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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/03/19 17:12:34 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@97d92b548345ba48bf65051765285ee4e35b5313)
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 c69e71f670 deploying docs (apache/tvm@97d92b548345ba48bf65051765285ee4e35b5313)
c69e71f670 is described below
commit c69e71f67021d303a2f74e307751b7223e3836be
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Tue Mar 19 17:12:26 2024 +0000
deploying docs (apache/tvm@97d92b548345ba48bf65051765285ee4e35b5313)
---
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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 cb06ea808c..42c606cdcd 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 41.106 seconds)
+ **Total running time of the script:** ( 1 minutes 30.208 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
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 4f9e9f6768..0ea36c8e9d 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -121,7 +121,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
- 0%| | 0.00/41.5M [00:00<?, ?B/s] 15%|#5 | 6.33M/41.5M [00:00<00:00, 41.7MB/s] 32%|###2 | 13.4M/41.5M [00:00<00:00, 57.0MB/s] 46%|####6 | 19.2M/41.5M [00:00<00:00, 35.3MB/s] 56%|#####6 | 23.4M/41.5M [00:00<00:00, 34.6MB/s] 65%|######5 | 27.1M/41.5M [00:00<00:00, 33.7MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 37.1MB/s] 96%|#########6| 40.0M/41.5M [00:01<00:00, 43.9MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 41.3MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 19%|#9 | 7.99M/41.5M [00:00<00:00, 65.4MB/s] 39%|###8 | 16.0M/41.5M [00:00<00:00, 56.9MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 51.0MB/s] 66%|######5 | 27.2M/41.5M [00:00<00:00, 38.2MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 38.1MB/s] 92%|#########2| 38.3M/41.5M [00:00<00:00, 43.2MB/s] 100%|##########| 41.5M/41.5M [00:00<00:00, 43.6MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index cccb7bc174..8df75c9e33 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -207,11 +207,6 @@ Look up prediction top 1 index in 1000 class synset.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 4.259 seconds)
-
-
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
.. only:: html
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 2fc92f5cac..3b8f3c2648 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 48.3MB/s] 32%|###2 | 14.3M/44.7M [00:00<00:00, 47.2MB/s] 42%|####2 | 18.8M/44.7M [00:00<00:00, 43.0MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 45.3MB/s] 72%|#######1 | 32.0M/44.7M [00:00<00:00, 40.4MB/s] 86%|########5 | 38.3M/44.7M [00:01<00:00, 36.5MB/s] 94%|#########3| 41.9M/44.7M [00:01<00:00, 29.3MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 31.0MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 18%|#7 | 7.99M/44.7M [00:00<00:00, 58.6MB/s] 36%|###5 | 16.0M/44.7M [00:00<00:00, 54.5MB/s] 54%|#####3 | 24.0M/44.7M [00:00<00:00, 54.8MB/s] 72%|#######1 | 32.0M/44.7M [00:00<00:00, 58.6MB/s] 90%|########9 | 40.0M/44.7M [00:00<00:00, 58.6MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 62.4MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 03359e2713..272c08b34b 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.839 seconds)
+ **Total running time of the script:** ( 1 minutes 27.732 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 3c81ad6984..76029e16df 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
@@ -5,24 +5,24 @@
Computation times
=================
-**07:03.088** total execution time for **how_to_compile_models** files:
+**06:21.417** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:41.106 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:30.208 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:36.839 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:27.732 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:04.259 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:58.889 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:45.852 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:41.794 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:40.981 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:36.483 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:30.990 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.228 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.929 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.629 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.038 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:10.705 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:03.094 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.748 | 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 fb06fff405..3ace8429b9 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)
- 4138.9903 4130.3751 4215.2482 4128.4925 25.4820
+ 3990.7422 3990.3732 3994.8019 3988.7071 1.9313
@@ -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 21.684 seconds)
+ **Total running time of the script:** ( 1 minutes 16.769 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 36e29aa1c5..634d005455 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
- 8192/102967424 [..............................] - ETA: 0s 8380416/102967424 [=>............................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s 33546240/102967424 [========>.....................] - ETA: 0s 41934848/102967424 [===========>..................] - ETA: 0s 50323456/102967424 [=============>................] - ETA: 0s 56967168/102967424 [===============>..............] - ETA: 0s
61538304/102967424 [================>.............] - ETA: 0s 67551232/102967424 [==================>...........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 100646912/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 1s 0us/step
+ 8192/102967424 [..............................] - ETA: 0s 2957312/102967424 [..............................] - ETA: 1s 11837440/102967424 [==>...........................] - ETA: 0s 23371776/102967424 [=====>........................] - ETA: 0s 28753920/102967424 [=======>......................] - ETA: 0s 35340288/102967424 [=========>....................] - ETA: 0s 40452096/102967424 [==========>...................] - ETA: 0s 41934848/102967424 [===========>..................] - ETA: 0s
50323456/102967424 [=============>................] - ETA: 0s 57556992/102967424 [===============>..............] - ETA: 0s 60309504/102967424 [================>.............] - ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 82968576/102967424 [=======================>......] - ETA: 0s 87932928/102967424 [========================>.....] - ETA: 0s 92266496/102967424 [=========================>....] -
ETA: 0s 100630528/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 1s 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 0c91fdf190..c9e5b2ebc5 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7183 15.5954 16.3048 15.2092 0.3967
+ 13.7543 13.7362 13.8853 13.6846 0.0613
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 2413eefdcb..bca7289595 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:04, 37.5MB/s] 8%|8 | 14.3M/170M [00:00<00:03, 42.9MB/s] 11%|# | 18.5M/170M [00:00<00:05, 29.7MB/s] 14%|#3 | 23.6M/170M [00:00<00:04, 35.2MB/s] 16%|#6 | 27.4M/170M [00:00<00:04, 33.8MB/s] 18%|#8 | 30.9M/170M [00:00<00:04, 34.1MB/s] 20%|## | 34.4M/170M [00:01<00:04, 31.6MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 38.1MB/s] 27%|##7 | 46.3M/170M [00:01<00:03, 41.1MB/s] 30%|##9 | 50.4M/170M [00:01<00:03, 39.9MB/s] 33%|###2 | 56.0M/170M [00:01<00:03, 34.2MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 37.4MB/s] 42%|####2 | 72.0M/170M [00:02<00:02, 41.7MB/s] 47%|####7 | 80.0M/170M [00:02<00:02, 43.5MB/s] 52%|#####1 | 88.0M/170M [00:02<00:01, 49.8MB/s] 57%|#####6 | 96.0M/170M [00:02<00:01, 46.8MB/s] 60%|###### | 102M/170M [00:02<00:01, 47.1MB/s
] 63%|######2 | 107M/170M [00:02<00:01, 37.6MB/s] 66%|######5 | 112M/170M [00:02<00:01, 40.6MB/s] 71%|####### | 120M/170M [00:03<00:01, 49.8MB/s] 74%|#######4 | 126M/170M [00:03<00:00, 52.9MB/s] 78%|#######7 | 132M/170M [00:03<00:00, 45.3MB/s] 80%|######## | 137M/170M [00:03<00:01, 33.8MB/s] 85%|########4 | 144M/170M [00:03<00:00, 37.0MB/s] 88%|########8 | 150M/170M [00:03<00:00, 39.8MB/s] 91%|######### | 154M/170M [00:04<00:00, 40.0MB/s] 94%|#########4| 160M/170M [00:04<00:00, 38.2MB/s] 98%|#########7| 166M/170M [00:04<00:00, 35.2MB/s] 100%|##########| 170M/170M [00:04<00:00, 39.6MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 5%|4 | 7.99M/170M [00:00<00:04, 41.2MB/s] 9%|9 | 16.0M/170M [00:00<00:04, 40.0MB/s] 13%|#3 | 22.3M/170M [00:00<00:03, 40.9MB/s] 15%|#5 | 26.2M/170M [00:00<00:03, 39.7MB/s] 18%|#7 | 30.3M/170M [00:00<00:03, 39.7MB/s] 20%|## | 34.1M/170M [00:00<00:04, 34.1MB/s] 24%|##3 | 40.0M/170M [00:01<00:03, 37.6MB/s] 27%|##7 | 46.3M/170M [00:01<00:03, 35.4MB/s] 31%|### | 52.1M/170M [00:01<00:03, 40.9MB/s] 33%|###3 | 56.2M/170M [00:01<00:02, 41.2MB/s] 38%|###7 | 64.0M/170M [00:01<00:02, 44.3MB/s] 42%|####2 | 72.0M/170M [00:01<00:01, 53.4MB/s] 47%|####7 | 80.0M/170M [00:01<00:01, 51.2MB/s] 51%|##### | 86.3M/170M [00:02<00:01, 51.4MB/s] 56%|#####5 | 94.3M/170M [00:02<00:01, 52.8MB/s] 59%|#####8 | 99.5M/170M [00:02<00:01, 50.5MB/s] 61%|######1 | 104M/170M [00:02<00:01, 41.7MB/s
] 66%|######5 | 112M/170M [00:02<00:01, 47.8MB/s] 71%|####### | 120M/170M [00:02<00:01, 50.3MB/s] 75%|#######5 | 128M/170M [00:03<00:00, 45.8MB/s] 79%|#######9 | 134M/170M [00:03<00:00, 45.4MB/s] 82%|########1 | 139M/170M [00:03<00:00, 33.1MB/s] 88%|########7 | 149M/170M [00:03<00:00, 45.6MB/s] 91%|######### | 154M/170M [00:03<00:00, 35.5MB/s] 93%|#########3| 159M/170M [00:03<00:00, 35.6MB/s] 96%|#########5| 163M/170M [00:04<00:00, 34.0MB/s] 98%|#########8| 166M/170M [00:04<00:00, 32.3MB/s] 100%|##########| 170M/170M [00:04<00:00, 41.4MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 50.946 seconds)
+ **Total running time of the script:** ( 3 minutes 20.268 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 d2ac1425d8..9c1932604c 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] 47%|####6 | 6.30M/13.6M [00:00<00:00, 63.7MB/s] 91%|#########1| 12.4M/13.6M [00:00<00:00, 33.4MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 38.9MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 59%|#####8 | 7.99M/13.6M [00:00<00:00, 35.6MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 54.8MB/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)
- 89.5861 89.1090 122.6341 88.8205 3.3713
+ 85.7626 85.7258 86.3337 85.4945 0.1797
@@ -457,7 +457,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.735 seconds)
+ **Total running time of the script:** ( 1 minutes 25.206 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 31d53b25af..2e794cc58e 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)
- 111.3935 111.3417 112.7962 110.5910 0.3760
+ 103.4025 103.2977 106.7831 102.5994 0.5810
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 534abc8a07..8e25609b1d 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
@@ -5,24 +5,24 @@
Computation times
=================
-**10:29.944** total execution time for **how_to_deploy_models** files:
+**09:21.530** 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``) | 03:50.946 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:20.268 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:36.735 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:25.206 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:21.684 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:16.769 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:55.162 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:49.186 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:54.191 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:48.838 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:49.211 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.159 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:31.009 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:28.597 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.995 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.499 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.009 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.008 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
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 f60e061cb9..02bb77729f 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
@@ -5,12 +5,12 @@
Computation times
=================
-**00:04.373** total execution time for **how_to_extend_tvm** files:
+**00:03.842** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:03.025 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.674 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.339 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.161 | 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 |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 93b2ca960c..7da3f2d832 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: 27287us [27287us] (48.14%; 48.14%)
