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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/08/12 05:57:41 UTC

[tvm-site] branch asf-site updated: deploying docs (apache/tvm@81fd9f3476ed64b4d23ab8f38286a9bd7accc947)

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 c400e341b8 deploying docs (apache/tvm@81fd9f3476ed64b4d23ab8f38286a9bd7accc947)
c400e341b8 is described below

commit c400e341b864cdb7f5f2594d07d3ef3bd32d2e85
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sat Aug 12 05:57:28 2023 +0000

    deploying docs (apache/tvm@81fd9f3476ed64b4d23ab8f38286a9bd7accc947)
---
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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 ae832d37c6..e6dc47ddc5 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  35.785 seconds)
+   **Total running time of the script:** ( 1 minutes  35.047 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 8803173429..6c373027a3 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.zip54be9a3f-ad4b-4815-878f-dc93a2613479 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+    Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipcf700b51-afca-44c1-9ca7-2641b1d57076 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 c1514462da..8e40f27cb6 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]
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diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 80e22eb362..a0de79e4b0 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  12.151 seconds)
+   **Total running time of the script:** ( 1 minutes  5.628 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 5b7e54bffc..bd59425209 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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+
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diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 411d528e11..2ae008a9b0 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  40.259 seconds)
+   **Total running time of the script:** ( 1 minutes  40.944 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 5a1202d979..369a9c7684 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
 =================
-**07:46.245** total execution time for **how_to_compile_models** files:
+**07:32.514** total execution time for **how_to_compile_models** files:
 
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:40.259 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:40.944 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)       | 01:35.785 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)       | 01:35.047 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)         | 01:12.151 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)         | 01:05.628 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)       | 00:44.028 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)       | 00:43.270 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)           | 00:38.789 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)         | 00:37.691 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)         | 00:37.546 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)           | 00:36.187 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)           | 00:30.184 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)       | 00:28.961 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)       | 00:29.353 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)           | 00:28.689 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)         | 00:15.043 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)         | 00:13.207 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)             | 00:03.107 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)             | 00:02.891 | 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 e2c5c64f7c..e4f8acc76d 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)  
-     4237.7885    4233.7190    4272.3148    4231.0619     11.7239                  
+     4227.6315    4227.5334    4234.1704    4221.7982      3.5077                  
 
 
 
@@ -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  21.501 seconds)
+   **Total running time of the script:** ( 1 minutes  21.467 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 3492d25574..fc70542c27 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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    102850560/102967424 [============================>.] - ETA: 0s
    102967424/102967424 [==============================] - 3s 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 76d4218de3..399feb2fd2 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)  
-      16.3245      16.2898      16.7488      16.0073       0.2469                  
+      15.6746      15.6405      16.1835      15.2908       0.2506                  
 
 
 
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 d39eded41c..021c1d23e5 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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    100%|##########| 170M/170M [00:05<00:00, 33.8MB/s]
     /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
       (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
     /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  47.204 seconds)
+   **Total running time of the script:** ( 3 minutes  39.197 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 e2fd45e6b7..cd58b445b8 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, 50.7MB/s]
     95%|#########4| 12.8M/13.6M [00:00<00:00, 29.2MB/s]
    100%|##########| 13.6M/13.6M [00:00<00:00, 33.2MB/s]
+
      0%|          | 0.00/13.6M [00:00<?, ?B/s]
     59%|#####8    | 7.99M/13.6M [00:00<00:00, 46.7MB/s]
     92%|#########1| 12.5M/13.6M [00:00<00:00, 45.2MB/s]
    100%|##########| 13.6M/13.6M [00:00<00:00, 48.6MB/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)  
-      89.1725      89.1476      92.0903      88.7578       0.3694                  
+      88.9137      88.8519      90.0473      88.5180       0.2548                  
 
 
 
@@ -457,7 +457,7 @@ TODO
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  39.479 seconds)
+   **Total running time of the script:** ( 1 minutes  28.946 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 d0c649b4ed..76733668ab 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)  
-      111.1047     111.0965     116.0725     110.1563      0.6066                  
+      109.7146     109.6867     110.6482     108.9642      0.3080                  
 
 
 
@@ -457,11 +457,6 @@ Here we give an example of how to measure performance of TVM compiled models.
      network for ARM CPU <tune_relay_arm>`.
 
 
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 1 minutes  1.590 seconds)
-
-
 .. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
 
 .. only:: html
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 a366eb902e..33f580cd35 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -257,7 +257,7 @@ We create a Relay VM to build and execute the model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  38.686 seconds)
+   **Total running time of the script:** ( 2 minutes  26.236 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 02419f2d52..38fa50996c 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
 =================
-**13:29.508** total execution time for **how_to_deploy_models** files:
+**12:34.818** 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``) | 03:47.204 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:39.197 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)                               | 02:38.686 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)                               | 02:26.236 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)                         | 01:39.479 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)                         | 01:28.946 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``)                   | 01:21.501 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``)                   | 01:21.467 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)           | 01:01.590 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)           | 00:55.841 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``)         | 00:57.532 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``)         | 00:50.902 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)                 | 00:57.059 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)                 | 00:50.529 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)                       | 00:33.397 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``)                       | 00:31.383 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``)                       | 00:33.053 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)                       | 00:30.313 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)                                     | 00:00.006 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 1b1eaa9046..f926b06fe1 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.zip5af97d2c-bd37-48e7-82a8-845e7ff4542a from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+    Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip6e21983b-f9e3-4951-8f08-223b1d44e101 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 23cea0e36e..168d91e640 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
 =================
-**01:00.571** total execution time for **how_to_extend_tvm** files:
+**01:00.676** 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``) | 00:56.154 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:56.548 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)           | 00:03.091 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)           | 00:02.865 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)                     | 00:01.317 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)                     | 00:01.254 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)       | 00:00.008 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 272b4aef63..075a4100d7 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: 30340us [30340us] (48.26%; 48.26%)
-    FoldScaleAxis: 32528us [11us] (51.74%; 51.74%)
-            FoldConstant: 32517us [1883us] (51.72%; 99.97%)
-                    InferType: 30634us [30634us] (48.73%; 94.21%)
+    InferType: 24150us [24150us] (48.34%; 48.34%)
+    FoldScaleAxis: 25807us [10us] (51.66%; 51.66%)
+            FoldConstant: 25797us [1927us] (51.64%; 99.96%)
+                    InferType: 23870us [23870us] (47.78%; 92.53%)
 
 
 
@@ -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: 30425us [30425us] (48.62%; 48.62%)
-    FoldScaleAxis: 32148us [10us] (51.38%; 51.38%)
-            FoldConstant: 32138us [1928us] (51.36%; 99.97%)
-                    InferType: 30210us [30210us] (48.28%; 94.00%)
+    InferType: 24197us [24197us] (48.75%; 48.75%)
+    FoldScaleAxis: 25438us [8us] (51.25%; 51.25%)
+            FoldConstant: 25430us [1895us] (51.23%; 99.97%)
+                    InferType: 23535us [23535us] (47.42%; 92.55%)
 
 
 
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 da6796e1d2..b56a43522d 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: 34.461982 ms
+    Convolution: 53.526847 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 45fd089331..2b9badff79 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: 12.269571 ms
+    conv2d with tensor core: 12.270982 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 fa8cb43cc6..66a614eefa 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: 0.019445
-    Baseline: 3.537474
+    Numpy running time: 0.019032
+    Baseline: 3.578296
 
 
 
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
 
  .. code-block:: none
 
-    Opt1: 0.305471
+    Opt1: 0.309980
 
 
 
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
 
  .. code-block:: none
 
-    Opt2: 0.299635
+    Opt2: 0.281913
 
 
 
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
 
  .. code-block:: none
 
-    Opt3: 0.119241
+    Opt3: 0.118874
 
 
 
@@ -523,7 +523,7 @@ flattening.
 
