (12/20) | 8.23 s
[Task 2/25] Current/Best: 6.60/ 17.18 GFLOPS | Progress: (16/20) | 11.02 s
[Task 2/25] Current/Best: 2.15/ 17.18 GFLOPS | Progress: (20/20) | 13.15 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 22.79/ 22.79 GFLOPS | Progress: (4/20) | 5.11 s
[Task 3/25] Current/Best: 17.42/ 22.79 GFLOPS | Progress: (8/20) | 7.72 s
[Task 3/25] Current/Best: 8.65/ 22.79 GFLOPS | Progress: (12/20) | 10.18 s
[Task 3/25] Current/Best: 21.25/ 22.79 GFLOPS | Progress: (16/20) | 13.13 s
[Task 3/25] Current/Best: 3.11/ 22.79 GFLOPS | Progress: (20/20) | 16.25 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 7.54/ 13.64 GFLOPS | Progress: (4/20) | 4.93 s
[Task 4/25] Current/Best: 14.19/ 21.31 GFLOPS | Progress: (8/20) | 10.49 s
[Task 4/25] Current/Best: 3.30/ 21.31 GFLOPS | Progress: (12/20) | 13.56 s
[Task 4/25] Current/Best: 9.88/ 21.31 GFLOPS | Progress: (16/20) | 16.55 s
[Task 4/25] Current/Best: 12.97/ 21.31 GFLOPS | Progress: (20/20) | 18.60 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 3.94/ 14.10 GFLOPS | Progress: (4/20) | 5.48 s
[Task 5/25] Current/Best: 4.13/ 15.58 GFLOPS | Progress: (8/20) | 7.57 s
[Task 5/25] Current/Best: 3.99/ 18.62 GFLOPS | Progress: (12/20) | 9.77 s
[Task 5/25] Current/Best: 15.23/ 18.62 GFLOPS | Progress: (16/20) | 12.30 s
[Task 5/25] Current/Best: 13.47/ 18.62 GFLOPS | Progress: (20/20) | 14.60 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 14.90/ 19.73 GFLOPS | Progress: (4/20) | 6.36 s
[Task 6/25] Current/Best: 17.55/ 19.73 GFLOPS | Progress: (8/20) | 8.88 s
[Task 6/25] Current/Best: 11.00/ 20.87 GFLOPS | Progress: (12/20) | 12.06 s
[Task 6/25] Current/Best: 11.94/ 20.87 GFLOPS | Progress: (16/20) | 15.77 s
[Task 6/25] Current/Best: 13.92/ 22.24 GFLOPS | Progress: (20/20) | 18.49 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 5.82/ 15.56 GFLOPS | Progress: (4/20) | 5.56 s
[Task 7/25] Current/Best: 12.46/ 18.09 GFLOPS | Progress: (8/20) | 8.23 s
[Task 7/25] Current/Best: 17.27/ 18.09 GFLOPS | Progress: (12/20) | 11.09 s
[Task 7/25] Current/Best: 15.21/ 18.09 GFLOPS | Progress: (16/20) | 13.70 s
[Task 7/25] Current/Best: 5.70/ 18.87 GFLOPS | Progress: (20/20) | 16.19 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 15.28/ 15.28 GFLOPS | Progress: (4/20) | 7.28 s
[Task 8/25] Current/Best: 10.65/ 15.28 GFLOPS | Progress: (8/20) | 18.26 s
[Task 8/25] Current/Best: 5.06/ 15.28 GFLOPS | Progress: (12/20) | 22.47 s
[Task 8/25] Current/Best: 3.53/ 19.43 GFLOPS | Progress: (16/20) | 31.30 s
[Task 8/25] Current/Best: 16.11/ 19.43 GFLOPS | Progress: (20/20) | 34.70 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 13.42/ 13.42 GFLOPS | Progress: (4/20) | 10.20 s
[Task 9/25] Current/Best: 16.79/ 16.79 GFLOPS | Progress: (8/20) | 11.88 s
[Task 9/25] Current/Best: 12.39/ 16.79 GFLOPS | Progress: (12/20) | 19.76 s
[Task 9/25] Current/Best: 12.21/ 16.79 GFLOPS | Progress: (16/20) | 23.91 s
[Task 9/25] Current/Best: 13.67/ 18.55 GFLOPS | Progress: (20/20) | 34.97 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 6.51/ 17.36 GFLOPS | Progress: (4/20) | 5.87 s
[Task 10/25] Current/Best: 9.34/ 17.36 GFLOPS | Progress: (8/20) | 9.83 s
[Task 10/25] Current/Best: 18.97/ 21.45 GFLOPS | Progress: (12/20) | 11.75 s Done.
+
[Task 10/25] Current/Best: 5.11/ 21.45 GFLOPS | Progress: (16/20) | 13.61 s
[Task 10/25] Current/Best: 13.60/ 21.45 GFLOPS | Progress: (20/20) | 16.47 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 6.14/ 21.26 GFLOPS | Progress: (4/20) | 5.19 s
[Task 11/25] Current/Best: 15.70/ 21.39 GFLOPS | Progress: (8/20) | 7.45 s
[Task 11/25] Current/Best: 19.53/ 22.84 GFLOPS | Progress: (12/20) | 10.58 s
[Task 11/25] Current/Best: 6.75/ 22.84 GFLOPS | Progress: (16/20) | 13.12 s
[Task 11/25] Current/Best: 16.44/ 22.84 GFLOPS | Progress: (20/20) | 15.58 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 12.99/ 16.02 GFLOPS | Progress: (4/20) | 6.44 s
[Task 12/25] Current/Best: 16.08/ 20.75 GFLOPS | Progress: (8/20) | 9.64 s
[Task 12/25] Current/Best: 8.48/ 20.75 GFLOPS | Progress: (12/20) | 13.07 s
[Task 12/25] Current/Best: 17.99/ 20.75 GFLOPS | Progress: (16/20) | 16.06 s
[Task 12/25] Current/Best: 3.06/ 20.75 GFLOPS | Progress: (20/20) | 19.36 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 7.41/ 14.13 GFLOPS | Progress: (4/20) | 5.87 s
[Task 13/25] Current/Best: 18.80/ 20.60 GFLOPS | Progress: (8/20) | 8.56 s
[Task 13/25] Current/Best: 1.56/ 20.60 GFLOPS | Progress: (12/20) | 12.30 s
[Task 13/25] Current/Best: 11.46/ 20.60 GFLOPS | Progress: (16/20) | 17.02 s
[Task 13/25] Current/Best: 12.18/ 22.56 GFLOPS | Progress: (20/20) | 20.28 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 17.13/ 17.13 GFLOPS | Progress: (4/20) | 4.94 s
[Task 14/25] Current/Best: 6.88/ 17.13 GFLOPS | Progress: (8/20) | 11.76 s
[Task 14/25] Current/Best: 19.22/ 19.22 GFLOPS | Progress: (12/20) | 20.35 s
[Task 14/25] Current/Best: 6.97/ 19.22 GFLOPS | Progress: (16/20) | 28.16 s
[Task 14/25] Current/Best: 10.84/ 19.22 GFLOPS | Progress: (20/20) | 36.59 s Done.