- FoldScaleAxis: 29395us [10us] (51.86%; 51.86%)
- FoldConstant: 29385us [2234us] (51.84%; 99.97%)
- InferType: 27151us [27151us] (47.90%; 92.40%)
+ InferType: 24537us [24537us] (48.40%; 48.40%)
+ FoldScaleAxis: 26156us [6us] (51.60%; 51.60%)
+ FoldConstant: 26149us [1883us] (51.58%; 99.98%)
+ InferType: 24267us [24267us] (47.87%; 92.80%)
@@ -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: 26596us [26596us] (48.22%; 48.22%)
- FoldScaleAxis: 28557us [11us] (51.78%; 51.78%)
- FoldConstant: 28546us [2011us] (51.76%; 99.96%)
- InferType: 26535us [26535us] (48.11%; 92.96%)
+ InferType: 23700us [23700us] (48.74%; 48.74%)
+ FoldScaleAxis: 24927us [6us] (51.26%; 51.26%)
+ FoldConstant: 24921us [1602us] (51.25%; 99.98%)
+ InferType: 23320us [23320us] (47.96%; 93.57%)
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 a3711900b4..955c817183 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.520416 ms
+ Convolution: 53.593025 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 4ddc346794..b94d7a7ca4 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 11.501059 ms
+ conv2d with tensor core: 11.216691 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 0bea136261..7b621328ca 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: 0.019450
- Baseline: 3.307527
+ Numpy running time: 0.013852
+ Baseline: 3.220233
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.300458
+ Opt1: 0.287637
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.297456
+ Opt2: 0.256215
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118929
+ Opt3: 0.109804
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.107393
+ Opt4: 0.103377
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.114247
+ Opt5: 0.097689
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.132767
+ Opt6: 0.116513
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 ee73b97937..3605f5fb81 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.143** total execution time for **how_to_optimize_operators** files:
+**00:31.267** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.067 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.329 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.885 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.785 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.191 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.152 | 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 d4c6efe44a..e68c3205a0 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**03:50.653** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:29.037** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:40.497 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:30.693 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:16.814 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:10.378 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:19.165 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:17.166 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.700 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.817 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:16.374 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.886 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.097 | 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 88257b7c33..fc970df537 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: 0.340 ms
+ Execution time of this operator: 0.333 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 00297bd505..df6fbd7a5e 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
@@ -633,7 +633,7 @@ 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)
- 3.2322 3.2316 3.2461 3.2190 0.0111
+ 3.2001 3.1965 3.2092 3.1946 0.0065
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 16.814 seconds)
+ **Total running time of the script:** ( 1 minutes 10.378 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 27b30ac48a..c1c0e952a4 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
@@ -655,7 +655,7 @@ 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)
- 759.8720 758.2567 764.1303 757.2290 3.0402
+ 708.0210 708.1602 708.4791 707.4239 0.4419
@@ -682,7 +682,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 40.497 seconds)
+ **Total running time of the script:** ( 1 minutes 30.693 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 5527a909c3..d77e34b7af 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
@@ -5,14 +5,14 @@
Computation times
=================
-**00:23.855** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.045** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.817 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.005 | 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``) | 00:00.025 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index b0d89c7a7c..6273a5be50 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: 0.037089
+ Time cost of this operator: 0.037283
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 c30a509bbd..cad1f2422e 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 302.4 98.745 (1, 2, 10, 10, 3) 2 1 [302.4]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.889 0.943 (1, 6, 10, 10) 1 1 [2.889]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.312 (1, 1, 10, 10, 3) 1 1 [0.955]
- Total_time - 306.244 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 294.5 98.743 (1, 2, 10, 10, 3) 2 1 [294.5]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.796 0.937 (1, 6, 10, 10) 1 1 [2.796]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.32 (1, 1, 10, 10, 3) 1 1 [0.954]
+ Total_time - 298.25 - - - - -
@@ -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 105.4 97.526 (1, 6, 10, 10, 1) 2 1 [105.4]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.824 1.688 (1, 6, 10, 10) 1 1 [1.824]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.85 0.786 (1, 3, 10, 10, 1) 1 1 [0.85]
- Total_time - 108.074 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 97.625 97.285 (1, 6, 10, 10, 1) 2 1 [97.625]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.743 1.737 (1, 6, 10, 10) 1 1 [1.743]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.982 0.978 (1, 1, 10, 10, 3) 1 1 [0.982]
+ Total_time - 100.35 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 31.758 seconds)
+ **Total running time of the script:** ( 1 minutes 22.104 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 6d63bd2bfe..00e955a4ba 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
- 0%| | 0.00/3.42M [00:00<?, ?B/s] 61%|###### | 2.09M/3.42M [00:00<00:00, 11.6MB/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 17.9MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 99.8MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -326,7 +326,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 35.130 seconds)
+ **Total running time of the script:** ( 1 minutes 24.523 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 c96dad555b..51c24b7985 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/tmpxj7a1vvs/images/random'
+ '/tmp/tmphdpfpsq2/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpxj7a1vvs/images/target contains 8144 images
- /tmp/tmpxj7a1vvs/images/random contains 5000 images
+ /tmp/tmphdpfpsq2/images/target contains 8144 images
+ /tmp/tmphdpfpsq2/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 40s - loss: 0.2335 - accuracy: 0.9210 - val_loss: 0.1320 - val_accuracy: 0.9547 - 40s/epoch - 122ms/step
+ 328/328 - 38s - loss: 0.2162 - accuracy: 0.9253 - val_loss: 0.1170 - val_accuracy: 0.9569 - 38s/epoch - 115ms/step
Epoch 2/3
- 328/328 - 36s - loss: 0.1049 - accuracy: 0.9606 - val_loss: 0.1249 - val_accuracy: 0.9566 - 36s/epoch - 110ms/step
+ 328/328 - 34s - loss: 0.1012 - accuracy: 0.9636 - val_loss: 0.1058 - val_accuracy: 0.9626 - 34s/epoch - 104ms/step
Epoch 3/3
- 328/328 - 36s - loss: 0.0689 - accuracy: 0.9748 - val_loss: 0.1215 - val_accuracy: 0.9569 - 36s/epoch - 109ms/step
+ 328/328 - 34s - loss: 0.0762 - accuracy: 0.9728 - val_loss: 0.1044 - val_accuracy: 0.9634 - 34s/epoch - 104ms/step
- <keras.callbacks.History object at 0x7fa522575f10>
+ <keras.callbacks.History object at 0x7fafe76a4400>
@@ -860,7 +860,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 35.051 seconds)
+ **Total running time of the script:** ( 4 minutes 20.097 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 f11d8557ca..82bf4d4cc8 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
=================
-**08:13.281** total execution time for **how_to_work_with_microtvm** files:
+**07:34.944** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:35.051 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:20.097 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:35.130 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:24.523 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:31.758 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:22.104 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.945 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.574 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:09.515 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.398 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.882 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.248 | 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 2e53f9b507..e263bd30d9 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
=================
-**00:41.675** total execution time for **how_to_work_with_relay** files:
+**00:36.891** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.967 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:32.034 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.454 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.062 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.247 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.788 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.007 | 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 705fbabf8c..3746bab1ee 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 0x7fa9dc85ec10>
+ <function my_cuda_math_rule at 0x7fb1c80e7550>
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 6f6e8f13a4..7705536765 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:06.155** total execution time for **how_to_work_with_schedules** files:
+**00:05.490** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.721 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:02.432 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.570 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.394 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.781 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.698 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.765 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.684 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.133 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.117 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.080 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.069 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.070 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.065 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.035 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.032 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 9003a5b004..9d78e3f70b 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 28.94s!
+ yolov3-tiny inference graph built in 25.54s!
@@ -445,11 +445,6 @@ Download test image
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 2.580 seconds)
-
-
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
.. only:: html
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 da89d9440d..e176a03516 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
@@ -5,8 +5,8 @@
Computation times
=================
-**01:02.580** total execution time for **topic_vta_tutorials_frontend** files:
+**00:57.884** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:02.580 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:57.884 | 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 25043c5df6..650546b885 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
=================
-**00:03.407** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.176** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.876 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.709 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.530 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.467 | 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 91a5e9882e..c04041477f 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
=================
-**00:00.842** total execution time for **topic_vta_tutorials** files:
+**00:00.773** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.439 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.404 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.403 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.368 | 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 3185d6446f..d2c0a83af0 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: 94.828 ms
+ Execution time of this operator: 92.091 ms
@@ -416,7 +416,7 @@ resume the status and do more 5 trials.
.. code-block:: none
Resume search:
- *E
+
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 46.279 seconds)
+ **Total running time of the script:** ( 1 minutes 27.126 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 d852027f70..9ee0a15a0e 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: 5.76/5.76 result: MeasureResult(costs=(0.0466200606,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.0229053497314453, timestamp=1710861090.9118295) [('tile_y', [-1, 2]), ('tile_x', [-1, 4])],None,21
- No: 2 GFLOPS: 10.66/10.66 result: MeasureResult(costs=(0.025188435000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6749799251556396, timestamp=1710861091.5831857) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
- No: 3 GFLOPS: 9.97/10.66 result: MeasureResult(costs=(0.026915468199999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7520039081573486, timestamp=1710861092.284731) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
- No: 4 GFLOPS: 0.50/10.66 result: MeasureResult(costs=(0.5352805696,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.855007886886597, timestamp=1710861101.1107087) [('tile_y', [-1, 64]), ('tile_x', [-1, 1])],None,6
- No: 5 GFLOPS: 8.30/10.66 result: MeasureResult(costs=(0.0323260914,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7970671653747559, timestamp=1710861102.027243) [('tile_y', [-1, 128]), ('tile_x', [-1, 16])],None,47
- No: 6 GFLOPS: 8.06/10.66 result: MeasureResult(costs=(0.033314937200000005,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7959713935852051, timestamp=1710861102.8211057) [('tile_y', [-1, 1]), ('tile_x', [-1, 8])],None,30
- No: 7 GFLOPS: 7.08/10.66 result: MeasureResult(costs=(0.0379020588,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8787758350372314, timestamp=1710861103.6939225) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
- No: 8 GFLOPS: 5.03/10.66 result: MeasureResult(costs=(0.0533276246,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1110692024230957, timestamp=1710861104.8074536) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 9 GFLOPS: 12.57/12.57 result: MeasureResult(costs=(0.0213544684,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.713761568069458, timestamp=1710861105.6346252) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
- No: 10 GFLOPS: 10.32/12.57 result: MeasureResult(costs=(0.026014929799999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.689124345779419, timestamp=1710861106.315267) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
+ No: 1 GFLOPS: 1.05/1.05 result: MeasureResult(costs=(0.2558824478,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.346071004867554, timestamp=1710864043.5133452) [('tile_y', [-1, 64]), ('tile_x', [-1, 2])],None,16
+ No: 2 GFLOPS: 1.90/1.90 result: MeasureResult(costs=(0.1409829138,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.512375593185425, timestamp=1710864046.0236225) [('tile_y', [-1, 8]), ('tile_x', [-1, 1])],None,3
+ No: 3 GFLOPS: 12.25/12.25 result: MeasureResult(costs=(0.021915528599999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.677790641784668, timestamp=1710864046.6297965) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+ No: 4 GFLOPS: 6.34/12.25 result: MeasureResult(costs=(0.0423073236,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.93923020362854, timestamp=1710864047.558919) [('tile_y', [-1, 16]), ('tile_x', [-1, 4])],None,24
+ No: 5 GFLOPS: 11.59/12.25 result: MeasureResult(costs=(0.0231631126,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6464016437530518, timestamp=1710864048.3854647) [('tile_y', [-1, 256]), ('tile_x', [-1, 256])],None,88
+ No: 6 GFLOPS: 8.89/12.25 result: MeasureResult(costs=(0.0301905902,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7931931018829346, timestamp=1710864049.130692) [('tile_y', [-1, 512]), ('tile_x', [-1, 128])],None,79
+ No: 7 GFLOPS: 8.60/12.25 result: MeasureResult(costs=(0.031210401199999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.824091911315918, timestamp=1710864049.8777792) [('tile_y', [-1, 512]), ('tile_x', [-1, 64])],None,69
+ No: 8 GFLOPS: 14.45/14.45 result: MeasureResult(costs=(0.0185766178,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5633754730224609, timestamp=1710864050.4281013) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 9 GFLOPS: 11.64/14.45 result: MeasureResult(costs=(0.0230655634,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6168653964996338, timestamp=1710864051.1717436) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+ No: 10 GFLOPS: 10.24/14.45 result: MeasureResult(costs=(0.026209627999999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.680119514465332, timestamp=1710864051.8460035) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 90f16ff95d..1c8bcdae54 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': 500.1930359100061, 'median': 501.0475809000127, 'std': 3.205093817229211}
+ {'mean': 466.7433671999788, 'median': 466.45811764992686, 'std': 1.3168505305302587}
@@ -582,31 +582,29 @@ 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: 23.76/ 23.76 GFLOPS | Progress: (4/20) | 8.91 s [Task 1/25] Current/Best: 7.03/ 23.76 GFLOPS | Progress: (8/20) | 12.56 s [Task 1/25] Current/Best: 22.69/ 23.76 GFLOPS | Progress: (12/20) | 14.91 s [Task 1/25] Current/Best: 12.83/ 23.76 GFLOPS | Progress: (16/20) | 17.12 s [Task 1/25] Current/Best: 15.58/ 23.76 GFLOPS | Progress: (20/20) | 19.28 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 21.04/ 21.04 GFLOPS | Progress: (4/20) | 4.22 s [Task 2/25] Current/Best: 13.84/ 21.04 GFLOPS | Progress: (8/20) | 7.09 s [Task 2/25] Current/Best: 5.41/ 21.04 GFLOPS | Progress: (12/20) | 8.74 s [Task 2/25] Current/Best: 14.70/ 21.35 GFLOPS | Progress: (16/20) | 10.64 s [Task 2/25] Current/Best: 18.20/ 21.35 GFLOPS | Progress: (20/20) | 12.50 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 11.09/ 19.94 GFLOPS | Progress: (4/20) | 6.01 s [Task 3/25] Current/Best: 21.31/ 21.31 GFLOPS | Progress: (8/20) | 8.41 s [Task 3/25] Current/Best: 17.02/ 21.31 GFLOPS | Progress: (12/20) | 11.81 s [Task 3/25] Current/Best: 12.73/ 21.31 GFLOPS | Progress: (16/20) | 14.67 s [Task 3/25] Current/Best: 13.47/ 21.31 GFLOPS | Progress: (20/20) | 16.79 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 15.88/ 15.88 GFLOPS | Progress: (4/20) | 4.79 s [Task 4/25] Current/Best: 13.42/ 16.92 GFLOPS | Progress: (8/20) | 6.64 s [Task 4/25] Current/Best: 6.19/ 16.92 GFLOPS | Progress: (12/20) | 8.82 s [Task 4/25] Current/Best: 4.74/ 18.31 GFLOPS | Progress: (16/20) | 11.72 s [Task 4/25] Current/Best: 13.30/ 19.68 GFLOPS | Progress: (20/20) | 13.97 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 13.50/ 14.94 GFLOPS | Progress: (4/20) | 4.77 s [Task 5/25] Current/Best: 10.33/ 18.96 GFLOPS | Progress: (8/20) | 7.00 s [Task 5/25] Current/Best: 7.99/ 20.11 GFLOPS | Progress: (12/20) | 8.99 s [Task 5/25] Current/Best: 15.76/ 22.33 GFLOPS | Progress: (16/20) | 10.96 s [Task 5/25] Current/Best: 12.20/ 22.33 GFLOPS | Progress: (20/20) | 13.10 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 17.97/ 18.31 GFLOPS | Progress: (4/20) | 5.83 s [Task 6/25] Current/Best: 15.23/ 18.60 GFLOPS | Progress: (8/20) | 7.81 s [Task 6/25] Current/Best: 15.37/ 18.60 GFLOPS | Progress: (12/20) | 10.18 s [Task 6/25] Current/Best: 15.96/ 18.84 GFLOPS | Progress: (16/20) | 13.69 s [Task 6/25] Current/Best: 10.49/ 20.40 GFLOPS | Progress: (20/20) | 16.60 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 14.20/ 20.93 GFLOPS | Progress: (4/20) | 4.86 s [Task 7/25] Current/Best: 3.14/ 20.93 GFLOPS | Progress: (8/20) | 7.95 s [Task 7/25] Current/Best: 17.88/ 20.93 GFLOPS | Progress: (12/20) | 10.39 s [Task 7/25] Current/Best: 8.24/ 22.28 GFLOPS | Progress: (16/20) | 13.41 s [Task 7/25] Current/Best: 12.56/ 22.28 GFLOPS | Progress: (20/20) | 15.94 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 15.45/ 19.87 GFLOPS | Progress: (4/20) | 5.77 s [Task 8/25] Current/Best: 13.09/ 19.87 GFLOPS | Progress: (8/20) | 8.66 s [Task 8/25] Current/Best: 5.72/ 19.87 GFLOPS | Progress: (12/20) | 20.31 s [Task 8/25] Current/Best: 13.89/ 19.87 GFLOPS | Progress: (16/20) | 26.22 s [Task 8/25] Current/Best: 10.73/ 19.87 GFLOPS | Progress: (20/20) | 32.03 s [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 16.06/ 18.51 GFLOPS | Progress: (4/20) | 4.41 s [Task 9/25] Current/Best: 5.96/ 18.51 GFLOPS | Progress: (8/20) | 6.36 s [Task 9/25] Current/Best: 16.40/ 18.51 GFLOPS | Progress: (12/20) | 11.62 s [Task 9/25] Current/Best: 16.56/ 18.82 GFLOPS | Progress: (16/20) | 19.07 s [Task 9/25] Current/Best: 12.23/ 18.82 GFLOPS | Progress: (20/20
) | 21.20 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 18.72/ 18.72 GFLOPS | Progress: (4/20) | 5.17 s [Task 10/25] Current/Best: 19.18/ 19.18 GFLOPS | Progress: (8/20) | 6.86 s [Task 10/25] Current/Best: 9.40/ 19.18 GFLOPS | Progress: (12/20) | 11.06 s [Task 10/25] Current/Best: 5.25/ 19.18 GFLOPS | Progress: (16/20) | 13.12 s [Task 10/25] Current/Best: 13.49/ 19.18 GFLOPS | Progress: (20/20) | 15.17 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 20.04/ 20.58 GFLOPS | Progress: (4/20) | 5.84 s [Task 11/25] Current/Best: 19.41/ 20.58 GFLOPS | Progress: (8/20) | 8.26 s [Task 11/25] Current/Best: 12.90/ 20.58 GFLOPS | Progress: (12/20) | 10.61 s [Task 11/25] Current/Best: 8.39/ 23.31 GFLOPS | Progress: (16/20) | 13.00 s [Task 11/25] Current/Best: 10.18/ 23.31 GFLOPS | Progress: (20/20) | 15.24 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 11.03/ 15.81 GFLOPS | Progress: (4/20) | 8.53 s [Task 12/25] Current/Best: 17.57/ 17.57 GFLOPS | Progress: (8/20) | 10.71 s [Task 12/25] Current/Best: 5.41/ 17.57 GFLOPS | Progress: (12/20) | 13.59 s [Task 12/25] Current/Best: 14.19/ 18.76 GFLOPS | Progress: (16/20) | 17.32 s [Task 12/25] Current/Best: 13.45/ 18.76 GFLOPS | Progress: (20/20) | 20.09 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 18.90/ 19.40 GFLOPS | Progress: (4/20) | 5.78 s [Task 13/25] Current/Best: 19.79/ 20.22 GFLOPS | Progress: (8/20) | 7.89 s [Task 13/25] Current/Best: 6.19/ 20.22 GFLOPS | Progress: (12/20) | 11.90 s [Task 13/25] Current/Best: 13.62/ 20.22 GFLOPS | Progress: (16/20) | 14.82 s [Task 13/25] Current/Best: 8.09/ 20.22 GFLOPS | Progress: (20/20) | 17.99 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 4.82/ 11.09 GFLOPS | Progress: (4/20) | 14.54 s [Task 14/25] Current/Best: 9.28/ 14.16 GFLOPS | Progress: (8/20) | 20.90 s [Task 14/25] Current/Best: 12.47/ 15.75 GFLOPS | Progress: (12/20) | 23.24 s [Task 14/25] Current/Best: 15.23/ 15.75 GFLOPS | Progress: (16/20) | 34.88 s [Task 14/25] Current/Best: 8.47/ 15.75 GFLOPS | Progress: (20/20) | 45.13 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 11.69/ 22.43 GFLOPS | Progress: (4/20) | 7.56 s [Task 1/25] Current/Best: 15.72/ 22.43 GFLOPS | Progress: (8/20) | 10.26 s [Task 1/25] Current/Best: 18.50/ 23.51 GFLOPS | Progress: (12/20) | 12.17 s [Task 1/25] Current/Best: 15.06/ 23.51 GFLOPS | Progress: (16/20) | 14.58 s [Task 1/25] Current/Best: 9.55/ 23.51 GFLOPS | Progress: (20/20) | 16.73 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 13.96/ 17.68 GFLOPS | Progress: (4/20) | 4.13 s [Task 2/25] Current/Best: 19.71/ 22.75 GFLOPS | Progress: (8/20) | 6.10 s [Task 2/25] Current/Best: 8.54/ 22.75 GFLOPS | Progress: (12/20) | 7.73 s [Task 2/25] Current/Best: 15.51/ 22.75 GFLOPS | Progress: (16/20) | 9.46 s [Task 2/25] Current/Best: 12.29/ 22.75 GFLOPS | Progress: (20/20) | 11.40 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 1.68/ 21.87 GFLOPS | Progress: (4/20) | 5.80 s [Task 3/25] Current/Best: 12.81/ 22.09 GFLOPS | Progress: (8/20) | 7.94 s [Task 3/25] Current/Best: 22.27/ 22.27 GFLOPS | Progress: (12/20) | 10.07 s [Task 3/25] Current/Best: 6.58/ 22.27 GFLOPS | Progress: (16/20) | 12.67 s [Task 3/25] Current/Best: 3.25/ 22.32 GFLOPS | Progress: (20/20) | 15.20 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 13.06/ 22.46 GFLOPS | Progress: (4/20) | 4.83 s [Task 4/25] Current/Best: 4.88/ 22.46 GFLOPS | Progress: (8/20) | 7.44 s [Task 4/25] Current/Best: 13.63/ 22.46 GFLOPS | Progress: (12/20) | 10.35 s [Task 4/25] Current/Best: 18.32/ 22.46 GFLOPS | Progress: (16/20) | 12.26 s [Task 4/25] Current/Best: 6.50/ 22.46 GFLOPS | Progress: (20/20) | 16.91 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 16.01/ 16.01 GFLOPS | Progress: (4/20) | 4.68 s [Task 5/25] Current/Best: 