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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/07/09 10:22:01 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@a60cd0fecfdbc56187c7632469c5f444f835e99f)
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 7afc27449d deploying docs (apache/tvm@a60cd0fecfdbc56187c7632469c5f444f835e99f)
7afc27449d is described below
commit 7afc27449d8352fe5f926f28ac93c859ed966c13
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sun Jul 9 10:21:52 2023 +0000
deploying docs (apache/tvm@a60cd0fecfdbc56187c7632469c5f444f835e99f)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 22 +-
.../deploy_models/deploy_model_on_adreno.rst.txt | 4 +-
.../deploy_model_on_adreno_tvmc.rst.txt | 2 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 2 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 20 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
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.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 14 +-
.../tune_conv2d_layer_cuda.rst.txt | 2 +-
.../tune_network_cuda.rst.txt | 4 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 10 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 2 +-
.../work_with_microtvm/micro_autotune.rst.txt | 18 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
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.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
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.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
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docs/how_to/compile_models/from_paddle.html | 2 +-
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256 files changed, 71518 insertions(+), 71607 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 b5724684d2..090246b8ab 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 33.642 seconds)
+ **Total running time of the script:** ( 1 minutes 33.833 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 6e6c3db2ed..32a560b683 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.zip593a557b-b42a-4a42-b0d8-1f617b23bd91 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip46666e96-71d4-4e04-92e6-f73863b54583 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 a1040db7cd..abebaa2020 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]
19%|#9 | 7.99M/41.5M [00:00<00:00, 64.8MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 49.8MB/s]
54%|#####3 | 22.3M/41.5M [00:00<00:00, 50.6MB/s]
66%|######5 | 27.3M/41.5M [00:00<00:00, 46.5MB/s]
81%|######## | 33.4M/41.5M [00:00<00:00, 51.6MB/s]
96%|#########6| 40.0M/41.5M [00:00<00:00, 54.2MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 50.0MB/s]
+
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:00, 45.4MB/s]
32%|###1 | 13.2M/41.5M [00:00<00:00, 58.3MB/s]
46%|####5 | 19.0M/41.5M [00:00<00:00, 41.0MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 34.7MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 44.8MB/s]
92%|#########2| 38.3M/41.5M [00:01<00:00, 32.4MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 37.2MB/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 4a50ae8054..6253b26ccd 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.235 seconds)
+ **Total running time of the script:** ( 1 minutes 3.848 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 e24fff609d..58c52f4edc 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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72%|#######1 | 32.0M/44.7M [00:00<00:00, 55.6MB/s]
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+
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100%|##########| 44.7M/44.7M [00:00<00:00, 47.7MB/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 cd8053387a..cb3b97636c 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 38.042 seconds)
+ **Total running time of the script:** ( 1 minutes 38.722 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 1b991abb3a..c472ad1092 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
=================
-**07:19.933** total execution time for **how_to_compile_models** files:
+**07:26.114** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:38.042 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:38.722 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:33.642 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:33.833 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:02.235 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:03.848 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.334 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.796 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.849 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:38.087 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.139 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:35.228 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.182 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.936 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:26.920 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.918 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.706 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.893 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.884 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.852 | 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 f242dd4be5..208cbbaf30 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)
- 4226.1123 4225.5254 4228.8788 4224.3069 1.5572
+ 4227.5178 4227.3995 4230.0351 4225.0069 1.4676
@@ -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 20.518 seconds)
+ **Total running time of the script:** ( 1 minutes 20.589 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 a29ddebaea..c0f99ff8f3 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
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+
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102967424/102967424 [==============================] - 2s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index d701879f96..7917602ab6 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.5095 16.5766 17.1052 15.9641 0.4135
+ 15.8064 15.6825 16.9634 15.3129 0.4632
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 c29162997c..23eb77d496 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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]
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+
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]
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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 34.703 seconds)
+ **Total running time of the script:** ( 3 minutes 36.147 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 f1ecc62748..e28e654016 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -227,7 +227,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
59%|#####8 | 7.99M/13.6M [00:00<00:00, 44.9MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 53.6MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
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100%|##########| 13.6M/13.6M [00:00<00:00, 74.3MB/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)
- 89.0741 89.0495 90.0488 88.6288 0.2377
+ 89.2934 89.2973 90.0723 88.7715 0.2510
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.445 seconds)
+ **Total running time of the script:** ( 1 minutes 28.029 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 701b6b4d6e..17f6b8a24b 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)
- 109.3124 109.2794 110.1223 108.7427 0.2779
+ 110.8880 110.8598 111.7700 110.2543 0.2920
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 5c5948becd..29a6762c97 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:** ( 1 minutes 47.289 seconds)
+ **Total running time of the script:** ( 1 minutes 59.862 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 f6cdad6383..55b78fa7a9 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**11:49.026** total execution time for **how_to_deploy_models** files:
+**12:05.315** 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``) | 03:34.703 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:36.147 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:47.289 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:59.862 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:28.445 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:28.029 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.518 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.589 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:56.797 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:58.097 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:50.814 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.388 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.138 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:50.256 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.799 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.911 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.516 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.031 | 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 ecb90ff276..73c754b27a 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.zip41081310-5aac-45a4-b2b9-b52020b55fef from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip0bd2d045-0ec6-4e87-b76f-9138c4f3fb10 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 a318b87f34..f64320af3b 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
=================
-**00:58.834** total execution time for **how_to_extend_tvm** files:
+**00:58.569** 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``) | 00:54.847 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:54.639 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.761 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.729 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.219 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.190 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.011 | 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 ff2180dfad..4e6b5fb620 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: 23319us [23319us] (48.03%; 48.03%)
- FoldScaleAxis: 25231us [10us] (51.97%; 51.97%)
- FoldConstant: 25221us [1965us] (51.95%; 99.96%)
- InferType: 23256us [23256us] (47.90%; 92.21%)
+ InferType: 23690us [23690us] (48.41%; 48.41%)
+ FoldScaleAxis: 25246us [9us] (51.59%; 51.59%)
+ FoldConstant: 25237us [1825us] (51.57%; 99.96%)
+ InferType: 23412us [23412us] (47.84%; 92.77%)
@@ -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: 23262us [23262us] (48.02%; 48.02%)
- FoldScaleAxis: 25183us [8us] (51.98%; 51.98%)
- FoldConstant: 25174us [1937us] (51.97%; 99.97%)
- InferType: 23237us [23237us] (47.97%; 92.30%)
+ InferType: 23470us [23470us] (48.27%; 48.27%)
+ FoldScaleAxis: 25155us [8us] (51.73%; 51.73%)
+ FoldConstant: 25147us [1888us] (51.72%; 99.97%)
+ InferType: 23259us [23259us] (47.83%; 92.49%)
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 4480189f97..6d527508d2 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.516929 ms
+ Convolution: 36.036800 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 cd771663cc..e129baa610 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.123843 ms
+ conv2d with tensor core: 12.267725 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 743d439490..dd61f60ba6 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: 0.018695
- Baseline: 3.297293
+ Numpy running time: 0.018630
+ Baseline: 3.268179
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.296455
+ Opt1: 0.303675
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.275121
+ Opt2: 0.283828
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118055
+ Opt3: 0.117801
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.108069
+ Opt4: 0.107550
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111617
+ Opt5: 0.112012
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.133539
+ Opt6: 0.134036
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 3fc28eaa8a..0da28e3380 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.113** total execution time for **how_to_optimize_operators** files:
+**00:34.364** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.571 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.891 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.135 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.132 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.407 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.340 | 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 bd4228b996..e525035019 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
=================
-**03:50.270** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:52.031** 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``) | 01:40.561 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:40.674 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:19.368 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:20.012 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.286 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.987 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.593 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.683 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.359 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.571 | 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``) | 00:00.104 | 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 e105c5249e..752732d707 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: 0.343 ms
+ Execution time of this operator: 0.353 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 9d8403d61e..a091a09997 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)
- 8.1556 8.1546 8.1616 8.1508 0.0045
+ 8.1237 8.1188 8.1356 8.1168 0.0085
@@ -674,7 +674,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.368 seconds)
+ **Total running time of the script:** ( 1 minutes 20.012 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 956f7d3e08..48c5186a52 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)
- 762.8610 763.0856 763.6271 761.8703 0.7346
+ 760.7598 760.9998 762.9208 758.3587 1.8702
@@ -693,7 +693,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 40.561 seconds)
+ **Total running time of the script:** ( 1 minutes 40.674 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 7d25106c5f..71b1761ebc 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
=================