+
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 13.65/ 15.60 GFLOPS | Progress: (4/20) | 5.74 s
[Task 15/25] Current/Best: 12.74/ 20.79 GFLOPS | Progress: (8/20) | 11.06 s
[Task 15/25] Current/Best: 8.20/ 20.79 GFLOPS | Progress: (12/20) | 14.90 s
[Task 15/25] Current/Best: 14.93/ 20.79 GFLOPS | Progress: (16/20) | 17.03 s
[Task 15/25] Current/Best: 8.94/ 20.79 GFLOPS | Progress: (20/20) | 20.64 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 13.65/ 19.26 GFLOPS | Progress: (4/20) | 6.03 s
[Task 16/25] Current/Best: 11.66/ 19.26 GFLOPS | Progress: (8/20) | 7.68 s
[Task 16/25] Current/Best: 21.08/ 21.08 GFLOPS | Progress: (12/20) | 9.82 s
[Task 16/25] Current/Best: 10.73/ 21.08 GFLOPS | Progress: (16/20) | 12.89 s
[Task 16/25] Current/Best: 9.44/ 21.08 GFLOPS | Progress: (20/20) | 16.61 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 21.41/ 21.41 GFLOPS | Progress: (4/20) | 5.67 s
[Task 17/25] Current/Best: 22.49/ 22.49 GFLOPS | Progress: (8/20) | 8.40 s
[Task 17/25] Current/Best: 12.59/ 22.49 GFLOPS | Progress: (12/20) | 12.86 s
[Task 17/25] Current/Best: 22.10/ 22.49 GFLOPS | Progress: (16/20) | 17.16 s
[Task 17/25] Current/Best: 10.27/ 23.82 GFLOPS | Progress: (20/20) | 19.58 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 14.31/ 18.23 GFLOPS | Progress: (4/20) | 5.03 s
[Task 18/25] Current/Best: 9.51/ 18.23 GFLOPS | Progress: (8/20) | 10.59 s
[Task 18/25] Current/Best: 9.76/ 18.23 GFLOPS | Progress: (12/20) | 12.86 s
[Task 18/25] Current/Best: 11.21/ 18.23 GFLOPS | Progress: (16/20) | 15.88 s
[Task 18/25] Current/Best: 10.21/ 18.23 GFLOPS | Progress: (20/20) | 19.10 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 9.42/ 17.58 GFLOPS | Progress: (4/20) | 6.94 s
[Task 19/25] Current/Best: 8.84/ 21.98 GFLOPS | Progress: (8/20) | 11.15 s
[Task 19/25] Current/Best: 11.92/ 21.98 GFLOPS | Progress: (12/20) | 14.57 s
[Task 19/25] Current/Best: 17.52/ 21.98 GFLOPS | Progress: (16/20) | 19.29 s
[Task 19/25] Current/Best: 12.03/ 21.98 GFLOPS | Progress: (20/20) | 21.81 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 2.29/ 11.03 GFLOPS | Progress: (4/20) | 7.06 s
[Task 20/25] Current/Best: 8.44/ 11.03 GFLOPS | Progress: (8/20) | 12.34 s
[Task 20/25] Current/Best: 13.77/ 17.66 GFLOPS | Progress: (12/20) | 20.53 s
[Task 20/25] Current/Best: 17.73/ 17.73 GFLOPS | Progress: (16/20) | 32.23 s
[Task 20/25] Current/Best: 14.40/ 17.73 GFLOPS | Progress: (20/20) | 44.62 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 18.86/ 18.86 GFLOPS | Progress: (4/20) | 10.36 s
[Task 21/25] Current/Best: 18.05/ 18.86 GFLOPS | Progress: (8/20) | 16.94 s
[Task 21/25] Current/Best: 8.83/ 18.86 GFLOPS | Progress: (12/20) | 19.82 s
[Task 21/25] Current/Best: 17.53/ 18.86 GFLOPS | Progress: (16/20) | 30.99 s
[Task 21/25] Current/Best: 7.87/ 22.65 GFLOPS | Progress: (20
/20) | 36.07 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 18.03/ 18.83 GFLOPS | Progress: (4/20) | 4.48 s
[Task 22/25] Current/Best: 17.74/ 18.83 GFLOPS | Progress: (8/20) | 6.14 s
[Task 22/25] Current/Best: 14.85/ 19.77 GFLOPS | Progress: (12/20) | 8.59 s
[Task 22/25] Current/Best: 6.52/ 19.77 GFLOPS | Progress: (16/20) | 13.19 s
[Task 22/25] Current/Best: 12.62/ 19.77 GFLOPS | Progress: (20/20) | 15.32 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 6.12/ 19.27 GFLOPS | Progress: (4/20) | 6.78 s
[Task 23/25] Current/Best: 4.93/ 19.27 GFLOPS | Progress: (8/20) | 12.14 s
[Task 23/25] Current/Best: 10.13/ 19.27 GFLOPS | Progress: (12/20) | 16.80 s
[Task 23/25] Current/Best: 4.55/ 19.95 GFLOPS | Progress: (16/20) | 19.84 s
[Task 23/25] Current/Best: 23.44/ 23.44 GFLOPS | Progress: (20/20) | 23.64 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 3.26/ 9.23 GFLOPS | Progress: (4/20) | 9.77 s
[Task 24/25] Current/Best: 6.27/ 9.23 GFLOPS | Progress: (8/20) | 12.52 s
[Task 24/25] Current/Best: 7.22/ 9.23 GFLOPS | Progress: (12/20) | 23.50 s
[Task 24/25] Current/Best: 2.02/ 9.23 GFLOPS | Progress: (16/20) | 29.59 s
[Task 24/25] Current/Best: 3.73/ 9.23 GFLOPS | Progress: (20/20) | 40.72 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
-
[Task 24/25] Current/Best: 3.77/ 8.95 GFLOPS | Progress: (12/20) | 37.76 s
[Task 24/25] Current/Best: 3.32/ 9.74 GFLOPS | Progress: (16/20) | 50.18 s
[Task 24/25] Current/Best: 2.40/ 9.74 GFLOPS | Progress: (20/20) | 53.61 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 8.44/ 8.78 GFLOPS | Progress: (4/20) | 4.59 s
[Task 25/25] Current/Best: 8.75/ 8.78 GFLOPS | Progress: (8/20) | 15.54 s
[Task 25/25] Current/Best: 9.63/ 9.63 GFLOPS | Progress: (12/20) | 18.19 s
[Task 25/25] Current/Best: 5.41/ 9.63 GFLOPS | Progress: (16/20) | 29.16 s
[Task 25/25] Current/Best: 1.55/ 9.63 GFLOPS | Progress: (20/20) | 41.46 s
+ Done.
