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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/05/13 23:55:29 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@05001bed646c26f788521d4d14a2bad9bf755e65)
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 609e6fbdcd deploying docs (apache/tvm@05001bed646c26f788521d4d14a2bad9bf755e65)
609e6fbdcd is described below
commit 609e6fbdcd6540d005ffce8c23526d1ab0f886dc
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sat May 13 23:55:22 2023 +0000
deploying docs (apache/tvm@05001bed646c26f788521d4d14a2bad9bf755e65)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_keras.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 5 +
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 22 +-
.../deploy_models/deploy_model_on_adreno.rst.txt | 4 +-
.../deploy_model_on_adreno_tvmc.rst.txt | 2 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 2 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 22 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 8 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 12 +-
.../tune_conv2d_layer_cuda.rst.txt | 2 +-
.../tune_network_cuda.rst.txt | 4 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 6 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 2 +-
.../work_with_microtvm/micro_autotune.rst.txt | 18 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 14 +-
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.../work_with_schedules/sg_execution_times.rst.txt | 14 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
.../frontend/deploy_classification.rst.txt | 4 +-
.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 4 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 62 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 22 +-
.../tutorial/tensor_expr_get_started.rst.txt | 51 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_keras.html | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 12 +-
docs/how_to/compile_models/from_paddle.html | 1 +
docs/how_to/compile_models/from_pytorch.html | 14 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 26 +-
.../deploy_models/deploy_model_on_adreno.html | 4 +-
.../deploy_models/deploy_model_on_adreno_tvmc.html | 28 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 49 +-
docs/how_to/deploy_models/deploy_prequantized.html | 9 +-
.../deploy_models/deploy_prequantized_tflite.html | 2 +-
docs/how_to/deploy_models/deploy_quantized.html | 2 +-
docs/how_to/deploy_models/sg_execution_times.html | 30 +-
.../extend_tvm/bring_your_own_datatypes.html | 2 +-
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docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
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.../tune_conv2d_layer_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_cuda.html | 4 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
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docs/how_to/work_with_microtvm/micro_pytorch.html | 4 +-
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329 files changed, 10894 insertions(+), 10843 deletions(-)
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index 0336516ff8..e397159712 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 27.711 seconds)
+ **Total running time of the script:** ( 1 minutes 30.238 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index 91bdc62360..94fb2d025d 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -232,7 +232,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
-
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 1s 1s/step
+
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 2s 2s/step
Keras top-1 id: 285, class name: Egyptian cat
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 8da74fd685..c22f91b850 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -116,7 +116,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipbcd3f873-883e-4774-b7d6-86ae83ef1c88 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipf178cd02-e780-40bb-a480-eddd574e62df from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index e872a0fc20..da7d42b9d9 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
-
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+
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diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 8df75c9e33..6ddc59b934 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -207,6 +207,11 @@ Look up prediction top 1 index in 1000 class synset.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.407 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 460f490b59..092f9152c7 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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97%|#########6| 43.3M/44.7M [00:00<00:00, 76.8MB/s]
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+
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80%|######## | 35.8M/44.7M [00:00<00:00, 52.0MB/s]
91%|#########1| 40.8M/44.7M [00:00<00:00, 50.5MB/s]
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diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 0a61fd702c..40d7182e1c 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 27.841 seconds)
+ **Total running time of the script:** ( 1 minutes 29.489 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 9bba42ecff..f974fe8821 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**06:46.880** total execution time for **how_to_compile_models** files:
+**07:07.506** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:27.841 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:30.238 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:27.711 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:29.489 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:59.634 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:01.407 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:39.838 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:42.921 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:34.899 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.844 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:31.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:34.723 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.095 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:28.380 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:24.845 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:27.460 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:10.387 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:11.939 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.756 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:03.105 | 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 6bd905052e..7c992d54e8 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)
- 4223.9162 4224.2572 4229.0392 4219.1234 3.0607
+ 4602.4472 4637.3949 4738.4299 4433.1458 83.9255
@@ -682,7 +682,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.890 seconds)
+ **Total running time of the script:** ( 1 minutes 24.046 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 4f8f764072..2aef8073fa 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
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+
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83877888/102967424 [=======================>......] -
ETA: 0s
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102088704/102967424 [============================>.] - ETA: 0s
102850560/102967424 [============================>.] - 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 9d9af35811..f0267b15f1 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)
- 15.2254 15.1690 15.5653 14.9848 0.1653
+ 15.7464 15.7272 16.3568 15.2300 0.3064
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 0b94e38aa1..b6bd1ec08e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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+
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]
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -295,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 30.597 seconds)
+ **Total running time of the script:** ( 3 minutes 45.798 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 c0785709ee..2059b48e92 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -227,7 +227,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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+
0%| | 0.00/13.6M [00:00<?, ?B/s]
59%|#####8 | 7.99M/13.6M [00:00<00:00, 43.4MB/s]
90%|########9 | 12.2M/13.6M [00:00<00:00, 35.4MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 40.1MB/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)
- 88.9038 88.7898 92.7808 88.4138 0.5638
+ 97.9974 98.1451 101.8753 92.7045 1.3640
@@ -458,7 +458,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 23.408 seconds)
+ **Total running time of the script:** ( 1 minutes 35.337 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 9dd838cbad..51776726ea 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)
- 108.4079 108.2276 126.5140 106.8795 1.9437
+ 109.1622 109.1748 110.5632 108.4884 0.3267
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index e0af0ad0e2..2ec5321516 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -257,7 +257,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 53.698 seconds)
+ **Total running time of the script:** ( 2 minutes 6.126 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index 8278494212..e7b0e37e9f 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**11:27.367** total execution time for **how_to_deploy_models** files:
+**12:27.079** 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``) | 03:30.597 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:45.798 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:53.698 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:06.126 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:23.408 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:35.337 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.890 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:24.046 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:50.905 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:53.927 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:47.675 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:53.023 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:43.787 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:46.549 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.403 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:32.433 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.997 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.833 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 7c9fab37b6..f75d045cba 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -463,7 +463,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip6122265f-7c12-485e-86e7-84b1ec14270e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip23ea02cf-5f7f-4644-a5a2-e57bdccc0c0e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index b6c2e9e773..96c600b014 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:55.232** total execution time for **how_to_extend_tvm** files:
+**00:56.324** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:51.497 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:52.432 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.615 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.698 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.113 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.186 | 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 e13c5d7b3c..a973847ac0 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: 23384us [23384us] (48.40%; 48.40%)