  .. code-block:: none
 
-    Opt4: 0.109402
+    Opt4: 0.107967
 
 
 
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
 
  .. code-block:: none
 
-    Opt5: 0.112595
+    Opt5: 0.112745
 
 
 
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
 
  .. code-block:: none
 
-    Opt6: 0.134538
+    Opt6: 0.133421
 
 
 
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 70a43b07e4..0546054f7d 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
 =================
-**00:35.332** total execution time for **how_to_optimize_operators** files:
+**00:34.950** total execution time for **how_to_optimize_operators** files:
 
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)                       | 00:31.989 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)                       | 00:31.720 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.100 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.001 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)             | 00:01.243 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)             | 00:01.229 | 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 15ae5457e8..4694e2a9bd 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
 =================
-**04:01.172** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:47.118** 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``)             | 01:45.692 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)             | 01:41.276 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)           | 01:18.802 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)           | 01:14.859 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:19.209 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.715 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)           | 00:18.709 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)           | 00:16.760 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)             | 00:18.656 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)             | 00:16.405 | 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 |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)               | 00:00.103 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index 0a6dadadcd..308e1e8477 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
@@ -766,7 +766,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 0.358 ms
+    Execution time of this operator: 0.348 ms
 
 
 
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 851de74e15..45b0be3042 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)  
-       3.2703       3.2695       3.2732       3.2683       0.0021                  
+       3.2980       3.3025       3.3041       3.2874       0.0075                  
 
 
 
@@ -674,7 +674,7 @@ Other Tips
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  18.802 seconds)
+   **Total running time of the script:** ( 1 minutes  14.859 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 48576a64fb..d91ec78a26 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)  
-      762.3943     763.2844     763.7169     760.1816      1.5745                  
+      762.4247     761.6711     764.6278     760.9752      1.5835                  
 
 
 
@@ -693,7 +693,7 @@ Other Tips
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  45.692 seconds)
+   **Total running time of the script:** ( 1 minutes  41.276 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 af64620278..4d79d1e67e 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**00:24.697** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.595** 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``)           | 00:24.658 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)           | 00:23.558 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)               | 00:00.023 | 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_cuda.py` (``tune_relay_cuda.py``)             | 00:00.006 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 5d7d1e8f1f..20c45f662c 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: 0.037222
+    Time cost of this operator: 0.037338
 
 
 
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 1be0b11698..1df85b8101 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  303.4     98.732   (1, 2, 10, 10, 3)  2       1        [303.4]           
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.935     0.955    (1, 6, 10, 10)     1       1        [2.935]           
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.961     0.313    (1, 1, 10, 10, 3)  1       1        [0.961]           
-    Total_time                                    -                                             307.296   -        -                  -       -        -                 
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  304.2     98.738   (1, 2, 10, 10, 3)  2       1        [304.2]           
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.913     0.946    (1, 6, 10, 10)     1       1        [2.913]           
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.974     0.316    (1, 1, 10, 10, 3)  1       1        [0.974]           
+    Total_time                                    -                                             308.087   -        -                  -       -        -                 
 
 
 
@@ -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  136.1     98.024   (1, 6, 10, 10, 1)  2       1        [136.1]           
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.874     1.35     (1, 6, 10, 10)     1       1        [1.874]           
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.869     0.626    (1, 3, 10, 10, 1)  1       1        [0.869]           
-    Total_time                                    -                                             138.843   -        -                  -       -        -                 
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  106.9     97.48    (1, 6, 10, 10, 1)  2       1        [106.9]           
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.808     1.649    (1, 6, 10, 10)     1       1        [1.808]           
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.956     0.871    (1, 1, 10, 10, 3)  1       1        [0.956]           
+    Total_time                                    -                                             109.664   -        -                  -       -        -                 
 
 
 
@@ -439,7 +439,7 @@ Timing the tuned program
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  31.871 seconds)
+   **Total running time of the script:** ( 1 minutes  27.650 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 5b2566ea82..3e60a038b3 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, 46.2MB/s]
+
      0%|          | 0.00/3.42M [00:00<?, ?B/s]
    100%|##########| 3.42M/3.42M [00:00<00:00, 38.7MB/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  39.348 seconds)
+   **Total running time of the script:** ( 1 minutes  29.096 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 368c35d9ac..3fadb30fb5 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/tmpuiv97dam/images/random'
+    '/tmp/tmpbh77eprb/images/random'
 
 
 
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
 
  .. code-block:: none
 
-    /tmp/tmpuiv97dam/images/target contains 8144 images
-    /tmp/tmpuiv97dam/images/random contains 5000 images
+    /tmp/tmpbh77eprb/images/target contains 8144 images
+    /tmp/tmpbh77eprb/images/random contains 5000 images
 
 
 
@@ -493,13 +493,13 @@ the time on our validation set).
  .. code-block:: none
 
     Epoch 1/3
-    328/328 - 42s - loss: 0.2128 - accuracy: 0.9284 - val_loss: 0.1175 - val_accuracy: 0.9566 - 42s/epoch - 127ms/step
+    328/328 - 41s - loss: 0.2247 - accuracy: 0.9226 - val_loss: 0.1019 - val_accuracy: 0.9637 - 41s/epoch - 124ms/step
     Epoch 2/3
-    328/328 - 36s - loss: 0.1011 - accuracy: 0.9631 - val_loss: 0.1001 - val_accuracy: 0.9649 - 36s/epoch - 109ms/step
+    328/328 - 36s - loss: 0.0947 - accuracy: 0.9659 - val_loss: 0.1284 - val_accuracy: 0.9547 - 36s/epoch - 109ms/step
     Epoch 3/3
-    328/328 - 36s - loss: 0.0624 - accuracy: 0.9787 - val_loss: 0.1254 - val_accuracy: 0.9585 - 36s/epoch - 109ms/step
+    328/328 - 35s - loss: 0.0683 - accuracy: 0.9754 - val_loss: 0.1103 - val_accuracy: 0.9626 - 35s/epoch - 108ms/step
 
-    <keras.callbacks.History object at 0x7f28daf60dc0>
+    <keras.callbacks.History object at 0x7f74d844ae80>
 
 
 
@@ -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:** ( 5 minutes  6.405 seconds)
+   **Total running time of the script:** ( 4 minutes  50.336 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 17fe32cd8e..7a5cafc9db 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
 
 Computation times
 =================
-**08:50.632** total execution time for **how_to_work_with_microtvm** files:
+**08:18.247** total execution time for **how_to_work_with_microtvm** files:
 
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)           | 05:06.405 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)           | 04:50.336 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``)       | 01:39.348 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``)       | 01:29.096 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)     | 01:31.871 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)     | 01:27.650 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``)               | 00:12.936 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``)               | 00:12.931 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.689 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)         | 00:09.380 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)         | 00:09.383 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.854 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)         | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 83215a14a1..be5fcbd92f 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
 =================
-**00:42.690** total execution time for **how_to_work_with_relay** files:
+**00:41.063** total execution time for **how_to_work_with_relay** files:
 
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:36.939 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.732 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)           | 00:03.601 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)           | 00:03.370 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)                             | 00:02.144 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)                             | 00:01.955 | 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 44f3992f3c..8e3272373b 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
@@ -281,7 +281,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
  .. code-block:: none
 
 
-    <function my_cuda_math_rule at 0x7f2c151ee8b0>
+    <function my_cuda_math_rule at 0x7f751e3418b0>
 
 
 
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 c33657428f..35b4e23067 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
 =================
-**00:07.069** total execution time for **how_to_work_with_schedules** files:
+**00:09.092** total execution time for **how_to_work_with_schedules** files:
 
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)                 | 00:03.512 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)                 | 00:05.763 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)                     | 00:01.581 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)                     | 00:01.475 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)                     | 00:00.821 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)                     | 00:00.784 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)                               | 00:00.810 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)                               | 00:00.764 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)                     | 00:00.127 | 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_tedd.py` (``tedd.py``)                               | 00:00.098 | 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``) | 00:00.082 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.071 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)               | 00:00.039 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)               | 00:00.033 | 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 84de51d66d..896b49f3f8 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
 =================
-**00:38.249** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:35.435** total execution time for **topic_vta_tutorials_autotvm** files:
 
 +---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:38.242 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:35.428 | 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 c36d395531..fcaf91ac66 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 43.96s!
+    resnet18_v1 inference graph built in 38.59s!
 
 
 
@@ -416,7 +416,7 @@ and an input test image.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  11.458 seconds)
+   **Total running time of the script:** ( 1 minutes  5.859 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 5ed01aa496..82d5c852af 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 28.93s!
+    yolov3-tiny inference graph built in 26.37s!
 
 
 
@@ -447,7 +447,7 @@ Download test image
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  12.469 seconds)
+   **Total running time of the script:** ( 1 minutes  10.114 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 3486b7dcff..2e6359197a 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
 =================
-**02:23.927** total execution time for **topic_vta_tutorials_frontend** files:
+**02:15.973** total execution time for **topic_vta_tutorials_frontend** files:
 
 +------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)           | 01:12.469 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)           | 01:10.114 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:11.458 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:05.859 | 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 a829a2aba2..efa7902e67 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
 =================
-**00:03.466** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.448** total execution time for **topic_vta_tutorials_optimize** files:
 
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)         | 00:02.907 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)         | 00:02.904 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.559 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.544 | 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 e44c3ef280..5ab98191a2 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
 =================
-**00:00.952** total execution time for **topic_vta_tutorials** files:
+**00:00.936** total execution time for **topic_vta_tutorials** files:
 
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.487 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.480 | 0.0 MB |
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.465 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.456 | 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 81a174345a..0b04e2648e 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 93.105 ms
+    Execution time of this operator: 95.905 ms
 
 
 