+
[Task 25/25] Current/Best: 5.81/ 5.81 GFLOPS | Progress: (4/20) | 13.74 s
[Task 25/25] Current/Best: 1.55/ 8.21 GFLOPS | Progress: (8/20) | 20.17 s
[Task 25/25] Current/Best: 4.15/ 8.21 GFLOPS | Progress: (12/20) | 31.16 s
[Task 25/25] Current/Best: 7.73/ 8.47 GFLOPS | Progress: (16/20) | 35.70 s
[Task 25/25] Current/Best: 9.26/ 9.26 GFLOPS | Progress: (20/20) | 38.31 s
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
- Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 410.5280910100191, 'median': 410.4814308499044, 'std': 1.4833603780222662}
- unoptimized: {'mean': 489.89261000002443, 'median': 489.3413210998915, 'std': 2.9164566676576857}
+ optimized: {'mean': 418.1201219399918, 'median': 416.98401969997576, 'std': 2.7110433287118547}
+ unoptimized: {'mean': 496.82192496000425, 'median': 496.40536309998424, 'std': 1.9683461116534566}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 19.276 seconds)
+ **Total running time of the script:** ( 14 minutes 2.228 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 3d03b18913..9a9495db40 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
- 1.264e-07 secs/op
+ 1.157e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 55420c74f7..1bf1307f51 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, 0x226fda80)), stage(b, placeholder(b, 0xeecd670)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T. [...]
+ [stage(a, placeholder(a, 0x263cb790)), stage(b, placeholder(b, 0x9fb6b40)), 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 65e2704c9a..587f899777 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
=================
-**17:04.001** total execution time for **tutorial** files:
+**17:32.945** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:19.276 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 14:02.228 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:35.220 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:23.834 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.514 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.651 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.344 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.633 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:24.637 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:20.995 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.003 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.375 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.831 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.018 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.175 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.211 | 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 5a5ac44b1b..eaedc1802f 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: 0.000008
+ Numpy running time: 0.000007
naive: 0.000007
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000041
+ vector: 0.000040
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.639470022695604e-06 1.0
- naive 6.629699999999999e-06 0.8678219798368546
- parallel 6.9417e-06 0.9086625092286972
- vector 4.10058e-05 5.3676236542821085
+ numpy 7.0648600012646054e-06 1.0
+ naive 6.832199999999999e-06 0.9670679955125847
+ parallel 7.235999999999999e-06 1.0242241174920323
+ vector 4.03412e-05 5.7101202278288525
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018520
+ Numpy running time: 0.019534
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.350202
+ none: 3.372613
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.299249
+ blocking: 0.302624
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.302404
+ vectorization: 0.265736
# 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: 0.112318
+ loop permutation: 0.116467
# 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: 0.104036
+ array packing: 0.106956
# 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: 0.111050
+ block caching: 0.111907
# 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: 0.131626
+ parallelization: 0.132140
# 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 3.3502015620999996 1.0
- blocking 0.2992493857 0.08932280048022607
- vectorization 0.30240384560000005 0.09026437364874396
- loop permutation 0.1123180525 0.03352575969476771
- array packing 0.10403632999999998 0.031053752459833227
- block caching 0.11105017130000001 0.03314731046522188
- parallelization 0.1316262242 0.039289046273828676
+ none 3.3726134132 1.0
+ blocking 0.30262411269999995 0.08972985504818486
+ vectorization 0.26573622770000005 0.07879237705096608
+ loop permutation 0.11646661380000001 0.034533045899706086
+ array packing 0.10695604639999998 0.03171310591999278
+ block caching 0.11190658319999999 0.03318096961899374
+ parallelization 0.13214015340000002 0.03918034390861979
diff --git a/docs/commit_hash b/docs/commit_hash
index 9d3ea40a35..ab645f38ff 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-4b183daa97f5bd3e9ff8f484bf97a5df8f0d6083
+d81e8809b81b0746d4a7230c10fde858f5a177ba
diff --git a/docs/genindex.html b/docs/genindex.html
index 3d20e78df6..99c71da9d8 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -3749,6 +3749,8 @@
</li>
</ul></li>
<li><a href="reference/api/python/target.html#tvm.target.riscv_cpu">riscv_cpu() (in module tvm.target)</a>
+</li>
+ <li><a href="reference/api/python/topi.html#tvm.topi.nn.rms_norm">rms_norm() (in module tvm.topi.nn)</a>
</li>
<li><a href="reference/api/python/runtime.html#tvm.runtime.rocm">rocm() (in module tvm.runtime)</a>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7d32375390..ca7ff62421 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 32.082 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 35.015 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 1f03a02218..23f933629c 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.zipf7fd45ce-e5e3-40f8-b941-09630fc23b8b 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.zip6a4eafe4-1cc7-4cd9-8ac0-0ac20e8dd14d 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 13bd5d9bd8..a020974c98 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,15 +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]
- 15%|#5 | 6.33M/41.5M [00:00<00:00, 41.4MB/s]
- 25%|##4 | 10.3M/41.5M [00:00<00:00, 36.4MB/s]
- 39%|###8 | 16.0M/41.5M [00:00<00:00, 31.2MB/s]
- 54%|#####3 | 22.3M/41.5M [00:00<00:00, 40.2MB/s]
- 64%|######4 | 26.6M/41.5M [00:00<00:00, 35.1MB/s]
- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.7MB/s]
- 87%|########7 | 36.1M/41.5M [00:01<00:00, 27.9MB/s]
- 95%|#########4| 39.4M/41.5M [00:01<00:00, 25.5MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 29.3MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 51.5MB/s]
+ 27%|##7 | 11.2M/41.5M [00:00<00:00, 39.7MB/s]
+ 39%|###8 | 16.0M/41.5M [00:00<00:00, 28.0MB/s]