8.49/ 16.59 GFLOPS | Progress: (8/20) | 7.04 s [Task 5/25] Current/Best: 5.01/ 16.59 GFLOPS | Progress: (12/20) | 9.32 s [Task 5/25] Current/Best: 3.35/ 19.83 GFLOPS | Progress: (16/20) | 11.19 s [Task 5/25] Current/Best: 14.16/ 19.83 GFLOPS | Progress: (20/20) | 13.32 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 18.47/ 18.47 GFLOPS | Progress: (4/20) | 4.70 s [Task 6/25] Current/Best: 11.15/ 18.47 GFLOPS | Progress: (8/20) | 9.64 s [Task 6/25] Current/Best: 15.27/ 18.47 GFLOPS | Progress: (12/20) | 12.55 s [Task 6/25] Current/Best: 17.08/ 18.47 GFLOPS | Progress: (16/20) | 14.93 s [Task 6/25] Current/Best: 13.79/ 18.47 GFLOPS | Progress: (20/20) | 17.92 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 13.27/ 21.84 GFLOPS | Progress: (4/20) | 4.52 s [Task 7/25] Current/Best: 20.48/ 21.84 GFLOPS | Progress: (8/20) | 6.54 s [Task 7/25] Current/Best: 10.63/ 21.84 GFLOPS | Progress: (12/20) | 8.96 s [Task 7/25] Current/Best: 20.03/ 21.84 GFLOPS | Progress: (16/20) | 11.27 s [Task 7/25] Current/Best: 22.25/ 22.25 GFLOPS | Progress: (20/20) | 14.17 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 6.16/ 17.21 GFLOPS | Progress: (4/20) | 13.46 s [Task 8/25] Current/Best: 11.51/ 17.21 GFLOPS | Progress: (8/20) | 24.64 s [Task 8/25] Current/Best: 14.86/ 17.21 GFLOPS | Progress: (12/20) | 30.79 s [Task 8/25] Current/Best: 12.73/ 17.21 GFLOPS | Progress: (16/20) | 33.07 s [Task 8/25] Current/Best: 3.67/ 17.21 GFLOPS | Progress: (20/20) | 39.24 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 17.68/ 20.51 GFLOPS | Progress: (4/20) | 4.43 s [Task 9/25] Current/Best: 15.60/ 22.13 GFLOPS | Progress: (8/20) | 7.80 s [Task 9/25] Current/Best: 12.53/ 22.13 GFLOPS | Progress: (12/20) | 11.01 s [Task 9/25] Current/Best: 6.13/ 22.13 GFLOPS | Progress: (16/20) | 15.81 s [Task 9/25] Current/Best: 13.99/ 22.13 GFLOPS | Progress: (20/20) | 25.98 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 11.84/ 13.99 GFLOPS | Progress: (4/20) | 4.93 s [Task 10/25] Current/Best: 11.11/ 13.99 GFLOPS | Progress: (8/20) | 8.95 s [Task 10/25] Current/Best: 12.30/ 16.18 GFLOPS | Progress: (12/20) | 11.39 s [Task 10/25] Current/Best: 10.29/ 16.18 GFLOPS | Progress: (16/20) | 14.69 s [Task 10/25] Current/Best: 5.31/ 16.18 GFLOPS | Progress: (20/20) | 18.03 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 20.27/ 20.27 GFLOPS | Progress: (4/20) | 4.41 s [Task 11/25] Current/Best: 18.39/ 20.27 GFLOPS | Progress: (8/20) | 7.01 s [Task 11/25] Current/Best: 18.61/ 20.27 GFLOPS | Progress: (12/20) | 9.26 s [Task 11/25] Current/Best: 6.67/ 20.27 GFLOPS | Progress: (16/20) | 12.71 s [Task 11/25] Current/Best: 24.46/ 24.46 GFLOPS | Progress: (20/20) | 14.79 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 17.36/ 17.36 GFLOPS | Progress: (4/20) | 10.66 s [Task 12/25] Current/Best: 16.00/ 20.23 GFLOPS | Progress: (8/20) | 13.10 s [Task 12/25] Current/Best: 20.49/ 20.49 GFLOPS | Progress: (12/20) | 15.86 s [Task 12/25] Current/Best: 10.53/ 20.49 GFLOPS | Progress: (16/20) | 18.42 s [Task 12/25] Current/Best: 6.60/ 20.49 GFLOPS | Progress: (20/20) | 21.28 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 12.66/ 17.56 GFLOPS | Progress: (4/20) | 5.12 s [Task 13/25] Current/Best: 14.68/ 17.56 GFLOPS | Progress: (8/20) | 8.68 s [Task 13/25] Current/Best: 16.30/ 22.47 GFLOPS | Progress: (12/20) | 10.96 s [Task 13/25] Current/Best: 9.19/ 22.47 GFLOPS | Progress: (16/20) | 14.19 s [Task 13/25] Current/Best: 21.77/ 22.47 GFLOPS | Progress: (20/20) | 17.32 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 8.76/ 17.73 GFLOPS | Progress: (4/20) | 8.78 s [Task 14/25] Current/Best: 21.85/ 21.85 GFLOPS | Progress: (8/20) | 12.55 s [Task 14/25] Current/Best: 8.02/ 21.85 GFLOPS | Progress: (12/20) | 19.41 s [Task 14/25] Current/Best: 13.70/ 21.85 GFLOPS | Progress: (16/20) | 30.84 s [Task 14/25] Current/Best: 23.17/ 23.17 GFLOPS | Progress: (20/20) | 34.38 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 15.24/ 19.24 GFLOPS | Progress: (4/20) | 7.51 s [Task 15/25] Current/Best: 17.31/ 19.24 GFLOPS | Progress: (8/20) | 9.23 s [Task 15/25] Current/Best: 10.53/ 19.24 GFLOPS | Progress: (12/20) | 13.20 s [Task 15/25] Current/Best: 14.58/ 19.24 GFLOPS | Progress: (16/20) | 15.36 s [Task 15/25] Current/Best: 22.27/ 22.27 GFLOPS | Progress: (20/2
0) | 16.96 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 14.59/ 14.59 GFLOPS | Progress: (4/20) | 4.91 s [Task 16/25] Current/Best: 11.32/ 20.93 GFLOPS | Progress: (8/20) | 6.93 s [Task 16/25] Current/Best: 12.51/ 20.93 GFLOPS | Progress: (12/20) | 9.39 s [Task 16/25] Current/Best: 16.08/ 20.93 GFLOPS | Progress: (16/20) | 11.41 s [Task 16/25] Current/Best: 6.20/ 20.93 GFLOPS | Progress: (20/20) | 14.34 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 18.61/ 23.13 GFLOPS | Progress: (4/20) | 4.59 s [Task 17/25] Current/Best: 18.57/ 23.13 GFLOPS | Progress: (8/20) | 6.74 s [Task 17/25] Current/Best: 10.37/ 23.13 GFLOPS | Progress: (12/20) | 11.79 s [Task 17/25] Current/Best: 23.33/ 23.33 GFLOPS | Progress: (16/20) | 14.05 s [Task 17/25] Current/Best: 18.28/ 23.33 GFLOPS | Progress: (20/20) | 17.24 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 18.75/ 21.85 GFLOPS | Progress: (4/20) | 4.69 s [Task 18/25] Current/Best: 20.28/ 21.85 GFLOPS | Progress: (8/20) | 7.92 s [Task 18/25] Current/Best: 20.52/ 22.09 GFLOPS | Progress: (12/20) | 13.02 s [Task 18/25] Current/Best: 17.50/ 22.09 GFLOPS | Progress: (16/20) | 14.93 s [Task 18/25] Current/Best: 16.69/ 22.09 GFLOPS | Progress: (20/20) | 19.73 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 18.85/ 22.05 GFLOPS | Progress: (4/20) | 6.00 s [Task 19/25] Current/Best: 16.53/ 22.05 GFLOPS | Progress: (8/20) | 10.35 s [Task 19/25] Current/Best: 18.47/ 23.05 GFLOPS | Progress: (12/20) | 12.98 s [Task 19/25] Current/Best: 10.46/ 23.05 GFLOPS | Progress: (16/20) | 17.43 s [Task 19/25] Current/Best: 9.49/ 23.05 GFLOPS | Progress: (20/20) | 23.07 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 11.06/ 16.36 GFLOPS | Progress: (4/20) | 15.15 s [Task 20/25] Current/Best: 7.88/ 16.36 GFLOPS | Progress: (8/20) | 25.83 s [Task 20/25] Current/Best: 10.19/ 21.72 GFLOPS | Progress: (12/20) | 30.07 s [Task 20/25] Current/Best: 10.91/ 21.72 GFLOPS | Progress: (16/20) | 34.03 s [Task 20/25] Current/Best: 5.56/ 21.72 GFLOPS | Progress: (20/20) | 38.79 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
- [Task 15/25] Current/Best: 9.74/ 19.47 GFLOPS | Progress: (4/20) | 4.50 s [Task 15/25] Current/Best: 12.49/ 19.47 GFLOPS | Progress: (8/20) | 15.76 s [Task 15/25] Current/Best: 20.67/ 20.67 GFLOPS | Progress: (12/20) | 20.39 s [Task 15/25] Current/Best: 21.23/ 21.23 GFLOPS | Progress: (16/20) | 28.05 s [Task 15/25] Current/Best: 14.16/ 21.23 GFLOPS | Progress: (20/20) | 29.98 s [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 18.06/ 19.06 GFLOPS | Progress: (4/20) | 4.90 s [Task 16/25] Current/Best: 17.18/ 21.82 GFLOPS | Progress: (8/20) | 6.50 s [Task 16/25] Current/Best: 11.17/ 21.82 GFLOPS | Progress: (12/20) | 9.18 s [Task 16/25] Current/Best: 13.43/ 21.82 GFLOPS | Progress: (16/20) | 11.60 s [Task 16/25] Current/Best: 16.83/ 21.82 GFLOPS | Progress: (20/20) | 13.44 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 19.84/ 19.86 GFLOPS | Progress: (4/20) | 5.77 s [Task 17/25] Current/Best: 9.38/ 19.86 GFLOPS | Progress: (8/20) | 9.32 s [Task 17/25] Current/Best: 8.03/ 20.62 GFLOPS | Progress: (12/20) | 12.72 s [Task 17/25] Current/Best: 3.08/ 21.93 GFLOPS | Progress: (16/20) | 15.41 s [Task 17/25] Current/Best: 21.27/ 22.95 GFLOPS | Progress: (20/20) | 19.39 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 15.50/ 17.97 GFLOPS | Progress: (4/20) | 5.77 s [Task 18/25] Current/Best: 16.83/ 18.68 GFLOPS | Progress: (8/20) | 9.03 s [Task 18/25] Current/Best: 10.78/ 18.68 GFLOPS | Progress: (12/20) | 11.84 s [Task 18/25] Current/Best: 16.81/ 18.68 GFLOPS | Progress: (16/20) | 14.16 s [Task 18/25] Current/Best: 16.09/ 18.68 GFLOPS | Progress: (20/20) | 16.40 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 8.09/ 10.54 GFLOPS | Progress: (4/20) | 5.93 s [Task 19/25] Current/Best: 5.32/ 21.53 GFLOPS | Progress: (8/20) | 9.14 s [Task 19/25] Current/Best: 3.08/ 22.24 GFLOPS | Progress: (12/20) | 12.04 s [Task 19/25] Current/Best: 17.91/ 22.24 GFLOPS | Progress: (16/20) | 15.22 s [Task 19/25] Current/Best: 16.67/ 22.24 GFLOPS | Progress: (20/20) | 18.84 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 10.79/ 21.51 GFLOPS | Progress: (4/20) | 5.88 s [Task 20/25] Current/Best: 19.36/ 21.51 GFLOPS | Progress: (8/20) | 17.71 s [Task 20/25] Current/Best: 17.01/ 21.51 GFLOPS | Progress: (12/20) | 23.96 s [Task 20/25] Current/Best: 10.33/ 21.51 GFLOPS | Progress: (16/20) | 29.87 s [Task 20/25] Current/Best: 11.12/ 21.51 GFLOPS | Progress: (20/20) | 33.31 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (4/20) | 4.68 s [Task 21/25] Current/Best: 15.52/ 18.96 GFLOPS | Progress: (8/20) | 8.29 s [Task 21/25] Current/Best: 9.12/ 18.96 GFLOPS | Progress: (12/20) | 10.68 s [Task 21/25] Current/Best: 5.20/ 18.96 GFLOPS | Progress: (16/20) | 22.07 s [Task 21/25] Current/Best: 6.52/ 18.96 GFLOPS | Progress: (20/2
0) | 28.04 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 2.66/ 10.27 GFLOPS | Progress: (4/20) | 6.88 s [Task 22/25] Current/Best: 20.70/ 20.70 GFLOPS | Progress: (8/20) | 9.48 s [Task 22/25] Current/Best: 11.99/ 20.70 GFLOPS | Progress: (12/20) | 11.65 s [Task 22/25] Current/Best: 10.62/ 20.70 GFLOPS | Progress: (16/20) | 15.53 s [Task 22/25] Current/Best: 7.35/ 20.70 GFLOPS | Progress: (20/20) | 18.89 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 9.67/ 12.65 GFLOPS | Progress: (4/20) | 7.68 s [Task 23/25] Current/Best: 21.38/ 21.38 GFLOPS | Progress: (8/20) | 11.11 s [Task 23/25] Current/Best: 7.97/ 21.38 GFLOPS | Progress: (12/20) | 17.84 s [Task 23/25] Current/Best: 10.17/ 21.38 GFLOPS | Progress: (16/20) | 23.24 s [Task 23/25] Current/Best: 2.66/ 22.22 GFLOPS | Progress: (20/20) | 26.26 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 3.04/ 7.12 GFLOPS | Progress: (4/20) | 13.78 s Done.
- Done.
- Done.
- [Task 24/25] Current/Best: 2.65/ 7.12 GFLOPS | Progress: (8/20) | 24.82 s [Task 24/25] Current/Best: 7.05/ 7.12 GFLOPS | Progress: (12/20) | 35.83 s [Task 24/25] Current/Best: 7.14/ 7.14 GFLOPS | Progress: (16/20) | 39.64 s [Task 24/25] Current/Best: 8.70/ 8.70 GFLOPS | Progress: (20/20) | 50.68 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 5.26/ 7.35 GFLOPS | Progress: (4/20) | 6.12 s [Task 25/25] Current/Best: 5.16/ 8.89 GFLOPS | Progress: (8/20) | 10.75 s [Task 25/25] Current/Best: 8.30/ 8.89 GFLOPS | Progress: (12/20) | 14.48 s [Task 25/25] Current/Best: 5.55/ 8.89 GFLOPS | Progress: (16/20) | 20.39 s [Task 25/25] Current/Best: 5.43/ 8.89 GFLOPS | Progress: (20/20) | 23.74 s Done.
-
+ [Task 21/25] Current/Best: 13.86/ 13.86 GFLOPS | Progress: (4/20) | 9.63 s [Task 21/25] Current/Best: 23.25/ 23.25 GFLOPS | Progress: (8/20) | 12.91 s [Task 21/25] Current/Best: 16.48/ 23.25 GFLOPS | Progress: (12/20) | 14.63 s [Task 21/25] Current/Best: 9.96/ 23.25 GFLOPS | Progress: (16/20) | 25.93 s [Task 21/25] Current/Best: 14.45/ 23.25 GFLOPS | Progress: (20/20) | 28.70 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 13.35/ 20.64 GFLOPS | Progress: (4/20) | 5.63 s [Task 22/25] Current/Best: 12.03/ 20.64 GFLOPS | Progress: (8/20) | 8.24 s [Task 22/25] Current/Best: 10.66/ 20.64 GFLOPS | Progress: (12/20) | 10.90 s [Task 22/25] Current/Best: 20.75/ 20.75 GFLOPS | Progress: (16/20) | 12.91 s [Task 22/25] Current/Best: 17.01/ 20.75 GFLOPS | Progress: (20/20) | 15.16 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 8.50/ 11.76 GFLOPS | Progress: (4/20) | 5.22 s [Task 23/25] Current/Best: 14.83/ 15.66 GFLOPS | Progress: (8/20) | 8.03 s [Task 23/25] Current/Best: 12.57/ 20.08 GFLOPS | Progress: (12/20) | 10.68 s [Task 23/25] Current/Best: 6.89/ 21.71 GFLOPS | Progress: (16/20) | 13.86 s [Task 23/25] Current/Best: 12.87/ 21.80 GFLOPS | Progress: (20/20) | 19.34 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 2.31/ 5.03 GFLOPS | Progress: (4/20) | 13.46 s [Task 24/25] Current/Best: 1.11/ 8.82 GFLOPS | Progress: (8/20) | 25.62 s [Task 24/25] Current/Best: 0.59/ 8.91 GFLOPS | Progress: (12/20) | 29.62 s [Task 24/25] Current/Best: 3.98/ 10.85 GFLOPS | Progress: (16/20) | 39.46 s [Task 24/25] Current/Best: 3.74/ 10.85 GFLOPS | Progress: (20/20) | 50.50 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 3.97/ 10.41 GFLOPS | Progress: (4/20) | 6.23 s [Task 25/25] Current/Best: 3.93/ 10.41 GFLOPS | Progress: (8/20) | 8.89 s [Task 25/25] Current/Best: 7.54/ 10.41 GFLOPS | Progress: (12/20) | 10.85 s [Task 25/25] Current/Best: 5.47/ 10.41 GFLOPS | Progress: (16/20) | 21.77 s [Task 25/25] Current/Best: 8.17/ 10.41 GFLOPS | Progress: (20/
20) | 31.04 s
@@ -675,6 +673,8 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
+ Done.
+ Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 400.16806858001473, 'median': 400.9833006000008, 'std': 3.572511811638212}
- unoptimized: {'mean': 500.1930359100061, 'median': 501.0475809000127, 'std': 3.205093817229211}
+ optimized: {'mean': 382.64284065004176, 'median': 382.3706999500246, 'std': 2.555507291539717}
+ unoptimized: {'mean': 466.7433671999788, 'median': 466.45811764992686, 'std': 1.3168505305302587}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 40.990 seconds)
+ **Total running time of the script:** ( 13 minutes 10.705 seconds)
.. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index 0c1f0fbc4d..6095629914 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
- 1.168e-07 secs/op
+ 2.94e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 32d881772b..6c852d5437 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, 0x11685200)), stage(b, placeholder(b, 0x282ff820)), 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, 0x13325b00)), stage(b, placeholder(b, 0x2dea46a0)), 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 04186bb76e..3b9613e244 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**17:38.613** total execution time for **tutorial** files:
+**16:36.590** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:40.990 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:10.705 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:46.279 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:27.126 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:00.647 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:56.568 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:45.563 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:41.777 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:23.068 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:18.513 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.931 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.879 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.906 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.840 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.229 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.183 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 7a0e6b46bb..aceeb5bc4e 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: 0.000007
- naive: 0.000007
+ Numpy running time: 0.000006
+ naive: 0.000006
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000007
+ parallel: 0.000006
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000039
+ vector: 0.000038
# 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 6.725359999109059e-06 1.0
- naive 6.9061e-06 1.0268743979377883
- parallel 7.1028e-06 1.0561219029079398
- vector 3.9165199999999996e-05 5.823509820320161
+ numpy 5.7613100034359376e-06 1.0
+ naive 5.8218e-06 1.0104993476358652
+ parallel 6.1032e-06 1.0593424058695273
+ vector 3.80478e-05 6.604018873712576
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.019348
+ Numpy running time: 0.013831
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.359106
+ none: 3.231289
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.327019
+ blocking: 0.289828
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.326725
+ vectorization: 0.267394
# 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: 0.133642
+ loop permutation: 0.109272
# 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: 0.105722
+ array packing: 0.102797
# 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: 0.113069
+ block caching: 0.096166
# 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: 0.133691
+ parallelization: 0.114535
# 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 3.3591061022000006 1.0
- blocking 0.32701894189999997 0.09735296592323278
- vectorization 0.3267247177 0.09726537589450245
- loop permutation 0.1336419403 0.03978497142810495
- array packing 0.1057218267 0.03147320253765099
- block caching 0.11306948339999998 0.0336605870609287
- parallelization 0.1336906377 0.039799468558745776
+ none 3.2312894035 1.0
+ blocking 0.2898281866 0.08969428311994276
+ vectorization 0.26739386880000005 0.08275144544786672
+ loop permutation 0.10927247469999998 0.03381698791251583
+ array packing 0.1027966669 0.031812893883368935
+ block caching 0.0961656218 0.029760757948773437
+ parallelization 0.1145345267 0.035445456100571156
@@ -1594,11 +1594,6 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 0.647 seconds)
-
-
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index 749684ea6f..2a5fc31348 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-8b5bd555d13fe07092aa4179625eb6c1b7c1fe9b
+97d92b548345ba48bf65051765285ee4e35b5313
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 2e3b8c52c0..988d9ef7ec 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -604,7 +604,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 41.106 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.208 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_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 8eb74e99c2..0a093ab04a 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -468,14 +468,13 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 15%|#5 | 6.33M/41.5M [00:00<00:00, 41.7MB/s]
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- 46%|####6 | 19.2M/41.5M [00:00<00:00, 35.3MB/s]
- 56%|#####6 | 23.4M/41.5M [00:00<00:00, 34.6MB/s]
- 65%|######5 | 27.1M/41.5M [00:00<00:00, 33.7MB/s]
- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 37.1MB/s]
- 96%|#########6| 40.0M/41.5M [00:01<00:00, 43.9MB/s]
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 7192329c03..12b26b40e5 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -503,7 +503,6 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.259 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 7c91fcb439..7037babb33 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -451,14 +451,12 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index f5033c0d68..c8993ad510 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -671,7 +671,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.839 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 27.732 seconds)</p>
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<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
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--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>07:03.088</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:21.417</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
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</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></td>
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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 f5c54eebf2..d399e78be3 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -850,10 +850,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4138.9903 4130.3751 4215.2482 4128.4925 25.4820
+ 3990.7422 3990.3732 3994.8019 3988.7071 1.9313
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 16.769 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 1c0056db5a..896a4657a5 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
@@ -458,19 +458,22 @@ to run this tutorial with a real device over rpc.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
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</pre></div>
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diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 590695f8d6..1522a793fd 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -682,7 +682,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7183 15.5954 16.3048 15.2092 0.3967
+ 13.7543 13.7362 13.8853 13.6846 0.0613
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index e07c7ccc55..ca4454aec7 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -474,35 +474,34 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -593,7 +592,7 @@ 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 50.946 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 20.268 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 d8d83cd185..bf3dc0d2ff 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -515,9 +515,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
0%| | 0.00/13.6M [00:00<?, ?B/s]
- 47%|####6 | 6.30M/13.6M [00:00<00:00, 63.7MB/s]
- 91%|#########1| 12.4M/13.6M [00:00<00:00, 33.4MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 38.9MB/s]
+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 35.6MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 54.8MB/s]
</pre></div>
</div>
</div>
@@ -608,7 +607,7 @@ 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)
- 89.5861 89.1090 122.6341 88.8205 3.3713
+ 85.7626 85.7258 86.3337 85.4945 0.1797
</pre></div>
</div>
<div class="admonition note">
@@ -647,7 +646,7 @@ 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 36.735 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.206 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 f96b7dbe71..30e723f7f7 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -600,7 +600,7 @@ 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)
- 111.3935 111.3417 112.7962 110.5910 0.3760
+ 103.4025 103.2977 106.7831 102.5994 0.5810
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index cfe8dd0b75..cdc68badb1 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -360,7 +360,7 @@
<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>10:29.944</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>09:21.530</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -369,39 +369,39 @@
</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>03:50.946</p></td>
+<td><p>03:20.268</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>01:36.735</p></td>
+<td><p>01:25.206</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:21.684</p></td>
+<td><p>01:16.769</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:55.162</p></td>
+<td><p>00:49.186</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>00:54.191</p></td>
+<td><p>00:48.838</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 Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:49.211</p></td>
+<td><p>00:44.159</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:31.009</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:28.597</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:30.995</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
+<td><p>00:28.499</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.009</p></td>
+<td><p>00:00.008</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 333eefa21c..5f727ea149 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:04.373</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:03.842</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,15 +369,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><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>00:03.025</p></td>
+<td><p>00:02.674</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:01.339</p></td>
+<td><p>00:01.161</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
-<td><p>00:00.008</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index bdc34c3c35..9f183b7525 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -545,10 +545,10 @@ 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: 27287us [27287us] (48.14%; 48.14%)
-FoldScaleAxis: 29395us [10us] (51.86%; 51.86%)
- FoldConstant: 29385us [2234us] (51.84%; 99.97%)
- InferType: 27151us [27151us] (47.90%; 92.40%)
+InferType: 24537us [24537us] (48.40%; 48.40%)
+FoldScaleAxis: 26156us [6us] (51.60%; 51.60%)
+ FoldConstant: 26149us [1883us] (51.58%; 99.98%)
+ InferType: 24267us [24267us] (47.87%; 92.80%)
</pre></div>
</div>
</div>
@@ -570,10 +570,10 @@ 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: 26596us [26596us] (48.22%; 48.22%)
-FoldScaleAxis: 28557us [11us] (51.78%; 51.78%)
- FoldConstant: 28546us [2011us] (51.76%; 99.96%)
- InferType: 26535us [26535us] (48.11%; 92.96%)
+InferType: 23700us [23700us] (48.74%; 48.74%)
+FoldScaleAxis: 24927us [6us] (51.26%; 51.26%)
+ FoldConstant: 24921us [1602us] (51.25%; 99.98%)
+ InferType: 23320us [23320us] (47.96%; 93.57%)
</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 5ed4de9d2e..d6c7285502 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -595,7 +595,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.520416 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.593025 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 966435d2f3..772dafd29c 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -877,7 +877,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.501059 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.216691 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 5c339926c8..9d7cfeb3c5 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -492,8 +492,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019450
-Baseline: 3.307527
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013852
+Baseline: 3.220233
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -552,7 +552,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.300458
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.287637
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -609,7 +609,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.297456
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.256215
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -664,7 +664,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118929
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.109804
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -741,7 +741,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.107393
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.103377
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -819,7 +819,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.114247
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.097689
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -899,7 +899,7 @@ class Module:
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.132767
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.116513
</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 f1fc3ea1ff..ca9ecb1f3c 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.143</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
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<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>
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diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
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@@ -360,7 +360,7 @@
<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>
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<table class="docutils align-default">
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@@ -369,27 +369,27 @@
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<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>
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<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>
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<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
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diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index f4084ab655..c15fba5eb7 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
@@ -1032,7 +1032,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
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+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.333 ms
</pre></div>
</div>
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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 9f73b0eefc..f29d82c432 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -923,7 +923,7 @@ 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)
- 3.2322 3.2316 3.2461 3.2190 0.0111
+ 3.2001 3.1965 3.2092 3.1946 0.0065
</pre></div>
</div>
</div>
@@ -945,7 +945,7 @@ to learn how to use the RPC Tracker and RPC Server.