@@ -434,7 +434,7 @@ operations.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  31.226 seconds)
+   **Total running time of the script:** ( 1 minutes  29.402 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 8a0a34bf9a..d425d00ff3 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@ reduce variance, we take 5 measurements and average them.
     waiting for device...
     device available
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 12.39/12.39     result: MeasureResult(costs=(0.0216717838,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7073981761932373, timestamp=1691783775.2324166)       [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
-    No: 2   GFLOPS: 14.96/14.96     result: MeasureResult(costs=(0.017941991999999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7941596508026123, timestamp=1691783775.7809474)       [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
-    No: 3   GFLOPS: 2.88/14.96      result: MeasureResult(costs=(0.09317181000000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7614877223968506, timestamp=1691783777.5333147)        [('tile_y', [-1, 16]), ('tile_x', [-1, 2])],None,14
-    No: 4   GFLOPS: 7.75/14.96      result: MeasureResult(costs=(0.034619311199999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8297405242919922, timestamp=1691783778.3564267)       [('tile_y', [-1, 512]), ('tile_x', [-1, 16])],None,49
-    No: 5   GFLOPS: 3.77/14.96      result: MeasureResult(costs=(0.0711699486,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.40824556350708, timestamp=1691783779.912721)  [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
-    No: 6   GFLOPS: 11.62/14.96     result: MeasureResult(costs=(0.0231105114,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6684379577636719, timestamp=1691783780.5436273)       [('tile_y', [-1, 128]), ('tile_x', [-1, 32])],None,57
-    No: 7   GFLOPS: 10.46/14.96     result: MeasureResult(costs=(0.0256682108,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7147643566131592, timestamp=1691783781.2163193)       [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
-    No: 8   GFLOPS: 11.85/14.96     result: MeasureResult(costs=(0.0226490346,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6147685050964355, timestamp=1691783781.8444748)       [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
-    No: 9   GFLOPS: 13.19/14.96     result: MeasureResult(costs=(0.020348601400000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5972418785095215, timestamp=1691783782.5525777)       [('tile_y', [-1, 128]), ('tile_x', [-1, 128])],None,77
-    No: 10  GFLOPS: 2.10/14.96      result: MeasureResult(costs=(0.12811583720000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.290436267852783, timestamp=1691783784.863335)  [('tile_y', [-1, 128]), ('tile_x', [-1, 4])],None,27
+    No: 1   GFLOPS: 11.20/11.20     result: MeasureResult(costs=(0.0239780674,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6427876949310303, timestamp=1691815580.9808567)       [('tile_y', [-1, 32]), ('tile_x', [-1, 512])],None,95
+    No: 2   GFLOPS: 7.31/11.20      result: MeasureResult(costs=(0.036709634799999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8554332256317139, timestamp=1691815581.830116)        [('tile_y', [-1, 512]), ('tile_x', [-1, 16])],None,49
+    No: 3   GFLOPS: 11.22/11.22     result: MeasureResult(costs=(0.0239331696,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.65142822265625, timestamp=1691815582.4796734) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+    No: 4   GFLOPS: 8.66/11.22      result: MeasureResult(costs=(0.0309884162,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7604248523712158, timestamp=1691815583.2482424)       [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
+    No: 5   GFLOPS: 0.51/11.22      result: MeasureResult(costs=(0.528327949,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.716147422790527, timestamp=1691815592.0956736) [('tile_y', [-1, 128]), ('tile_x', [-1, 1])],None,7
+    No: 6   GFLOPS: 9.07/11.22      result: MeasureResult(costs=(0.029587877800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7551007270812988, timestamp=1691815592.8451188)       [('tile_y', [-1, 512]), ('tile_x', [-1, 256])],None,89
+    No: 7   GFLOPS: 11.92/11.92     result: MeasureResult(costs=(0.0225135606,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6382317543029785, timestamp=1691815593.4676185)       [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
+    No: 8   GFLOPS: 11.39/11.92     result: MeasureResult(costs=(0.0235603302,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6684978008270264, timestamp=1691815594.116169)        [('tile_y', [-1, 64]), ('tile_x', [-1, 512])],None,96
+    No: 9   GFLOPS: 6.52/11.92      result: MeasureResult(costs=(0.041151971200000005,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8854708671569824, timestamp=1691815595.1369298)       [('tile_y', [-1, 2]), ('tile_x', [-1, 4])],None,21
+    No: 10  GFLOPS: 11.48/11.92     result: MeasureResult(costs=(0.023381396000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6173808574676514, timestamp=1691815595.7700748)       [('tile_y', [-1, 32]), ('tile_x', [-1, 32])],None,55
 
 
 
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 37ccb65bab..42b0b946c5 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': 497.68446000999575, 'median': 496.7346291999547, 'std': 3.2581165093678317}
+    {'mean': 500.67015277999417, 'median': 501.1268954499883, 'std': 2.3389088102530704}
 
 
 
@@ -582,30 +582,31 @@ the tuning data to.
 