- FoldScaleAxis: 24933us [8us] (51.60%; 51.60%)
- FoldConstant: 24925us [1785us] (51.59%; 99.97%)
- InferType: 23141us [23141us] (47.89%; 92.84%)
+ InferType: 24087us [24087us] (49.51%; 49.51%)
+ FoldScaleAxis: 24563us [8us] (50.49%; 50.49%)
+ FoldConstant: 24555us [1808us] (50.47%; 99.97%)
+ InferType: 22747us [22747us] (46.76%; 92.64%)
@@ -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: 22854us [22854us] (48.32%; 48.32%)
- FoldScaleAxis: 24445us [6us] (51.68%; 51.68%)
- FoldConstant: 24439us [1788us] (51.67%; 99.98%)
- InferType: 22651us [22651us] (47.89%; 92.68%)
+ InferType: 22793us [22793us] (48.28%; 48.28%)
+ FoldScaleAxis: 24419us [6us] (51.72%; 51.72%)
+ FoldConstant: 24413us [1813us] (51.71%; 99.97%)
+ InferType: 22600us [22600us] (47.87%; 92.57%)
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 4081b93ec3..5977638e80 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: 53.526752 ms
+ Convolution: 53.577152 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 b37ee65331..00811c9151 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: 11.146528 ms
+ conv2d with tensor core: 11.087904 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 3a21b87734..0c34de6abe 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: 0.017728
- Baseline: 3.338099
+ Numpy running time: 0.017881
+ Baseline: 3.528650
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.292644
+ Opt1: 0.294164
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.271410
+ Opt2: 0.268354
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.113882
+ Opt3: 0.117984
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.106886
+ Opt4: 0.108368
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111743
+ Opt5: 0.112178
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.132038
+ Opt6: 0.133152
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 8e6d303336..ffc42cef83 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:33.799** total execution time for **how_to_optimize_operators** files:
+**00:34.335** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:30.357 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.047 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.986 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.051 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.456 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.237 | 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 d8cbf9b9cc..319b425a21 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
=================
-**03:29.657** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:42.138** 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``) | 01:26.947 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:32.136 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:13.683 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:16.889 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:18.146 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:17.918 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:15.547 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:17.564 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.231 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.527 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index 4ebaf76f4e..9896b36929 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -767,7 +767,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.349 ms
+ Execution time of this operator: 0.346 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 9046889079..a37f62f76b 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -647,7 +647,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 8.1178 8.1183 8.1231 8.1120 0.0046
+ 8.0973 8.0992 8.0994 8.0934 0.0028
@@ -675,7 +675,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 13.683 seconds)
+ **Total running time of the script:** ( 1 minutes 16.889 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 8e92708a09..a8a0744eba 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -666,7 +666,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 753.1324 754.0751 754.1440 751.1780 1.3822
+ 760.0259 760.5550 761.5224 758.0002 1.4858
@@ -694,7 +694,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.947 seconds)
+ **Total running time of the script:** ( 1 minutes 32.136 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 b2a0b22fa0..0e8919c298 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:23.666** total execution time for **how_to_tune_with_autotvm** files:
+**00:24.536** 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``) | 00:23.623 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.496 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.027 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.024 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 889d03ba73..21864b4b2a 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: 0.037168
+ Time cost of this operator: 0.037155
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 ff080009cd..743e08537c 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 302.6 98.739 (1, 2, 10, 10, 3) 2 1 [302.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.903 0.947 (1, 6, 10, 10) 1 1 [2.903]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 0.314 (1, 1, 10, 10, 3) 1 1 [0.962]
- Total_time - 306.465 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 324.6 98.775 (1, 2, 10, 10, 3) 2 1 [324.6]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.933 (1, 6, 10, 10) 1 1 [3.067]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.961 0.292 (1, 1, 10, 10, 3) 1 1 [0.961]
+ Total_time - 328.627 - - - - -
@@ -428,10 +428,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.5 97.374 (1, 6, 10, 10, 1) 2 1 [100.5]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.752 1.698 (1, 6, 10, 10) 1 1 [1.752]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.928 (1, 1, 10, 10, 3) 1 1 [0.958]
- Total_time - 103.21 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 149.4 98.249 (1, 6, 10, 10, 1) 2 1 [149.4]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.803 1.186 (1, 6, 10, 10) 1 1 [1.803]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.859 0.565 (1, 3, 10, 10, 1) 1 1 [0.859]
+ Total_time - 152.062 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 26.908 seconds)
+ **Total running time of the script:** ( 1 minutes 30.768 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 47f4e88fd1..7a83f31841 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
-
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 88.1MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 42.5MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -326,7 +326,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 24.250 seconds)
+ **Total running time of the script:** ( 1 minutes 32.342 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 f8c96a4514..b8047695d5 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/tmp_16m210o/images/random'
+ '/tmp/tmp6mw5g6e1/images/random'
@@ -317,8 +317,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmp_16m210o/images/target contains 8144 images
- /tmp/tmp_16m210o/images/random contains 5000 images
+ /tmp/tmp6mw5g6e1/images/target contains 8144 images
+ /tmp/tmp6mw5g6e1/images/random contains 5000 images
@@ -493,13 +493,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 41s - loss: 0.2192 - accuracy: 0.9261 - val_loss: 0.1638 - val_accuracy: 0.9505 - 41s/epoch - 123ms/step
+ 328/328 - 42s - loss: 0.2278 - accuracy: 0.9225 - val_loss: 0.1217 - val_accuracy: 0.9547 - 42s/epoch - 127ms/step
Epoch 2/3
- 328/328 - 35s - loss: 0.0998 - accuracy: 0.9633 - val_loss: 0.1043 - val_accuracy: 0.9622 - 35s/epoch - 107ms/step
+ 328/328 - 37s - loss: 0.1029 - accuracy: 0.9622 - val_loss: 0.1048 - val_accuracy: 0.9668 - 37s/epoch - 112ms/step
Epoch 3/3
- 328/328 - 35s - loss: 0.0643 - accuracy: 0.9752 - val_loss: 0.1019 - val_accuracy: 0.9671 - 35s/epoch - 107ms/step
+ 328/328 - 37s - loss: 0.0700 - accuracy: 0.9741 - val_loss: 0.1051 - val_accuracy: 0.9660 - 37s/epoch - 111ms/step
- <keras.callbacks.History object at 0x7f3002558a90>
+ <keras.callbacks.History object at 0x7f3a4c51db20>
@@ -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 13.862 seconds)
+ **Total running time of the script:** ( 4 minutes 24.546 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 e930ad097c..8e96ffe9ac 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**07:34.485** total execution time for **how_to_work_with_microtvm** files:
+**07:57.356** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:13.862 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:24.546 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:26.908 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:32.342 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:24.250 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:30.768 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.489 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.466 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:09.404 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.788 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.572 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:08.446 | 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 ebca584483..cfd4417b04 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:39.990** total execution time for **how_to_work_with_relay** files:
+**00:41.695** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:34.704 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:36.297 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.155 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.378 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.125 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.013 | 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 5bc7e952e7..f38e9d8389 100644
--- a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
@@ -278,7 +278,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f2e980ff820>
+ <function my_cuda_math_rule at 0x7f3920652820>
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 3139c45aeb..8508cd7a2f 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
=================
-**00:06.789** total execution time for **how_to_work_with_schedules** files:
+**00:06.535** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.621 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:03.457 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.385 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.314 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.768 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.771 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.764 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.737 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.114 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.115 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.060 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.051 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.054 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.027 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index dbf7244984..6744c1fb15 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:33.549** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:36.428** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:33.541 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:36.419 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.008 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 2d4a493962..5b57492674 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -293,7 +293,7 @@ The compilation steps are:
warnings.warn(
/workspace/vta/tutorials/frontend/deploy_classification.py:212: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
graph, lib, params = relay.build(
- resnet18_v1 inference graph built in 35.45s!
+ resnet18_v1 inference graph built in 39.76s!
@@ -416,7 +416,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.803 seconds)
+ **Total running time of the script:** ( 1 minutes 8.837 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_classification.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index e77fb2506c..71a68a7329 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 24.35s!
+ yolov3-tiny inference graph built in 27.85s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 7.623 seconds)
+ **Total running time of the script:** ( 1 minutes 13.696 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 381e5016cd..ca223e3884 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**02:10.426** total execution time for **topic_vta_tutorials_frontend** files:
+**02:22.533** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:07.623 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:13.696 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:02.803 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 01:08.837 | 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 70bea1586e..760573d324 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:03.446** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.689** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.892 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:03.144 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.554 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.546 | 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 046861e768..89167a1a26 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:00.952** total execution time for **topic_vta_tutorials** files:
+**00:00.834** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.490 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.428 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.462 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.406 | 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 a4b75c0171..0692993e3b 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.143 ms
+ Execution time of this operator: 96.070 ms
@@ -434,7 +434,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.558 seconds)
+ **Total running time of the script:** ( 1 minutes 30.322 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 d9a0ef7f35..d472982ad6 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: 6.14/6.14 result: MeasureResult(costs=(0.0437203908,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9675338268280029, timestamp=1684014304.7180526) [('tile_y', [-1, 16]), ('tile_x', [-1, 4])],None,24
- No: 2 GFLOPS: 10.35/10.35 result: MeasureResult(costs=(0.0259244592,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6957743167877197, timestamp=1684014305.3927226) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
- No: 3 GFLOPS: 11.11/11.11 result: MeasureResult(costs=(0.0241509534,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6531670093536377, timestamp=1684014306.0355837) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
- No: 4 GFLOPS: 0.47/11.11 result: MeasureResult(costs=(0.5766298304,), error_no=MeasureErrorNo.NO_ERROR, all_cost=9.463722467422485, timestamp=1684014315.520278) [('tile_y', [-1, 512]), ('tile_x', [-1, 1])],None,9
- No: 5 GFLOPS: 2.02/11.11 result: MeasureResult(costs=(0.1328974898,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3940601348876953, timestamp=1684014318.0361364) [('tile_y', [-1, 64]), ('tile_x', [-1, 4])],None,26
- No: 6 GFLOPS: 10.64/11.11 result: MeasureResult(costs=(0.0252202612,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6835484504699707, timestamp=1684014318.7020216) [('tile_y', [-1, 8]), ('tile_x', [-1, 16])],None,43
- No: 7 GFLOPS: 7.33/11.11 result: MeasureResult(costs=(0.0366314298,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8410866260528564, timestamp=1684014319.5472894) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
- No: 8 GFLOPS: 3.77/11.11 result: MeasureResult(costs=(0.07126489059999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.405729055404663, timestamp=1684014320.945339) [('tile_y', [-1, 32]), ('tile_x', [-1, 8])],None,35
- No: 9 GFLOPS: 8.12/11.11 result: MeasureResult(costs=(0.0330670972,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7652127742767334, timestamp=1684014321.8217785) [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
- No: 10 GFLOPS: 10.85/11.11 result: MeasureResult(costs=(0.0247337506,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6296267509460449, timestamp=1684014322.4852831) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 1 GFLOPS: 8.74/8.74 result: MeasureResult(costs=(0.030724173,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8333892822265625, timestamp=1684018835.8713295) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
+ No: 2 GFLOPS: 3.67/8.74 result: MeasureResult(costs=(0.0731355296,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4485836029052734, timestamp=1684018837.304297) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 3 GFLOPS: 9.80/9.80 result: MeasureResult(costs=(0.0273964838,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.768608808517456, timestamp=1684018838.0115695) [('tile_y', [-1, 512]), ('tile_x', [-1, 128])],None,79
+ No: 4 GFLOPS: 8.88/9.80 result: MeasureResult(costs=(0.030239371999999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7357444763183594, timestamp=1684018838.7542446) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
+ No: 5 GFLOPS: 1.95/9.80 result: MeasureResult(costs=(0.1375958652,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.477661609649658, timestamp=1684018841.3444138) [('tile_y', [-1, 64]), ('tile_x', [-1, 4])],None,26
+ No: 6 GFLOPS: 3.49/9.80 result: MeasureResult(costs=(0.0768982854,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5081675052642822, timestamp=1684018842.844073) [('tile_y', [-1, 256]), ('tile_x', [-1, 8])],None,38
+ No: 7 GFLOPS: 10.85/10.85 result: MeasureResult(costs=(0.024732028,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6530442237854004, timestamp=1684018843.4990392) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
+ No: 8 GFLOPS: 12.59/12.59 result: MeasureResult(costs=(0.021316896800000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6047744750976562, timestamp=1684018844.100529) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 9 GFLOPS: 1.02/12.59 result: MeasureResult(costs=(0.2621831436,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.452666521072388, timestamp=1684018848.6641333) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+ No: 10 GFLOPS: 8.97/12.59 result: MeasureResult(costs=(0.029924459200000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7144591808319092, timestamp=1684018849.4028518) [('tile_y', [-1, 128]), ('tile_x', [-1, 16])],None,47
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 9fb0359a38..d751977545 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': 492.443102159923, 'median': 492.58702424995136, 'std': 2.5520380245361642}
+ {'mean': 495.4493903200637, 'median': 495.00540520020877, 'std': 2.031572185495168}
@@ -582,31 +582,30 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 9.39/ 15.85 GFLOPS | Progress: (4/20) | 9.62 s
[Task 1/25] Current/Best: 13.35/ 15.85 GFLOPS | Progress: (8/20) | 12.51 s
[Task 1/25] Current/Best: 18.64/ 18.69 GFLOPS | Progress: (12/20) | 14.71 s
[Task 1/25] Current/Best: 20.79/ 20.79 GFLOPS | Progress: (16/20) | 17.55 s
[Task 1/25] Current/Best: 21.80/ 21.80 GFLOPS | Progress: (20/20) | 20.63 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 13.86/ 17.10 GFLOPS | Progress: (4/20) | 4.32 s
[Task 2/25] Current/Best: 15.93/ 17.10 GFLOPS | Progress: (8/20) | 5.68 s
[Task 2/25] Current/Best: 12.53/ 17.10 GFLOPS | Progress: (12/20) | 8.60 s
[Task 2/25] Current/Best: 12.04/ 17.10 GFLOPS | Progress: (16/20) | 11.47 s
[Task 2/25] Current/Best: 16.15/ 17.10 GFLOPS | Progress: (20/20) | 13.14 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 17.38/ 17.38 GFLOPS | Progress: (4/20) | 5.21 s
[Task 3/25] Current/Best: 23.11/ 23.11 GFLOPS | Progress: (8/20) | 7.29 s
[Task 3/25] Current/Best: 6.71/ 23.11 GFLOPS | Progress: (12/20) | 10.43 s
[Task 3/25] Current/Best: 19.32/ 23.11 GFLOPS | Progress: (16/20) | 12.30 s
[Task 3/25] Current/Best: 23.89/ 23.89 GFLOPS | Progress: (20/20) | 14.95 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 15.90/ 15.90 GFLOPS | Progress: (4/20) | 5.67 s
[Task 4/25] Current/Best: 7.60/ 15.90 GFLOPS | Progress: (8/20) | 8.38 s
[Task 4/25] Current/Best: 20.22/ 20.22 GFLOPS | Progress: (12/20) | 11.25 s
[Task 4/25] Current/Best: 7.21/ 20.22 GFLOPS | Progress: (16/20) | 13.26 s
[Task 4/25] Current/Best: 16.41/ 20.22 GFLOPS | Progress: (20/20) | 15.13 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 13.59/ 13.59 GFLOPS | Progress: (4/20) | 4.95 s
[Task 5/25] Current/Best: 16.69/ 17.02 GFLOPS | Progress: (8/20) | 6.88 s
[Task 5/25] Current/Best: 14.23/ 17.27 GFLOPS | Progress: (12/20) | 8.62 s
[Task 5/25] Current/Best: 13.67/ 17.63 GFLOPS | Progress: (16/20) | 11.05 s
[Task 5/25] Current/Best: 14.36/ 22.14 GFLOPS | Progress: (20/20) | 13.18 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 6.72/ 14.22 GFLOPS | Progress: (4/20) | 5.25 s
[Task 6/25] Current/Best: 13.95/ 23.29 GFLOPS | Progress: (8/20) | 7.28 s
[Task 6/25] Current/Best: 7.68/ 23.29 GFLOPS | Progress: (12/20) | 9.74 s
[Task 6/25] Current/Best: 14.16/ 23.29 GFLOPS | Progress: (16/20) | 12.18 s
[Task 6/25] Current/Best: 6.43/ 23.29 GFLOPS | Progress: (20/20) | 14.76 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.51/ 12.43 GFLOPS | Progress: (4/20) | 5.57 s
[Task 7/25] Current/Best: 14.83/ 14.83 GFLOPS | Progress: (8/20) | 7.89 s
[Task 7/25] Current/Best: 16.04/ 21.10 GFLOPS | Progress: (12/20) | 9.83 s
[Task 7/25] Current/Best: 12.51/ 21.74 GFLOPS | Progress: (16/20) | 12.56 s
[Task 7/25] Current/Best: 21.85/ 21.85 GFLOPS | Progress: (20/20) | 15.18 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.88/ 14.52 GFLOPS | Progress: (4/20) | 5.39 s
[Task 8/25] Current/Best: 4.06/ 20.60 GFLOPS | Progress: (8/20) | 7.76 s
[Task 8/25] Current/Best: 12.15/ 20.60 GFLOPS | Progress: (12/20) | 12.63 s
[Task 8/25] Current/Best: 16.81/ 20.60 GFLOPS | Progress: (16/20) | 17.40 s
[Task 8/25] Current/Best: 5.66/ 20.60 GFLOPS | Progress: (20/20) | 22.41 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 6.98/ 13.31 GFLOPS | Progress: (4/20) | 5.64 s
[Task 9/25] Current/Best: 11.85/ 18.84 GFLOPS | Progress: (8/20) | 10.57 s
[Task 9/25] Current/Best: 16.59/ 18.84 GFLOPS | Progress: (12/20) | 15.80 s
[Task 9/25] Current/Best: 14.35/ 18.84 GFLOPS | Progress: (16/20) | 22.58 s
[Task 9/25] Current/Best: 14.34/ 18.84 GFLOPS | Progress: (20/20) | 33.71 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 13.52/ 15.74 GFLOPS | Progress: (4/20) | 6.26 s Done.
-
[Task 10/25] Current/Best: 3.16/ 15.74 GFLOPS | Progress: (8/20) | 9.02 s
[Task 10/25] Current/Best: 4.85/ 15.74 GFLOPS | Progress: (12/20) | 11.46 s
[Task 10/25] Current/Best: 3.09/ 19.33 GFLOPS | Progress: (16/20) | 13.36 s
[Task 10/25] Current/Best: 5.02/ 19.33 GFLOPS | Progress: (20/20) | 15.19 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 24.01/ 24.01 GFLOPS | Progress: (4/20) | 4.59 s