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<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 4aba51c358..d80593524d 100644
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+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -945,7 +945,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 759.8720 758.2567 764.1303 757.2290 3.0402
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</pre></div>
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@@ -967,7 +967,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
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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 35f54d0d1f..2a027bf26e 100644
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+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:23.855</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
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<table class="docutils align-default">
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<col style="width: 84%" />
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diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 09d122e18d..e4ede0d8a0 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -630,7 +630,7 @@ and measure running time.</p>
Best config:
,None
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index b17913a6b9..ea89a583f8 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -664,10 +664,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
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-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.955 0.312 (1, 1, 10, 10, 3) 1 1 [0.955]
-Total_time - 306.244 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 294.5 98.743 (1, 2, 10, 10, 3) 2 1 [294.5]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.796 0.937 (1, 6, 10, 10) 1 1 [2.796]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.954 0.32 (1, 1, 10, 10, 3) 1 1 [0.954]
+Total_time - 298.25 - - - - -
</pre></div>
</div>
</div>
@@ -719,13 +719,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 105.4 97.526 (1, 6, 10, 10, 1) 2 1 [105.4]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.824 1.688 (1, 6, 10, 10) 1 1 [1.824]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.85 0.786 (1, 3, 10, 10, 1) 1 1 [0.85]
-Total_time - 108.074 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 97.625 97.285 (1, 6, 10, 10, 1) 2 1 [97.625]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.743 1.737 (1, 6, 10, 10) 1 1 [1.743]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.982 0.978 (1, 1, 10, 10, 3) 1 1 [0.982]
+Total_time - 100.35 - - - - -
</pre></div>
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<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 2fbabd8a3b..baad90a473 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -475,8 +475,7 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
- 61%|###### | 2.09M/3.42M [00:00<00:00, 11.6MB/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 17.9MB/s]
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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()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -604,7 +603,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 3c63b8bfc4..5334c9f12a 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -543,7 +543,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpxj7a1vvs/images/random'
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</pre></div>
</div>
</div>
@@ -603,8 +603,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpxj7a1vvs/images/target contains 8144 images
-/tmp/tmpxj7a1vvs/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmphdpfpsq2/images/target contains 8144 images
+/tmp/tmphdpfpsq2/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -716,13 +716,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 40s - loss: 0.2335 - accuracy: 0.9210 - val_loss: 0.1320 - val_accuracy: 0.9547 - 40s/epoch - 122ms/step
+328/328 - 38s - loss: 0.2162 - accuracy: 0.9253 - val_loss: 0.1170 - val_accuracy: 0.9569 - 38s/epoch - 115ms/step
Epoch 2/3
-328/328 - 36s - loss: 0.1049 - accuracy: 0.9606 - val_loss: 0.1249 - val_accuracy: 0.9566 - 36s/epoch - 110ms/step
+328/328 - 34s - loss: 0.1012 - accuracy: 0.9636 - val_loss: 0.1058 - val_accuracy: 0.9626 - 34s/epoch - 104ms/step
Epoch 3/3
-328/328 - 36s - loss: 0.0689 - accuracy: 0.9748 - val_loss: 0.1215 - val_accuracy: 0.9569 - 36s/epoch - 109ms/step
+328/328 - 34s - loss: 0.0762 - accuracy: 0.9728 - val_loss: 0.1044 - val_accuracy: 0.9634 - 34s/epoch - 104ms/step
-<keras.callbacks.History object at 0x7fa522575f10>
+<keras.callbacks.History object at 0x7fafe76a4400>
</pre></div>
</div>
</div>
@@ -986,7 +986,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 35.051 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 20.097 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 14ad45ed53..97cfe84eca 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>08:13.281</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>07:34.944</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -369,27 +369,27 @@
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">5. Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
-<td><p>04:35.051</p></td>
+<td><p>04:20.097</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_pytorch.html#sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">4. microTVM PyTorch Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_pytorch.py</span></code>)</p></td>
-<td><p>01:35.130</p></td>
+<td><p>01:24.523</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">6. Model Tuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>01:31.758</p></td>
+<td><p>01:22.104</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">3. microTVM Ahead-of-Time (AOT) Compilation</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:12.945</p></td>
+<td><p>00:11.574</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
-<td><p>00:09.515</p></td>
+<td><p>00:08.398</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:08.882</p></td>
+<td><p>00:08.248</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index f3c2ba1a2c..83a940241b 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:41.675</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:36.891</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -369,19 +369,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:35.967</p></td>
+<td><p>00:32.034</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
-<td><p>00:03.454</p></td>
+<td><p>00:03.062</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
-<td><p>00:02.247</p></td>
+<td><p>00:01.788</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 64d358f442..ffc42a57dc 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -572,7 +572,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<a href="../../reference/api/python/ir.html#tvm.ir.register_intrin_lowering" title="tvm.ir.register_intrin_lowering" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-function"><span class="n">register_intrin_lowering</span></a><span class="p">(</span><span class="s2">"tir.exp"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">f</span><span class="o">= [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fa9dc85ec10>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fb1c80e7550>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index 569d27e586..56d4d6c169 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -360,7 +360,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:06.155</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.490</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -369,35 +369,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
-<td><p>00:02.721</p></td>
+<td><p>00:02.432</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
-<td><p>00:01.570</p></td>
+<td><p>00:01.394</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
-<td><p>00:00.781</p></td>
+<td><p>00:00.698</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
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+<td><p>00:00.684</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
-<td><p>00:00.133</p></td>
+<td><p>00:00.117</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
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+<td><p>00:00.069</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
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+<td><p>00:00.065</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
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+<td><p>00:00.032</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator-members.html b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator-members.html
index d7da32fe9c..ad2f6214b0 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator-members.html
@@ -72,16 +72,15 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643">Alloc</a>(size_t nbytes, size_t alignment, DLDataType type_hint)=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">Alloc</a>(ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope="")=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74">Alloc</a>(Device dev, ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a79a611871161884ffa6342c329e6ca77">Allocator</a>(AllocatorType type)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3">Clear</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a02c8cb0f3a8fe2062f744f9a5f4206a0">Empty</a>(ShapeTuple shape, DLDataType dtype, Device dev, Optional< String > mem_scope=NullOpt)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(const Buffer &buffer)=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">type</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">UsedMemory</a>() const =0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a1716aedc61a0cf4b16d0b7c844e6c4d1">~Allocator</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">Alloc</a>(Device dev, size_t nbytes, size_t alignment, DLDataType type_hint)=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">Alloc</a>(Device dev, ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope="")=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a79a611871161884ffa6342c329e6ca77">Allocator</a>(AllocatorType type)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3">Clear</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a02c8cb0f3a8fe2062f744f9a5f4206a0">Empty</a>(ShapeTuple shape, DLDataType dtype, Device dev, Optional< String > mem_scope=NullOpt)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(const Buffer &buffer)=0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">type</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">UsedMemory</a>() const =0</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a1716aedc61a0cf4b16d0b7c844e6c4d1">~Allocator</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">tvm::runtime::memory::Allocator</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator.html b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator.html
index 0cc08d70c3..070a182f0c 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator.html
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1memory_1_1Allocator.html
@@ -67,7 +67,6 @@ $(function() {
<div class="header">
<div class="summary">
<a href="#pub-methods">Public Member Functions</a> |
-<a href="#pro-methods">Protected Member Functions</a> |
<a href="classtvm_1_1runtime_1_1memory_1_1Allocator-members.html">List of all members</a> </div>
<div class="headertitle">
<div class="title">tvm::runtime::memory::Allocator Class Reference<span class="mlabels"><span class="mlabel">abstract</span></span></div> </div>
@@ -78,7 +77,7 @@ $(function() {
<div class="dynheader">
Collaboration diagram for tvm::runtime::memory::Allocator:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1runtime_1_1memory_1_1Allocator__coll__graph.svg" width="182" height="235"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1runtime_1_1memory_1_1Allocator__coll__graph.svg" width="182" height="220"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
@@ -94,12 +93,12 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:a5690ce0a8e9e25bd8e5568173cc0b1bb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacetvm_1_1runtime_1_1memory.html#a902bbce5a86071aad9677b07d93b5518">AllocatorType</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">type</a> () const</td></tr>
<tr class="memdesc:a5690ce0a8e9e25bd8e5568173cc0b1bb"><td class="mdescLeft"> </td><td class="mdescRight">Return the allocator type. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">More...</a><br /></td></tr>
<tr class="separator:a5690ce0a8e9e25bd8e5568173cc0b1bb"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2b639ddbb667f7c5116adf7b86666643"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643">Alloc</a> (size_t nbytes, size_t alignment, DLDataType type_hint)=0</td></tr>
-<tr class="memdesc:a2b639ddbb667f7c5116adf7b86666643"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a buffer given a size, alignment and type. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643">More...</a><br /></td></tr>
-<tr class="separator:a2b639ddbb667f7c5116adf7b86666643"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa5c5c3b6e2bb66f08bcce52b6857102a"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">Alloc</a> (<a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html">ShapeTuple</a> shape, DLDataType type_hint, const std::string &mem_scope [...]
-<tr class="memdesc:aa5c5c3b6e2bb66f08bcce52b6857102a"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a buffer given a shape and type. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">More...</a><br /></td></tr>
-<tr class="separator:aa5c5c3b6e2bb66f08bcce52b6857102a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9424dbcbabea65c34a058bbc12e60160"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">Alloc</a> (<a class="el" href="namespacetvm.html#a7c2095aed90b2129ba631b90103313a2">Device</a> dev, size_t nbytes, size_t alignment, DLDataType typ [...]
+<tr class="memdesc:a9424dbcbabea65c34a058bbc12e60160"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a buffer given a size, alignment and type. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">More...</a><br /></td></tr>
+<tr class="separator:a9424dbcbabea65c34a058bbc12e60160"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa2cc212d965760c6f8a50581f3962a9b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">Alloc</a> (<a class="el" href="namespacetvm.html#a7c2095aed90b2129ba631b90103313a2">Device</a> dev, <a class="el" href="classtvm_1_1runtime_1_1Shap [...]
+<tr class="memdesc:aa2cc212d965760c6f8a50581f3962a9b"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a buffer given a shape and type. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">More...</a><br /></td></tr>
+<tr class="separator:aa2cc212d965760c6f8a50581f3962a9b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0532e931a439c2f7a2a281c42ab54473"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a> (const <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> &buffer)=0</td></tr>
<tr class="memdesc:a0532e931a439c2f7a2a281c42ab54473"><td class="mdescLeft"> </td><td class="mdescRight">Free a buffer allocated by the allocator. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">More...</a><br /></td></tr>
<tr class="separator:a0532e931a439c2f7a2a281c42ab54473"><td class="memSeparator" colspan="2"> </td></tr>
@@ -109,11 +108,6 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:a2c5e075e9b1ee86a6b39791e8e78683b"><td class="memItemLeft" align="right" valign="top">virtual size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">UsedMemory</a> () const =0</td></tr>
<tr class="memdesc:a2c5e075e9b1ee86a6b39791e8e78683b"><td class="mdescLeft"> </td><td class="mdescRight">The amount of memory currently allocated. <a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">More...</a><br /></td></tr>
<tr class="separator:a2c5e075e9b1ee86a6b39791e8e78683b"><td class="memSeparator" colspan="2"> </td></tr>
-</table><table class="memberdecls">
-<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
-Protected Member Functions</h2></td></tr>
-<tr class="memitem:abb73532df0074eaed9d89b925ab62c74"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74">Alloc</a> (<a class="el" href="namespacetvm.html#a7c2095aed90b2129ba631b90103313a2">Device</a> dev, <a class="el" href="classtvm_1_1runtime_1_1Shap [...]
-<tr class="separator:abb73532df0074eaed9d89b925ab62c74"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
<a id="a79a611871161884ffa6342c329e6ca77"></a>
@@ -168,8 +162,8 @@ Protected Member Functions</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="abb73532df0074eaed9d89b925ab62c74"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abb73532df0074eaed9d89b925ab62c74">◆ </a></span>Alloc() <span class="overload">[1/3]</span></h2>
+<a id="aa2cc212d965760c6f8a50581f3962a9b"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aa2cc212d965760c6f8a50581f3962a9b">◆ </a></span>Alloc() <span class="overload">[1/2]</span></h2>
<div class="memitem">
<div class="memproto">
@@ -195,48 +189,6 @@ Protected Member Functions</h2></td></tr>
<td class="paramtype">DLDataType </td>
<td class="paramname"><em>type_hint</em>, </td>
</tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">const std::string & </td>
- <td class="paramname"><em>mem_scope</em> </td>
- </tr>
- <tr>
- <td></td>
- <td>)</td>
- <td></td><td></td>
- </tr>
- </table>
- </td>
- <td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">protected</span><span class="mlabel">virtual</span></span> </td>
- </tr>
-</table>
-</div><div class="memdoc">
-
-</div>
-</div>
-<a id="aa5c5c3b6e2bb66f08bcce52b6857102a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa5c5c3b6e2bb66f08bcce52b6857102a">◆ </a></span>Alloc() <span class="overload">[2/3]</span></h2>
-
-<div class="memitem">
-<div class="memproto">
-<table class="mlabels">
- <tr>
- <td class="mlabels-left">
- <table class="memname">
- <tr>
- <td class="memname">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> tvm::runtime::memory::Allocator::Alloc </td>
- <td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html">ShapeTuple</a> </td>
- <td class="paramname"><em>shape</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">DLDataType </td>
- <td class="paramname"><em>type_hint</em>, </td>
- </tr>
<tr>
<td class="paramkey"></td>
<td></td>
@@ -259,6 +211,7 @@ Protected Member Functions</h2></td></tr>
<p>Allocate a buffer given a shape and type. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
+ <tr><td class="paramname">dev</td><td>The device where the array is allocated. </td></tr>
<tr><td class="paramname">shape</td><td>The shape of the tensor. </td></tr>
<tr><td class="paramname">type_hint</td><td>A type hint to the allocator. </td></tr>
<tr><td class="paramname">mem_scope</td><td>A memory scope of the buffer. </td></tr>
@@ -269,8 +222,8 @@ Protected Member Functions</h2></td></tr>
</div>
</div>
-<a id="a2b639ddbb667f7c5116adf7b86666643"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2b639ddbb667f7c5116adf7b86666643">◆ </a></span>Alloc() <span class="overload">[3/3]</span></h2>
+<a id="a9424dbcbabea65c34a058bbc12e60160"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a9424dbcbabea65c34a058bbc12e60160">◆ </a></span>Alloc() <span class="overload">[2/2]</span></h2>
<div class="memitem">
<div class="memproto">
@@ -281,6 +234,12 @@ Protected Member Functions</h2></td></tr>
<tr>
<td class="memname">virtual <a class="el" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> tvm::runtime::memory::Allocator::Alloc </td>
<td>(</td>
+ <td class="paramtype"><a class="el" href="namespacetvm.html#a7c2095aed90b2129ba631b90103313a2">Device</a> </td>
+ <td class="paramname"><em>dev</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
<td class="paramtype">size_t </td>
<td class="paramname"><em>nbytes</em>, </td>
</tr>
@@ -312,6 +271,7 @@ Protected Member Functions</h2></td></tr>
<p>Allocate a buffer given a size, alignment and type. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
+ <tr><td class="paramname">dev</td><td>The device where the array is allocated. </td></tr>
<tr><td class="paramname">nbytes</td><td>The size of the buffer. </td></tr>