-**00:23.581** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.829** 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``) | 00:23.544 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.787 | 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``) | 00:00.026 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
-+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
++--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 38f311509f..8e59151d37 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: 0.037317
+ Time cost of this operator: 0.037289
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 5d4c12c22f..52f5b4d427 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 330.4 98.834 (1, 2, 10, 10, 3) 2 1 [330.4]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.901 0.868 (1, 6, 10, 10) 1 1 [2.901]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.996 0.298 (1, 1, 10, 10, 3) 1 1 [0.996]
- Total_time - 334.297 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 305.3 98.717 (1, 2, 10, 10, 3) 2 1 [305.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.948 0.953 (1, 6, 10, 10) 1 1 [2.948]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.021 0.33 (1, 1, 10, 10, 3) 1 1 [1.021]
+ Total_time - 309.269 - - - - -
@@ -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 102.6 97.411 (1, 6, 10, 10, 1) 2 1 [102.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.772 1.683 (1, 6, 10, 10) 1 1 [1.772]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.906 (1, 1, 10, 10, 3) 1 1 [0.955]
- Total_time - 105.327 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 140.7 98.147 (1, 6, 10, 10, 1) 2 1 [140.7]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.255 (1, 6, 10, 10) 1 1 [1.799]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.858 0.598 (1, 3, 10, 10, 1) 1 1 [0.858]
+ Total_time - 143.357 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.685 seconds)
+ **Total running time of the script:** ( 1 minutes 29.693 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 b50f25a38a..56959d393f 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]
100%|##########| 3.42M/3.42M [00:00<00:00, 81.7MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
61%|###### | 2.09M/3.42M [00:00<00:00, 11.2MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 17.8MB/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 30.014 seconds)
+ **Total running time of the script:** ( 1 minutes 30.395 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 1e54e9295b..5a3b800e1b 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/tmppd11vpjq/images/random'
+ '/tmp/tmp58fil9c1/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmppd11vpjq/images/target contains 8144 images
- /tmp/tmppd11vpjq/images/random contains 5000 images
+ /tmp/tmp58fil9c1/images/target contains 8144 images
+ /tmp/tmp58fil9c1/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 40s - loss: 0.2200 - accuracy: 0.9229 - val_loss: 0.0987 - val_accuracy: 0.9671 - 40s/epoch - 122ms/step
+ 328/328 - 43s - loss: 0.2367 - accuracy: 0.9224 - val_loss: 0.1440 - val_accuracy: 0.9513 - 43s/epoch - 132ms/step
Epoch 2/3
- 328/328 - 36s - loss: 0.1004 - accuracy: 0.9656 - val_loss: 0.1099 - val_accuracy: 0.9603 - 36s/epoch - 108ms/step
+ 328/328 - 37s - loss: 0.0973 - accuracy: 0.9642 - val_loss: 0.1058 - val_accuracy: 0.9615 - 37s/epoch - 112ms/step
Epoch 3/3
- 328/328 - 36s - loss: 0.0691 - accuracy: 0.9743 - val_loss: 0.1061 - val_accuracy: 0.9615 - 36s/epoch - 108ms/step
+ 328/328 - 35s - loss: 0.0700 - accuracy: 0.9728 - val_loss: 0.2164 - val_accuracy: 0.9316 - 35s/epoch - 108ms/step
- <keras.callbacks.History object at 0x7fd269e33bb0>
+ <keras.callbacks.History object at 0x7f97e15062b0>
@@ -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 12.548 seconds)
+ **Total running time of the script:** ( 4 minutes 22.932 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 52362a1490..b9cdb344ae 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
@@ -5,24 +5,24 @@
Computation times
=================
-**07:39.213** total execution time for **how_to_work_with_microtvm** files:
+**07:54.885** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:12.548 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:22.932 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:30.014 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:30.395 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.685 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:29.693 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.500 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.828 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.798 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:10.316 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.668 | 0.0 MB |
-+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``) | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.721 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``) | 00:00.000 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_mlperftiny.py` (``micro_mlperftiny.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 140ad74aef..111ef15990 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:40.244** total execution time for **how_to_work_with_relay** files:
+**00:40.870** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.094 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.277 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.311 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.314 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.833 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.273 | 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 e97c5e8302..c7a7612430 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 0x7fd27d781c10>
+ <function my_cuda_math_rule at 0x7f9ac40be940>
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 07a946d7ad..2e88f3830e 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
=================
-**00:06.183** total execution time for **how_to_work_with_schedules** files:
+**00:06.992** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.219 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.552 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.150 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.620 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.767 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.769 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.755 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.759 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_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``) | 00:00.119 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.084 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.083 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.059 | 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 14716c0485..7a884da54d 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
=================
-**00:36.310** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:35.519** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:36.302 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.512 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 51241b848f..6b57c63404 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -293,7 +293,7 @@ The compilation steps are:
warnings.warn(
/workspace/vta/tutorials/frontend/deploy_classification.py:212: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
graph, lib, params = relay.build(
- resnet18_v1 inference graph built in 38.15s!
+ resnet18_v1 inference graph built in 38.38s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.504 seconds)
+ **Total running time of the script:** ( 1 minutes 5.806 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 ec1d27cbc6..b5ea9aa66b 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 26.46s!
+ yolov3-tiny inference graph built in 26.43s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.975 seconds)
+ **Total running time of the script:** ( 1 minutes 9.484 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 f809cbcf38..de2e13e56b 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
=================
-**02:15.479** total execution time for **topic_vta_tutorials_frontend** files:
+**02:15.290** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:09.975 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:09.484 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:05.504 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:05.806 | 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 41e75d2e91..c765be7fb1 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
=================
-**00:03.441** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.469** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.880 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.913 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.561 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.557 | 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 c41bd047bc..b05f51c4bd 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
=================
-**00:00.937** total execution time for **topic_vta_tutorials** files:
+**00:00.984** 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``) | 00:00.527 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.457 | 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 03d6f66817..b8443cd14c 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -207,6 +207,13 @@ trials, we can load the best schedule from the log file and apply it.
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ *E
+
+
@@ -318,7 +325,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 94.761 ms
+ Execution time of this operator: 94.036 ms
@@ -434,7 +441,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.778 seconds)
+ **Total running time of the script:** ( 1 minutes 44.823 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 7acbd9a49b..9fa23350a2 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,198 +454,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 12.57/12.57 result: MeasureResult(costs=(0.0213560062,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6900322437286377, timestamp=1688887790.2731388) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
- No: 2 GFLOPS: 9.16/12.57 result: MeasureResult(costs=(0.0292996068,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7408277988433838, timestamp=1688887791.011845) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
- No: 3 GFLOPS: 11.84/12.57 result: MeasureResult(costs=(0.0226773216,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.640437126159668, timestamp=1688887791.6343591) [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
- No: 4 GFLOPS: 11.31/12.57 result: MeasureResult(costs=(0.0237338246,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7278769016265869, timestamp=1688887792.2727885) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
- No: 5 GFLOPS: 11.69/12.57 result: MeasureResult(costs=(0.0229708924,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6505699157714844, timestamp=1688887793.0357366) [('tile_y', [-1, 32]), ('tile_x', [-1, 32])],None,55
- No: 6 GFLOPS: 0.00/12.57 result: Traceback (most recent call last):
- File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 712, in __call__
- yield remote, remote.load_module(os.path.split(build_result.filename)[1])
- File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 679, in run_through_rpc
- costs = time_f(*args).results
- File "/workspace/python/tvm/runtime/module.py", line 393, in evaluator
- blob = feval(*args)
- File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
- File "tvm/_ffi/_cython/./packed_func.pxi", line 262, in tvm._ffi._cy3.core.FuncCall
- File "tvm/_ffi/_cython/./packed_func.pxi", line 251, in tvm._ffi._cy3.core.FuncCall3
- File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
- tvm.error.RPCSessionTimeoutError: Traceback (most recent call last):
- 41: 0xffffffffffffffff
- 40: _start
- 39: __libc_start_main
- 38: 0x00007f48cc5aed8f
- 37: Py_BytesMain
- 36: Py_RunMain
- 35: 0x00000000005f3021
- 34: PyObject_Call
- 33: _PyFunction_Vectorcall
- 32: _PyEval_EvalCodeWithName
- 31: _PyEval_EvalFrameDefault
- 30: _PyFunction_Vectorcall
- 29: _PyEval_EvalCodeWithName
- 28: _PyEval_EvalFrameDefault
- 27: 0x000000000051546f
- 26: 0x00000000005dabd0
- 25: PyEval_EvalCode
- 24: _PyEval_EvalCodeWithName
- 23: _PyEval_EvalFrameDefault
- 22: _PyFunction_Vectorcall
- 21: _PyEval_EvalCodeWithName
- 20: _PyEval_EvalFrameDefault
- 19: PyObject_Call
- 18: _PyFunction_Vectorcall
- 17: _PyEval_EvalCodeWithName
- 16: _PyEval_EvalFrameDefault
- 15: PyObject_Call
- 14: _PyFunction_Vectorcall
- 13: _PyEval_EvalCodeWithName
- 12: _PyEval_EvalFrameDefault
- 11: PyObject_Call
- 10: __pyx_pw_3tvm_4_ffi_4_cy3_4core_14PackedFuncBase_5__call__
- at tvm/_ffi/_cython/core.cpp:8724
- 9: __pyx_pf_3tvm_4_ffi_4_cy3_4core_14PackedFuncBase_4__call__
- at tvm/_ffi/_cython/core.cpp:8760
- 8: __pyx_f_3tvm_4_ffi_4_cy3_4core_FuncCall
- at tvm/_ffi/_cython/core.cpp:7821
- 7: __pyx_f_3tvm_4_ffi_4_cy3_4core_FuncCall3
- at tvm/_ffi/_cython/core.cpp:7699
- 6: tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
- at /workspace/src/runtime/rpc/rpc_module.cc:129
- 5: tvm::runtime::RPCClientSession::CallFunc(void*, TVMValue const*, int const*, int, std::function<void (tvm::runtime::TVMArgs)> const&)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:1016
- 4: tvm::runtime::RPCEndpoint::CallFunc(void*, TVMValue const*, int const*, int, std::function<void (tvm::runtime::TVMArgs)>)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:807
- 3: tvm::runtime::RPCEndpoint::HandleUntilReturnEvent(bool, std::function<void (tvm::runtime::TVMArgs)>)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:651
- 2: tvm::runtime::RPCEndpoint::EventHandler::HandleNextEvent(bool, bool, std::function<void (tvm::runtime::TVMArgs)>)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:136
- 1: tvm::runtime::RPCEndpoint::EventHandler::HandleProcessPacket(std::function<void (tvm::runtime::TVMArgs)>)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:316
- 0: tvm::runtime::RPCEndpoint::EventHandler::HandleReturn(tvm::runtime::RPCCode, std::function<void (tvm::runtime::TVMArgs)>)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:380
- File "/workspace/src/runtime/rpc/rpc_endpoint.cc", line 380
- RPCSessionTimeoutError: Your 10.0s session has expired, try to increase the "session_timeout" value.