  .. code-block:: none
 
-
    [Task  1/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  1/25]  Current/Best:   15.15/  17.01 GFLOPS | Progress: (4/20) | 9.13 s
    [Task  1/25]  Current/Best:   12.71/  17.01 GFLOPS | Progress: (8/20) | 11.69 s
    [Task  1/25]  Current/Best:    6.54/  17.01 GFLOPS | Progress: (12/20) | 17.55 s
    [Task  1/25]  Current/Best:   14.00/  17.01 GFLOPS | Progress: (16/20) | 20.41 s
    [Task  1/25]  Current/Best:   11.04/  19.41 GFLOPS | Progress: (20/20) | 23.75 s Done.
-
    [Task  2/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  2/25]  Current/Best:   14.20/  14.20 GFLOPS | Progress: (4/20) | 4.68 s
    [Task  2/25]  Current/Best:   17.71/  17.71 GFLOPS | Progress: (8/20) | 6.38 s
    [Task  2/25]  Current/Best:   19.84/  19.84 GFLOPS | Progress: (12/20) | 8.04 s
    [Task  2/25]  Current/Best:   18.23/  19.84 GFLOPS | Progress: (16/20) | 9.74 s
    [Task  2/25]  Current/Best:   21.13/  21.13 GFLOPS | Progress: (20/20) | 11.57 s Done.
-
    [Task  3/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  3/25]  Current/Best:   19.27/  19.27 GFLOPS | Progress: (4/20) | 5.29 s
    [Task  3/25]  Current/Best:   15.20/  19.92 GFLOPS | Progress: (8/20) | 7.79 s
    [Task  3/25]  Current/Best:   19.30/  19.92 GFLOPS | Progress: (12/20) | 10.39 s
    [Task  3/25]  Current/Best:   10.69/  19.92 GFLOPS | Progress: (16/20) | 12.91 s
    [Task  3/25]  Current/Best:   20.20/  20.20 GFLOPS | Progress: (20/20) | 15.77 s Done.
-
    [Task  4/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  4/25]  Current/Best:   14.27/  18.63 GFLOPS | Progress: (4/20) | 6.03 s
    [Task  4/25]  Current/Best:   13.65/  18.63 GFLOPS | Progress: (8/20) | 8.93 s
    [Task  4/25]  Current/Best:    6.46/  18.63 GFLOPS | Progress: (12/20) | 11.98 s
    [Task  4/25]  Current/Best:   13.43/  18.63 GFLOPS | Progress: (16/20) | 16.32 s
    [Task  4/25]  Current/Best:   21.40/  21.44 GFLOPS | Progress: (20/20) | 22.78 s Done.
-
    [Task  5/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  5/25]  Current/Best:   22.42/  22.42 GFLOPS | Progress: (4/20) | 5.29 s
    [Task  5/25]  Current/Best:   14.97/  23.17 GFLOPS | Progress: (8/20) | 7.34 s
    [Task  5/25]  Current/Best:   16.82/  23.17 GFLOPS | Progress: (12/20) | 9.41 s
    [Task  5/25]  Current/Best:    6.73/  23.17 GFLOPS | Progress: (16/20) | 11.40 s
    [Task  5/25]  Current/Best:   10.53/  23.17 GFLOPS | Progress: (20/20) | 13.47 s Done.
-
    [Task  6/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  6/25]  Current/Best:   11.89/  17.40 GFLOPS | Progress: (4/20) | 5.48 s
    [Task  6/25]  Current/Best:    5.58/  17.40 GFLOPS | Progress: (8/20) | 8.37 s
    [Task  6/25]  Current/Best:   12.71/  18.45 GFLOPS | Progress: (12/20) | 11.07 s
    [Task  6/25]  Current/Best:   16.07/  22.58 GFLOPS | Progress: (16/20) | 13.13 s
    [Task  6/25]  Current/Best:   15.20/  22.58 GFLOPS | Progress: (20/20) | 15.92 s Done.
-
    [Task  7/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  7/25]  Current/Best:   14.96/  14.96 GFLOPS | Progress: (4/20) | 5.98 s
    [Task  7/25]  Current/Best:   11.65/  21.07 GFLOPS | Progress: (8/20) | 8.62 s
    [Task  7/25]  Current/Best:   11.50/  21.07 GFLOPS | Progress: (12/20) | 12.60 s
    [Task  7/25]  Current/Best:    7.73/  21.07 GFLOPS | Progress: (16/20) | 15.25 s
    [Task  7/25]  Current/Best:   17.23/  21.07 GFLOPS | Progress: (20/20) | 17.73 s Done.
-
    [Task  8/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  8/25]  Current/Best:   13.63/  14.46 GFLOPS | Progress: (4/20) | 6.44 s
    [Task  8/25]  Current/Best:   14.46/  14.46 GFLOPS | Progress: (8/20) | 18.13 s
    [Task  8/25]  Current/Best:   11.20/  14.46 GFLOPS | Progress: (12/20) | 26.71 s
    [Task  8/25]  Current/Best:   18.30/  18.30 GFLOPS | Progress: (16/20) | 39.30 s
    [Task  8/25]  Current/Best:    9.39/  18.30 GFLOPS | Progress: (20/20) | 45.24 s
    [Task  9/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
-
    [Task  9/25]  Current/Best:    7.32/  14.28 GFLOPS | Progress: (4/20) | 6.00 s
    [Task  9/25]  Current/Best:   11.81/  19.16 GFLOPS | Progress: (8/20) | 8.15 s
    [Task  9/25]  Current/Best:   21.11/  22.98 GFLOPS | Progress: (12/20) | 10.59 s
    [Task  9/25]  Current/Best:   16.83/  22.98 GFLOPS | Progress: (16/20) | 16.13 s
    [Task  9/25]  Current/Best:   16.28/  22.98 GFLOPS | Progress: (20/20) | 27.03 s Done.
-
    [Task 10/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 10/25]  Current/Best:   16.33/  16.33 GFLOPS | Progress: (4/20) | 4.90 s
    [Task 10/25]  Current/Best:    8.47/  16.46 GFLOPS | Progress: (8/20) | 6.97 s
    [Task 10/25]  Current/Best:    9.26/  16.46 GFLOPS | Progress: (12/20) | 8.85 s
    [Task 10/25]  Current/Best:   12.07/  16.46 GFLOPS | Progress: (16/20) | 11.06 s
    [Task 10/25]  Current/Best:   19.43/  21.16 GFLOPS | Progress: (20/20) | 12.80 s Done.
-
    [Task 11/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 11/25]  Current/Best:   13.15/  13.48 GFLOPS | Progress: (4/20) | 5.56 s
    [Task 11/25]  Current/Best:   13.14/  16.69 GFLOPS | Progress: (8/20) | 7.92 s
    [Task 11/25]  Current/Best:   13.87/  23.12 GFLOPS | Progress: (12/20) | 9.91 s
    [Task 11/25]  Current/Best:   24.21/  24.21 GFLOPS | Progress: (16/20) | 12.06 s
    [Task 11/25]  Current/Best:   23.49/  24.21 GFLOPS | Progress: (20/20) | 14.28 s Done.
-
    [Task 12/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 12/25]  Current/Best:   11.53/  20.42 GFLOPS | Progress: (4/20) | 7.54 s
    [Task 12/25]  Current/Best:    5.88/  20.42 GFLOPS | Progress: (8/20) | 10.55 s
    [Task 12/25]  Current/Best:   12.25/  20.42 GFLOPS | Progress: (12/20) | 14.97 s
    [Task 12/25]  Current/Best:   10.23/  20.42 GFLOPS | Progress: (16/20) | 17.12 s
    [Task 12/25]  Current/Best:   12.21/  20.42 GFLOPS | Progress: (20/20) | 20.26 s Done.
-
    [Task 13/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 13/25]  Current/Best:    9.78/  20.48 GFLOPS | Progress: (4/20) | 5.90 s
    [Task 13/25]  Current/Best:    6.06/  20.48 GFLOPS | Progress: (8/20) | 8.88 s
    [Task 13/25]  Current/Best:   22.02/  22.02 GFLOPS | Progress: (12/20) | 12.02 s
    [Task 13/25]  Current/Best:   18.37/  22.02 GFLOPS | Progress: (16/20) | 14.46 s
    [Task 13/25]  Current/Best:   14.00/  22.02 GFLOPS | Progress: (20/20) | 17.25 s Done.
-
    [Task 14/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 14/25]  Current/Best:   17.74/  17.74 GFLOPS | Progress: (4/20) | 14.72 s
    [Task 14/25]  Current/Best:   18.68/  19.15 GFLOPS | Progress: (8/20) | 17.97 s
    [Task 14/25]  Current/Best:   17.30/  19.15 GFLOPS | Progress: (12/20) | 25.69 s
    [Task 14/25]  Current/Best:    3.01/  19.15 GFLOPS | Progress: (16/20) | 31.77 s
    [Task 14/25]  Current/Best:   16.38/  19.15 GFLOPS | Progress: (20/20) | 34.42 s
    [Task 15/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 15/25]  Current/Best:   19.20/  19.20 GFLOPS | Progress: (4/20) | 6.13 s
    [Task 15/25]  Current/Best:   15.98/  19.20 GFLOPS | Progress: (8/20) | 12.21 s
    [Task 15/25]  Current/Best:   20.13/  20.13 GFLOPS | Progress: (12/20) | 16.79 s
    [Task 15/25]  Current/Best:    8.47/  20.13 GFLOPS | Progress: (16/20) | 21.77 s
    [Task 15/25]  Current/Best:   20.55/  21.36 GFLOPS | Progress: (20
 /20) | 24.46 s Done.
-
    [Task 16/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 16/25]  Current/Best:   18.50/  18.50 GFLOPS | Progress: (4/20) | 5.05 s
    [Task 16/25]  Current/Best:   17.74/  18.50 GFLOPS | Progress: (8/20) | 6.90 s
    [Task 16/25]  Current/Best:   11.28/  18.50 GFLOPS | Progress: (12/20) | 10.21 s
    [Task 16/25]  Current/Best:    8.53/  20.23 GFLOPS | Progress: (16/20) | 12.35 s
    [Task 16/25]  Current/Best:   14.96/  20.23 GFLOPS | Progress: (20/20) | 16.15 s Done.
-
    [Task 17/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 17/25]  Current/Best:   14.18/  22.30 GFLOPS | Progress: (4/20) | 5.03 s
    [Task 17/25]  Current/Best:   18.64/  22.30 GFLOPS | Progress: (8/20) | 7.60 s
    [Task 17/25]  Current/Best:   22.15/  22.30 GFLOPS | Progress: (12/20) | 9.99 s