<tr><td class="paramname">alignment</td><td>The alignment of the buffer. </td></tr>
<tr><td class="paramname">type_hint</td><td>A type hint to the allocator. </td></tr>
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+<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ type()</text>
<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Alloc()</text>
-<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Free()</text>
-<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Clear()</text>
-<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ UsedMemory()</text>
-<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00"># Alloc()</text>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Alloc()</text>
+<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Free()</text>
+<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Clear()</text>
+<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ UsedMemory()</text>
</a>
</g>
</g>
diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index 1f882fcf82..5a77795975 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -182,7 +182,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#acd70148b03897204efea4e579f812173">tvm::runtime::Array< T, typename ></a>
</li>
<li>AgregateImpl()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a16a9dc37f218594b41ddeeb3ef803bec">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a89af06579f52883d941137d54784c231">tvm::runtime::Array< T, typename ></a>
</li>
<li>align_corners
: <a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html#a529d4b973c43fc878b40da71d50e95ad">tvm::relay::GridSampleAttrs</a>
@@ -202,7 +202,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1StageNode.html#ae4592502d1c99f2515be61a6503bb7a6">tvm::te::StageNode</a>
</li>
<li>Alloc()
-: <a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74">tvm::runtime::memory::Allocator</a>
+: <a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">tvm::runtime::memory::Allocator</a>
</li>
<li>alloc_buffers
: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode.html#a80df6ba11a31b3bca4461dc811838f21">tvm::script::ir_builder::tir::BlockFrameNode</a>
@@ -446,7 +446,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a360b264ed892e620935b648e5a91a5ea">tvm::runtime::vm::Instruction</a>
</li>
<li>Array()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a7c03ae71aa89ec2591ae9ba7b3acc9d1">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a478af2e3338db97bf9dd3411de6d9a8a">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a23070656da6784a7e4c33c4b0ea9de35">tvm::runtime::ArrayNode</a>
</li>
<li>array
@@ -545,9 +545,9 @@ $(function() {
</li>
<li>at()
: <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a410953f5ac23f6ca5caa257def8b08bd">tvm::runtime::ArrayNode</a>
-, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a773d2e9099e34ab3bcf3b1870d0aee28">tvm::runtime::DenseMapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#a6071908cdeb00617d3b28a70d05ac649">tvm::runtime::DenseMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map< K, V, typename, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a29503ec61af7a5bb9b030a00cfdff01a">tvm::runtime::MapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a07d50937020663f46ce9b1f31f066a7a">tvm::runtime::ShapeTuple</a>
, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#a0593c84ceb05afb1a3f87045a3dc3a59">tvm::runtime::SmallMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaeda6a88310d41a22ce884fb1570b0d2">tvm::runtime::String</a>
diff --git a/docs/reference/api/doxygen/functions_func_a.html b/docs/reference/api/doxygen/functions_func_a.html
index c3c39e1a74..9494736064 100644
--- a/docs/reference/api/doxygen/functions_func_a.html
+++ b/docs/reference/api/doxygen/functions_func_a.html
@@ -153,13 +153,13 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1Array.html#acd70148b03897204efea4e579f812173">tvm::runtime::Array< T, typename ></a>
</li>
<li>AgregateImpl()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a89af06579f52883d941137d54784c231">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#a4b2fe0e5e58e0b41109df62ddd647814">tvm::runtime::Array< T, typename ></a>
</li>
<li>All()
: <a class="el" href="classtvm_1_1relay_1_1FeatureSet.html#abbedfbde4f544c66e6129181b3c29c2a">tvm::relay::FeatureSet</a>
</li>
<li>Alloc()
-: <a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">tvm::runtime::memory::Allocator</a>
+: <a class="el" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">tvm::runtime::memory::Allocator</a>
</li>
<li>AllocADT()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#aa3abb881f27ea29f0fb2eed670130a6f">tvm::runtime::vm::Instruction</a>
diff --git a/docs/reference/api/doxygen/memory__manager_8h_source.html b/docs/reference/api/doxygen/memory__manager_8h_source.html
index d79edb5395..c4d6e49227 100644
--- a/docs/reference/api/doxygen/memory__manager_8h_source.html
+++ b/docs/reference/api/doxygen/memory__manager_8h_source.html
@@ -125,67 +125,64 @@ $(function() {
<div class="line"><a name="l00069"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a02c8cb0f3a8fe2062f744f9a5f4206a0"> 69</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a02c8cb0f3a8fe2062f744f9a5f4206a0">Empty</a>(<a class="code" href="classtvm_1_1runtime_1_1ShapeTuple.html">ShapeTuple</a> <a class="code" href="namespacet [...]
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="classtvm_1_1runtime_1_1Optional.html">Optional<String></a> mem_scope = <a class="code" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a>);</div>
<div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb"> 72</a></span>  <span class="keyword">inline</span> <a class="code" href="namespacetvm_1_1runtime_1_1memory.html#a902bbce5a86071aad9677b07d93b5518">AllocatorType</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">type</a>()<span class="keyword"> const </span>{ [...]
-<div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643"> 79</a></span>  TVM_DLL <span class="keyword">virtual</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643">Alloc</a>(<span class="keywordtype">size_t</span> nbytes, <span class="ke [...]
-<div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a"> 86</a></span>  TVM_DLL <span class="keyword">virtual</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">Alloc</a>(<a class="code" href="classtvm_1_1runtime_1_1ShapeTuple.html">S [...]
-<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> std::string& mem_scope = <span class="stringliteral">""</span>) = 0;</div>
-<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473"> 91</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(<span class="keyword">const</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Bu [...]
-<div class="line"><a name="l00093"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3"> 93</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3">Clear</a>();</div>
-<div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b"> 97</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">size_t</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">UsedMemory</a>() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  </div>
-<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">protected</span>:</div>
-<div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74"> 100</a></span>  TVM_DLL <span class="keyword">virtual</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74">Alloc</a>(<a class="code" href="namespacetvm.html#a7c2095aed90b2129ba631b [...]
-<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> std::string& mem_scope);</div>
-<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  </div>
-<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="namespacetvm_1_1runtime_1_1memory.html#a902bbce5a86071aad9677b07d93b5518">AllocatorType</a> type_;</div>
-<div class="line"><a name="l00105"></a><span class="lineno"> 105</span> };</div>
-<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  </div>
-<div class="line"><a name="l00107"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html"> 107</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a> {</div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247"> 109</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247">Global</a>();</div>
-<div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8497de2de7221c3b040bd04f438c00f0"> 116</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">Allocator</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8497de2de7221c3b040bd04f438c00f0">GetOrCreateAllocator</a>(<a class="code" href="namespacetvm. [...]
-<div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc"> 123</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">Allocator</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc">GetAllocator</a>(<a class="code" href="namespacetvm.html#a7c [...]
-<div class="line"><a name="l00125"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a1b5eda2f2fb67fcc69e16536d13d1058"> 125</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a1b5eda2f2fb67fcc69e16536d13d1058">Clear</a>();</div>
-<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  </div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a>() {}</div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  </div>
-<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">protected</span>:</div>
-<div class="line"><a name="l00131"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910"> 131</a></span>  std::mutex <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910">mu_</a>;</div>
-<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::unordered_map<Device, std::unordered_map<AllocatorType, std::unique_ptr<Allocator>>></div>
-<div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763"> 133</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763">allocators_</a>;</div>
-<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> };</div>
-<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  </div>
-<div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html"> 137</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">StorageObj</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a> {</div>
-<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a"> 140</a></span>  <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">buffer</a>;</div>
-<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  </div>
-<div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#afb23b5fa2eb4ae135f86e07694dd051a"> 143</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#afb23b5fa2eb4ae135f86e07694dd051a">AllocNDArray</a>(int64_t offset, <a class="code" href="classtvm_1_1runtime_1_1ShapeTuple.html">ShapeTuple</a> <a clas [...]
-<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  </div>
-<div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#acb218eea55f3c166dcd98158282c88c0"> 146</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#acb218eea55f3c166dcd98158282c88c0">Deleter</a>(<a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>* ptr);</div>
-<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  </div>
-<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117"> 148</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117">~StorageObj</a>() {</div>
-<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">auto</span> alloc = <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247">MemoryManager::Global</a>()-><a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc">GetAllocator</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3 [...]
-<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  alloc-><a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">buffer</a>);</div>
-<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div>
-<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  </div>
-<div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e"> 153</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> uint32_t <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e">_type_index</a> = <a class="code" href="structtvm_1_1runtime_1_1TypeIndex.html#aed93c7318efc8052201d4c404b [...]
-<div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f"> 154</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f">_type_key</a> = <span class="stringliteral">"vm.Storage"</span>;</div>
-<div class="line"><a name="l00155"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a976196638d5057be589023a5f100b047"> 155</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a976196638d5057be589023a5f100b047">TVM_DECLARE_FINAL_OBJECT_INFO</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">StorageObj</a>, <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>);</div>
-<div class="line"><a name="l00156"></a><span class="lineno"> 156</span> };</div>
-<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  </div>
-<div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html"> 159</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html">Storage</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> {</div>
-<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ab09671cb565811751b306620811f236a"> 161</a></span>  TVM_DLL <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ab09671cb565811751b306620811f236a">Storage</a>(<a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> buffer);</div>
-<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  </div>
-<div class="line"><a name="l00163"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ac6e3caab9cd04a1b443f7104b4b19c0e"> 163</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ac6e3caab9cd04a1b443f7104b4b19c0e">TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html">Storage</a>, <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a [...]
-<div class="line"><a name="l00164"></a><span class="lineno"> 164</span> };</div>
-<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  </div>
-<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> } <span class="comment">// namespace memory</span></div>
-<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> } <span class="comment">// namespace runtime</span></div>
-<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  </div>
-<div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_MEMORY_MEMORY_MANAGER_H_</span></div>
+<div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160"> 80</a></span>  TVM_DLL <span class="keyword">virtual</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">Alloc</a>(<a class="code" href="namespacetvm.html#a7c2095aed90b2129ba631b [...]
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  DLDataType type_hint) = 0;</div>
+<div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b"> 89</a></span>  TVM_DLL <span class="keyword">virtual</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">Alloc</a>(<a class="code" href="namespacetvm.html#a7c2095aed90b2129ba631b [...]
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> std::string& mem_scope = <span class="stringliteral">""</span>) = 0;</div>
+<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473"> 94</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(<span class="keyword">const</span> <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Bu [...]
+<div class="line"><a name="l00096"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3"> 96</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3">Clear</a>();</div>
+<div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b"> 100</a></span>  TVM_DLL <span class="keyword">virtual</span> <span class="keywordtype">size_t</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">UsedMemory</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  </div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacetvm_1_1runtime_1_1memory.html#a902bbce5a86071aad9677b07d93b5518">AllocatorType</a> type_;</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span> };</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  </div>
+<div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html"> 106</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a> {</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247"> 108</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247">Global</a>();</div>
+<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8497de2de7221c3b040bd04f438c00f0"> 115</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">Allocator</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8497de2de7221c3b040bd04f438c00f0">GetOrCreateAllocator</a>(<a class="code" href="namespacetvm. [...]
+<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc"> 122</a></span>  TVM_DLL <span class="keyword">static</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html">Allocator</a>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc">GetAllocator</a>(<a class="code" href="namespacetvm.html#a7c [...]
+<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a1b5eda2f2fb67fcc69e16536d13d1058"> 124</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a1b5eda2f2fb67fcc69e16536d13d1058">Clear</a>();</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  </div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">MemoryManager</a>() {}</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keyword">protected</span>:</div>
+<div class="line"><a name="l00130"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910"> 130</a></span>  std::mutex <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910">mu_</a>;</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::unordered_map<Device, std::unordered_map<AllocatorType, std::unique_ptr<Allocator>>></div>
+<div class="line"><a name="l00132"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763"> 132</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763">allocators_</a>;</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span> };</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  </div>
+<div class="line"><a name="l00136"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html"> 136</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">StorageObj</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a> {</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a"> 139</a></span>  <a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">buffer</a>;</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  </div>
+<div class="line"><a name="l00142"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#afb23b5fa2eb4ae135f86e07694dd051a"> 142</a></span>  TVM_DLL <a class="code" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#afb23b5fa2eb4ae135f86e07694dd051a">AllocNDArray</a>(int64_t offset, <a class="code" href="classtvm_1_1runtime_1_1ShapeTuple.html">ShapeTuple</a> <a clas [...]
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  </div>
+<div class="line"><a name="l00145"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#acb218eea55f3c166dcd98158282c88c0"> 145</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#acb218eea55f3c166dcd98158282c88c0">Deleter</a>(<a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>* ptr);</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  </div>
+<div class="line"><a name="l00147"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117"> 147</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117">~StorageObj</a>() {</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">auto</span> alloc = <a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247">MemoryManager::Global</a>()-><a class="code" href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc">GetAllocator</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3 [...]
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  alloc-><a class="code" href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">Free</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">buffer</a>);</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  </div>
+<div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e"> 152</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> uint32_t <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e">_type_index</a> = <a class="code" href="structtvm_1_1runtime_1_1TypeIndex.html#aed93c7318efc8052201d4c404b [...]