-
- During handling of the above exception, another exception occurred:
-
- Traceback (most recent call last):
- File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 679, in run_through_rpc
- costs = time_f(*args).results
- File "/usr/lib/python3.8/contextlib.py", line 131, in __exit__
- self.gen.throw(type, value, traceback)
- File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 716, in __call__
- remote.remove(build_result.filename)
- File "/workspace/python/tvm/rpc/client.py", line 145, in remove
- self._remote_funcs["remove"] = self.get_function("tvm.rpc.server.remove")
- File "/workspace/python/tvm/rpc/client.py", line 73, in get_function
- return self._sess.get_function(name)
- File "/workspace/python/tvm/runtime/module.py", line 171, in get_function
- check_call(
- File "/workspace/python/tvm/_ffi/base.py", line 348, in check_call
- raise get_last_ffi_error()
- tvm.error.InternalError: Traceback (most recent call last):
- 50: 0xffffffffffffffff
- 49: _start
- 48: __libc_start_main
- 47: 0x00007f48cc5aed8f
- 46: Py_BytesMain
- 45: Py_RunMain
- 44: 0x00000000005f3021
- 43: PyObject_Call
- 42: _PyFunction_Vectorcall
- 41: _PyEval_EvalCodeWithName
- 40: _PyEval_EvalFrameDefault
- 39: _PyFunction_Vectorcall
- 38: _PyEval_EvalCodeWithName
- 37: _PyEval_EvalFrameDefault
- 36: 0x000000000051546f
- 35: 0x00000000005dabd0
- 34: PyEval_EvalCode
- 33: _PyEval_EvalCodeWithName
- 32: _PyEval_EvalFrameDefault
- 31: _PyFunction_Vectorcall
- 30: _PyEval_EvalCodeWithName
- 29: _PyEval_EvalFrameDefault
- 28: PyObject_Call
- 27: _PyFunction_Vectorcall
- 26: _PyEval_EvalCodeWithName
- 25: _PyEval_EvalFrameDefault
- 24: 0x0000000000521f82
- 23: _PyFunction_Vectorcall
- 22: _PyEval_EvalFrameDefault
- 21: 0x000000000052f0a9
- 20: 0x0000000000626e1c
- 19: 0x0000000000626f00
- 18: 0x00000000005291f0
- 17: _PyEval_EvalFrameDefault
- 16: _PyFunction_Vectorcall
- 15: _PyEval_EvalFrameDefault
- 14: _PyFunction_Vectorcall
- 13: _PyEval_EvalFrameDefault
- 12: _PyFunction_Vectorcall
- 11: _PyEval_EvalCodeWithName
- 10: _PyEval_EvalFrameDefault
- 9: _PyObject_MakeTpCall
- 8: 0x00007f48cc3ca429
- 7: _ctypes_callproc
- 6: 0x00007f48cc12b492
- 5: 0x00007f48cc12ee2d
- 4: tvm::runtime::ModuleNode::GetFunction(tvm::runtime::String const&, bool)
- at /workspace/src/runtime/module.cc:66
- 3: tvm::runtime::RPCModuleNode::GetFunction(tvm::runtime::String const&, tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)
- at /workspace/src/runtime/rpc/rpc_module.cc:187
- 2: tvm::runtime::RPCClientSession::GetFunction(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:1011
- 1: tvm::runtime::TVMRetValue tvm::runtime::RPCEndpoint::SysCallRemote<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&>(tvm::runtime::RPCCode, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)
- at /workspace/src/runtime/rpc/rpc_endpoint.h:223
- 0: operator()
- at /workspace/src/runtime/rpc/rpc_endpoint.cc:688
- File "/workspace/src/runtime/rpc/rpc_endpoint.cc", line 688
- InternalError: Check failed: (code == RPCCode::kReturn) is false: code=1
-
- Traceback (most recent call last):
- 50: 0xffffffffffffffff
- 49: _start
- 48: __libc_start_main
- 47: 0x00007f48cc5aed8f
- 46: Py_BytesMain
- 45: Py_RunMain
- 44: 0x00000000005f3021
- 43: PyObject_Call
- 42: _PyFunction_Vectorcall
- 41: _PyEval_EvalCodeWithName
- 40: _PyEval_EvalFrameDefault
- 39: _PyFunction_Vectorcall
- 38: _PyEval_EvalCodeWithName
- 37: _PyEval_EvalFrameDefault
- 36: 0x000000000051546f
- 35: 0x00000000005dabd0
- 34: PyEval_EvalCode
- 33: _PyEval_EvalCodeWithName
- 32: _PyEval_EvalFrameDefault
- 31: _PyFunction_Vectorcall
- 30: _PyEval_EvalCodeWithName
- 29: _PyEval_EvalFrameDefault
- 28: PyObject_Call
- 27: _PyFunction_Vectorcall
- 26: _PyEval_EvalCodeWithName
- 25: _PyEval_EvalFrameDefault
- 24: 0x0000000000521f82
- 23: _PyFunction_Vectorcall
- 22: _PyEval_EvalFrameDefault
- 21: 0x000000000052f0a9
- 20: 0x0000000000626e1c
- 19: 0x0000000000626f00
- 18: 0x00000000005291f0
- 17: _PyEval_EvalFrameDefault
- 16: _PyFunction_Vectorcall
- 15: _PyEval_EvalFrameDefault
- 14: _PyFunction_Vectorcal [('tile_y', [-1, 512]), ('tile_x', [-1, 1])],None,9
- No: 7 GFLOPS: 11.16/12.57 result: MeasureResult(costs=(0.024046382800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6556534767150879, timestamp=1688887804.338284) [('tile_y', [-1, 16]), ('tile_x', [-1, 256])],None,84
- No: 8 GFLOPS: 9.88/12.57 result: MeasureResult(costs=(0.0271734774,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7077329158782959, timestamp=1688887805.0333476) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
- No: 9 GFLOPS: 3.61/12.57 result: MeasureResult(costs=(0.0743743854,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4171814918518066, timestamp=1688887806.5624955) [('tile_y', [-1, 256]), ('tile_x', [-1, 8])],None,38
- No: 10 GFLOPS: 2.12/12.57 result: MeasureResult(costs=(0.12661515939999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.2517290115356445, timestamp=1688887808.8506064) [('tile_y', [-1, 32]), ('tile_x', [-1, 4])],None,25
+ No: 1 GFLOPS: 2.01/2.01 result: MeasureResult(costs=(0.1332824924,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.398159980773926, timestamp=1688893977.8324547) [('tile_y', [-1, 8]), ('tile_x', [-1, 1])],None,3
+ No: 2 GFLOPS: 11.38/11.38 result: MeasureResult(costs=(0.0235828566,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6757068634033203, timestamp=1688893978.478863) [('tile_y', [-1, 64]), ('tile_x', [-1, 512])],None,96
+ No: 3 GFLOPS: 6.13/11.38 result: MeasureResult(costs=(0.0437800284,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9717411994934082, timestamp=1688893979.4477112) [('tile_y', [-1, 16]), ('tile_x', [-1, 4])],None,24
+ No: 4 GFLOPS: 0.49/11.38 result: MeasureResult(costs=(0.5427402014,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.930859088897705, timestamp=1688893988.3890743) [('tile_y', [-1, 32]), ('tile_x', [-1, 1])],None,5
+ No: 5 GFLOPS: 12.35/12.35 result: MeasureResult(costs=(0.0217401226,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7130405902862549, timestamp=1688893989.2144935) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
+ No: 6 GFLOPS: 1.02/12.35 result: MeasureResult(costs=(0.26311481800000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.486556053161621, timestamp=1688893993.6908717) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+ No: 7 GFLOPS: 1.76/12.35 result: MeasureResult(costs=(0.1525950734,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.6902096271514893, timestamp=1688893996.3894851) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
+ No: 8 GFLOPS: 3.78/12.35 result: MeasureResult(costs=(0.0710576206,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4146358966827393, timestamp=1688893997.7917156) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
+ No: 9 GFLOPS: 9.11/12.35 result: MeasureResult(costs=(0.0294794228,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7677388191223145, timestamp=1688893998.670842) [('tile_y', [-1, 512]), ('tile_x', [-1, 64])],None,69