    [Task 17/25]  Current/Best:   19.40/  22.30 GFLOPS | Progress: (16/20) | 13.66 s
    [Task 17/25]  Current/Best:   18.53/  22.56 GFLOPS | Progress: (20/20) | 15.67 s Done.
-
    [Task 18/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 18/25]  Current/Best:   12.72/  14.23 GFLOPS | Progress: (4/20) | 6.76 s
    [Task 18/25]  Current/Best:    9.76/  20.36 GFLOPS | Progress: (8/20) | 10.40 s
    [Task 18/25]  Current/Best:    3.65/  20.36 GFLOPS | Progress: (12/20) | 14.90 s
    [Task 18/25]  Current/Best:   15.90/  20.36 GFLOPS | Progress: (16/20) | 17.01 s
    [Task 18/25]  Current/Best:    3.16/  20.45 GFLOPS | Progress: (20/20) | 19.65 s Done.
-
    [Task 19/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 19/25]  Current/Best:   14.37/  21.75 GFLOPS | Progress: (4/20) | 5.59 s
    [Task 19/25]  Current/Best:   13.55/  21.75 GFLOPS | Progress: (8/20) | 9.49 s
    [Task 19/25]  Current/Best:   18.04/  21.75 GFLOPS | Progress: (12/20) | 13.48 s
    [Task 19/25]  Current/Best:   14.04/  21.75 GFLOPS | Progress: (16/20) | 16.40 s
    [Task 19/25]  Current/Best:   12.49/  21.75 GFLOPS | Progress: (20/20) | 20.08 s Done.
-
    [Task 20/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 20/25]  Current/Best:    8.49/  14.59 GFLOPS | Progress: (4/20) | 9.26 s
    [Task 20/25]  Current/Best:   10.74/  14.59 GFLOPS | Progress: (8/20) | 17.30 s
    [Task 20/25]  Current/Best:   18.37/  18.37 GFLOPS | Progress: (12/20) | 19.75 s
    [Task 20/25]  Current/Best:   10.12/  18.87 GFLOPS | Progress: (16/20) | 26.64 s
    [Task 20/25]  Current/Best:    5.60/  19.94 GFLOPS | Progress: (20/20) | 29.25 s Done.
-
    [Task 21/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 21/25]  Current/Best:    2.81/  20.79 GFLOPS | Progress: (4/20) | 5.02 s
    [Task 21/25]  Current/Best:   22.58/  22.58 GFLOPS | Progress: (8/20) | 8.23 s
    [Task 21/25]  Current/Best:   18.70/  22.58 GFLOPS | Progress: (12/20) | 13.51 s
    [Task 21/25]  Current/Best:    2.78/  22.58 GFLOPS | Progress: (16/20) | 21.44 s
    [Task 21/25]  Current/Best:   20.18/  22.58 GFLOPS | Progress: (20/20) | 24.61 s Done.
-
    [Task 22/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 22/25]  Current/Best:   17.20/  20.42 GFLOPS | Progress: (4/20) | 4.97 s
    [Task 22/25]  Current/Best:    9.31/  22.87 GFLOPS | Progress: (8/20) | 8.09 s
    [Task 22/25]  Current/Best:   11.10/  22.87 GFLOPS | Progress: (12/20) | 11.01 s
    [Task 22/25]  Current/Best:   16.40/  22.87 GFLOPS | Progress: (16/20) | 13.00 s
    [Task 22/25]  Current/Best:   12.69/  22.87 GFLOPS | Progress: (20/20) | 16.76 s Done.
-
    [Task 23/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 23/25]  Current/Best:   14.44/  23.06 GFLOPS | Progress: (4/20) | 6.24 s
    [Task 23/25]  Current/Best:   14.41/  23.06 GFLOPS | Progress: (8/20) | 12.55 s
    [Task 23/25]  Current/Best:   23.19/  23.19 GFLOPS | Progress: (12/20) | 16.40 s
    [Task 23/25]  Current/Best:    1.60/  23.94 GFLOPS | Progress: (16/20) | 21.07 s
    [Task 23/25]  Current/Best:   11.77/  23.94 GFLOPS | Progress: (20/20) | 25.33 s Done.
-
    [Task 24/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 24/25]  Current/Best:    7.32/   7.32 GFLOPS | Progress: (4/20) | 4.74 s
    [Task 24/25]  Current/Best:    2.51/   8.24 GFLOPS | Progress: (8/20) | 15.74 s
    [Task 24/25]  Current/Best:    8.18/   8.24 GFLOPS | Progress: (12/20) | 20.50 s
    [Task 24/25]  Current/Best:    0.90/  10.00 GFLOPS | Progress: (16/20) | 27.47 s
    [Task 24/25]  Current/Best:    3.14/  10.00 GFLOPS | Progress: (20/20) | 36.85 s
    [Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 25/25]  Current/Best:    7.63/   7.63 GFLOPS | Progress: (4/20) | 6.21 s
    [Task 25/25]  Current/Best:    4.25/   7.63 GFLOPS | Progress: (8/20) | 8.00 s
    [Task 25/25]  Current/Best:    3.08/   7.63 GFLOPS | Progress: (12/20) | 11.48 s
    [Task 25/25]  Current/Best:    4.18/   7.63 GFLOPS | Progress: (16/20) | 13.59 s
    [Task 25/25]  Current/Best:    8.30/   8.67 GFLOPS | Progress: (20/2
 0) | 16.00 s Done.
-
+
    [Task  1/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  1/25]  Current/Best:   10.77/  23.61 GFLOPS | Progress: (4/20) | 13.14 s
    [Task  1/25]  Current/Best:   14.46/  23.61 GFLOPS | Progress: (8/20) | 16.06 s
    [Task  1/25]  Current/Best:   12.32/  23.61 GFLOPS | Progress: (12/20) | 18.71 s
    [Task  1/25]  Current/Best:    9.28/  23.61 GFLOPS | Progress: (16/20) | 27.52 s
    [Task  1/25]  Current/Best:   12.84/  23.61 GFLOPS | Progress: (20/20) | 30.07 s Done.
+
    [Task  2/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  2/25]  Current/Best:    3.88/  14.58 GFLOPS | Progress: (4/20) | 4.73 s
    [Task  2/25]  Current/Best:    3.70/  14.58 GFLOPS | Progress: (8/20) | 6.61 s
    [Task  2/25]  Current/Best:   14.80/  14.80 GFLOPS | Progress: (12/20) | 8.76 s
    [Task  2/25]  Current/Best:   19.39/  19.39 GFLOPS | Progress: (16/20) | 10.58 s
    [Task  2/25]  Current/Best:    9.78/  19.39 GFLOPS | Progress: (20/20) | 12.76 s Done.
+
    [Task  3/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  3/25]  Current/Best:   22.40/  22.40 GFLOPS | Progress: (4/20) | 5.29 s
    [Task  3/25]  Current/Best:   15.35/  22.40 GFLOPS | Progress: (8/20) | 7.55 s
    [Task  3/25]  Current/Best:    3.17/  22.40 GFLOPS | Progress: (12/20) | 10.80 s
    [Task  3/25]  Current/Best:   15.67/  22.40 GFLOPS | Progress: (16/20) | 14.70 s
    [Task  3/25]  Current/Best:   20.82/  22.40 GFLOPS | Progress: (20/20) | 17.88 s Done.
+
    [Task  4/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  4/25]  Current/Best:   18.97/  18.97 GFLOPS | Progress: (4/20) | 4.63 s
    [Task  4/25]  Current/Best:   16.02/  18.97 GFLOPS | Progress: (8/20) | 12.63 s
    [Task  4/25]  Current/Best:    5.62/  18.97 GFLOPS | Progress: (12/20) | 15.04 s
    [Task  4/25]  Current/Best:   13.73/  18.97 GFLOPS | Progress: (16/20) | 17.43 s
    [Task  4/25]  Current/Best:   13.43/  18.97 GFLOPS | Progress: (20/20) | 19.35 s Done.
+
    [Task  5/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  5/25]  Current/Best:   11.51/  15.74 GFLOPS | Progress: (4/20) | 5.05 s
    [Task  5/25]  Current/Best:   10.55/  16.10 GFLOPS | Progress: (8/20) | 7.99 s
    [Task  5/25]  Current/Best:   17.57/  17.57 GFLOPS | Progress: (12/20) | 10.29 s
    [Task  5/25]  Current/Best:    3.00/  17.89 GFLOPS | Progress: (16/20) | 12.74 s
    [Task  5/25]  Current/Best:   14.11/  17.89 GFLOPS | Progress: (20/20) | 14.89 s Done.
+
    [Task  6/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  6/25]  Current/Best:   12.48/  12.48 GFLOPS | Progress: (4/20) | 5.88 s
    [Task  6/25]  Current/Best:   18.56/  18.56 GFLOPS | Progress: (8/20) | 8.55 s
    [Task  6/25]  Current/Best:   15.45/  21.69 GFLOPS | Progress: (12/20) | 10.91 s
    [Task  6/25]  Current/Best:   12.00/  21.69 GFLOPS | Progress: (16/20) | 13.66 s
    [Task  6/25]  Current/Best:   13.46/  21.69 GFLOPS | Progress: (20/20) | 17.08 s Done.
+
    [Task  7/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  7/25]  Current/Best:   14.91/  14.91 GFLOPS | Progress: (4/20) | 5.27 s
    [Task  7/25]  Current/Best:   11.23/  17.49 GFLOPS | Progress: (8/20) | 8.20 s
    [Task  7/25]  Current/Best:   14.97/  21.37 GFLOPS | Progress: (12/20) | 10.38 s
    [Task  7/25]  Current/Best:   11.49/  21.37 GFLOPS | Progress: (16/20) | 12.80 s
    [Task  7/25]  Current/Best:   12.08/  21.37 GFLOPS | Progress: (20/20) | 15.23 s Done.
+
    [Task  8/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  8/25]  Current/Best:   15.80/  17.38 GFLOPS | Progress: (4/20) | 4.94 s
    [Task  8/25]  Current/Best:    8.59/  17.38 GFLOPS | Progress: (8/20) | 8.36 s
    [Task  8/25]  Current/Best:    7.50/  17.38 GFLOPS | Progress: (12/20) | 20.87 s
    [Task  8/25]  Current/Best:   14.40/  17.38 GFLOPS | Progress: (16/20) | 23.94 s
    [Task  8/25]  Current/Best:   11.99/  17.38 GFLOPS | Progress: (20/20) | 32.87 s Done.
+
    [Task  9/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task  9/25]  Current/Best:    9.42/  17.04 GFLOPS | Progress: (4/20) | 7.46 s