+ 58%|#####7 | 24.0M/41.5M [00:00<00:00, 27.7MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 36.9MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 41.6MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 36.5MB/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 ea55a2e205..ea9909a4bd 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 0.985 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.102 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
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<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 07675747e9..2562a21ba6 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,14 +442,15 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 773d6e8ead..c6af7006ff 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 36.155 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.133 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
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<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 b90f3b02e6..662f5a4e43 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>07:14.676</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:32.525</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
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+<td><p>01:42.133</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>
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+<td><p>01:35.015</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>
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+<td><p>01:04.102</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>00:41.856</p></td>
+<td><p>00:43.854</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>00:36.623</p></td>
+<td><p>00:37.259</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>00:34.792</p></td>
+<td><p>00:35.110</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:28.630</p></td>
+<td><p>00:29.512</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>00:27.961</p></td>
+<td><p>00:28.911</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>00:12.749</p></td>
+<td><p>00:13.753</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>00:02.844</p></td>
+<td><p>00:02.876</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 d6df8eebbf..4ca7e2b501 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -840,10 +840,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4226.8723 4225.5390 4232.2363 4223.2627 3.2770
+ 4223.6853 4223.2203 4226.5434 4220.8838 1.9966
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.846 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.941 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
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<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index b098a85f8c..eb3d21232c 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
@@ -448,20 +448,24 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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@@ -471,7 +475,7 @@ to run this tutorial with a real device over rpc.</p>
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</pre></div>
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diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index df0aeb4405..ca81e8087e 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)
- 15.5401 15.4043 16.2310 15.0227 0.4120
+ 15.5451 15.6149 16.1295 14.9890 0.4366
</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 f7dd6d3647..7333f876b9 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,37 +464,39 @@ 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).
@@ -588,7 +590,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 32.834 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 34.647 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 d3da16cc06..b120dae517 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,10 +505,9 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
0%| | 0.00/13.6M [00:00<?, ?B/s]
- 34%|###3 | 4.60M/13.6M [00:00<00:00, 48.2MB/s]
- 68%|######7 | 9.20M/13.6M [00:00<00:00, 31.2MB/s]
- 92%|#########2| 12.5M/13.6M [00:00<00:00, 28.2MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 32.0MB/s]
+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 56.4MB/s]
+ 99%|#########8| 13.4M/13.6M [00:00<00:00, 54.1MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 55.0MB/s]
</pre></div>
</div>
</div>
@@ -599,7 +598,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.2562 89.2117 89.9230 88.7683 0.2514
+ 88.7899 88.7312 91.4161 88.5616 0.3152
</pre></div>
</div>
<div class="admonition note">
@@ -638,7 +637,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.883 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.262 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 667c124261..0d41d8bdf6 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -590,7 +590,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 110.1213 109.9974 117.4976 109.2398 1.0009
+ 109.8250 109.7896 110.8237 109.2390 0.2706
</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 4ca5f56dfa..df55874084 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 11.346 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 7.403 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 b7c60f7a43..c7dbf76e44 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>12:07.853</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:08.471</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:32.834</p></td>
+<td><p>03:34.647</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>02:11.346</p></td>
+<td><p>02:07.403</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>01:27.883</p></td>
+<td><p>01:27.262</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:19.846</p></td>
+<td><p>01:20.941</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>00:56.695</p></td>
+<td><p>00:57.611</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>00:49.504</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
+<td><p>00:50.270</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>00:49.386</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:49.760</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:30.236</p></td>
+<td><p>00:30.573</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>00:30.115</p></td>
+<td><p>00:29.999</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index facd0b4fa1..8d6504e2e8 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.zipcd940e61-6e7e-410a-af10-2fb3b9bce92e 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.zip21af5192-da3f-460a-955e-e977a0d7d28e 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 17eed48588..e8de1559a4 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>00:57.956</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:57.196</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>00:54.071</p></td>
+<td><p>00:53.274</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>00:02.698</p></td>
+<td><p>00:02.713</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>00:01.180</p></td>
+<td><p>00:01.202</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 8f1cf25483..7423b27ec6 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: 23187us [23187us] (48.34%; 48.34%)