+ No: 10 GFLOPS: 6.82/12.35 result: MeasureResult(costs=(0.0393871998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8636128902435303, timestamp=1688893999.5679152) [('tile_y', [-1, 512]), ('tile_x', [-1, 16])],None,49
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index cd3ccbf9b0..6f75d8cf56 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': 495.82078362989705, 'median': 496.2372947498807, 'std': 1.7682756869910616}
+ {'mean': 476.76003235996177, 'median': 476.3056827498076, 'std': 1.5709036914563033}
@@ -582,30 +582,30 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 3.41/ 9.89 GFLOPS | Progress: (4/20) | 9.19 s
[Task 1/25] Current/Best: 15.20/ 15.20 GFLOPS | Progress: (8/20) | 17.04 s
[Task 1/25] Current/Best: 7.49/ 15.80 GFLOPS | Progress: (12/20) | 23.31 s
[Task 1/25] Current/Best: 12.83/ 20.82 GFLOPS | Progress: (16/20) | 25.55 s
[Task 1/25] Current/Best: 10.60/ 20.82 GFLOPS | Progress: (20/20) | 29.23 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 10.27/ 12.05 GFLOPS | Progress: (4/20) | 5.04 s
[Task 2/25] Current/Best: 7.42/ 16.18 GFLOPS | Progress: (8/20) | 6.59 s
[Task 2/25] Current/Best: 12.98/ 19.54 GFLOPS | Progress: (12/20) | 8.40 s
[Task 2/25] Current/Best: 7.41/ 19.54 GFLOPS | Progress: (16/20) | 10.30 s
[Task 2/25] Current/Best: 11.24/ 19.54 GFLOPS | Progress: (20/20) | 12.25 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 12.82/ 15.30 GFLOPS | Progress: (4/20) | 5.66 s
[Task 3/25] Current/Best: 9.99/ 15.30 GFLOPS | Progress: (8/20) | 7.99 s
[Task 3/25] Current/Best: 15.36/ 15.36 GFLOPS | Progress: (12/20) | 10.61 s
[Task 3/25] Current/Best: 6.47/ 19.99 GFLOPS | Progress: (16/20) | 13.09 s
[Task 3/25] Current/Best: 4.74/ 22.50 GFLOPS | Progress: (20/20) | 15.67 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 12.98/ 12.98 GFLOPS | Progress: (4/20) | 4.84 s
[Task 4/25] Current/Best: 10.99/ 12.98 GFLOPS | Progress: (8/20) | 7.67 s
[Task 4/25] Current/Best: 15.82/ 15.82 GFLOPS | Progress: (12/20) | 13.18 s
[Task 4/25] Current/Best: 7.29/ 19.66 GFLOPS | Progress: (16/20) | 15.26 s
[Task 4/25] Current/Best: 5.18/ 19.66 GFLOPS | Progress: (20/20) | 17.60 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 21.02/ 21.02 GFLOPS | Progress: (4/20) | 4.86 s
[Task 5/25] Current/Best: 3.68/ 21.02 GFLOPS | Progress: (8/20) | 6.98 s
[Task 5/25] Current/Best: 4.31/ 21.02 GFLOPS | Progress: (12/20) | 9.48 s
[Task 5/25] Current/Best: 14.55/ 21.02 GFLOPS | Progress: (16/20) | 11.21 s
[Task 5/25] Current/Best: 3.25/ 21.02 GFLOPS | Progress: (20/20) | 13.76 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 18.59/ 18.59 GFLOPS | Progress: (4/20) | 4.89 s
[Task 6/25] Current/Best: 16.84/ 18.59 GFLOPS | Progress: (8/20) | 8.09 s
[Task 6/25] Current/Best: 11.11/ 18.59 GFLOPS | Progress: (12/20) | 10.82 s
[Task 6/25] Current/Best: 6.78/ 18.59 GFLOPS | Progress: (16/20) | 14.07 s
[Task 6/25] Current/Best: 14.88/ 18.59 GFLOPS | Progress: (20/20) | 16.57 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 9.51/ 15.18 GFLOPS | Progress: (4/20) | 5.18 s
[Task 7/25] Current/Best: 13.83/ 21.93 GFLOPS | Progress: (8/20) | 7.54 s
[Task 7/25] Current/Best: 14.59/ 21.93 GFLOPS | Progress: (12/20) | 11.04 s
[Task 7/25] Current/Best: 18.83/ 21.93 GFLOPS | Progress: (16/20) | 13.50 s
[Task 7/25] Current/Best: 11.66/ 21.93 GFLOPS | Progress: (20/20) | 17.28 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.27/ 14.03 GFLOPS | Progress: (4/20) | 8.02 s
[Task 8/25] Current/Best: 16.18/ 16.18 GFLOPS | Progress: (8/20) | 10.47 s
[Task 8/25] Current/Best: 11.85/ 16.18 GFLOPS | Progress: (12/20) | 16.21 s
[Task 8/25] Current/Best: 14.02/ 16.18 GFLOPS | Progress: (16/20) | 19.94 s
[Task 8/25] Current/Best: 13.78/ 16.18 GFLOPS | Progress: (20/20) | 31.53 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 13.89/ 16.73 GFLOPS | Progress: (4/20) | 4.68 s
[Task 9/25] Current/Best: 9.47/ 16.73 GFLOPS | Progress: (8/20) | 8.54 s
[Task 9/25] Current/Best: 6.43/ 16.73 GFLOPS | Progress: (12/20) | 10.52 s
[Task 9/25] Current/Best: 4.93/ 16.73 GFLOPS | Progress: (16/20) | 12.95 s
[Task 9/25] Current/Best: 20.94/ 20.94 GFLOPS | Progress: (20/20) | 14.73 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 4.39/ 14.94 GFLOPS | Progress: (4/20) | 4.82 s
[Task 10/25] Current/Best: 3.96/ 15.12 GFLOPS | Progress: (8/20) | 7.50 s
[Task 10/25] Current/Best: 5.39/ 15.12 GFLOPS | Progress: (12/20) | 9.56 s
[Task 10/25] Current/Best: 18.89/ 18.89 GFLOPS | Progress: (16/20) | 12.47 s
[Task 10/25] Current/Best: 15.27/ 18.89 GFLOPS | Progress: (20/20) | 15.06 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 8.48/ 23.89 GFLOPS | Progress: (4/20) | 5.60 s
[Task 11/25] Current/Best: 8.17/ 23.89 GFLOPS | Progress: (8/20) | 7.73 s
[Task 11/25] Current/Best: 21.45/ 23.89 GFLOPS | Progress: (12/20) | 10.21 s
[Task 11/25] Current/Best: 19.44/ 23.89 GFLOPS | Progress: (16/20) | 12.61 s
[Task 11/25] Current/Best: 16.45/ 23.89 GFLOPS | Progress: (20/20) | 14.60 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 16.83/ 16.83 GFLOPS | Progress: (4/20) | 8.98 s
[Task 12/25] Current/Best: 13.21/ 16.83 GFLOPS | Progress: (8/20) | 15.04 s
[Task 12/25] Current/Best: 3.02/ 16.92 GFLOPS | Progress: (12/20) | 17.66 s
[Task 12/25] Current/Best: 18.00/ 18.72 GFLOPS | Progress: (16/20) | 23.48 s
[Task 12/25] Current/Best: 12.65/ 18.72 GFLOPS | Progress: (20/20) | 26.16 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 17.01/ 18.26 GFLOPS | Progress: (4/20) | 5.45 s
[Task 13/25] Current/Best: 12.61/ 18.26 GFLOPS | Progress: (8/20) | 9.20 s
[Task 13/25] Current/Best: 6.21/ 18.26 GFLOPS | Progress: (12/20) | 13.35 s
[Task 13/25] Current/Best: 1.56/ 20.47 GFLOPS | Progress: (16/20) | 18.47 s
[Task 13/25] Current/Best: 19.15/ 20.47 GFLOPS | Progress: (20/20) | 23.35 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 14.57/ 15.00 GFLOPS | Progress: (4/20) | 14.23 s
[Task 14/25] Current/Best: 13.74/ 15.00 GFLOPS | Progress: (8/20) | 18.94 s
[Task 14/25] Current/Best: 11.70/ 15.00 GFLOPS | Progress: (12/20) | 23.07 s
[Task 14/25] Current/Best: 8.38/ 17.79 GFLOPS | Progress: (16/20) | 25.59 s
[Task 14/25] Current/Best: 7.58/ 17.79 GFLOPS | Progress: (20/20) | 35.26 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 11.26/ 11.26 GFLOPS | Progress: (4/20) | 6.05 s
[Task 15/25] Current/Best: 13.76/ 18.32 GFLOPS | Progress: (8/20) | 7.98 s
[Task 15/25] Current/Best: 6.75/ 18.32 GFLOPS | Progress: (12/20) | 14.46 s
[Task 15/25] Current/Best: 16.25/ 18.32 GFLOPS | Progress: (16/20) | 20.78 s
[Task 15/25] Current/Best: 11.76/ 18.32 GFLOPS | Progress: (20/20) | 23.67 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 17.31/ 18.33 GFLOPS | Progress: (4/20) | 5.22 s
[Task 16/25] Current/Best: 15.63/ 18.33 GFLOPS | Progress: (8/20) | 7.40 s
[Task 16/25] Current/Best: 9.37/ 18.33 GFLOPS | Progress: (12/20) | 10.57 s