    [Task  9/25]  Current/Best:   13.88/  17.92 GFLOPS | Progress: (8/20) | 9.21 s
    [Task  9/25]  Current/Best:    6.24/  19.25 GFLOPS | Progress: (12/20) | 12.37 s
    [Task  9/25]  Current/Best:   14.86/  19.25 GFLOPS | Progress: (16/20) | 14.67 s
    [Task  9/25]  Current/Best:   11.18/  21.79 GFLOPS | Progress: (20/20) | 20.19 s Done.
+
    [Task 10/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 10/25]  Current/Best:   10.74/  18.26 GFLOPS | Progress: (4/20) | 4.74 s
    [Task 10/25]  Current/Best:   15.06/  18.26 GFLOPS | Progress: (8/20) | 6.40 s
    [Task 10/25]  Current/Best:   18.62/  19.18 GFLOPS | Progress: (12/20) | 8.87 s
    [Task 10/25]  Current/Best:    7.25/  19.18 GFLOPS | Progress: (16/20) | 10.70 s
    [Task 10/25]  Current/Best:    7.22/  19.18 GFLOPS | Progress: (20/20) | 13.74 s Done.
+
    [Task 11/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 11/25]  Current/Best:   16.38/  23.46 GFLOPS | Progress: (4/20) | 5.07 s
    [Task 11/25]  Current/Best:   13.29/  23.46 GFLOPS | Progress: (8/20) | 7.83 s
    [Task 11/25]  Current/Best:    6.27/  23.46 GFLOPS | Progress: (12/20) | 10.35 s
    [Task 11/25]  Current/Best:   18.75/  23.46 GFLOPS | Progress: (16/20) | 13.78 s
    [Task 11/25]  Current/Best:    8.44/  23.46 GFLOPS | Progress: (20/20) | 16.46 s Done.
+
    [Task 12/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 12/25]  Current/Best:   14.09/  19.08 GFLOPS | Progress: (4/20) | 5.01 s
    [Task 12/25]  Current/Best:   18.95/  19.08 GFLOPS | Progress: (8/20) | 7.73 s
    [Task 12/25]  Current/Best:    3.15/  19.08 GFLOPS | Progress: (12/20) | 10.41 s
    [Task 12/25]  Current/Best:   21.07/  21.07 GFLOPS | Progress: (16/20) | 14.60 s
    [Task 12/25]  Current/Best:   13.41/  21.07 GFLOPS | Progress: (20/20) | 17.14 s Done.
+
    [Task 13/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 13/25]  Current/Best:    9.86/  20.39 GFLOPS | Progress: (4/20) | 6.69 s
    [Task 13/25]  Current/Best:   12.14/  20.39 GFLOPS | Progress: (8/20) | 9.19 s
    [Task 13/25]  Current/Best:    8.90/  20.39 GFLOPS | Progress: (12/20) | 12.10 s
    [Task 13/25]  Current/Best:   12.00/  20.39 GFLOPS | Progress: (16/20) | 14.77 s
    [Task 13/25]  Current/Best:   22.27/  22.27 GFLOPS | Progress: (20/20) | 18.44 s Done.
+
    [Task 14/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 14/25]  Current/Best:    9.65/  10.69 GFLOPS | Progress: (4/20) | 10.93 s
    [Task 14/25]  Current/Best:   16.08/  16.08 GFLOPS | Progress: (8/20) | 13.23 s
    [Task 14/25]  Current/Best:   19.99/  19.99 GFLOPS | Progress: (12/20) | 15.62 s
    [Task 14/25]  Current/Best:   14.84/  19.99 GFLOPS | Progress: (16/20) | 17.76 s
    [Task 14/25]  Current/Best:    3.19/  19.99 GFLOPS | Progress: (20/20) | 20.89 s Done.
+
    [Task 15/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 15/25]  Current/Best:   16.25/  18.45 GFLOPS | Progress: (4/20) | 11.06 s
    [Task 15/25]  Current/Best:   15.71/  18.45 GFLOPS | Progress: (8/20) | 22.13 s
    [Task 15/25]  Current/Best:    1.72/  22.57 GFLOPS | Progress: (12/20) | 25.46 s
    [Task 15/25]  Current/Best:   10.24/  22.57 GFLOPS | Progress: (16/20) | 30.09 s
    [Task 15/25]  Current/Best:    5.86/  22.57 GFLOPS | Progress: (20/20) | 41.35 s
    [Task 16/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 16/25]  Current/Best:    9.03/  19.76 GFLOPS | Progress: (4/20) | 5.77 s
    [Task 16/25]  Current/Best:   15.46/  19.76 GFLOPS | Progress: (8/20) | 7.85 s
    [Task 16/25]  Current/Best:    9.38/  19.76 GFLOPS | Progress: (12/20) | 11.52 s
    [Task 16/25]  Current/Best:   14.60/  19.76 GFLOPS | Progress: (16/20) | 13.26 s
    [Task 16/25]  Current/Best:   15.56/  19.76 GFLOPS | Progress: (20/
 20) | 16.08 s Done.
+
    [Task 17/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 17/25]  Current/Best:   17.85/  18.80 GFLOPS | Progress: (4/20) | 6.00 s
    [Task 17/25]  Current/Best:   11.66/  20.40 GFLOPS | Progress: (8/20) | 8.82 s
    [Task 17/25]  Current/Best:   10.98/  21.67 GFLOPS | Progress: (12/20) | 12.72 s
    [Task 17/25]  Current/Best:    3.08/  21.67 GFLOPS | Progress: (16/20) | 16.43 s
    [Task 17/25]  Current/Best:   12.58/  21.67 GFLOPS | Progress: (20/20) | 19.24 s Done.
+
    [Task 18/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 18/25]  Current/Best:   10.39/  13.77 GFLOPS | Progress: (4/20) | 6.86 s
    [Task 18/25]  Current/Best:   15.65/  16.49 GFLOPS | Progress: (8/20) | 11.27 s
    [Task 18/25]  Current/Best:    9.76/  16.49 GFLOPS | Progress: (12/20) | 19.35 s
    [Task 18/25]  Current/Best:    2.98/  19.77 GFLOPS | Progress: (16/20) | 21.97 s
    [Task 18/25]  Current/Best:   10.96/  19.77 GFLOPS | Progress: (20/20) | 24.50 s Done.
+
    [Task 19/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 19/25]  Current/Best:    9.68/  14.00 GFLOPS | Progress: (4/20) | 7.39 s
    [Task 19/25]  Current/Best:   21.37/  21.37 GFLOPS | Progress: (8/20) | 10.63 s
    [Task 19/25]  Current/Best:    6.15/  21.37 GFLOPS | Progress: (12/20) | 14.46 s
    [Task 19/25]  Current/Best:   19.69/  21.37 GFLOPS | Progress: (16/20) | 18.56 s
    [Task 19/25]  Current/Best:    1.55/  21.45 GFLOPS | Progress: (20/20) | 23.05 s Done.
+
    [Task 20/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 20/25]  Current/Best:   15.37/  17.49 GFLOPS | Progress: (4/20) | 5.72 s
    [Task 20/25]  Current/Best:   12.41/  17.49 GFLOPS | Progress: (8/20) | 9.10 s
    [Task 20/25]  Current/Best:    8.25/  17.49 GFLOPS | Progress: (12/20) | 14.52 s
    [Task 20/25]  Current/Best:    4.86/  17.49 GFLOPS | Progress: (16/20) | 21.61 s
    [Task 20/25]  Current/Best:   20.87/  20.87 GFLOPS | Progress: (20/20) | 23.70 s Done.
+
    [Task 21/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 21/25]  Current/Best:   12.23/  18.90 GFLOPS | Progress: (4/20) | 14.05 s
    [Task 21/25]  Current/Best:   18.81/  18.90 GFLOPS | Progress: (8/20) | 17.02 s
    [Task 21/25]  Current/Best:    9.10/  18.90 GFLOPS | Progress: (12/20) | 19.46 s
    [Task 21/25]  Current/Best:    6.94/  18.90 GFLOPS | Progress: (16/20) | 23.96 s
    [Task 21/25]  Current/Best:   21.59/  21.59 GFLOPS | Progress: (20/20) | 26.42 s
    [Task 22/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 22/25]  Current/Best:   18.58/  18.58 GFLOPS | Progress: (4/20) | 4.72 s
    [Task 22/25]  Current/Best:    8.59/  21.72 GFLOPS | Progress: (8/20) | 7.90 s
    [Task 22/25]  Current/Best:    6.69/  21.72 GFLOPS | Progress: (12/20) | 10.00 s
    [Task 22/25]  Current/Best:   18.08/  21.72 GFLOPS | Progress: (16/20) | 12.02 s
    [Task 22/25]  Current/Best:   10.37/  21.72 GFLOPS | Progress: (20/
 20) | 14.25 s Done.
+
    [Task 23/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 23/25]  Current/Best:    1.55/  19.68 GFLOPS | Progress: (4/20) | 8.71 s
    [Task 23/25]  Current/Best:   11.50/  19.68 GFLOPS | Progress: (8/20) | 11.34 s
    [Task 23/25]  Current/Best:    3.08/  19.68 GFLOPS | Progress: (12/20) | 15.09 s
    [Task 23/25]  Current/Best:   13.80/  22.70 GFLOPS | Progress: (16/20) | 17.49 s
    [Task 23/25]  Current/Best:   22.18/  22.70 GFLOPS | Progress: (20/20) | 20.53 s Done.
+
    [Task 24/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 24/25]  Current/Best:    9.58/   9.58 GFLOPS | Progress: (4/20) | 5.09 s
    [Task 24/25]  Current/Best:    5.16/   9.58 GFLOPS | Progress: (8/20) | 16.12 s
    [Task 24/25]  Current/Best:    3.18/   9.58 GFLOPS | Progress: (12/20) | 27.16 s
    [Task 24/25]  Current/Best:    2.44/   9.58 GFLOPS | Progress: (16/20) | 30.61 s
    [Task 24/25]  Current/Best:    7.33/  10.03 GFLOPS | Progress: (20/20) | 37.23 s
    [Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/20) | 0.00 s
    [Task 25/25]  Current/Best:    5.41/   7.54 GFLOPS | Progress: (4/20) | 5.39 s Done.
+     Done.
+     Done.
+
    [Task 25/25]  Current/Best:    7.22/   7.54 GFLOPS | Progress: (8/20) | 16.37 s
    [Task 25/25]  Current/Best:    6.14/   7.54 GFLOPS | Progress: (12/20) | 21.71 s
    [Task 25/25]  Current/Best:    1.54/   7.54 GFLOPS | Progress: (16/20) | 25.39 s
    [Task 25/25]  Current/Best:    8.96/   8.96 GFLOPS | Progress: (20/20) | 32.75 s
 