[Task 11/25] Current/Best: 3.11/ 24.01 GFLOPS | Progress: (8/20) | 8.66 s
[Task 11/25] Current/Best: 22.95/ 24.01 GFLOPS | Progress: (12/20) | 10.66 s
[Task 11/25] Current/Best: 15.91/ 24.01 GFLOPS | Progress: (16/20) | 13.61 s
[Task 11/25] Current/Best: 19.78/ 24.01 GFLOPS | Progress: (20/20) | 15.65 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 19.47/ 19.47 GFLOPS | Progress: (4/20) | 6.27 s
[Task 12/25] Current/Best: 14.81/ 19.47 GFLOPS | Progress: (8/20) | 8.29 s
[Task 12/25] Current/Best: 11.68/ 19.47 GFLOPS | Progress: (12/20) | 13.06 s
[Task 12/25] Current/Best: 11.76/ 19.47 GFLOPS | Progress: (16/20) | 16.87 s
[Task 12/25] Current/Best: 12.86/ 19.47 GFLOPS | Progress: (20/20) | 20.54 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 11.96/ 19.96 GFLOPS | Progress: (4/20) | 5.21 s
[Task 13/25] Current/Best: 16.80/ 19.96 GFLOPS | Progress: (8/20) | 8.62 s
[Task 13/25] Current/Best: 10.61/ 19.96 GFLOPS | Progress: (12/20) | 12.38 s
[Task 13/25] Current/Best: 21.63/ 21.63 GFLOPS | Progress: (16/20) | 14.70 s
[Task 13/25] Current/Best: 12.75/ 21.63 GFLOPS | Progress: (20/20) | 16.82 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 11.80/ 14.99 GFLOPS | Progress: (4/20) | 9.98 s
[Task 14/25] Current/Best: 17.81/ 17.81 GFLOPS | Progress: (8/20) | 12.54 s
[Task 14/25] Current/Best: 9.17/ 20.78 GFLOPS | Progress: (12/20) | 23.64 s
[Task 14/25] Current/Best: 12.93/ 20.78 GFLOPS | Progress: (16/20) | 26.24 s
[Task 14/25] Current/Best: 13.32/ 20.78 GFLOPS | Progress: (20/20) | 29.13 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 12.77/ 12.77 GFLOPS | Progress: (4/20) | 10.00 s
[Task 15/25] Current/Best: 9.71/ 12.87 GFLOPS | Progress: (8/20) | 15.46 s
[Task 15/25] Current/Best: 8.60/ 12.87 GFLOPS | Progress: (12/20) | 20.78 s
[Task 15/25] Current/Best: 21.53/ 21.53 GFLOPS | Progress: (16/20) | 23.16 s
[Task 15/25] Current/Best: 17.53/ 21.53 GFLOPS | Progress: (20/20) | 24.71 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 12.98/ 19.29 GFLOPS | Progress: (4/20) | 4.83 s
[Task 16/25] Current/Best: 12.25/ 21.14 GFLOPS | Progress: (8/20) | 6.79 s
[Task 16/25] Current/Best: 16.23/ 21.14 GFLOPS | Progress: (12/20) | 8.30 s
[Task 16/25] Current/Best: 19.95/ 21.14 GFLOPS | Progress: (16/20) | 10.08 s
[Task 16/25] Current/Best: 6.12/ 21.14 GFLOPS | Progress: (20/20) | 13.15 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 7.71/ 22.67 GFLOPS | Progress: (4/20) | 5.39 s
[Task 17/25] Current/Best: 23.27/ 23.27 GFLOPS | Progress: (8/20) | 7.95 s
[Task 17/25] Current/Best: 8.15/ 23.27 GFLOPS | Progress: (12/20) | 11.00 s
[Task 17/25] Current/Best: 20.61/ 23.27 GFLOPS | Progress: (16/20) | 13.81 s
[Task 17/25] Current/Best: 22.61/ 23.27 GFLOPS | Progress: (20/20) | 17.30 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 10.93/ 18.30 GFLOPS | Progress: (4/20) | 5.28 s
[Task 18/25] Current/Best: 5.50/ 18.30 GFLOPS | Progress: (8/20) | 11.42 s
[Task 18/25] Current/Best: 12.04/ 18.30 GFLOPS | Progress: (12/20) | 14.57 s
[Task 18/25] Current/Best: 14.84/ 18.30 GFLOPS | Progress: (16/20) | 19.01 s
[Task 18/25] Current/Best: 15.64/ 18.30 GFLOPS | Progress: (20/20) | 21.51 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 11.88/ 11.88 GFLOPS | Progress: (4/20) | 7.25 s
[Task 19/25] Current/Best: 21.69/ 21.85 GFLOPS | Progress: (8/20) | 12.64 s
[Task 19/25] Current/Best: 18.50/ 21.85 GFLOPS | Progress: (12/20) | 17.04 s
[Task 19/25] Current/Best: 6.90/ 21.85 GFLOPS | Progress: (16/20) | 21.41 s
[Task 19/25] Current/Best: 8.13/ 21.85 GFLOPS | Progress: (20/20) | 28.21 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.41/ 15.55 GFLOPS | Progress: (4/20) | 10.37 s
[Task 20/25] Current/Best: 13.76/ 19.93 GFLOPS | Progress: (8/20) | 12.44 s
[Task 20/25] Current/Best: 12.28/ 19.93 GFLOPS | Progress: (12/20) | 15.41 s
[Task 20/25] Current/Best: 7.45/ 19.93 GFLOPS | Progress: (16/20) | 20.45 s
[Task 20/25] Current/Best: 9.46/ 19.93 GFLOPS | Progress: (20/20) | 24.83 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 5.02/ 13.94 GFLOPS | Progress: (4/20) | 6.16 s
[Task 21/25] Current/Best: 8.27/ 20.24 GFLOPS | Progress: (8/20) | 13.68 s
[Task 21/25] Current/Best: 4.33/ 20.24 GFLOPS | Progress: (12/20) | 15.99 s
[Task 21/25] Current/Best: 12.26/ 20.24 GFLOPS | Progress: (16/20) | 22.69 s
[Task 21/25] Current/Best: 7.47/ 21.32 GFLOPS | Progress: (20/20) | 28.74 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 5.15/ 21.03 GFLOPS | Progress: (4/20) | 4.50 s
[Task 22/25] Current/Best: 6.57/ 21.03 GFLOPS | Progress: (8/20) | 9.80 s
[Task 22/25] Current/Best: 10.65/ 21.03 GFLOPS | Progress: (12/20) | 11.90 s
[Task 22/25] Current/Best: 5.63/ 21.03 GFLOPS | Progress: (16/20) | 13.90 s
[Task 22/25] Current/Best: 3.10/ 21.03 GFLOPS | Progress: (20/20) | 15.93 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 23.16/ 23.16 GFLOPS | Progress: (4/20) | 5.23 s
[Task 23/25] Current/Best: 5.21/ 23.16 GFLOPS | Progress: (8/20) | 10.38 s
[Task 23/25] Current/Best: 22.42/ 23.16 GFLOPS | Progress: (12/20) | 12.71 s
[Task 23/25] Current/Best: 12.18/ 23.16 GFLOPS | Progress: (16/20) | 16.01 s
[Task 23/25] Current/Best: 18.80/ 23.16 GFLOPS | Progress: (20/20) | 19.92 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 2.07/ 5.20 GFLOPS | Progress: (4/20) | 11.83 s
[Task 24/25] Current/Best: 10.28/ 10.28 GFLOPS | Progress: (8/20) | 22.57 s
[Task 24/25] Current/Best: 4.09/ 10.28 GFLOPS | Progress: (12/20) | 25.40 s
[Task 24/25] Current/Best: 3.78/ 10.28 GFLOPS | Progress: (16/20) | 36.39 s
[Task 24/25] Current/Best: 8.10/ 10.28 GFLOPS | Progress: (20/20) | 47.09 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 7.64 GFLOPS | Progress: (4/20) | 5.87 s
[Task 25/25] Current/Best: 4.40/ 8.02 GFLOPS | Progress: (8/20) | 7.46 s
[Task 25/25] Current/Best: 8.44/ 8.44 GFLOPS | Progress: (12/20) | 18.42 s Done.
-
[Task 25/25] Current/Best: 1.55/ 8.44 GFLOPS | Progress: (16/20) | 29.46 s
[Task 25/25] Current/Best: 1.55/ 8.44 GFLOPS | Progress: (20/20) | 32.08 s
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 9.74/ 22.65 GFLOPS | Progress: (4/20) | 9.89 s
[Task 1/25] Current/Best: 6.67/ 22.65 GFLOPS | Progress: (8/20) | 12.95 s
[Task 1/25] Current/Best: 23.01/ 23.01 GFLOPS | Progress: (12/20) | 15.49 s
[Task 1/25] Current/Best: 11.05/ 23.01 GFLOPS | Progress: (16/20) | 19.03 s
[Task 1/25] Current/Best: 12.01/ 23.01 GFLOPS | Progress: (20/20) | 21.57 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 6.55/ 11.61 GFLOPS | Progress: (4/20) | 4.71 s
[Task 2/25] Current/Best: 12.01/ 15.35 GFLOPS | Progress: (8/20) | 7.49 s
[Task 2/25] Current/Best: 17.35/ 17.35 GFLOPS | Progress: (12/20) | 9.40 s
[Task 2/25] Current/Best: 19.43/ 19.43 GFLOPS | Progress: (16/20) | 11.17 s
[Task 2/25] Current/Best: 7.45/ 21.20 GFLOPS | Progress: (20/20) | 12.65 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 13.27/ 20.68 GFLOPS | Progress: (4/20) | 5.13 s
[Task 3/25] Current/Best: 13.70/ 21.40 GFLOPS | Progress: (8/20) | 7.34 s
[Task 3/25] Current/Best: 22.65/ 23.06 GFLOPS | Progress: (12/20) | 9.42 s
[Task 3/25] Current/Best: 15.68/ 23.06 GFLOPS | Progress: (16/20) | 11.65 s
[Task 3/25] Current/Best: 11.10/ 23.06 GFLOPS | Progress: (20/20) | 14.06 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 16.81/ 16.94 GFLOPS | Progress: (4/20) | 4.97 s
[Task 4/25] Current/Best: 14.58/ 16.94 GFLOPS | Progress: (8/20) | 7.31 s
[Task 4/25] Current/Best: 7.88/ 17.51 GFLOPS | Progress: (12/20) | 10.85 s
[Task 4/25] Current/Best: 18.10/ 19.18 GFLOPS | Progress: (16/20) | 17.77 s
[Task 4/25] Current/Best: 6.06/ 19.18 GFLOPS | Progress: (20/20) | 22.44 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 20.64/ 20.64 GFLOPS | Progress: (4/20) | 4.77 s
[Task 5/25] Current/Best: 20.92/ 20.92 GFLOPS | Progress: (8/20) | 6.46 s
[Task 5/25] Current/Best: 17.79/ 20.92 GFLOPS | Progress: (12/20) | 8.61 s
[Task 5/25] Current/Best: 16.81/ 20.92 GFLOPS | Progress: (16/20) | 10.47 s
[Task 5/25] Current/Best: 10.10/ 20.92 GFLOPS | Progress: (20/20) | 12.48 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 16.81/ 18.17 GFLOPS | Progress: (4/20) | 5.76 s
[Task 6/25] Current/Best: 16.85/ 18.17 GFLOPS | Progress: (8/20) | 8.14 s
[Task 6/25] Current/Best: 18.11/ 18.17 GFLOPS | Progress: (12/20) | 10.58 s
[Task 6/25] Current/Best: 15.37/ 18.75 GFLOPS | Progress: (16/20) | 13.26 s
[Task 6/25] Current/Best: 14.83/ 18.75 GFLOPS | Progress: (20/20) | 15.88 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.13/ 19.98 GFLOPS | Progress: (4/20) | 5.05 s
[Task 7/25] Current/Best: 17.89/ 20.59 GFLOPS | Progress: (8/20) | 7.16 s
[Task 7/25] Current/Best: 17.21/ 20.59 GFLOPS | Progress: (12/20) | 9.26 s
[Task 7/25] Current/Best: 22.97/ 22.97 GFLOPS | Progress: (16/20) | 13.06 s
[Task 7/25] Current/Best: 11.87/ 22.97 GFLOPS | Progress: (20/20) | 15.12 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 13.32/ 13.32 GFLOPS | Progress: (4/20) | 5.79 s
[Task 8/25] Current/Best: 13.07/ 14.88 GFLOPS | Progress: (8/20) | 7.96 s
[Task 8/25] Current/Best: 12.81/ 14.88 GFLOPS | Progress: (12/20) | 15.11 s
[Task 8/25] Current/Best: 6.27/ 14.88 GFLOPS | Progress: (16/20) | 19.53 s
[Task 8/25] Current/Best: 18.97/ 18.97 GFLOPS | Progress: (20/20) | 28.29 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 13.97/ 15.08 GFLOPS | Progress: (4/20) | 4.64 s
[Task 9/25] Current/Best: 5.00/ 15.08 GFLOPS | Progress: (8/20) | 10.41 s
[Task 9/25] Current/Best: 15.40/ 15.40 GFLOPS | Progress: (12/20) | 21.59 s
[Task 9/25] Current/Best: 12.95/ 19.32 GFLOPS | Progress: (16/20) | 24.33 s
[Task 9/25] Current/Best: 18.90/ 19.32 GFLOPS | Progress: (20/20) | 26.63 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 10/25] Current/Best: 9.42/ 14.01 GFLOPS | Progress: (4/20) | 5.28 s
[Task 10/25] Current/Best: 19.15/ 19.15 GFLOPS | Progress: (8/20) | 7.19 s
[Task 10/25] Current/Best: 13.64/ 19.15 GFLOPS | Progress: (12/20) | 9.60 s
[Task 10/25] Current/Best: 4.78/ 19.15 GFLOPS | Progress: (16/20) | 12.14 s
[Task 10/25] Current/Best: 15.15/ 19.15 GFLOPS | Progress: (20/20) | 15.54 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.13/ 21.37 GFLOPS | Progress: (4/20) | 5.16 s
[Task 11/25] Current/Best: 6.28/ 21.37 GFLOPS | Progress: (8/20) | 7.39 s
[Task 11/25] Current/Best: 12.17/ 21.37 GFLOPS | Progress: (12/20) | 9.55 s
[Task 11/25] Current/Best: 1.57/ 21.37 GFLOPS | Progress: (16/20) | 13.46 s
[Task 11/25] Current/Best: 12.36/ 21.37 GFLOPS | Progress: (20/20) | 16.09 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 13.13/ 14.45 GFLOPS | Progress: (4/20) | 6.91 s
[Task 12/25] Current/Best: 11.67/ 15.32 GFLOPS | Progress: (8/20) | 10.40 s
[Task 12/25] Current/Best: 16.29/ 16.29 GFLOPS | Progress: (12/20) | 13.64 s
[Task 12/25] Current/Best: 19.10/ 19.10 GFLOPS | Progress: (16/20) | 16.78 s
[Task 12/25] Current/Best: 17.27/ 19.10 GFLOPS | Progress: (20/20) | 20.83 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 9.02/ 14.77 GFLOPS | Progress: (4/20) | 6.57 s
[Task 13/25] Current/Best: 16.73/ 21.21 GFLOPS | Progress: (8/20) | 9.61 s
[Task 13/25] Current/Best: 18.55/ 21.21 GFLOPS | Progress: (12/20) | 13.26 s
[Task 13/25] Current/Best: 11.39/ 21.21 GFLOPS | Progress: (16/20) | 16.95 s
[Task 13/25] Current/Best: 8.41/ 21.21 GFLOPS | Progress: (20/20) | 21.93 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 5.55/ 16.44 GFLOPS | Progress: (4/20) | 5.31 s
[Task 14/25] Current/Best: 13.21/ 16.44 GFLOPS | Progress: (8/20) | 8.13 s
[Task 14/25] Current/Best: 10.75/ 22.76 GFLOPS | Progress: (12/20) | 11.52 s
[Task 14/25] Current/Best: 6.16/ 22.76 GFLOPS | Progress: (16/20) | 21.04 s
[Task 14/25] Current/Best: 14.53/ 22.76 GFLOPS | Progress: (20/20) | 24.26 s Done.