[Task 16/25] Current/Best: 11.70/ 19.85 GFLOPS | Progress: (16/20) | 13.21 s
[Task 16/25] Current/Best: 16.80/ 19.85 GFLOPS | Progress: (20/20) | 14.83 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 17.36/ 21.67 GFLOPS | Progress: (4/20) | 5.36 s
[Task 17/25] Current/Best: 21.27/ 21.67 GFLOPS | Progress: (8/20) | 8.78 s
[Task 17/25] Current/Best: 20.77/ 21.67 GFLOPS | Progress: (12/20) | 10.71 s
[Task 17/25] Current/Best: 16.99/ 21.67 GFLOPS | Progress: (16/20) | 13.39 s
[Task 17/25] Current/Best: 21.69/ 21.69 GFLOPS | Progress: (20/20) | 16.45 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 13.32/ 14.17 GFLOPS | Progress: (4/20) | 6.29 s
[Task 18/25] Current/Best: 11.63/ 16.36 GFLOPS | Progress: (8/20) | 9.33 s
[Task 18/25] Current/Best: 8.14/ 16.36 GFLOPS | Progress: (12/20) | 13.09 s
[Task 18/25] Current/Best: 18.48/ 18.88 GFLOPS | Progress: (16/20) | 15.84 s
[Task 18/25] Current/Best: 11.03/ 18.88 GFLOPS | Progress: (20/20) | 20.50 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 20.84/ 22.60 GFLOPS | Progress: (4/20) | 5.53 s
[Task 19/25] Current/Best: 11.32/ 22.60 GFLOPS | Progress: (8/20) | 9.07 s
[Task 19/25] Current/Best: 16.88/ 22.60 GFLOPS | Progress: (12/20) | 12.58 s
[Task 19/25] Current/Best: 7.61/ 22.60 GFLOPS | Progress: (16/20) | 19.23 s
[Task 19/25] Current/Best: 9.69/ 22.60 GFLOPS | Progress: (20/20) | 22.90 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 15.54/ 18.71 GFLOPS | Progress: (4/20) | 5.00 s
[Task 20/25] Current/Best: 20.27/ 20.27 GFLOPS | Progress: (8/20) | 9.57 s
[Task 20/25] Current/Best: 7.88/ 20.27 GFLOPS | Progress: (12/20) | 20.68 s
[Task 20/25] Current/Best: 3.08/ 20.27 GFLOPS | Progress: (16/20) | 27.31 s
[Task 20/25] Current/Best: 12.56/ 20.27 GFLOPS | Progress: (20/20) | 31.26 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 9.88/ 13.00 GFLOPS | Progress: (4/20) | 5.77 s
[Task 21/25] Current/Best: 6.34/ 13.00 GFLOPS | Progress: (8/20) | 17.25 s
[Task 21/25] Current/Best: 5.37/ 16.31 GFLOPS | Progress: (12/20) | 20.72 s
[Task 21/25] Current/Best: 7.27/ 19.10 GFLOPS | Progress: (16/20) | 25.59 s
[Task 21/25] Current/Best: 13.74/ 19.10 GFLOPS | Progress: (20/2
0) | 34.75 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 12.22/ 18.29 GFLOPS | Progress: (4/20) | 4.88 s
[Task 22/25] Current/Best: 17.60/ 18.76 GFLOPS | Progress: (8/20) | 8.37 s
[Task 22/25] Current/Best: 10.07/ 20.04 GFLOPS | Progress: (12/20) | 10.73 s
[Task 22/25] Current/Best: 10.65/ 20.04 GFLOPS | Progress: (16/20) | 14.74 s
[Task 22/25] Current/Best: 5.33/ 20.04 GFLOPS | Progress: (20/20) | 18.48 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.62/ 20.40 GFLOPS | Progress: (4/20) | 5.24 s
[Task 23/25] Current/Best: 6.56/ 20.86 GFLOPS | Progress: (8/20) | 12.27 s
[Task 23/25] Current/Best: 20.80/ 23.94 GFLOPS | Progress: (12/20) | 17.74 s
[Task 23/25] Current/Best: 18.10/ 23.94 GFLOPS | Progress: (16/20) | 20.20 s
[Task 23/25] Current/Best: 13.07/ 23.94 GFLOPS | Progress: (20/20) | 22.86 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 3.64/ 3.64 GFLOPS | Progress: (4/20) | 13.62 s
[Task 24/25] Current/Best: 5.64/ 5.64 GFLOPS | Progress: (8/20) | 16.82 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 14.39/ 23.37 GFLOPS | Progress: (4/20) | 8.26 s
[Task 1/25] Current/Best: 17.88/ 23.37 GFLOPS | Progress: (8/20) | 10.79 s
[Task 1/25] Current/Best: 3.44/ 23.41 GFLOPS | Progress: (12/20) | 16.12 s
[Task 1/25] Current/Best: 9.24/ 23.41 GFLOPS | Progress: (16/20) | 19.24 s
[Task 1/25] Current/Best: 19.88/ 23.41 GFLOPS | Progress: (20/20) | 21.41 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 13.40/ 15.47 GFLOPS | Progress: (4/20) | 4.69 s
[Task 2/25] Current/Best: 15.24/ 17.13 GFLOPS | Progress: (8/20) | 6.24 s
[Task 2/25] Current/Best: 17.23/ 21.86 GFLOPS | Progress: (12/20) | 7.61 s
[Task 2/25] Current/Best: 18.38/ 21.86 GFLOPS | Progress: (16/20) | 9.47 s
[Task 2/25] Current/Best: 16.37/ 21.86 GFLOPS | Progress: (20/20) | 11.26 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 12.48/ 23.81 GFLOPS | Progress: (4/20) | 5.51 s
[Task 3/25] Current/Best: 14.98/ 23.81 GFLOPS | Progress: (8/20) | 8.42 s
[Task 3/25] Current/Best: 12.46/ 23.81 GFLOPS | Progress: (12/20) | 10.88 s
[Task 3/25] Current/Best: 21.60/ 23.81 GFLOPS | Progress: (16/20) | 12.91 s
[Task 3/25] Current/Best: 12.79/ 23.81 GFLOPS | Progress: (20/20) | 15.72 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 7.21/ 16.80 GFLOPS | Progress: (4/20) | 8.13 s
[Task 4/25] Current/Best: 10.33/ 16.80 GFLOPS | Progress: (8/20) | 12.21 s
[Task 4/25] Current/Best: 14.98/ 16.80 GFLOPS | Progress: (12/20) | 14.29 s
[Task 4/25] Current/Best: 18.74/ 18.74 GFLOPS | Progress: (16/20) | 17.39 s
[Task 4/25] Current/Best: 20.10/ 20.10 GFLOPS | Progress: (20/20) | 19.12 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 15.00/ 15.00 GFLOPS | Progress: (4/20) | 5.16 s
[Task 5/25] Current/Best: 13.51/ 15.00 GFLOPS | Progress: (8/20) | 8.21 s
[Task 5/25] Current/Best: 4.07/ 19.47 GFLOPS | Progress: (12/20) | 10.28 s
[Task 5/25] Current/Best: 18.00/ 19.47 GFLOPS | Progress: (16/20) | 12.26 s
[Task 5/25] Current/Best: 11.25/ 19.47 GFLOPS | Progress: (20/20) | 14.44 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 18.32/ 18.32 GFLOPS | Progress: (4/20) | 6.43 s
[Task 6/25] Current/Best: 3.30/ 18.32 GFLOPS | Progress: (8/20) | 10.63 s
[Task 6/25] Current/Best: 8.24/ 18.97 GFLOPS | Progress: (12/20) | 12.79 s
[Task 6/25] Current/Best: 6.89/ 18.97 GFLOPS | Progress: (16/20) | 15.52 s
[Task 6/25] Current/Best: 7.97/ 21.17 GFLOPS | Progress: (20/20) | 17.82 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 8.56/ 15.68 GFLOPS | Progress: (4/20) | 6.66 s
[Task 7/25] Current/Best: 17.18/ 18.02 GFLOPS | Progress: (8/20) | 9.15 s
[Task 7/25] Current/Best: 13.87/ 19.58 GFLOPS | Progress: (12/20) | 12.11 s
[Task 7/25] Current/Best: 6.37/ 22.60 GFLOPS | Progress: (16/20) | 14.68 s
[Task 7/25] Current/Best: 17.24/ 22.60 GFLOPS | Progress: (20/20) | 18.38 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 22.79/ 22.79 GFLOPS | Progress: (4/20) | 8.59 s
[Task 8/25] Current/Best: 11.77/ 22.79 GFLOPS | Progress: (8/20) | 12.37 s
[Task 8/25] Current/Best: 13.01/ 22.79 GFLOPS | Progress: (12/20) | 14.70 s
[Task 8/25] Current/Best: 2.20/ 22.92 GFLOPS | Progress: (16/20) | 18.73 s
[Task 8/25] Current/Best: 11.37/ 22.92 GFLOPS | Progress: (20/20) | 22.22 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 21.42/ 21.42 GFLOPS | Progress: (4/20) | 4.68 s
[Task 9/25] Current/Best: 16.21/ 21.42 GFLOPS | Progress: (8/20) | 6.90 s
[Task 9/25] Current/Best: 14.75/ 21.42 GFLOPS | Progress: (12/20) | 8.94 s
[Task 9/25] Current/Best: 15.23/ 21.42 GFLOPS | Progress: (16/20) | 19.55 s
[Task 9/25] Current/Best: 13.02/ 21.42 GFLOPS | Progress: (20/20) | 21.95 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 16.51/ 16.51 GFLOPS | Progress: (4/20) | 4.94 s
[Task 10/25] Current/Best: 12.82/ 16.51 GFLOPS | Progress: (8/20) | 7.34 s