 
 
@@ -674,7 +675,6 @@ model using optimized operators to speed up our computations.
  .. code-block:: none
 
      Done.
-     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': 391.63384844000575, 'median': 392.0628854500137, 'std': 3.479379386910943}
-    unoptimized: {'mean': 497.68446000999575, 'median': 496.7346291999547, 'std': 3.2581165093678317}
+    optimized: {'mean': 412.21054737000486, 'median': 411.811772450028, 'std': 1.0512261355036214}
+    unoptimized: {'mean': 500.67015277999417, 'median': 501.1268954499883, 'std': 2.3389088102530704}
 
 
 
@@ -790,7 +790,7 @@ profiling/benchmarking.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 13 minutes  10.652 seconds)
+   **Total running time of the script:** ( 13 minutes  20.180 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 1b93802e8c..7273f43785 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
 
-    1.183e-07 secs/op
+    1.175e-07 secs/op
 
 
 
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 1ef0793e47..c85b99df2d 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, 0xebbc8a0)), stage(b, placeholder(b, 0x149c9f00)), 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, 0x232f7fd0)), stage(b, placeholder(b, 0x24234cf0)), 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 8c269cc4f6..576416658a 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,31 +5,31 @@
 
 Computation times
 =================
-**16:48.516** total execution time for **tutorial** files:
+**16:58.598** total execution time for **tutorial** files:
 
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)                 | 13:10.652 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)                 | 13:20.180 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:31.226 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:29.402 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)     | 01:02.218 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)     | 01:00.798 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)                 | 00:44.840 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)                 | 00:43.904 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)               | 00:17.495 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)               | 00:22.250 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)                               | 00:01.023 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)                               | 00:01.008 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)       | 00:00.859 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)       | 00:00.856 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.202 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.200 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_uma.py` (``uma.py``)                                             | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)                             | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)   | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)                             | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)                           | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_install.py` (``install.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 00fc3fc514..528243f429 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
 
  .. code-block:: none
 
-    Numpy running time: 0.000006
-    naive: 0.000008
+    Numpy running time: 0.000007
+    naive: 0.000007
 
 
 
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
 
  .. code-block:: none
 
-    parallel: 0.000006
+    parallel: 0.000007
 
 
 
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
 
  .. code-block:: none
 
-    vector: 0.000038
+    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    6.358800001180498e-06                    1.0
-                   naive              7.6778e-06      1.2074290744440197
-                parallel    6.124600000000001e-06     0.9631691512334059
-                  vector             3.82533e-05       6.015804867726357
+                   numpy    6.816329996581771e-06                    1.0
+                   naive              6.8172e-06      1.0001276351671156
+                parallel    7.0171999999999995e-06    1.0294689376129031
+                  vector    3.9341799999999997e-05     5.771698262808429
 
 
 
@@ -922,7 +922,7 @@ matrix multiplication.
 
  .. code-block:: none
 
-    Numpy running time: 0.017651
+    Numpy running time: 0.018774
 
 
 
@@ -980,7 +980,7 @@ optimizations.
 
  .. code-block:: none
 
-    none: 3.537286
+    none: 3.405789
 
 
 
@@ -1080,7 +1080,7 @@ schedule.
 
  .. code-block:: none
 
-    blocking: 0.316766
+    blocking: 0.306474
 
 
 
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
 
  .. code-block:: none
 
-    vectorization: 0.307279
+    vectorization: 0.293340
     # 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: 0.126559
+    loop permutation: 0.120841
     # 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: 0.105482
+    array packing: 0.105600
     # 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: 0.101364
+    block caching: 0.111556
     # 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: 0.121882
+    parallelization: 0.132594
     # 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      3.5372859606999993                     1.0
-                blocking            0.3167657963     0.08955051975422273
-           vectorization            0.3072793822     0.08686868565729189
-        loop permutation     0.12655948420000002      0.0357786974550836
-           array packing            0.1054818948     0.02982000776073134
-           block caching            0.1013640142    0.028655872136484243
-         parallelization     0.12188166900000001    0.034456266853777534
+                    none      3.4057888549000004                     1.0
+                blocking            0.3064737387      0.0899861241424488
+           vectorization            0.2933403671      0.0861299333568384
+        loop permutation            0.1208412083     0.03548112153991651
+           array packing             0.105600192     0.03100608889716406
+           block caching     0.11155612020000001      0.0327548550285204
+         parallelization     0.13259394549999998    0.038931933583972324
 
 
 
@@ -1596,7 +1596,7 @@ the computation for specific platforms.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  2.218 seconds)
+   **Total running time of the script:** ( 1 minutes  0.798 seconds)
 
 
 .. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/api/rust/help.html b/docs/api/rust/help.html
index 131b87bfc3..8d27f9b944 100644
--- a/docs/api/rust/help.html
+++ b/docs/api/rust/help.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Documentation for Rustdoc"><title>Rustdoc help</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c14 [...]
\ No newline at end of file
diff --git a/docs/api/rust/settings.html b/docs/api/rust/settings.html
index 9987e08388..ab27dc3ccb 100644
--- a/docs/api/rust/settings.html
+++ b/docs/api/rust/settings.html
@@ -1 +1 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Settings of Rustdoc"><title>Rustdoc settings</title><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/SourceSerif4-Regular-46f98efaafac5295.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="./static.files/FiraSans-Regular-018c141b [...]
\ No newline at end of file
diff --git a/docs/commit_hash b/docs/commit_hash
index 025c828cf0..53b71caa63 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-326f8e74355368eb91639155f3a0adfa53b10aca
+81fd9f3476ed64b4d23ab8f38286a9bd7accc947
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7373707d94..54fb74848c 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -600,7 +600,7 @@ class:[&#39;truck 0.9266&#39;] left:471 top:83 right:689 bottom:169
 class:[&#39;bicycle 0.9984&#39;] 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  35.785 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  35.047 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 e8c856c6b6..276262139e 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -454,7 +454,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;x&quot;</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.zip54be9a3f-ad4b-4815-878f-dc93a2613479 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.zipcf700b51-afca-44c1-9ca7-2641b1d57076 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 06cfca3bfc..3c32b5d67e 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -464,14 +464,15 @@ 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: &quot;https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip&quot; to /workspace/.oneflow/flowvision_cache/resnet18.zip
 
   0%|          | 0.00/41.5M [00:00&lt;?, ?B/s]
- 15%|#5        | 6.33M/41.5M [00:00&lt;00:01, 32.5MB/s]
- 23%|##2       | 9.43M/41.5M [00:00&lt;00:01, 25.2MB/s]
- 39%|###8      | 16.0M/41.5M [00:00&lt;00:00, 28.1MB/s]
- 54%|#####3    | 22.3M/41.5M [00:00&lt;00:00, 34.8MB/s]
- 62%|######2   | 25.8M/41.5M [00:00&lt;00:00, 28.4MB/s]
- 77%|#######7  | 32.0M/41.5M [00:01&lt;00:00, 35.2MB/s]
- 92%|#########2| 38.3M/41.5M [00:01&lt;00:00, 39.0MB/s]
-100%|##########| 41.5M/41.5M [00:01&lt;00:00, 34.1MB/s]
+  8%|7         | 3.27M/41.5M [00:00&lt;00:01, 34.3MB/s]
+ 19%|#9        | 7.99M/41.5M [00:00&lt;00:01, 33.2MB/s]
+ 35%|###4      | 14.3M/41.5M [00:00&lt;00:00, 36.7MB/s]
+ 44%|####3     | 18.2M/41.5M [00:00&lt;00:00, 38.0MB/s]
+ 54%|#####3    | 22.3M/41.5M [00:00&lt;00:00, 39.5MB/s]
+ 63%|######3   | 26.1M/41.5M [00:00&lt;00:00, 34.2MB/s]
+ 82%|########2 | 34.1M/41.5M [00:00&lt;00:00, 42.6MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01&lt;00:00, 36.5MB/s]
+100%|##########| 41.5M/41.5M [00:01&lt;00:00, 37.3MB/s]
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 87d8d12a24..3ba718ccc7 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -499,7 +499,7 @@ To begin, we’ll install PaddlePaddle&gt;=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: &#39;tiger cat&#39;,
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  12.151 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  5.628 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 218651bd1a..6effccfeb8 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -447,14 +447,15 @@ be unstable.</p>
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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 c8875c561b..f39a030b71 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -667,7 +667,7 @@ banana (score = 0.00022)
 desk (score = 0.00019)
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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 a3492e2458..ba5c88e064 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -845,10 +845,10 @@ Top5 predictions:
 Evaluate inference time cost...
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  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
- 4237.7885    4233.7190    4272.3148    4231.0619     11.7239
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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 b5a1fa36ee..56c259cb4e 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
@@ -453,31 +453,26 @@ to run this tutorial with a real device over rpc.</p>
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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
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--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -677,7 +677,7 @@ to the remote android device.</p>
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diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
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+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
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+ 98%|#########7| 166M/170M [00:05&lt;00:00, 34.5MB/s]
+100%|##########| 170M/170M [00:05&lt;00:00, 33.8MB/s]
 /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
   (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
 /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -596,7 +593,7 @@ 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  47.204 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  39.197 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 10e011239c..7f057514ad 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -510,9 +510,9 @@ training. Other models require a full post training calibration.</p>
 Downloading: &quot;https://download.pytorch.org/models/mobilenet_v2-b0353104.pth&quot; to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
 