+
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 20.26/ 20.26 GFLOPS | Progress: (4/20) | 6.43 s
[Task 15/25] Current/Best: 12.62/ 20.26 GFLOPS | Progress: (8/20) | 12.16 s
[Task 15/25] Current/Best: 12.67/ 20.26 GFLOPS | Progress: (12/20) | 17.82 s
[Task 15/25] Current/Best: 15.98/ 20.26 GFLOPS | Progress: (16/20) | 22.63 s
[Task 15/25] Current/Best: 14.92/ 20.26 GFLOPS | Progress: (20/20) | 26.51 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 17.37/ 17.37 GFLOPS | Progress: (4/20) | 5.18 s
[Task 16/25] Current/Best: 17.83/ 17.83 GFLOPS | Progress: (8/20) | 7.51 s
[Task 16/25] Current/Best: 17.06/ 20.95 GFLOPS | Progress: (12/20) | 9.31 s
[Task 16/25] Current/Best: 12.90/ 20.95 GFLOPS | Progress: (16/20) | 12.33 s
[Task 16/25] Current/Best: 10.05/ 20.95 GFLOPS | Progress: (20/20) | 14.59 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.63/ 20.54 GFLOPS | Progress: (4/20) | 5.41 s
[Task 17/25] Current/Best: 12.93/ 20.54 GFLOPS | Progress: (8/20) | 8.54 s
[Task 17/25] Current/Best: 19.97/ 21.92 GFLOPS | Progress: (12/20) | 11.81 s
[Task 17/25] Current/Best: 18.13/ 22.42 GFLOPS | Progress: (16/20) | 15.36 s
[Task 17/25] Current/Best: 17.58/ 22.42 GFLOPS | Progress: (20/20) | 17.86 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 17.06/ 17.06 GFLOPS | Progress: (4/20) | 6.60 s
[Task 18/25] Current/Best: 11.17/ 17.06 GFLOPS | Progress: (8/20) | 9.48 s
[Task 18/25] Current/Best: 10.30/ 17.06 GFLOPS | Progress: (12/20) | 14.10 s
[Task 18/25] Current/Best: 11.06/ 18.74 GFLOPS | Progress: (16/20) | 16.45 s
[Task 18/25] Current/Best: 17.78/ 18.74 GFLOPS | Progress: (20/20) | 19.44 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 2.83/ 18.85 GFLOPS | Progress: (4/20) | 8.63 s
[Task 19/25] Current/Best: 12.61/ 18.85 GFLOPS | Progress: (8/20) | 12.61 s
[Task 19/25] Current/Best: 18.01/ 18.85 GFLOPS | Progress: (12/20) | 15.63 s
[Task 19/25] Current/Best: 12.14/ 18.85 GFLOPS | Progress: (16/20) | 19.65 s
[Task 19/25] Current/Best: 20.20/ 20.20 GFLOPS | Progress: (20/20) | 22.24 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 14.61/ 14.61 GFLOPS | Progress: (4/20) | 12.04 s
[Task 20/25] Current/Best: 5.94/ 14.69 GFLOPS | Progress: (8/20) | 14.75 s
[Task 20/25] Current/Best: 8.92/ 16.18 GFLOPS | Progress: (12/20) | 17.74 s
[Task 20/25] Current/Best: 10.15/ 16.18 GFLOPS | Progress: (16/20) | 20.41 s
[Task 20/25] Current/Best: 17.05/ 17.05 GFLOPS | Progress: (20/20) | 29.42 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.37/ 12.46 GFLOPS | Progress: (4/20) | 5.58 s
[Task 21/25] Current/Best: 15.37/ 15.90 GFLOPS | Progress: (8/20) | 7.32 s
[Task 21/25] Current/Best: 15.60/ 15.90 GFLOPS | Progress: (12/20) | 13.87 s
[Task 21/25] Current/Best: 16.91/ 17.66 GFLOPS | Progress: (16/20) | 15.71 s
[Task 21/25] Current/Best: 15.48/ 17.66 GFLOPS | Progress: (20/20) | 18.35 s Done.
+
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.60/ 16.71 GFLOPS | Progress: (4/20) | 6.78 s
[Task 22/25] Current/Best: 21.63/ 21.63 GFLOPS | Progress: (8/20) | 10.81 s
[Task 22/25] Current/Best: 4.89/ 21.63 GFLOPS | Progress: (12/20) | 12.68 s
[Task 22/25] Current/Best: 9.38/ 21.63 GFLOPS | Progress: (16/20) | 15.29 s
[Task 22/25] Current/Best: 9.43/ 21.63 GFLOPS | Progress: (20/20) | 18.11 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.72/ 17.72 GFLOPS | Progress: (4/20) | 8.64 s
[Task 23/25] Current/Best: 5.64/ 22.56 GFLOPS | Progress: (8/20) | 11.77 s
[Task 23/25] Current/Best: 11.01/ 22.56 GFLOPS | Progress: (12/20) | 15.55 s
[Task 23/25] Current/Best: 9.82/ 22.56 GFLOPS | Progress: (16/20) | 18.12 s
[Task 23/25] Current/Best: 9.99/ 22.56 GFLOPS | Progress: (20/20) | 21.26 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 9.94/ 10.34 GFLOPS | Progress: (4/20) | 10.71 s
[Task 24/25] Current/Best: 3.72/ 10.34 GFLOPS | Progress: (8/20) | 21.45 s
[Task 24/25] Current/Best: 3.88/ 10.34 GFLOPS | Progress: (12/20) | 25.49 s
[Task 24/25] Current/Best: 7.55/ 10.34 GFLOPS | Progress: (16/20) | 31.40 s
[Task 24/25] Current/Best: 6.60/ 10.34 GFLOPS | Progress: (20/20) | 32.78 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 5.09/ 5.52 GFLOPS | Progress: (4/20) | 13.75 s
[Task 25/25] Current/Best: 5.81/ 5.81 GFLOPS | Progress: (8/20) | 16.56 s
[Task 25/25] Current/Best: 6.06/ 6.06 GFLOPS | Progress: (12/20) | 19.85 s
[Task 25/25] Current/Best: 8.79/ 8.79 GFLOPS | Progress: (16/20) | 23.38 s
[Task 25/25] Current/Best: 9.21/ 9.21 GFLOPS | Progress: (2
0/20) | 24.91 s
@@ -675,6 +674,7 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
+ Done.
@@ -708,8 +708,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621103
- class='n02123159 tiger cat' with probability=0.356379
+ class='n02123045 tabby, tabby cat' with probability=0.621105
+ class='n02123159 tiger cat' with probability=0.356377
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 401.6122560399526, 'median': 401.75347454987786, 'std': 0.9607900980920305}
- unoptimized: {'mean': 492.443102159923, 'median': 492.58702424995136, 'std': 2.5520380245361642}
+ optimized: {'mean': 418.9006518100359, 'median': 418.8475400000243, 'std': 2.998710569035691}
+ unoptimized: {'mean': 495.4493903200637, 'median': 495.00540520020877, 'std': 2.031572185495168}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 12 minutes 54.114 seconds)
+ **Total running time of the script:** ( 12 minutes 43.293 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 4c946cb506..af84ce412d 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
- 3.729e-07 secs/op
+ 1.211e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 1425067e8b..b7e796eedc 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, 0xbd87b30)), stage(b, placeholder(b, 0xa150030)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.R [...]