[Task 10/25] Current/Best: 12.76/ 22.52 GFLOPS | Progress: (12/20) | 9.80 s
[Task 10/25] Current/Best: 16.77/ 22.52 GFLOPS | Progress: (16/20) | 11.99 s
[Task 10/25] Current/Best: 5.65/ 22.52 GFLOPS | Progress: (20/20) | 14.19 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.05/ 23.23 GFLOPS | Progress: (4/20) | 5.02 s
[Task 11/25] Current/Best: 14.07/ 23.23 GFLOPS | Progress: (8/20) | 7.29 s
[Task 11/25] Current/Best: 14.06/ 23.23 GFLOPS | Progress: (12/20) | 9.96 s
[Task 11/25] Current/Best: 12.11/ 23.23 GFLOPS | Progress: (16/20) | 12.77 s
[Task 11/25] Current/Best: 18.55/ 23.23 GFLOPS | Progress: (20/20) | 15.06 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 12.78/ 18.14 GFLOPS | Progress: (4/20) | 4.94 s
[Task 12/25] Current/Best: 17.59/ 18.14 GFLOPS | Progress: (8/20) | 7.50 s
[Task 12/25] Current/Best: 18.39/ 18.39 GFLOPS | Progress: (12/20) | 10.08 s
[Task 12/25] Current/Best: 21.63/ 21.63 GFLOPS | Progress: (16/20) | 12.66 s
[Task 12/25] Current/Best: 20.07/ 21.63 GFLOPS | Progress: (20/20) | 16.05 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 5.48/ 12.77 GFLOPS | Progress: (4/20) | 5.76 s
[Task 13/25] Current/Best: 18.54/ 18.54 GFLOPS | Progress: (8/20) | 9.09 s
[Task 13/25] Current/Best: 20.26/ 20.26 GFLOPS | Progress: (12/20) | 13.18 s
[Task 13/25] Current/Best: 12.55/ 20.26 GFLOPS | Progress: (16/20) | 16.02 s
[Task 13/25] Current/Best: 11.73/ 21.81 GFLOPS | Progress: (20/20) | 19.27 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 14.19/ 14.19 GFLOPS | Progress: (4/20) | 9.29 s
[Task 14/25] Current/Best: 10.72/ 14.19 GFLOPS | Progress: (8/20) | 14.15 s
[Task 14/25] Current/Best: 12.85/ 14.19 GFLOPS | Progress: (12/20) | 17.31 s
[Task 14/25] Current/Best: 12.08/ 14.19 GFLOPS | Progress: (16/20) | 28.98 s
[Task 14/25] Current/Best: 7.95/ 14.19 GFLOPS | Progress: (20/20) | 37.60 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 15/25] Current/Best: 18.26/ 19.46 GFLOPS | Progress: (4/20) | 13.82 s
[Task 15/25] Current/Best: 13.59/ 19.46 GFLOPS | Progress: (8/20) | 24.65 s
[Task 15/25] Current/Best: 22.54/ 22.54 GFLOPS | Progress: (12/20) | 27.80 s
[Task 15/25] Current/Best: 15.74/ 22.54 GFLOPS | Progress: (16/20) | 30.31 s
[Task 15/25] Current/Best: 10.17/ 22.54 GFLOPS | Progress: (20/20) | 34.20 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 18.86/ 18.86 GFLOPS | Progress: (4/20) | 4.67 s
[Task 16/25] Current/Best: 17.46/ 18.86 GFLOPS | Progress: (8/20) | 6.43 s
[Task 16/25] Current/Best: 15.95/ 18.86 GFLOPS | Progress: (12/20) | 8.47 s
[Task 16/25] Current/Best: 11.04/ 18.86 GFLOPS | Progress: (16/20) | 10.84 s
[Task 16/25] Current/Best: 15.49/ 18.86 GFLOPS | Progress: (20/20) | 14.07 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 19.01/ 23.88 GFLOPS | Progress: (4/20) | 5.30 s
[Task 17/25] Current/Best: 22.43/ 23.88 GFLOPS | Progress: (8/20) | 8.02 s
[Task 17/25] Current/Best: 22.95/ 23.88 GFLOPS | Progress: (12/20) | 11.24 s
[Task 17/25] Current/Best: 15.60/ 23.88 GFLOPS | Progress: (16/20) | 13.68 s
[Task 17/25] Current/Best: 10.01/ 23.88 GFLOPS | Progress: (20/20) | 16.33 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 12.49/ 16.22 GFLOPS | Progress: (4/20) | 5.24 s
[Task 18/25] Current/Best: 15.96/ 16.22 GFLOPS | Progress: (8/20) | 11.57 s
[Task 18/25] Current/Best: 21.14/ 21.14 GFLOPS | Progress: (12/20) | 13.97 s
[Task 18/25] Current/Best: 9.55/ 21.14 GFLOPS | Progress: (16/20) | 18.37 s
[Task 18/25] Current/Best: 13.33/ 21.14 GFLOPS | Progress: (20/20) | 21.09 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 5.37/ 12.04 GFLOPS | Progress: (4/20) | 7.13 s
[Task 19/25] Current/Best: 20.87/ 20.87 GFLOPS | Progress: (8/20) | 12.15 s
[Task 19/25] Current/Best: 9.02/ 20.87 GFLOPS | Progress: (12/20) | 17.30 s
[Task 19/25] Current/Best: 5.35/ 20.87 GFLOPS | Progress: (16/20) | 21.92 s
[Task 19/25] Current/Best: 20.37/ 20.87 GFLOPS | Progress: (20/20) | 26.31 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 19.23/ 19.23 GFLOPS | Progress: (4/20) | 7.20 s
[Task 20/25] Current/Best: 5.33/ 19.23 GFLOPS | Progress: (8/20) | 9.98 s
[Task 20/25] Current/Best: 11.31/ 19.23 GFLOPS | Progress: (12/20) | 12.69 s
[Task 20/25] Current/Best: 10.04/ 19.23 GFLOPS | Progress: (16/20) | 16.42 s
[Task 20/25] Current/Best: 10.31/ 19.23 GFLOPS | Progress: (20/20) | 23.69 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.36/ 22.51 GFLOPS | Progress: (4/20) | 4.56 s
[Task 21/25] Current/Best: 14.06/ 22.51 GFLOPS | Progress: (8/20) | 6.49 s
[Task 21/25] Current/Best: 18.25/ 22.51 GFLOPS | Progress: (12/20) | 17.89 s
[Task 21/25] Current/Best: 12.65/ 22.51 GFLOPS | Progress: (16/20) | 22.56 s
[Task 21/25] Current/Best: 13.34/ 22.51 GFLOPS | Progress: (20/20) | 25.69 s Done.
+
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 11.41/ 15.03 GFLOPS | Progress: (4/20) | 5.49 s
[Task 22/25] Current/Best: 18.57/ 21.38 GFLOPS | Progress: (8/20) | 8.15 s
[Task 22/25] Current/Best: 12.28/ 22.06 GFLOPS | Progress: (12/20) | 9.95 s
[Task 22/25] Current/Best: 22.65/ 22.65 GFLOPS | Progress: (16/20) | 12.31 s
[Task 22/25] Current/Best: 6.81/ 22.65 GFLOPS | Progress: (20/20) | 14.38 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 2.78/ 19.33 GFLOPS | Progress: (4/20) | 14.99 s
[Task 23/25] Current/Best: 12.50/ 21.96 GFLOPS | Progress: (8/20) | 20.73 s
[Task 23/25] Current/Best: 22.11/ 22.11 GFLOPS | Progress: (12/20) | 23.81 s
[Task 23/25] Current/Best: 6.98/ 22.11 GFLOPS | Progress: (16/20) | 27.46 s
[Task 23/25] Current/Best: 21.46/ 22.11 GFLOPS | Progress: (20/20) | 31.57 s
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 3.86/ 7.11 GFLOPS | Progress: (4/20) | 13.56 s
[Task 24/25] Current/Best: 3.87/ 7.11 GFLOPS | Progress: (8/20) | 24.60 s Done.
Done.
-
[Task 24/25] Current/Best: 0.63/ 5.64 GFLOPS | Progress: (12/20) | 26.59 s
[Task 24/25] Current/Best: 4.35/ 5.64 GFLOPS | Progress: (16/20) | 37.00 s
[Task 24/25] Current/Best: 2.81/ 8.46 GFLOPS | Progress: (20/20) | 49.94 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 3.03/ 7.46 GFLOPS | Progress: (4/20) | 10.56 s
[Task 25/25] Current/Best: 5.74/ 9.65 GFLOPS | Progress: (8/20) | 14.89 s
[Task 25/25] Current/Best: 4.18/ 9.65 GFLOPS | Progress: (12/20) | 16.65 s
[Task 25/25] Current/Best: 8.70/ 9.65 GFLOPS | Progress: (16/20) | 18.82 s
[Task 25/25] Current/Best: 1.54/ 9.65 GFLOPS | Progress: (20/20) | 20.60 s Done.
+
[Task 24/25] Current/Best: 3.31/ 7.34 GFLOPS | Progress: (12/20) | 34.63 s
[Task 24/25] Current/Best: 6.86/ 7.34 GFLOPS | Progress: (16/20) | 44.21 s
[Task 24/25] Current/Best: 7.23/ 7.34 GFLOPS | Progress: (20/20) | 55.21 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.59/ 10.53 GFLOPS | Progress: (4/20) | 7.72 s
[Task 25/25] Current/Best: 1.60/ 10.53 GFLOPS | Progress: (8/20) | 9.14 s
[Task 25/25] Current/Best: 4.28/ 10.53 GFLOPS | Progress: (12/20) | 12.18 s
[Task 25/25] Current/Best: 5.59/ 10.53 GFLOPS | Progress: (16/20) | 13.70 s
[Task 25/25] Current/Best: 8.93/ 10.53 GFLOPS | Progress: (20/20) | 15.87 s Done.