+<div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f"> 153</a></span>  <span class="keyword">static</span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f">_type_key</a> = <span class="stringliteral">"vm.Storage"</span>;</div>
+<div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a976196638d5057be589023a5f100b047"> 154</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a976196638d5057be589023a5f100b047">TVM_DECLARE_FINAL_OBJECT_INFO</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">StorageObj</a>, <a class="code" href="classtvm_1_1runtime_1_1Object.html">Object</a>);</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span> };</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  </div>
+<div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html"> 158</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html">Storage</a> : <span class="keyword">public</span> <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> {</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ab09671cb565811751b306620811f236a"> 160</a></span>  TVM_DLL <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ab09671cb565811751b306620811f236a">Storage</a>(<a class="code" href="structtvm_1_1runtime_1_1memory_1_1Buffer.html">Buffer</a> buffer);</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  </div>
+<div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ac6e3caab9cd04a1b443f7104b4b19c0e"> 162</a></span>  <a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ac6e3caab9cd04a1b443f7104b4b19c0e">TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS</a>(<a class="code" href="classtvm_1_1runtime_1_1memory_1_1Storage.html">Storage</a>, <a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a>, <a [...]
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> };</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  </div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> } <span class="comment">// namespace memory</span></div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> } <span class="comment">// namespace runtime</span></div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  </div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_MEMORY_MEMORY_MANAGER_H_</span></div>
<div class="ttc" id="ac__runtime__api_8h_html"><div class="ttname"><a href="c__runtime__api_8h.html">c_runtime_api.h</a></div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1NDArray_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1NDArray.html">tvm::runtime::NDArray</a></div><div class="ttdoc">Managed NDArray. The array is backed by reference counted blocks.</div><div class="ttdef"><b>Definition:</b> ndarray.h:51</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference.</div><div class="ttdef"><b>Definition:</b> object.h:519</div></div>
@@ -196,29 +193,28 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a02c8cb0f3a8fe2062f744f9a5f4206a0"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a02c8cb0f3a8fe2062f744f9a5f4206a0">tvm::runtime::memory::Allocator::Empty</a></div><div class="ttdeci">NDArray Empty(ShapeTuple shape, DLDataType dtype, Device dev, Optional< String > mem_scope=NullOpt)</div><div class="ttdoc">Allocate an empty NDArray using from the allocator.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a0532e931a439c2f7a2a281c42ab54473"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a0532e931a439c2f7a2a281c42ab54473">tvm::runtime::memory::Allocator::Free</a></div><div class="ttdeci">virtual void Free(const Buffer &buffer)=0</div><div class="ttdoc">Free a buffer allocated by the allocator.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a1716aedc61a0cf4b16d0b7c844e6c4d1"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a1716aedc61a0cf4b16d0b7c844e6c4d1">tvm::runtime::memory::Allocator::~Allocator</a></div><div class="ttdeci">virtual ~Allocator()=default</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a2b639ddbb667f7c5116adf7b86666643"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2b639ddbb667f7c5116adf7b86666643">tvm::runtime::memory::Allocator::Alloc</a></div><div class="ttdeci">virtual Buffer Alloc(size_t nbytes, size_t alignment, DLDataType type_hint)=0</div><div class="ttdoc">Allocate a buffer given a size, alignment and type.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a2c5e075e9b1ee86a6b39791e8e78683b"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a2c5e075e9b1ee86a6b39791e8e78683b">tvm::runtime::memory::Allocator::UsedMemory</a></div><div class="ttdeci">virtual size_t UsedMemory() const =0</div><div class="ttdoc">The amount of memory currently allocated.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a5690ce0a8e9e25bd8e5568173cc0b1bb"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a5690ce0a8e9e25bd8e5568173cc0b1bb">tvm::runtime::memory::Allocator::type</a></div><div class="ttdeci">AllocatorType type() const</div><div class="ttdoc">Return the allocator type.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:72</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a79a611871161884ffa6342c329e6ca77"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a79a611871161884ffa6342c329e6ca77">tvm::runtime::memory::Allocator::Allocator</a></div><div class="ttdeci">Allocator(AllocatorType type)</div><div class="ttdef"><b>Definition:</b> memory_manager.h:60</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_aa5c5c3b6e2bb66f08bcce52b6857102a"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa5c5c3b6e2bb66f08bcce52b6857102a">tvm::runtime::memory::Allocator::Alloc</a></div><div class="ttdeci">virtual Buffer Alloc(ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope="")=0</div><div class="ttdoc">Allocate a buffer given a shape and type.</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_a9424dbcbabea65c34a058bbc12e60160"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#a9424dbcbabea65c34a058bbc12e60160">tvm::runtime::memory::Allocator::Alloc</a></div><div class="ttdeci">virtual Buffer Alloc(Device dev, size_t nbytes, size_t alignment, DLDataType type_hint)=0</div><div class="ttdoc">Allocate a buffer given a size, alignment and type.</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_aa2cc212d965760c6f8a50581f3962a9b"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa2cc212d965760c6f8a50581f3962a9b">tvm::runtime::memory::Allocator::Alloc</a></div><div class="ttdeci">virtual Buffer Alloc(Device dev, ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope="")=0</div><div class="ttdoc">Allocate a buffer given a shape and type.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_aa9fc901b598595e7ec0e3cbe55217be3"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#aa9fc901b598595e7ec0e3cbe55217be3">tvm::runtime::memory::Allocator::Clear</a></div><div class="ttdeci">virtual void Clear()</div><div class="ttdoc">Clear the allocated memory.</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Allocator_html_abb73532df0074eaed9d89b925ab62c74"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Allocator.html#abb73532df0074eaed9d89b925ab62c74">tvm::runtime::memory::Allocator::Alloc</a></div><div class="ttdeci">virtual Buffer Alloc(Device dev, ShapeTuple shape, DLDataType type_hint, const std::string &mem_scope)</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">tvm::runtime::memory::MemoryManager</a></div><div class="ttdef"><b>Definition:</b> memory_manager.h:107</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html">tvm::runtime::memory::MemoryManager</a></div><div class="ttdef"><b>Definition:</b> memory_manager.h:106</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_a1b5eda2f2fb67fcc69e16536d13d1058"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a1b5eda2f2fb67fcc69e16536d13d1058">tvm::runtime::memory::MemoryManager::Clear</a></div><div class="ttdeci">static void Clear()</div><div class="ttdoc">Clear the allocators.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_a8497de2de7221c3b040bd04f438c00f0"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8497de2de7221c3b040bd04f438c00f0">tvm::runtime::memory::MemoryManager::GetOrCreateAllocator</a></div><div class="ttdeci">static Allocator * GetOrCreateAllocator(Device dev, AllocatorType type)</div><div class="ttdoc">Get or create an allocator given the context and allocator type.</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_a8cbf0c3786cda612ee0b9bc6133c5910"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910">tvm::runtime::memory::MemoryManager::mu_</a></div><div class="ttdeci">std::mutex mu_</div><div class="ttdef"><b>Definition:</b> memory_manager.h:131</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_a8cbf0c3786cda612ee0b9bc6133c5910"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a8cbf0c3786cda612ee0b9bc6133c5910">tvm::runtime::memory::MemoryManager::mu_</a></div><div class="ttdeci">std::mutex mu_</div><div class="ttdef"><b>Definition:</b> memory_manager.h:130</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_a90e44d168ef57fb22be271f446b475cc"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#a90e44d168ef57fb22be271f446b475cc">tvm::runtime::memory::MemoryManager::GetAllocator</a></div><div class="ttdeci">static Allocator * GetAllocator(Device dev, AllocatorType type)</div><div class="ttdoc">Get an allocator given the context.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_abbb8736dde248b5a2811c53a28171247"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#abbb8736dde248b5a2811c53a28171247">tvm::runtime::memory::MemoryManager::Global</a></div><div class="ttdeci">static MemoryManager * Global()</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_adb5375ee7892c72fe8105216a6936763"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763">tvm::runtime::memory::MemoryManager::allocators_</a></div><div class="ttdeci">std::unordered_map< Device, std::unordered_map< AllocatorType, std::unique_ptr< Allocator > > > allocators_</div><div class="ttdef"><b>Definition:</b> memory_manager.h:133 [...]
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">tvm::runtime::memory::StorageObj</a></div><div class="ttdoc">An object representing a storage allocation.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:137</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a3c7977cfe3349b828981b9ea840e3a4a"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">tvm::runtime::memory::StorageObj::buffer</a></div><div class="ttdeci">Buffer buffer</div><div class="ttdoc">The index into the VM function table.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:140</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a4a7ed495e72911b721ce076130b90117"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117">tvm::runtime::memory::StorageObj::~StorageObj</a></div><div class="ttdeci">~StorageObj()</div><div class="ttdef"><b>Definition:</b> memory_manager.h:148</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a71d645c37f459f99327454746831fa2f"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f">tvm::runtime::memory::StorageObj::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> memory_manager.h:154</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1MemoryManager_html_adb5375ee7892c72fe8105216a6936763"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1MemoryManager.html#adb5375ee7892c72fe8105216a6936763">tvm::runtime::memory::MemoryManager::allocators_</a></div><div class="ttdeci">std::unordered_map< Device, std::unordered_map< AllocatorType, std::unique_ptr< Allocator > > > allocators_</div><div class="ttdef"><b>Definition:</b> memory_manager.h:132 [...]
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html">tvm::runtime::memory::StorageObj</a></div><div class="ttdoc">An object representing a storage allocation.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:136</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a3c7977cfe3349b828981b9ea840e3a4a"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a3c7977cfe3349b828981b9ea840e3a4a">tvm::runtime::memory::StorageObj::buffer</a></div><div class="ttdeci">Buffer buffer</div><div class="ttdoc">The index into the VM function table.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:139</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a4a7ed495e72911b721ce076130b90117"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a4a7ed495e72911b721ce076130b90117">tvm::runtime::memory::StorageObj::~StorageObj</a></div><div class="ttdeci">~StorageObj()</div><div class="ttdef"><b>Definition:</b> memory_manager.h:147</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a71d645c37f459f99327454746831fa2f"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a71d645c37f459f99327454746831fa2f">tvm::runtime::memory::StorageObj::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> memory_manager.h:153</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_a976196638d5057be589023a5f100b047"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#a976196638d5057be589023a5f100b047">tvm::runtime::memory::StorageObj::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(StorageObj, Object)</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_ab4932069dec2780d53026fb58e4f396e"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e">tvm::runtime::memory::StorageObj::_type_index</a></div><div class="ttdeci">static constexpr const uint32_t _type_index</div><div class="ttdef"><b>Definition:</b> memory_manager.h:153</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_ab4932069dec2780d53026fb58e4f396e"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#ab4932069dec2780d53026fb58e4f396e">tvm::runtime::memory::StorageObj::_type_index</a></div><div class="ttdeci">static constexpr const uint32_t _type_index</div><div class="ttdef"><b>Definition:</b> memory_manager.h:152</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_acb218eea55f3c166dcd98158282c88c0"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#acb218eea55f3c166dcd98158282c88c0">tvm::runtime::memory::StorageObj::Deleter</a></div><div class="ttdeci">static void Deleter(Object *ptr)</div><div class="ttdoc">The deleter for an NDArray when allocated from underlying storage.</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1StorageObj_html_afb23b5fa2eb4ae135f86e07694dd051a"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1StorageObj.html#afb23b5fa2eb4ae135f86e07694dd051a">tvm::runtime::memory::StorageObj::AllocNDArray</a></div><div class="ttdeci">NDArray AllocNDArray(int64_t offset, ShapeTuple shape, DLDataType dtype)</div><div class="ttdoc">Allocate an NDArray from a given piece of storage.</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Storage_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Storage.html">tvm::runtime::memory::Storage</a></div><div class="ttdoc">reference to storage.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:159</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Storage_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Storage.html">tvm::runtime::memory::Storage</a></div><div class="ttdoc">reference to storage.</div><div class="ttdef"><b>Definition:</b> memory_manager.h:158</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Storage_html_ab09671cb565811751b306620811f236a"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ab09671cb565811751b306620811f236a">tvm::runtime::memory::Storage::Storage</a></div><div class="ttdeci">Storage(Buffer buffer)</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1memory_1_1Storage_html_ac6e3caab9cd04a1b443f7104b4b19c0e"><div class="ttname"><a href="classtvm_1_1runtime_1_1memory_1_1Storage.html#ac6e3caab9cd04a1b443f7104b4b19c0e">tvm::runtime::memory::Storage::TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS</a></div><div class="ttdeci">TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(Storage, ObjectRef, StorageObj)</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_1_1memory_html_a902bbce5a86071aad9677b07d93b5518"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1memory.html#a902bbce5a86071aad9677b07d93b5518">tvm::runtime::memory::AllocatorType</a></div><div class="ttdeci">AllocatorType</div><div class="ttdef"><b>Definition:</b> memory_manager.h:42</div></div>
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