-FoldScaleAxis: 24779us [9us] (51.66%; 51.66%)
- FoldConstant: 24770us [1834us] (51.64%; 99.96%)
- InferType: 22936us [22936us] (47.82%; 92.60%)
+InferType: 23327us [23327us] (48.32%; 48.32%)
+FoldScaleAxis: 24954us [8us] (51.68%; 51.68%)
+ FoldConstant: 24945us [1838us] (51.67%; 99.97%)
+ InferType: 23108us [23108us] (47.86%; 92.63%)
</pre></div>
</div>
</div>
@@ -561,10 +561,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 22874us [22874us] (48.36%; 48.36%)
-FoldScaleAxis: 24421us [7us] (51.64%; 51.64%)
- FoldConstant: 24415us [1819us] (51.62%; 99.97%)
- InferType: 22595us [22595us] (47.77%; 92.55%)
+InferType: 23374us [23374us] (47.95%; 47.95%)
+FoldScaleAxis: 25373us [8us] (52.05%; 52.05%)
+ FoldConstant: 25365us [2043us] (52.03%; 99.97%)
+ InferType: 23322us [23322us] (47.84%; 91.94%)
</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 1cedcf3e3f..291e48e8a4 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: 50.781280 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 34.940639 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 ae457e07dd..df62fd6e75 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: 11.369632 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.270390 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 57cbaf0309..02ebca40a1 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: 0.018759
-Baseline: 3.230064
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018685
+Baseline: 3.342723
</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: 0.307181
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.295032
</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: 0.283013
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.274847
</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: 0.115540
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.113569
</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: 0.108210
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110018
</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: 0.111626
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112191
</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: 0.132803
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.133091
</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 8d96224332..32383f6fba 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>00:33.800</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.240</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>00:30.527</p></td>
+<td><p>00:30.639</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
-<td><p>00:02.045</p></td>
+<td><p>00:02.185</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>00:01.227</p></td>
+<td><p>00:01.415</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 43efa91251..b3f24eb360 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>03:47.740</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:50.301</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>01:39.235</p></td>
+<td><p>01:40.103</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>01:18.625</p></td>
+<td><p>01:19.023</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>00:17.124</p></td>
+<td><p>00:18.347</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:16.479</p></td>
+<td><p>00:16.437</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:16.173</p></td>
+<td><p>00:16.285</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.103</p></td>
+<td><p>00:00.105</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index b5dd1c51b4..5e5dae0a9b 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: 0.344 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>
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 8f4f34be6f..0a6594bbeb 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)
- 8.1135 8.1113 8.1203 8.1087 0.0050
+ 8.1215 8.1214 8.1248 8.1182 0.0027
</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 18.625 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.023 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 2d2260c7ca..987b31353e 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)
- 753.2856 753.3022 753.4253 753.1293 0.1214
+ 760.8718 760.9270 762.0119 759.6764 0.9543
</pre></div>
</div>
</div>
@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.235 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.103 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 5f80f5352b..3aaee33f16 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>00:23.563</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.877</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>00:23.526</p></td>
+<td><p>00:23.839</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.021</p></td>
+<td><p>00:00.022</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<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>
+<tr class="row-even"><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>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
+<tr class="row-odd"><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>
<td><p>0.0 MB</p></td>
</tr>
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 eab7a95466..aa8822a2be 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: 0.037153
+Time cost of this operator: 0.037136
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index be18309256..2e275adc5e 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 304.7 98.728 (1, 2, 10, 10, 3) 2 1 [304.7]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.951 0.956 (1, 6, 10, 10) 1 1 [2.951]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.975 0.316 (1, 1, 10, 10, 3) 1 1 [0.975]
-Total_time - 308.625 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.0 98.727 (1, 2, 10, 10, 3) 2 1 [304.0]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.953 0.959 (1, 6, 10, 10) 1 1 [2.953]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.967 0.314 (1, 1, 10, 10, 3) 1 1 [0.967]
+Total_time - 307.92 - - - - -
</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 135.9 98.11 (1, 6, 10, 10, 1) 2 1 [135.9]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.77 1.278 (1, 6, 10, 10) 1 1 [1.77]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.848 0.612 (1, 3, 10, 10, 1) 1 1 [0.848]
-Total_time - 138.518 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 101.2 97.339 (1, 6, 10, 10, 1) 2 1 [101.2]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.73 (1, 6, 10, 10) 1 1 [1.799]
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</pre></div>
</div>
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index 9f4f70946a..d60878c943 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -465,8 +465,7 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
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+100%|##########| 3.42M/3.42M [00:00<00:00, 60.5MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -594,7 +593,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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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 9ddef9adb5..a8fba53425 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/tmpku3g7t96/images/random'