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621104
- class='n02123159 tiger cat' with probability=0.356378
+ class='n02123045 tabby, tabby cat' with probability=0.621103
+ class='n02123159 tiger cat' with probability=0.356379
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 432.23786198002927, 'median': 432.35294695004995, 'std': 1.084171931460595}
- unoptimized: {'mean': 495.82078362989705, 'median': 496.2372947498807, 'std': 1.7682756869910616}
+ optimized: {'mean': 409.7355061800772, 'median': 409.26886740016926, 'std': 2.2259035858128096}
+ unoptimized: {'mean': 476.76003235996177, 'median': 476.3056827498076, 'std': 1.5709036914563033}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 31.935 seconds)
+ **Total running time of the script:** ( 13 minutes 10.705 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 434fd0d4ae..0cc9476c30 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
- 1.186e-07 secs/op
+ 1.164e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index f738efb9d0..93b9b60b39 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, 0x1c241b50)), stage(b, placeholder(b, 0x882bf60)), 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, 0x1cab2bc0)), stage(b, placeholder(b, 0x122dd6d0)), 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 14842888c9..5f5112f156 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
=================
-**17:08.899** total execution time for **tutorial** files:
+**17:11.153** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:31.935 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:10.705 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:25.778 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:44.823 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:59.561 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.917 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.547 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.781 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:25.995 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:30.896 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.009 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:01.013 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.861 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.841 | 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_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.176 | 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 650e3bbab1..58a52535ad 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,7 +285,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000007
+ Numpy running time: 0.000006
naive: 0.000007
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000041
+ vector: 0.000039
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.03416000760626e-06 1.0
- naive 6.837300000000001e-06 0.9720137148723673
- parallel 7.0572e-06 1.0032754433178697
- vector 4.11824e-05 5.854629402155789
+ numpy 6.457599956775084e-06 1.0
+ naive 6.624900000000001e-06 1.0259074647461541
+ parallel 6.9202e-06 1.071636528481386
+ vector 3.926e-05 6.079658117999367
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.021103
+ Numpy running time: 0.018410
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.325344
+ none: 3.280614
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.307322
+ blocking: 0.311922
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.281173
+ vectorization: 0.296108
# 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: 0.119389
+ loop permutation: 0.117800
# 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: 0.107632
+ array packing: 0.107205
# 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: 0.111438
+ block caching: 0.111025
# 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: 0.132715
+ parallelization: 0.132412
# 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 3.3253435988999995 1.0
- blocking 0.3073217385 0.09241804022948483
- vectorization 0.2811730062 0.08455457243366071
- loop permutation 0.119388821 0.0359027022168455
- array packing 0.10763162740000001 0.032367069506923675
- block caching 0.111438207 0.03351178718399596
- parallelization 0.1327146145 0.039910045549548946
+ none 3.2806141424 1.0
+ blocking 0.311921536 0.09508022658580854
+ vectorization 0.2961077377 0.09025984917670822
+ loop permutation 0.11780023040000001 0.03590798103242366
+ array packing 0.10720494480000001 0.03267831575022476
+ block caching 0.1110246563 0.033842643932144256
+ parallelization 0.1324122447 0.04036202947144863
diff --git a/docs/api/rust/help.html b/docs/api/rust/help.html
index 9505cd4cca..6efdf48682 100644
--- a/docs/api/rust/help.html
+++ b/docs/api/rust/help.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
diff --git a/docs/api/rust/settings.html b/docs/api/rust/settings.html
index 7297aebcc3..95bf831b71 100644
--- a/docs/api/rust/settings.html
+++ b/docs/api/rust/settings.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
diff --git a/docs/commit_hash b/docs/commit_hash
index 028848a544..54300ac3f1 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-0c1aad78f9ef6071cc383905f11a37fc29e1a0a6
+a60cd0fecfdbc56187c7632469c5f444f835e99f
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7b3866c75d..af98ed236f 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -595,7 +595,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 33.642 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 33.833 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 50385ba324..fd7e40570c 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.zip593a557b-b42a-4a42-b0d8-1f617b23bd91 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.zip46666e96-71d4-4e04-92e6-f73863b54583 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 e20e9364a6..107dabab33 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -459,13 +459,13 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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-100%|##########| 41.5M/41.5M [00:00<00:00, 50.0MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 45.4MB/s]
+ 32%|###1 | 13.2M/41.5M [00:00<00:00, 58.3MB/s]
+ 46%|####5 | 19.0M/41.5M [00:00<00:00, 41.0MB/s]
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+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 44.8MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 32.4MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 37.2MB/s]
</pre></div>
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</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 282b2fba06..98198f98e9 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -494,7 +494,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.235 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.848 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 296d329281..016b862e5c 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -442,12 +442,13 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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+100%|##########| 44.7M/44.7M [00:00<00:00, 47.7MB/s]
</pre></div>
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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 f893347896..96ff1af5b8 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 38.042 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.722 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 3b2faab357..49f421c7a9 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>07:19.933</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:26.114</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -359,43 +359,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
-<td><p>01:38.042</p></td>
+<td><p>01:38.722</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:33.642</p></td>
+<td><p>01:33.833</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:02.235</p></td>
+<td><p>01:03.848</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:42.334</p></td>
+<td><p>00:42.796</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:37.849</p></td>
+<td><p>00:38.087</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></td>
-<td><p>00:35.139</p></td>
+<td><p>00:35.228</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>00:28.182</p></td>
+<td><p>00:28.936</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>00:26.920</p></td>
+<td><p>00:28.918</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>00:12.706</p></td>
+<td><p>00:12.893</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>00:02.884</p></td>
+<td><p>00:02.852</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 86959794f6..dd336a98a1 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)
- 4226.1123 4225.5254 4228.8788 4224.3069 1.5572
+ 4227.5178 4227.3995 4230.0351 4225.0069 1.4676
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.518 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.589 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 6bf2554956..e2ad6f1bef 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
@@ -448,26 +448,31 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index a27c4bd235..9884d97703 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -672,7 +672,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.5095 16.5766 17.1052 15.9641 0.4135
+ 15.8064 15.6825 16.9634 15.3129 0.4632
</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 68aa03d62d..c06d9ea4c8 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -464,34 +464,32 @@ 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).
@@ -585,7 +583,7 @@ 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 34.703 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 36.147 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 351235cdbb..925a7f2a69 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -505,8 +505,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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</pre></div>
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@@ -597,7 +597,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.0741 89.0495 90.0488 88.6288 0.2377
+ 89.2934 89.2973 90.0723 88.7715 0.2510
</pre></div>
</div>
<div class="admonition note">
@@ -636,7 +636,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.445 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.029 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 ba8009bec2..a910eb639e 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -590,7 +590,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 109.3124 109.2794 110.1223 108.7427 0.2779
+ 110.8880 110.8598 111.7700 110.2543 0.2920
</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 185ad74fc7..ed51e1e181 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> ( 1 minutes 47.289 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 59.862 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 74bccb0400..025a4bb974 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>11:49.026</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:05.315</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -359,39 +359,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:34.703</p></td>
+<td><p>03:36.147</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>01:47.289</p></td>
+<td><p>01:59.862</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></td>
-<td><p>01:28.445</p></td>
+<td><p>01:28.029</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:20.518</p></td>
+<td><p>01:20.589</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>00:56.797</p></td>
+<td><p>00:58.097</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:50.814</p></td>
+<td><p>00:51.388</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>00:49.138</p></td>
+<td><p>00:50.256</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>00:30.799</p></td>
+<td><p>00:30.911</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>00:30.516</p></td>
+<td><p>00:30.031</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index f92c40332e..85d5d7823f 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.zip41081310-5aac-45a4-b2b9-b52020b55fef 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.zip0bd2d045-0ec6-4e87-b76f-9138c4f3fb10 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 ec591f373d..34e45f1ef4 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>00:58.834</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:58.569</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -359,19 +359,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:54.847</p></td>
+<td><p>00:54.639</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>00:02.761</p></td>
+<td><p>00:02.729</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>00:01.219</p></td>
+<td><p>00:01.190</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.011</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 0cd6c74ea3..9d1ae34181 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: 23319us [23319us] (48.03%; 48.03%)
-FoldScaleAxis: 25231us [10us] (51.97%; 51.97%)
- FoldConstant: 25221us [1965us] (51.95%; 99.96%)
- InferType: 23256us [23256us] (47.90%; 92.21%)
+InferType: 23690us [23690us] (48.41%; 48.41%)
+FoldScaleAxis: 25246us [9us] (51.59%; 51.59%)
+ FoldConstant: 25237us [1825us] (51.57%; 99.96%)
+ InferType: 23412us [23412us] (47.84%; 92.77%)
</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: 23262us [23262us] (48.02%; 48.02%)
-FoldScaleAxis: 25183us [8us] (51.98%; 51.98%)
- FoldConstant: 25174us [1937us] (51.97%; 99.97%)
- InferType: 23237us [23237us] (47.97%; 92.30%)
+InferType: 23470us [23470us] (48.27%; 48.27%)
+FoldScaleAxis: 25155us [8us] (51.73%; 51.73%)
+ FoldConstant: 25147us [1888us] (51.72%; 99.97%)
+ InferType: 23259us [23259us] (47.83%; 92.49%)
</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 789f84180f..38d1741492 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -585,7 +585,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.516929 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 36.036800 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 e1f1acb744..1966ffb5df 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -867,7 +867,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.123843 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.267725 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 209e9bea15..bb8572d295 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: 0.018695
-Baseline: 3.297293
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018630
+Baseline: 3.268179
</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: 0.296455
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.303675
</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: 0.275121
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.283828
</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: 0.118055
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117801
</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: 0.108069
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107550
</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: 0.111617
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112012
</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: 0.133539
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.134036
</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 7d8cd01624..de7cdca604 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -350,7 +350,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.113</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.364</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>00:30.571</p></td>
+<td><p>00:30.891</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>00:02.135</p></td>
+<td><p>00:02.132</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>00:01.407</p></td>
+<td><p>00:01.340</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 8712878ad8..072e1408d9 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>03:50.270</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
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@@ -359,27 +359,27 @@
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<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>
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<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>
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+<td><p>01:20.012</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>
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+<td><p>00:17.987</p></td>
<td><p>0.0 MB</p></td>
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-<td><p>00:16.593</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
+<td><p>00:16.683</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>00:16.359</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
+<td><p>00:16.571</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>
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+<td><p>00:00.104</p></td>
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</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 5cf94cdcbc..f362ea69f1 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: 0.343 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.353 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 ba21b62c81..ec5a56a8db 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)
- 8.1556 8.1546 8.1616 8.1508 0.0045
+ 8.1237 8.1188 8.1356 8.1168 0.0085
</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 19.368 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.012 seconds)</p>
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<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 045e8d0742..632dd12796 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)
- 762.8610 763.0856 763.6271 761.8703 0.7346
+ 760.7598 760.9998 762.9208 758.3587 1.8702
</pre></div>
</div>
</div>
@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.674 seconds)</p>
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<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index a003c1c4d3..a1340ddc51 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>00:23.581</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.829</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>00:23.544</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="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_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-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<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>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</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 968c77a5ad..e14dcdc25a 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: 0.037317
+Time cost of this operator: 0.037289
</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 f9bbb5643d..f2e6c27f35 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 330.4 98.834 (1, 2, 10, 10, 3) 2 1 [330.4]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.901 0.868 (1, 6, 10, 10) 1 1 [2.901]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.996 0.298 (1, 1, 10, 10, 3) 1 1 [0.996]
-Total_time - 334.297 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 305.3 98.717 (1, 2, 10, 10, 3) 2 1 [305.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.948 0.953 (1, 6, 10, 10) 1 1 [2.948]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.021 0.33 (1, 1, 10, 10, 3) 1 1 [1.021]
+Total_time - 309.269 - - - - -
</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 102.6 97.411 (1, 6, 10, 10, 1) 2 1 [102.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.772 1.683 (1, 6, 10, 10) 1 1 [1.772]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.906 (1, 1, 10, 10, 3) 1 1 [0.955]
-Total_time - 105.327 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 140.7 98.147 (1, 6, 10, 10, 1) 2 1 [140.7]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.799 1.255 (1, 6, 10, 10) 1 1 [1.799]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.858 0.598 (1, 3, 10, 10, 1) 1 1 [0.858]
+Total_time - 143.357 - - - - -
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.693 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 011d602636..a5d2ab6117 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -465,7 +465,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 81.7MB/s]
+ 61%|###### | 2.09M/3.42M [00:00<00:00, 11.2MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 17.8MB/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.