   0%|          | 0.00/13.6M [00:00&lt;?, ?B/s]
- 59%|#####8    | 7.99M/13.6M [00:00&lt;00:00, 50.7MB/s]
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-100%|##########| 13.6M/13.6M [00:00&lt;00:00, 33.2MB/s]
+ 59%|#####8    | 7.99M/13.6M [00:00&lt;00:00, 46.7MB/s]
+ 92%|#########1| 12.5M/13.6M [00:00&lt;00:00, 45.2MB/s]
+100%|##########| 13.6M/13.6M [00:00&lt;00:00, 48.6MB/s]
 </pre></div>
 </div>
 </div>
@@ -603,7 +603,7 @@ 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)
-  89.1725      89.1476      92.0903      88.7578       0.3694
+  88.9137      88.8519      90.0473      88.5180       0.2548
 </pre></div>
 </div>
 <div class="admonition note">
@@ -642,7 +642,7 @@ 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  39.479 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  28.946 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 0bb34a5506..18bafda0c7 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -595,7 +595,7 @@ 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)
-  111.1047     111.0965     116.0725     110.1563      0.6066
+  109.7146     109.6867     110.6482     108.9642      0.3080
 </pre></div>
 </div>
 <div class="admonition note">
@@ -623,7 +623,6 @@ network for ARM CPU</span></a>.</p></li>
 </ul>
 </div></blockquote>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  1.590 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.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_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index 5adcf1c3f1..06992fceca 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -536,7 +536,7 @@ for calibration. But the accuracy might be impacted.</p>
   warnings.warn(
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  38.686 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  26.236 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 6f08fde322..7db6fc4587 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -355,7 +355,7 @@
             
   <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>13:29.508</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:34.818</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 86%" />
@@ -364,39 +364,39 @@
 </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>03:47.204</p></td>
+<td><p>03:39.197</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></td>
-<td><p>02:38.686</p></td>
+<td><p>02:26.236</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>01:39.479</p></td>
+<td><p>01:28.946</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 Adrenoâ„¢</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:21.501</p></td>
+<td><p>01:21.467</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>01:01.590</p></td>
+<td><p>00:55.841</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adrenoâ„¢ 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>00:57.532</p></td>
+<td><p>00:50.902</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:57.059</p></td>
+<td><p>00:50.529</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:33.397</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:31.383</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:33.053</p></td>
+<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>00:30.313</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 ba941aff91..2f7620e714 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -634,7 +634,7 @@ 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.zip5af97d2c-bd37-48e7-82a8-845e7ff4542a 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.zip6e21983b-f9e3-4951-8f08-223b1d44e101 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 a4bbe9ec53..292c5a61aa 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -355,7 +355,7 @@
             
   <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>01:00.571</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>01:00.676</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 84%" />
@@ -364,15 +364,15 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:56.154</p></td>
+<td><p>00:56.548</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>00:03.091</p></td>
+<td><p>00:02.865</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>00:01.317</p></td>
+<td><p>00:01.254</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 676bf5007e..ea31940e9a 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -541,10 +541,10 @@ 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: 30340us [30340us] (48.26%; 48.26%)
-FoldScaleAxis: 32528us [11us] (51.74%; 51.74%)
-        FoldConstant: 32517us [1883us] (51.72%; 99.97%)
-                InferType: 30634us [30634us] (48.73%; 94.21%)
+InferType: 24150us [24150us] (48.34%; 48.34%)
+FoldScaleAxis: 25807us [10us] (51.66%; 51.66%)
+        FoldConstant: 25797us [1927us] (51.64%; 99.96%)
+                InferType: 23870us [23870us] (47.78%; 92.53%)
 </pre></div>
 </div>
 </div>
@@ -566,10 +566,10 @@ 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: 30425us [30425us] (48.62%; 48.62%)
-FoldScaleAxis: 32148us [10us] (51.38%; 51.38%)
-        FoldConstant: 32138us [1928us] (51.36%; 99.97%)
-                InferType: 30210us [30210us] (48.28%; 94.00%)
+InferType: 24197us [24197us] (48.75%; 48.75%)
+FoldScaleAxis: 25438us [8us] (51.25%; 51.25%)
+        FoldConstant: 25430us [1895us] (51.23%; 99.97%)
+                InferType: 23535us [23535us] (47.42%; 92.55%)
 </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 fc866ea89c..51b8c069ac 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -590,7 +590,7 @@ latency of convolution.</p>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Convolution: </span><span class="si">%f</span><span class="s2"> ms&quot;</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: 34.461982 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.526847 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 129b616eb8..17434ec3f6 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -872,7 +872,7 @@ be able to run on our build server</p>
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms&quot;</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: 12.269571 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.270982 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 3e833186f8..9f0e17f827 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -487,8 +487,8 @@ 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">&quot;Baseline: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.019445
-Baseline: 3.537474
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019032
+Baseline: 3.578296
 </pre></div>
 </div>
 <p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -547,7 +547,7 @@ 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">&quot;Opt1: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.305471
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.309980
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -604,7 +604,7 @@ vastly.</p>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt2: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.299635
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.281913
 </pre></div>
 </div>
 <p>Here is the generated IR after vectorization.</p>
@@ -659,7 +659,7 @@ the access pattern for A matrix is more cache friendly.</p>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt3: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.119241
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118874
 </pre></div>
 </div>
 <p>Here is the generated IR after loop permutation.</p>
@@ -736,7 +736,7 @@ flattening.</p>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt4: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.109402
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107967
 </pre></div>
 </div>
 <p>Here is the generated IR after array packing.</p>
@@ -814,7 +814,7 @@ write to C when all the block results are ready.</p>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt5: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.112595
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112745
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -894,7 +894,7 @@ class Module:
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt6: </span><span class="si">%f</span><span class="s2">&quot;</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: 0.134538
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.133421
 </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 8b1b6d7ad0..13a32da56f 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -355,7 +355,7 @@
             
   <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>00:35.332</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.950</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 83%" />
@@ -364,15 +364,15 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:31.989</p></td>
+<td><p>00:31.720</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>00:02.100</p></td>
+<td><p>00:02.001</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>00:01.243</p></td>
+<td><p>00:01.229</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 4c438bca17..de1cb5803e 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -355,7 +355,7 @@
             
   <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>04:01.172</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:47.118</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 85%" />
@@ -364,27 +364,27 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:45.692</p></td>
+<td><p>01:41.276</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:18.802</p></td>
+<td><p>01:14.859</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:19.209</p></td>
+<td><p>00:17.715</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:18.709</p></td>
+<td><p>00:16.760</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>00:18.656</p></td>
+<td><p>00:16.405</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.104</p></td>
+<td><p>00:00.103</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 </tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index 7a956808be..eefa130f7c 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
@@ -1027,7 +1027,7 @@ 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: 0.358 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.348 ms
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index 8c53f1210a..8f100314fa 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -931,7 +931,7 @@ 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)
-   3.2703       3.2695       3.2732       3.2683       0.0021
+   3.2980       3.3025       3.3041       3.2874       0.0075
 </pre></div>
 </div>
 </div>
@@ -953,7 +953,7 @@ to learn how to use the RPC Tracker and RPC Server.
 To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
 with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
 </ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  18.802 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  14.859 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 6a5b7db85b..63f421dfcb 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -950,7 +950,7 @@ 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)
-  762.3943     763.2844     763.7169     760.1816      1.5745
+  762.4247     761.6711     764.6278     760.9752      1.5835
 </pre></div>
 </div>
 </div>
@@ -972,7 +972,7 @@ 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  45.692 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  41.276 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 2fabbaeb26..b93f950b50 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -355,7 +355,7 @@
             
   <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>00:24.697</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.595</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 84%" />
@@ -364,11 +364,11 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:24.658</p></td>
+<td><p>00:23.558</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.023</p></td>
+<td><p>00:00.021</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
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 eaa050e7d3..bc48a5fb8e 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -625,7 +625,7 @@ and measure running time.</p>
 
 Best config:
 ,None
-Time cost of this operator: 0.037222
+Time cost of this operator: 0.037338
 </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 21fb5da3b4..cc6f1c3d6b 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -659,10 +659,10 @@ 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  303.4     98.732   (1, 2, 10, 10, 3)  2       1        [303.4]
-tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.935     0.955    (1, 6, 10, 10)     1       1        [2.935]
-tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.961     0.313    (1, 1, 10, 10, 3)  1       1        [0.961]
-Total_time                                    -                                             307.296   -        -                  -       -        -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  304.2     98.738   (1, 2, 10, 10, 3)  2       1        [304.2]
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+tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.808     1.649    (1, 6, 10, 10)     1       1        [1.808]
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+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -470,7 +470,7 @@ download a cat image and preprocess it to use as the model input.</p>
 Downloading: &quot;https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth&quot; to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
 
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 /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()
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@@ -598,7 +598,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
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diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
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 <p>Register the rule to TVM with override option to override existing rule.
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