+ [stage(a, placeholder(a, 0x1f0ae900)), stage(b, placeholder(b, 0x1f0d9370)), 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 a181072d55..e243a562b2 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,32 +5,32 @@
Computation times
=================
-**16:25.254** total execution time for **tutorial** files:
+**16:22.634** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:54.114 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 12:43.293 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:25.558 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:30.322 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.977 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:02.021 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:39.369 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:43.478 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:25.227 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:21.474 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.964 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.997 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.855 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.877 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.190 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.173 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 3d4dcd5268..25c2532c82 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: 0.000009
+ Numpy running time: 0.000008
+ naive: 0.000007
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000008
+ parallel: 0.000007
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000039
+ vector: 0.000041
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.14399000571575e-06 1.0
- naive 8.5399e-06 1.1953964091729428
- parallel 8.299e-06 1.161675757295314
- vector 3.92524e-05 5.494464573521941
+ numpy 7.987770004547201e-06 1.0
+ naive 6.685199999999999e-06 0.8369294554292765
+ parallel 6.9706e-06 0.8726590770680482
+ vector 4.13294e-05 5.174084879318307
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.017620
+ Numpy running time: 0.017733
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.346783
+ none: 3.539758
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.294149
+ blocking: 0.303445
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.273124
+ vectorization: 0.297754
# 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: 0.113373
+ loop permutation: 0.117362
# 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: 0.105291
+ array packing: 0.107421
# 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: 0.109988
+ block caching: 0.112151
# 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: 0.131098
+ parallelization: 0.133267
# 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 3.3467825759 1.0
- blocking 0.2941486142 0.0878899682095121
- vectorization 0.27312446160000003 0.0816080684675349
- loop permutation 0.11337312629999999 0.03387525891774199
- array packing 0.1052905054 0.03146021679394151
- block caching 0.10998791510000001 0.032863776658817644
- parallelization 0.131098254 0.039171428387380594
+ none 3.5397578798000007 1.0
+ blocking 0.30344489119999996 0.08572475901011196
+ vectorization 0.2977544521 0.08411718038659283
+ loop permutation 0.11736193399999999 0.03315535637895975
+ array packing 0.1074212905 0.030347072920724565
+ block caching 0.11215067320000001 0.031683148115863966
+ parallelization 0.1332672165 0.03764868135770058
@@ -1594,6 +1594,11 @@ 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 2.021 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index caff7f23c6..a6e2f27a48 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-e54bbc73a7bc9d942fa8d4794a4c2a6ff78a6421
+05001bed646c26f788521d4d14a2bad9bf755e65
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 8dbe9d3075..cad794b0ea 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -590,7 +590,7 @@ 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 27.711 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.238 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_keras.html b/docs/how_to/compile_models/from_keras.html
index c87458d826..edd3732e34 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -511,7 +511,7 @@ Tensorflow is also required since it’s used as the default backend of keras.</
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 1s/step
+1/1 [==============================] - 2s 2s/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index fecc0c21fe..f4b478c2fa 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -444,7 +444,7 @@
<span class="nb">print</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipbcd3f873-883e-4774-b7d6-86ae83ef1c88 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipf178cd02-e780-40bb-a480-eddd574e62df from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index a63653f499..79d4a2abdf 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -454,12 +454,12 @@ 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]
- 19%|#9 | 7.99M/41.5M [00:00<00:00, 65.2MB/s]
- 39%|###8 | 16.0M/41.5M [00:00<00:00, 51.4MB/s]
- 58%|#####7 | 24.0M/41.5M [00:00<00:00, 52.3MB/s]
- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 57.7MB/s]
- 96%|#########6| 40.0M/41.5M [00:00<00:00, 64.8MB/s]
-100%|##########| 41.5M/41.5M [00:00<00:00, 61.8MB/s]
+ 19%|#9 | 7.99M/41.5M [00:00<00:00, 70.8MB/s]
+ 39%|###8 | 16.0M/41.5M [00:00<00:00, 63.3MB/s]
+ 54%|#####3 | 22.3M/41.5M [00:00<00:00, 61.9MB/s]
+ 73%|#######3 | 30.4M/41.5M [00:00<00:00, 69.9MB/s]
+ 90%|########9 | 37.2M/41.5M [00:00<00:00, 63.6MB/s]
+100%|##########| 41.5M/41.5M [00:00<00:00, 61.7MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index cd86dc8824..c65770ce61 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -489,6 +489,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.407 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 1a4a374e7f..455824adce 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -437,13 +437,13 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
0%| | 0.00/44.7M [00:00<?, ?B/s]
- 14%|#4 | 6.30M/44.7M [00:00<00:01, 37.3MB/s]
- 22%|##2 | 9.86M/44.7M [00:00<00:01, 34.7MB/s]
- 36%|###5 | 16.0M/44.7M [00:00<00:00, 43.4MB/s]
- 54%|#####3 | 24.0M/44.7M [00:00<00:00, 54.9MB/s]
- 72%|#######1 | 32.0M/44.7M [00:00<00:00, 59.4MB/s]
- 97%|#########6| 43.3M/44.7M [00:00<00:00, 76.8MB/s]
-100%|##########| 44.7M/44.7M [00:00<00:00, 59.5MB/s]
+ 18%|#7 | 7.99M/44.7M [00:00<00:00, 43.6MB/s]
+ 36%|###5 | 16.0M/44.7M [00:00<00:00, 53.4MB/s]
+ 54%|#####3 | 24.0M/44.7M [00:00<00:00, 59.8MB/s]
+ 68%|######7 | 30.3M/44.7M [00:00<00:00, 56.8MB/s]
+ 80%|######## | 35.8M/44.7M [00:00<00:00, 52.0MB/s]
+ 91%|#########1| 40.8M/44.7M [00:00<00:00, 50.5MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 56.3MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index bb48285045..435d5db3cd 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -657,7 +657,7 @@ 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 27.841 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.489 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 9627619070..0933f9d16e 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -345,7 +345,7 @@
<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>06:46.880</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>07:07.506</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -353,44 +353,44 @@
<col style="width: 8%" />
</colgroup>
<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
-<td><p>01:27.841</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
+<td><p>01:30.238</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:27.711</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
+<td><p>01:29.489</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>00:59.634</p></td>
+<td><p>01:01.407</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:39.838</p></td>
+<td><p>00:42.921</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:34.899</p></td>
+<td><p>00:37.844</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></td>
-<td><p>00:31.874</p></td>
+<td><p>00:34.723</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:27.095</p></td>
+<td><p>00:28.380</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:24.845</p></td>
+<td><p>00:27.460</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
-<td><p>00:10.387</p></td>
+<td><p>00:11.939</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></td>
-<td><p>00:02.756</p></td>
+<td><p>00:03.105</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index d73f9ebada..f5a827a369 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -835,10 +835,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4223.9162 4224.2572 4229.0392 4219.1234 3.0607
+ 4602.4472 4637.3949 4738.4299 4433.1458 83.9255
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.890 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 24.046 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 a600c81386..af5756b14f 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
@@ -443,25 +443,25 @@ 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
8192/102967424 [..............................] - ETA: 0s
- 8069120/102967424 [=>............................] - ETA: 0s
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+102967424/102967424 [==============================] - 1s 0us/step
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 5f8ddb42b1..2d02b37ff7 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -667,7 +667,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.2254 15.1690 15.5653 14.9848 0.1653
+ 15.7464 15.7272 16.3568 15.2300 0.3064
</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 058ecd0986..874cf68a5a 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -459,27 +459,32 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
(torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -573,7 +578,7 @@ 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 30.597 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 45.798 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 fe06215533..f96f3b67b6 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -500,8 +500,9 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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+100%|##########| 13.6M/13.6M [00:00<00:00, 40.1MB/s]
</pre></div>
</div>
</div>
@@ -592,7 +593,7 @@ 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)
- 88.9038 88.7898 92.7808 88.4138 0.5638
+ 97.9974 98.1451 101.8753 92.7045 1.3640
</pre></div>
</div>
<div class="admonition note">
@@ -631,7 +632,7 @@ 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 23.408 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 35.337 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 b04727c72d..ba22d93d72 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -585,7 +585,7 @@ 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)
- 108.4079 108.2276 126.5140 106.8795 1.9437
+ 109.1622 109.1748 110.5632 108.4884 0.3267
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index 2ff001fa11..da5807a29c 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -526,7 +526,7 @@ for calibration. But the accuracy might be impacted.</p>
warnings.warn(
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 53.698 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 6.126 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 42c6ba4509..14a3f5fcf2 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -345,7 +345,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>11:27.367</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:27.079</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -354,43 +354,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:30.597</p></td>
+<td><p>03:45.798</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></td>
-<td><p>01:53.698</p></td>
+<td><p>02:06.126</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></td>
-<td><p>01:23.408</p></td>
+<td><p>01:35.337</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:18.890</p></td>
+<td><p>01:24.046</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:50.905</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:53.927</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:47.675</p></td>
+<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>00:53.023</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:43.787</p></td>
+<td><p>00:46.549</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>00:29.403</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>00:32.433</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>00:28.997</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:29.833</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index ac3e9b80af..982796a251 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -624,7 +624,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
<span class="n">module</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="n">get_mobilenet</span><span class="p">()</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip6122265f-7c12-485e-86e7-84b1ec14270e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip23ea02cf-5f7f-4644-a5a2-e57bdccc0c0e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index ac4c01f8c0..5844b66991 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -345,7 +345,7 @@
<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>00:55.232</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:56.324</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -354,15 +354,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:51.497</p></td>
+<td><p>00:52.432</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:02.615</p></td>
+<td><p>00:02.698</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></td>
-<td><p>00:01.113</p></td>
+<td><p>00:01.186</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index e15c1bbeaa..c4338ba86c 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -531,10 +531,10 @@ 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: 23384us [23384us] (48.40%; 48.40%)
-FoldScaleAxis: 24933us [8us] (51.60%; 51.60%)
- FoldConstant: 24925us [1785us] (51.59%; 99.97%)
- InferType: 23141us [23141us] (47.89%; 92.84%)
+InferType: 24087us [24087us] (49.51%; 49.51%)
+FoldScaleAxis: 24563us [8us] (50.49%; 50.49%)
+ FoldConstant: 24555us [1808us] (50.47%; 99.97%)
+ InferType: 22747us [22747us] (46.76%; 92.64%)
</pre></div>
</div>
</div>
@@ -556,10 +556,10 @@ 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: 22854us [22854us] (48.32%; 48.32%)
-FoldScaleAxis: 24445us [6us] (51.68%; 51.68%)
- FoldConstant: 24439us [1788us] (51.67%; 99.98%)
- InferType: 22651us [22651us] (47.89%; 92.68%)
+InferType: 22793us [22793us] (48.28%; 48.28%)
+FoldScaleAxis: 24419us [6us] (51.72%; 51.72%)
+ FoldConstant: 24413us [1813us] (51.71%; 99.97%)
+ InferType: 22600us [22600us] (47.87%; 92.57%)
</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 1b02aa915d..b773bfc7e0 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -580,7 +580,7 @@ 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: 53.526752 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.577152 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 1b4f223a0d..27073de610 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -862,7 +862,7 @@ 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: 11.146528 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.087904 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 42bd94d589..367c7d31f1 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -477,8 +477,8 @@ 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: 0.017728