@@ -593,7 +594,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.395 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 9e4c2d9d2a..fa11f34315 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/tmppd11vpjq/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp58fil9c1/images/random'
</pre></div>
</div>
</div>
@@ -593,8 +593,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
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</pre></div>
</div>
</div>
@@ -706,13 +706,13 @@ the time on our validation set).</p>
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</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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Epoch 3/3
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+328/328 - 35s - loss: 0.0700 - accuracy: 0.9728 - val_loss: 0.2164 - val_accuracy: 0.9316 - 35s/epoch - 108ms/step
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</pre></div>
</div>
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@@ -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
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<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_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">1. microTVM CLI Tool</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></td>
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<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<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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</pre></div>
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<p>Register the rule to TVM with override option to override existing rule.
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<title>Node102->Node41</title>
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<!-- Node102->Node42 -->
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<title>Node102->Node42</title>
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<!-- Node102->Node43 -->
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<title>Node102->Node43</title>
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<!-- Node102->Node44 -->
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<title>Node102->Node44</title>
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<!-- Node102->Node45 -->
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<title>Node102->Node45</title>
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<!-- Node102->Node46 -->
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<title>Node102->Node46</title>
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<!-- Node102->Node47 -->
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<title>Node102->Node47</title>
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<!-- Node102->Node48 -->
<g id="edge173" class="edge">
<title>Node102->Node48</title>
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<!-- Node102->Node49 -->
<g id="edge230" class="edge">
<title>Node102->Node49</title>
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<!-- Node102->Node50 -->
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<title>Node102->Node50</title>
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<!-- Node102->Node51 -->
<g id="edge208" class="edge">
<title>Node102->Node51</title>
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<!-- Node102->Node52 -->
<g id="edge209" class="edge">
<title>Node102->Node52</title>
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<!-- Node102->Node53 -->
<g id="edge233" class="edge">
<title>Node102->Node53</title>
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<!-- Node102->Node54 -->
<g id="edge234" class="edge">
<title>Node102->Node54</title>
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<!-- Node102->Node55 -->
<g id="edge235" class="edge">
<title>Node102->Node55</title>
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<!-- Node102->Node77 -->
<g id="edge180" class="edge">
<title>Node102->Node77</title>
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<!-- Node102->Node80 -->
<g id="edge225" class="edge">
<title>Node102->Node80</title>
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<!-- Node102->Node82 -->
<g id="edge223" class="edge">
<title>Node102->Node82</title>
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<!-- Node102->Node83 -->
<g id="edge224" class="edge">
<title>Node102->Node83</title>
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<!-- Node102->Node84 -->
<g id="edge232" class="edge">
<title>Node102->Node84</title>
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<!-- Node102->Node85 -->
<g id="edge210" class="edge">
<title>Node102->Node85</title>
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<!-- Node102->Node86 -->
<g id="edge231" class="edge">
<title>Node102->Node86</title>
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<!-- Node102->Node87 -->
<g id="edge179" class="edge">
<title>Node102->Node87</title>
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<!-- Node102->Node89 -->
<g id="edge206" class="edge">
<title>Node102->Node89</title>
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<!-- Node102->Node90 -->
<g id="edge211" class="edge">
<title>Node102->Node90</title>
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<!-- Node102->Node91 -->
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<title>Node120->Node14</title>
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<!-- Node120->Node118 -->
<g id="edge248" class="edge">
<title>Node120->Node118</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="509.27,-392 507.25,-402.4 515.31,-395.53 509.27,-392"/>
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<!-- Node121->Node4 -->
<g id="edge254" class="edge">
<title>Node121->Node4</title>
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<!-- Node121->Node40 -->
<g id="edge255" class="edge">
<title>Node121->Node40</title>
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<!-- Node121->Node42 -->
<g id="edge256" class="edge">
<title>Node121->Node42</title>
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<!-- Node121->Node44 -->
<g id="edge257" class="edge">
<title>Node121->Node44</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2610.27,-396.2 2601.74,-402.48 2612.33,-402.89 2610.27,-396.2"/>
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<!-- Node121->Node46 -->
<g id="edge258" class="edge">
<title>Node121->Node46</title>
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<!-- Node121->Node48 -->
<g id="edge259" class="edge">
<title>Node121->Node48</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2478.42,-412.56 2488.71,-410.03 2479.09,-405.59 2478.42,-412.56"/>
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<!-- Node121->Node50 -->
<g id="edge260" class="edge">
<title>Node121->Node50</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2505.26,-402.38 2515.86,-402.4 2507.58,-395.78 2505.26,-402.38"/>
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<!-- Node121->Node51 -->
<g id="edge261" class="edge">
<title>Node121->Node51</title>
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<!-- Node121->Node52 -->
<g id="edge262" class="edge">
<title>Node121->Node52</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2478.15,-406.63 2488.6,-404.88 2479.34,-399.73 2478.15,-406.63"/>
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<!-- Node121->Node54 -->
<g id="edge263" class="edge">
<title>Node121->Node54</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2577.26,-393.17 2572.06,-402.4 2581.88,-398.43 2577.26,-393.17"/>
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<!-- Node122->Node117 -->
<g id="edge265" class="edge">
<title>Node122->Node117</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2076.16,-597.73 2067.25,-603.48 2077.8,-604.54 2076.16,-597.73"/>
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<!-- Node123->Node36 -->
<g id="edge267" class="edge">
<title>Node123->Node36</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="4167.53,-536.52 4178.12,-536.4 4169.76,-529.89 4167.53,-536.52"/>
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<!-- Node123->Node86 -->
<g id="edge269" class="edge">
<title>Node123->Node86</title>
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<!-- Node123->Node89 -->
<g id="edge268" class="edge">
<title>Node123->Node89</title>
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<!-- Node127->Node36 -->
<g id="edge282" class="edge">
<title>Node127->Node36</title>
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<!-- Node127->Node61 -->
<g id="edge280" class="edge">
<title>Node127->Node61</title>
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<!-- Node127->Node63 -->
<g id="edge277" class="edge">
<title>Node127->Node63</title>
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<!-- Node127->Node64 -->
<g id="edge278" class="edge">
<title>Node127->Node64</title>
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<!-- Node127->Node69 -->
<g id="edge279" class="edge">
<title>Node127->Node69</title>
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<!-- Node127->Node74 -->
<g id="edge281" class="edge">
<title>Node127->Node74</title>
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<!-- Node128->Node4 -->
<g id="edge284" class="edge">
<title>Node128->Node4</title>
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<!-- Node129->Node30 -->
<g id="edge291" class="edge">
<title>Node129->Node30</title>
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<!-- Node129->Node31 -->
<g id="edge303" class="edge">
<title>Node129->Node31</title>
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<!-- Node129->Node130 -->
<g id="edge288" class="edge">
<title>Node129->Node130</title>
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<!-- Node129->Node131 -->
<g id="edge292" class="edge">
<title>Node129->Node131</title>
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<!-- Node129->Node133 -->
<g id="edge297" class="edge">
<title>Node129->Node133</title>
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