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</pre></div>
</div>
</div>
@@ -593,8 +593,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
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+<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/tmp2p1svjhr/images/target contains 8144 images
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</pre></div>
</div>
</div>
@@ -706,13 +706,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
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Epoch 2/3
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+328/328 - 36s - loss: 0.1016 - accuracy: 0.9634 - val_loss: 0.1121 - val_accuracy: 0.9611 - 36s/epoch - 108ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0679 - accuracy: 0.9757 - val_loss: 0.0970 - val_accuracy: 0.9656 - 35s/epoch - 108ms/step
+328/328 - 35s - loss: 0.0665 - accuracy: 0.9754 - val_loss: 0.1127 - val_accuracy: 0.9641 - 35s/epoch - 108ms/step
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</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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<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
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index c7569e7ff3..2a77b17d31 100644
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+++ b/docs/how_to/work_with_relay/sg_execution_times.html
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<div class="section" id="computation-times">
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<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
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diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
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--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -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">= [...]
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</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
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@@ -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>
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<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
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<g id="edge46" class="edge">
-<title>Node28->Node31</title>
-<path fill="none" stroke="midnightblue" d="M3793.65,-402.38C3856.72,-390.29 3946.16,-373.14 4003.38,-362.16"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3792.93,-398.95 3783.76,-404.27 3794.24,-405.83 3792.93,-398.95"/>
+<title>Node29->Node31</title>
+<path fill="none" stroke="midnightblue" d="M3522.21,-406.93C3424.59,-394.26 3253.99,-372.13 3160.64,-360.02"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3521.93,-410.42 3532.29,-408.23 3522.83,-403.48 3521.93,-410.42"/>
</g>
-<!-- Node28->Node32 -->
+<!-- Node29->Node32 -->
<g id="edge47" class="edge">
-<title>Node28->Node32</title>
-<path fill="none" stroke="midnightblue" d="M3700.91,-394.74C3692.48,-385.12 3682.81,-374.09 3675.34,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3698.41,-397.18 3707.63,-402.4 3703.67,-392.57 3698.41,-397.18"/>
+<title>Node29->Node32</title>
+<path fill="none" stroke="midnightblue" d="M3669.65,-402.38C3732.72,-390.29 3822.16,-373.14 3879.38,-362.16"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3668.93,-398.95 3659.76,-404.27 3670.24,-405.83 3668.93,-398.95"/>
</g>
-<!-- Node29->Node27 -->
-<g id="edge44" class="edge">
-<title>Node29->Node27</title>
-<path fill="none" stroke="midnightblue" d="M3534.67,-262.64C3516.55,-250.37 3494.23,-235.26 3480.56,-226.01"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3532.93,-265.69 3543.17,-268.4 3536.85,-259.89 3532.93,-265.69"/>
+<!-- Node29->Node33 -->
+<g id="edge48" class="edge">
+<title>Node29->Node33</title>
+<path fill="none" stroke="midnightblue" d="M3558.13,-397.99C3537.05,-387.69 3511.22,-375.07 3491.8,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3556.62,-401.15 3567.14,-402.4 3559.69,-394.86 3556.62,-401.15"/>
</g>
-<!-- Node33->Node5 -->
-<g id="edge53" class="edge">
-<title>Node33->Node5</title>
-<path fill="none" stroke="midnightblue" d="M1996.61,-397.44C2015.12,-387.24 2037.52,-374.9 2054.45,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1994.67,-394.51 1987.6,-402.4 1998.05,-400.64 1994.67,-394.51"/>
+<!-- Node30->Node28 -->
+<g id="edge45" class="edge">
+<title>Node30->Node28</title>
+<path fill="none" stroke="midnightblue" d="M3490.36,-262.64C3472.06,-250.37 3449.51,-235.26 3435.7,-226.01"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3488.7,-265.74 3498.95,-268.4 3492.59,-259.92 3488.7,-265.74"/>
</g>
-<!-- Node33->Node8 -->
+<!-- Node34->Node5 -->
<g id="edge54" class="edge">
-<title>Node33->Node8</title>
-<path fill="none" stroke="midnightblue" d="M1950.21,-393.28C1945.51,-384.02 1940.25,-373.66 1936.15,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1947.19,-395.06 1954.84,-402.4 1953.43,-391.89 1947.19,-395.06"/>
-</g>
-<!-- Node34->Node9 -->
-<g id="edge56" class="edge">
-<title>Node34->Node9</title>
-<path fill="none" stroke="midnightblue" d="M1114.77,-398.68C1088.31,-388.26 1055.37,-375.28 1030.73,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1113.61,-401.99 1124.2,-402.4 1116.18,-395.47 1113.61,-401.99"/>
+<title>Node34->Node5</title>
+<path fill="none" stroke="midnightblue" d="M1939.53,-395.6C1928.5,-385.78 1915.66,-374.34 1905.81,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1937.36,-398.36 1947.16,-402.4 1942.02,-393.13 1937.36,-398.36"/>
</g>
-<!-- Node34->Node10 -->
-<g id="edge66" class="edge">
-<title>Node34->Node10</title>
-<path fill="none" stroke="midnightblue" d="M1146.71,-393.59C1135.51,-375.83 1118.11,-351.54 1098,-335 1079.08,-319.44 1054.56,-307.18 1034.19,-298.63"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1143.78,-395.51 1151.98,-402.21 1149.75,-391.85 1143.78,-395.51"/>
+<!-- Node34->Node8 -->
+<g id="edge55" class="edge">
+<title>Node34->Node8</title>
+<path fill="none" stroke="midnightblue" d="M1997.32,-397.44C2015.67,-387.24 2037.88,-374.9 2054.66,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1995.43,-394.48 1988.39,-402.4 1998.83,-400.6 1995.43,-394.48"/>
</g>
-<!-- Node34->Node11 -->
+<!-- Node35->Node9 -->
<g id="edge57" class="edge">
-<title>Node34->Node11</title>
-<path fill="none" stroke="midnightblue" d="M1233.65,-414.87C1421.76,-410.38 1935.19,-396.14 2362,-366 2389.95,-364.03 2420.84,-361.07 2446.98,-358.34"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1233.52,-411.37 1223.6,-415.1 1233.68,-418.37 1233.52,-411.37"/>
+<title>Node35->Node9</title>
+<path fill="none" stroke="midnightblue" d="M1123.3,-397.71C1103.27,-387.46 1078.88,-374.98 1060.5,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1121.95,-400.96 1132.44,-402.4 1125.14,-394.72 1121.95,-400.96"/>