-Baseline: 3.338099
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.017881
+Baseline: 3.528650
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -537,7 +537,7 @@ 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: 0.292644
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.294164
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -594,7 +594,7 @@ 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: 0.271410
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.268354
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -649,7 +649,7 @@ 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: 0.113882
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.117984
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -726,7 +726,7 @@ 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: 0.106886
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.108368
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -804,7 +804,7 @@ 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: 0.111743
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112178
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -884,7 +884,7 @@ 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: 0.132038
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.133152
</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 54a5eba7ef..e6f16e9064 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -345,7 +345,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:33.799</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.335</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -354,15 +354,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:30.357</p></td>
+<td><p>00:31.047</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
-<td><p>00:01.986</p></td>
+<td><p>00:02.051</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.456</p></td>
+<td><p>00:01.237</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 7fe1c31129..3df398f678 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -345,7 +345,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:29.657</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:42.138</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -354,23 +354,23 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:26.947</p></td>
+<td><p>01:32.136</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:13.683</p></td>
+<td><p>01:16.889</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:18.146</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
+<td><p>00:17.918</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:15.547</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
+<td><p>00:17.564</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:15.231</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
+<td><p>00:17.527</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
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 14e12b1a4c..d23f4398a5 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
@@ -1018,7 +1018,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.349 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.346 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index f4e0b9d8f0..90dfa48278 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -921,7 +921,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 8.1178 8.1183 8.1231 8.1120 0.0046
+ 8.0973 8.0992 8.0994 8.0934 0.0028
</pre></div>
</div>
</div>
@@ -943,7 +943,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
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<p><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 e99f98fa6f..0158eb7dce 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
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@@ -940,7 +940,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 753.1324 754.0751 754.1440 751.1780 1.3822
+ 760.0259 760.5550 761.5224 758.0002 1.4858
</pre></div>
</div>
</div>
@@ -962,7 +962,7 @@ 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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<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 3994b5c7ae..cd01d42e01 100644
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+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -345,7 +345,7 @@
<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>00:23.666</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:24.536</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -354,11 +354,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
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 06d33614c2..66ac8feab2 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -615,7 +615,7 @@ and measure running time.</p>
Best config:
,None
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</pre></div>
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diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index d0408ae760..4ccdbe58e7 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -649,10 +649,10 @@ 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_1 tvmgen_default_fused_layout_transform_1 2.903 0.947 (1, 6, 10, 10) 1 1 [2.903]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 0.314 (1, 1, 10, 10, 3) 1 1 [0.962]
-Total_time - 306.465 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 324.6 98.775 (1, 2, 10, 10, 3) 2 1 [324.6]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.933 (1, 6, 10, 10) 1 1 [3.067]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.961 0.292 (1, 1, 10, 10, 3) 1 1 [0.961]
+Total_time - 328.627 - - - - -
</pre></div>
</div>
</div>
@@ -704,13 +704,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.5 97.374 (1, 6, 10, 10, 1) 2 1 [100.5]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.752 1.698 (1, 6, 10, 10) 1 1 [1.752]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.928 (1, 1, 10, 10, 3) 1 1 [0.958]
-Total_time - 103.21 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 149.4 98.249 (1, 6, 10, 10, 1) 2 1 [149.4]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.803 1.186 (1, 6, 10, 10) 1 1 [1.803]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.859 0.565 (1, 3, 10, 10, 1) 1 1 [0.859]
+Total_time - 152.062 - - - - -
</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 70d04ed150..a41949d6f8 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -460,7 +460,7 @@ 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]
-100%|##########| 3.42M/3.42M [00:00<00:00, 88.1MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 42.5MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
device=storage.device,
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -588,7 +588,7 @@ 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>
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<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 59d7d9e071..f2b2dead02 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -528,7 +528,7 @@ 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/tmp_16m210o/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp6mw5g6e1/images/random'
</pre></div>
</div>
</div>
@@ -588,8 +588,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
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-/tmp/tmp_16m210o/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/tmp6mw5g6e1/images/target contains 8144 images
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</pre></div>
</div>
</div>
@@ -701,13 +701,13 @@ 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 - 41s - loss: 0.2192 - accuracy: 0.9261 - val_loss: 0.1638 - val_accuracy: 0.9505 - 41s/epoch - 123ms/step
+328/328 - 42s - loss: 0.2278 - accuracy: 0.9225 - val_loss: 0.1217 - val_accuracy: 0.9547 - 42s/epoch - 127ms/step
Epoch 2/3
-328/328 - 35s - loss: 0.0998 - accuracy: 0.9633 - val_loss: 0.1043 - val_accuracy: 0.9622 - 35s/epoch - 107ms/step
+328/328 - 37s - loss: 0.1029 - accuracy: 0.9622 - val_loss: 0.1048 - val_accuracy: 0.9668 - 37s/epoch - 112ms/step
Epoch 3/3
-328/328 - 35s - loss: 0.0643 - accuracy: 0.9752 - val_loss: 0.1019 - val_accuracy: 0.9671 - 35s/epoch - 107ms/step
+328/328 - 37s - loss: 0.0700 - accuracy: 0.9741 - val_loss: 0.1051 - val_accuracy: 0.9660 - 37s/epoch - 111ms/step
-<keras.callbacks.History object at 0x7f3002558a90>
+<keras.callbacks.History object at 0x7f3a4c51db20>
</pre></div>
</div>
</div>
@@ -971,7 +971,7 @@ 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 13.862 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 24.546 seconds)</p>
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<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
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -345,7 +345,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>07:34.485</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -354,27 +354,27 @@
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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>
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<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>
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<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">2. microTVM TFLite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_custom_ide.html#sphx-glr-how-to-work-with-microtvm-micro-custom-ide-py"><span class="std std-ref">9. Bring microTVM to your own development environment</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_custom_ide.py</span></code>)</p></td>
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<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 94e32d05e5..0fbd27a7f1 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -345,7 +345,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:39.990</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:41.695</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -354,15 +354,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:34.704</p></td>
+<td><p>00:36.297</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>00:03.155</p></td>
+<td><p>00:03.378</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>00:02.125</p></td>
+<td><p>00:02.013</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index b409ad676b..bce8071647 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -554,7 +554,7 @@ 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 0x7f2e980ff820>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f3920652820>
</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 5cf8f1c4f8..010b96f679 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -345,7 +345,7 @@
<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>00:06.789</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:06.535</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -354,23 +354,23 @@
</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>00:03.621</p></td>
+<td><p>00:03.457</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>00:01.385</p></td>
+<td><p>00:01.314</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>00:00.768</p></td>
+<td><p>00:00.771</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
-<td><p>00:00.764</p></td>
+<td><p>00:00.737</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>00:00.114</p></td>
+<td><p>00:00.115</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
@@ -378,7 +378,7 @@
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
-<td><p>00:00.051</p></td>
+<td><p>00:00.054</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>
diff --git a/docs/reference/api/doxygen/codegen_8h.html b/docs/reference/api/doxygen/codegen_8h.html
index 3172afa88a..2d777f8a91 100644
--- a/docs/reference/api/doxygen/codegen_8h.html
+++ b/docs/reference/api/doxygen/codegen_8h.html
@@ -103,12 +103,12 @@ Functions</h2></td></tr>
<tr class="memitem:a0d6322c2dda54a66a3b82022f5f3632c"><td class="memItemLeft" align="right" valign="top">runtime::Module </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">tvm::codegen::Build</a> (IRModule mod, Target target)</td></tr>
<tr class="memdesc:a0d6322c2dda54a66a3b82022f5f3632c"><td class="mdescLeft"> </td><td class="mdescRight">Build a module from array of lowered function. <a href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">More...</a><br /></td></tr>
<tr class="separator:a0d6322c2dda54a66a3b82022f5f3632c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abf02059ebadcdb8bbbe5c840b646d67b"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">tvm::codegen::PackImportsToC</a> (const runtime::Module &m, bool system_lib)</td></tr>
-<tr class="memdesc:abf02059ebadcdb8bbbe5c840b646d67b"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a C file. Compile the C file and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">More...</a><br /></td></tr>
-<tr class="separator:abf02059ebadcdb8bbbe5c840b646d67b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="memItemLeft" align="right" valign="top">runtime::Module </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">tvm::codegen::PackImportsToLLVM</a> (const runtime::Module &m, bool system_lib, const std::string &target_triple)</td></tr>
-<tr class="memdesc:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a LLVM module. Compile the LLVM module and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">More...</a><br /></td></tr>
-<tr class="separator:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8b151920f583ba2612f4020879afb844"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">tvm::codegen::PackImportsToC</a> (const runtime::Module &m, bool system_lib, const std::string &c_symbol_prefix="")</td></tr>
+<tr class="memdesc:a8b151920f583ba2612f4020879afb844"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a C file. Compile the C file and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">More...</a><br /></td></tr>
+<tr class="separator:a8b151920f583ba2612f4020879afb844"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a08cb4d3bf9e767ce04908b843132aec8"><td class="memItemLeft" align="right" valign="top">runtime::Module </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">tvm::codegen::PackImportsToLLVM</a> (const runtime::Module &m, bool system_lib, const std::string &target_triple, const std::string &c_symbol_prefix="")</td></tr>
+<tr class="memdesc:a08cb4d3bf9e767ce04908b843132aec8"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a LLVM module. Compile the LLVM module and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">More...</a><br /></td></tr>
+<tr class="separator:a08cb4d3bf9e767ce04908b843132aec8"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Translates IRModule to runtime::Module. </p>
diff --git a/docs/reference/api/doxygen/codegen_8h_source.html b/docs/reference/api/doxygen/codegen_8h_source.html
index 569428ed9e..58a43ae63b 100644
--- a/docs/reference/api/doxygen/codegen_8h_source.html
+++ b/docs/reference/api/doxygen/codegen_8h_source.html
@@ -107,13 +107,15 @@ $(function() {
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  </div>
<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c"> 48</a></span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> <a class="code" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">Build</a>(<a class="code" href="classtvm_1_1IRModule.html">IRModule</a> <a class="code" href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">mod</ [...]