</g>
-<!-- Node34->Node12 -->
+<!-- Node35->Node10 -->
<g id="edge67" class="edge">
-<title>Node34->Node12</title>
-<path fill="none" stroke="midnightblue" d="M1169.23,-393.11C1178.25,-373.75 1193.99,-347.47 1217,-335 1270.36,-306.07 2248.38,-302.01 2309,-299 2361.47,-296.39 2420.97,-292.36 2464.54,-289.21"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1165.93,-391.93 1165.12,-402.49 1172.34,-394.73 1165.93,-391.93"/>
+<title>Node35->Node10</title>
+<path fill="none" stroke="midnightblue" d="M1146.9,-393.62C1136.37,-376.38 1120.49,-352.74 1103,-335 1089.12,-320.91 1070.94,-307.92 1056.59,-298.65"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1144.05,-395.67 1152.18,-402.47 1150.06,-392.09 1144.05,-395.67"/>
</g>
-<!-- Node34->Node13 -->
+<!-- Node35->Node11 -->
<g id="edge58" class="edge">
-<title>Node34->Node13</title>
-<path fill="none" stroke="midnightblue" d="M1086.3,-411.55C972.26,-403.61 746.22,-386.8 555,-366 539.77,-364.34 523.36,-362.28 508.05,-360.24"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1086.1,-415.04 1096.31,-412.24 1086.58,-408.06 1086.1,-415.04"/>
+<title>Node35->Node11</title>
+<path fill="none" stroke="midnightblue" d="M1233.9,-414.99C1426.23,-410.74 1958.12,-396.91 2400,-366 2427.95,-364.04 2458.84,-361.09 2484.98,-358.36"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1233.55,-411.5 1223.63,-415.22 1233.71,-418.5 1233.55,-411.5"/>
</g>
-<!-- Node34->Node14 -->
+<!-- Node35->Node12 -->
<g id="edge68" class="edge">
-<title>Node34->Node14</title>
-<path fill="none" stroke="midnightblue" d="M1086.41,-409.48C1038.62,-402.83 975.98,-390.1 925,-366 904.98,-356.54 906.48,-343.41 886,-335 748.71,-278.6 701.33,-316.97 554,-299 539.49,-297.23 523.88,-295.19 509.2,-293.21"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1086,-412.95 1096.37,-410.81 1086.92,-406.01 1086,-412.95"/>
+<title>Node35->Node12</title>
+<path fill="none" stroke="midnightblue" d="M1233.76,-414.77C1432.75,-409.78 1977.5,-393.9 2155,-366 2207.72,-357.71 2218.8,-346.1 2271,-335 2358.9,-316.31 2461.83,-301.13 2525.99,-292.45"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1233.61,-411.27 1223.7,-415.02 1233.78,-418.27 1233.61,-411.27"/>
</g>
-<!-- Node34->Node15 -->
+<!-- Node35->Node13 -->
<g id="edge59" class="edge">
-<title>Node34->Node15</title>
-<path fill="none" stroke="midnightblue" d="M1086.21,-408.13C1001.06,-398.39 856.83,-381.63 733,-366 717.8,-364.08 701.4,-361.92 686.09,-359.87"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1085.97,-411.63 1096.3,-409.28 1086.76,-404.67 1085.97,-411.63"/>
+<title>Node35->Node13</title>
+<path fill="none" stroke="midnightblue" d="M1086.32,-412.04C966.69,-404.5 722.84,-387.88 517,-366 501.76,-364.38 485.35,-362.33 470.04,-360.29"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1086.26,-415.54 1096.46,-412.67 1086.7,-408.56 1086.26,-415.54"/>
</g>
-<!-- Node34->Node16 -->
+<!-- Node35->Node14 -->
<g id="edge69" class="edge">
-<title>Node34->Node16</title>
-<path fill="none" stroke="midnightblue" d="M1133.95,-395.96C1122.74,-386.94 1109.57,-376.13 1098,-366 1082.86,-352.75 1083.26,-343.47 1065,-335 999.34,-304.55 792.32,-291.58 687.34,-286.84"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1132.09,-398.95 1142.09,-402.46 1136.46,-393.48 1132.09,-398.95"/>
+<title>Node35->Node14</title>
+<path fill="none" stroke="midnightblue" d="M1086.35,-411.15C1037.06,-405.2 971.94,-392.6 920,-366 901.8,-356.68 904.69,-343.3 886,-335 810.3,-301.38 597.27,-308.61 515,-299 500.79,-297.34 485.54,-295.37 471.16,-293.42"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1085.97,-414.63 1096.31,-412.29 1086.77,-407.68 1085.97,-414.63"/>
</g>
-<!-- Node34->Node35 -->
+<!-- Node35->Node15 -->
<g id="edge60" class="edge">
-<title>Node34->Node35</title>
-<path fill="none" stroke="midnightblue" d="M1086.16,-402.79C1024.51,-391.34 937.35,-375.15 877.53,-364.04"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1085.74,-406.27 1096.21,-404.65 1087.02,-399.39 1085.74,-406.27"/>
+<title>Node35->Node15</title>
+<path fill="none" stroke="midnightblue" d="M1086.44,-408.16C972.22,-395.2 754.35,-370.48 648.19,-358.44"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1086.16,-411.65 1096.49,-409.3 1086.95,-404.69 1086.16,-411.65"/>
</g>
-<!-- Node34->Node36 -->
+<!-- Node35->Node16 -->
+<g id="edge70" class="edge">
+<title>Node35->Node16</title>
+<path fill="none" stroke="midnightblue" d="M1086.3,-404.41C1048.3,-396.51 1001.89,-384.21 963,-366 941.66,-356.01 941.81,-343.9 920,-335 830.13,-298.34 716.87,-287.97 648.02,-285.23"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1085.78,-407.88 1096.27,-406.43 1087.17,-401.02 1085.78,-407.88"/>
+</g>
+<!-- Node35->Node36 -->
<g id="edge61" class="edge">
-<title>Node34->Node36</title>
-<path fill="none" stroke="midnightblue" d="M1160.18,-392.23C1161.73,-373.54 1167.14,-348.66 1184,-335 1228.49,-298.95 1608.85,-288.34 1766.24,-285.47"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1156.68,-392.22 1159.66,-402.39 1163.67,-392.58 1156.68,-392.22"/>
+<title>Node35->Node36</title>
+<path fill="none" stroke="midnightblue" d="M1086.16,-402.79C1024.51,-391.34 937.35,-375.15 877.53,-364.04"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1085.74,-406.27 1096.21,-404.65 1087.02,-399.39 1085.74,-406.27"/>
</g>
-<!-- Node34->Node37 -->
-<g id="edge64" class="edge">
-<title>Node34->Node37</title>
-<path fill="none" stroke="midnightblue" d="M1233.52,-408.82C1324.7,-399.21 1484.89,-382.06 1622,-366 1637.62,-364.17 1654.42,-362.12 1670.24,-360.15"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1233.14,-405.34 1223.56,-409.87 1233.87,-412.3 1233.14,-405.34"/>
+<!-- Node35->Node37 -->
+<g id="edge62" class="edge">
+<title>Node35->Node37</title>
+<path fill="none" stroke="midnightblue" d="M1158.67,-392.15C1159.1,-373.63 1163.02,-349.02 1179,-335 1211.76,-306.27 1492.03,-292 1623.47,-286.83"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1155.17,-392.25 1158.76,-402.22 1162.17,-392.19 1155.17,-392.25"/>
</g>
-<!-- Node34->Node38 -->
-<g id="edge70" class="edge">
-<title>Node34->Node38</title>
-<path fill="none" stroke="midnightblue" d="M1196.13,-397.71C1215.84,-387.46 1239.84,-374.98 1257.93,-365.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1194.38,-394.68 1187.12,-402.4 1197.61,-400.89 1194.38,-394.68"/>
+<!-- Node35->Node38 -->
+<g id="edge65" class="edge">
+<title>Node35->Node38</title>
+<path fill="none" stroke="midnightblue" d="M1189.19,-396.73C1204.05,-386.68 1221.76,-374.68 1235.21,-365.58"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1187.15,-393.89 1180.83,-402.4 1191.07,-399.69 1187.15,-393.89"/>
</g>
-<!-- Node34->Node39 -->
+<!-- Node35->Node39 -->
<g id="edge71" class="edge">
-<title>Node34->Node39</title>
-<path fill="none" stroke="midnightblue" d="M1226.06,-399.98C1268.53,-389.37 1323.12,-375.72 1363.56,-365.61"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1224.95,-396.66 1216.1,-402.48 1226.65,-403.45 1224.95,-396.66"/>
+<title>Node35->Node39</title>
+<path fill="none" stroke="midnightblue" d="M1221.48,-399.85C1260.61,-389.25 1310.73,-375.68 1347.9,-365.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1220.52,-396.48 1211.78,-402.48 1222.35,-403.24 1220.52,-396.48"/>
</g>
-<!-- Node34->Node40 -->
+<!-- Node35->Node40 -->
<g id="edge72" class="edge">
-<title>Node34->Node40</title>
-<path fill="none" stroke="midnightblue" d="M1233.74,-406.33C1300.15,-396.89 1400.34,-381.92 1487,-366 1490.09,-365.43 1493.25,-364.83 1496.44,-364.21"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1233.2,-402.88 1223.79,-407.74 1234.18,-409.81 1233.2,-402.88"/>
+<title>Node35->Node40</title>
+<path fill="none" stroke="midnightblue" d="M1233.78,-405.36C1296.07,-395.72 1387.56,-380.99 1467,-366 1470.08,-365.42 1473.24,-364.8 1476.43,-364.17"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1233,-401.94 1223.65,-406.92 1234.07,-408.86 1233,-401.94"/>
</g>
-<!-- Node36->Node6 -->
-<g id="edge62" class="edge">
-<title>Node36->Node6</title>
-<path fill="none" stroke="midnightblue" d="M1905.02,-266.24C1951.74,-255.55 2012.26,-241.7 2056.84,-231.5"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1904.24,-262.83 1895.27,-268.48 1905.8,-269.66 1904.24,-262.83"/>
+<!-- Node35->Node41 -->
+<g id="edge73" class="edge">
+<title>Node35->Node41</title>
+<path fill="none" stroke="midnightblue" d="M1233.73,-410.27C1321.72,-402.32 1473.14,-387.05 1602,-366 1605.1,-365.49 1608.27,-364.94 1611.46,-364.36"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1233.23,-406.8 1223.58,-411.18 1233.85,-413.77 1233.23,-406.8"/>
</g>
-<!-- Node36->Node7 -->
+<!-- Node37->Node6 -->
<g id="edge63" class="edge">
-<title>Node36->Node7</title>
-<path fill="none" stroke="midnightblue" d="M1858.36,-261.84C1879.3,-244.56 1910.67,-219.8 1940,-201 1961.25,-187.39 1986.55,-174.19 2005.85,-164.7"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1856.05,-259.21 1850.6,-268.3 1860.53,-264.59 1856.05,-259.21"/>
+<title>Node37->Node6</title>
+<path fill="none" stroke="midnightblue" d="M1750.14,-265.71C1787.4,-255.14 1834.97,-241.63 1870.29,-231.61"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1749.07,-262.38 1740.41,-268.48 1750.98,-269.11 1749.07,-262.38"/>
</g>
-<!-- Node37->Node36 -->
-<g id="edge65" class="edge">
-<title>Node37->Node36</title>
-<path fill="none" stroke="midnightblue" d="M1767.19,-329.73C1782.05,-319.68 1799.76,-307.68 1813.21,-298.58"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1765.15,-326.89 1758.83,-335.4 1769.07,-332.69 1765.15,-326.89"/>
+<!-- Node37->Node7 -->
+<g id="edge64" class="edge">
+<title>Node37->Node7</title>
+<path fill="none" stroke="midnightblue" d="M1719.18,-262.28C1756.88,-235.24 1822.76,-187.98 1855.45,-164.52"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1716.78,-259.7 1710.69,-268.37 1720.86,-265.39 1716.78,-259.7"/>
</g>
-<!-- Node41->Node22 -->
-<g id="edge74" class="edge">
-<title>Node41->Node22</title>
-<path fill="none" stroke="midnightblue" d="M1035.74,-209.33C1201.68,-195.48 1617.17,-161.02 1965,-134 2563.69,-87.49 3289.91,-35.14 3494.19,-20.49"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1035.32,-205.85 1025.65,-210.17 1035.9,-212.83 1035.32,-205.85"/>
+<!-- Node38->Node37 -->
+<g id="edge66" class="edge">
+<title>Node38->Node37</title>
+<path fill="none" stroke="midnightblue" d="M1333.5,-334.79C1440.31,-317.28 1553.27,-302.06 1623.36,-293.01"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1332.81,-331.36 1323.51,-336.44 1333.95,-338.27 1332.81,-331.36"/>
</g>
-<!-- Node42->Node21 -->
-<g id="edge77" class="edge">
-<title>Node42->Node21</title>
-<path fill="none" stroke="midnightblue" d="M3345.44,-396.2C3371.19,-376.88 3412.95,-348.76 3454,-335 3529.6,-309.66 3757.52,-358.14 3811,-299 3840.21,-266.69 3833.52,-238.28 3811,-201 3797.86,-179.24 3771.88,-166.38 3750.26,-159.1"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3343.17,-393.53 3337.35,-402.38 3347.42,-399.09 3343.17,-393.53"/>
+<!-- Node42->Node22 -->
+<g id="edge75" class="edge">
+<title>Node42->Node22</title>
+<path fill="none" stroke="midnightblue" d="M4133.71,-193.2C4119.63,-174.44 4097.02,-148.43 4071,-134 3894.66,-36.23 3648.48,-19.17 3539.58,-16.63"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="4131.03,-195.46 4139.74,-201.49 4136.69,-191.35 4131.03,-195.46"/>
</g>
-<!-- Node42->Node27 -->
+<!-- Node43->Node21 -->
<g id="edge78" class="edge">
-<title>Node42->Node27</title>
-<path fill="none" stroke="midnightblue" d="M3308.83,-392.79C3295.73,-361.11 3279,-304.7 3306,-268 3325.38,-241.66 3358.41,-228.67 3389.61,-222.4"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3305.7,-394.37 3312.9,-402.15 3312.12,-391.59 3305.7,-394.37"/>
+<title>Node43->Node21</title>
+<path fill="none" stroke="midnightblue" d="M3049.51,-398.15C3033.36,-390.24 3016.41,-379.62 3004,-366 2973.49,-332.52 2955.89,-308.59 2976,-268 3007.09,-205.26 3088.66,-172.89 3135.19,-159.04"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3048.34,-401.47 3058.89,-402.5 3051.29,-395.12 3048.34,-401.47"/>
</g>
-<!-- Node42->Node30 -->
-<g id="edge76" class="edge">
-<title>Node42->Node30</title>
-<path fill="none" stroke="midnightblue" d="M3281,-398.13C3254.44,-385.62 3220.51,-369.64 3200.06,-360.01"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3279.52,-401.3 3290.06,-402.4 3282.5,-394.97 3279.52,-401.3"/>
+<!-- Node43->Node28 -->
+<g id="edge79" class="edge">
+<title>Node43->Node28</title>
+<path fill="none" stroke="midnightblue" d="M3048.6,-397.75C3034.95,-390.08 3021.8,-379.71 3014,-366 3007.19,-354.03 3005.6,-345.92 3014,-335 3052.14,-285.45 3090.12,-316.99 3150,-299 3191.32,-286.58 3200.76,-280.69 3242,-268 3293.6,-252.12 3354.13,-235.67 3390.48,-226.02"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3047.11,-400.91 3057.6,-402.4 3050.33,-394.7 3047.11,-400.91"/>
</g>
-<!-- Node43->Node27 -->
-<g id="edge81" class="edge">
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+<title>Node103->Node52</title>
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+<title>Node103->Node54</title>
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+<title>Node103->Node77</title>
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+<title>Node103->Node80</title>
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+<title>Node103->Node82</title>
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+<title>Node103->Node84</title>
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+<title>Node105->Node42</title>
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+<title>Node105->Node43</title>
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<g id="edge192" class="edge">
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<g id="edge186" class="edge">
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+<title>Node105->Node50</title>
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