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div>
-<div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b"> 60</a></span> std::string <a class="code" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">PackImportsToC</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a>& m, <span class="keywordtype">bool</span> system_lib);</div>
-<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  </div>
-<div class="line"><a name="l00073"></a><span class="lineno"><a class="line" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6"> 73</a></span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> <a class="code" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">PackImportsToLLVM</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a>& m, <span class="keyw [...]
-<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> std::string& target_triple);</div>
-<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> } <span class="comment">// namespace codegen</span></div>
-<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TARGET_CODEGEN_H_</span></div>
+<div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844"> 61</a></span> std::string <a class="code" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">PackImportsToC</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a>& m, <span class="keywordtype">bool</span> system_lib,</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">const</span> std::string& c_symbol_prefix = <span class="stringliteral">""</span>);</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  </div>
+<div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8"> 77</a></span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> <a class="code" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">PackImportsToLLVM</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a>& m, <span class="keyw [...]
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> std::string& target_triple,</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">const</span> std::string& c_symbol_prefix = <span class="stringliteral">""</span>);</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> } <span class="comment">// namespace codegen</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TARGET_CODEGEN_H_</span></div>
<div class="ttc" id="aclasstvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode.</div><div class="ttdef"><b>Definition:</b> module.h:348</div></div>
<div class="ttc" id="aclasstvm_1_1Target_html"><div class="ttname"><a href="classtvm_1_1Target.html">tvm::Target</a></div><div class="ttdoc">Managed reference class to TargetNode.</div><div class="ttdef"><b>Definition:</b> target.h:183</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM.</div><div class="ttdef"><b>Definition:</b> module.h:79</div></div>
@@ -121,9 +123,9 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1runtime_1_1TVMRetValue_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1TVMRetValue.html">tvm::runtime::TVMRetValue</a></div><div class="ttdoc">Return Value container, Unlike TVMArgValue, which only holds reference and do not delete the underlyi...</div><div class="ttdef"><b>Definition:</b> packed_func.h:799</div></div>
<div class="ttc" id="acrt_2packed__func_8h_html_a7e6f4ecedf23b1d2bf1aaf360ae1a2e7"><div class="ttname"><a href="crt_2packed__func_8h.html#a7e6f4ecedf23b1d2bf1aaf360ae1a2e7">TVMArgs</a></div><div class="ttdeci">struct TVMArgs TVMArgs</div></div>
<div class="ttc" id="air_2module_8h_html"><div class="ttname"><a href="ir_2module_8h.html">module.h</a></div><div class="ttdoc">IRModule that holds the functions and type definitions.</div></div>
+<div class="ttc" id="anamespacetvm_1_1codegen_html_a08cb4d3bf9e767ce04908b843132aec8"><div class="ttname"><a href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">tvm::codegen::PackImportsToLLVM</a></div><div class="ttdeci">runtime::Module PackImportsToLLVM(const runtime::Module &m, bool system_lib, const std::string &target_triple, const std::string &c_symbol_prefix="")</div><div class="ttdoc">Pack imported device library to a LLVM module. Compile t [...]
<div class="ttc" id="anamespacetvm_1_1codegen_html_a0d6322c2dda54a66a3b82022f5f3632c"><div class="ttname"><a href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">tvm::codegen::Build</a></div><div class="ttdeci">runtime::Module Build(IRModule mod, Target target)</div><div class="ttdoc">Build a module from array of lowered function.</div></div>
-<div class="ttc" id="anamespacetvm_1_1codegen_html_ab2cd2a65bac4b26427a8ca0abe4e0bd6"><div class="ttname"><a href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">tvm::codegen::PackImportsToLLVM</a></div><div class="ttdeci">runtime::Module PackImportsToLLVM(const runtime::Module &m, bool system_lib, const std::string &target_triple)</div><div class="ttdoc">Pack imported device library to a LLVM module. Compile the LLVM module and link with the host library...</div></div>
-<div class="ttc" id="anamespacetvm_1_1codegen_html_abf02059ebadcdb8bbbe5c840b646d67b"><div class="ttname"><a href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">tvm::codegen::PackImportsToC</a></div><div class="ttdeci">std::string PackImportsToC(const runtime::Module &m, bool system_lib)</div><div class="ttdoc">Pack imported device library to a C file. Compile the C file and link with the host library will allo...</div></div>
+<div class="ttc" id="anamespacetvm_1_1codegen_html_a8b151920f583ba2612f4020879afb844"><div class="ttname"><a href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">tvm::codegen::PackImportsToC</a></div><div class="ttdeci">std::string PackImportsToC(const runtime::Module &m, bool system_lib, const std::string &c_symbol_prefix="")</div><div class="ttdoc">Pack imported device library to a C file. Compile the C file and link with the host library will allo... [...]
<div class="ttc" id="anamespacetvm_1_1topi_html_aaa95d3ad68932ab206efbe0a326db6a2"><div class="ttname"><a href="namespacetvm_1_1topi.html#aaa95d3ad68932ab206efbe0a326db6a2">tvm::topi::mod</a></div><div class="ttdeci">tvm::PrimExpr mod(const tvm::PrimExpr &a, const tvm::PrimExpr &b)</div><div class="ttdef"><b>Definition:</b> broadcast.h:290</div></div>
<div class="ttc" id="anamespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript.</div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="apacked__func_8h_html"><div class="ttname"><a href="packed__func_8h.html">packed_func.h</a></div><div class="ttdoc">Type-erased function used across TVM API.</div></div>
diff --git a/docs/reference/api/doxygen/ir_2module_8h.html b/docs/reference/api/doxygen/ir_2module_8h.html
index 47de81f9e2..eedb1e0351 100644
--- a/docs/reference/api/doxygen/ir_2module_8h.html
+++ b/docs/reference/api/doxygen/ir_2module_8h.html
@@ -144,6 +144,9 @@ Variables</h2></td></tr>
<tr class="memitem:a8604338aa4e9293d10d427a00a2c9f13"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">tvm::attr::kExternalMods</a> = "external_mods"</td></tr>
<tr class="memdesc:a8604338aa4e9293d10d427a00a2c9f13"><td class="mdescLeft"> </td><td class="mdescRight">All the runtime::Modules accumulated during compilation by external codegen. These modules must be either directly linked or captured in the final compilation artifact. <a href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">More...</a><br /></td></tr>
<tr class="separator:a8604338aa4e9293d10d427a00a2c9f13"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aae652055bd020f8aa1cc32436e155603"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">tvm::attr::kSystemLibPrefix</a> = "system_lib_prefix"</td></tr>
+<tr class="memdesc:aae652055bd020f8aa1cc32436e155603"><td class="mdescLeft"> </td><td class="mdescRight">A prefix for generating C symbols system lib creation. <a href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">More...</a><br /></td></tr>
+<tr class="separator:aae652055bd020f8aa1cc32436e155603"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a38f40a3fc908b9359c4ca46bdd18811f"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">tvm::attr::kConstNameToConstant</a> = "const_name_to_constant"</td></tr>
<tr class="memdesc:a38f40a3fc908b9359c4ca46bdd18811f"><td class="mdescLeft"> </td><td class="mdescRight">All the named runtime::NDArrays accumulated during compilation by external codegen. Generally the associated <a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM.">runtime::Module</a> will indicate it requires bindings for these names, and during module initialization these bindings will be recovered from a ConstLoaderModule. See also kConstantsA [...]
<tr class="separator:a38f40a3fc908b9359c4ca46bdd18811f"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/ir_2module_8h_source.html b/docs/reference/api/doxygen/ir_2module_8h_source.html
index 5dcdfd089e..11f90e8eec 100644
--- a/docs/reference/api/doxygen/ir_2module_8h_source.html
+++ b/docs/reference/api/doxygen/ir_2module_8h_source.html
@@ -276,11 +276,13 @@ $(function() {
<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  </div>
<div class="line"><a name="l00504"></a><span class="lineno"><a class="line" href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13"> 504</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">kExternalMods</a> = <span class="stringliteral">"external_mods"</span>;</div>
<div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  </div>
-<div class="line"><a name="l00514"></a><span class="lineno"><a class="line" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f"> 514</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">kConstNameToConstant</a> = <span class="stringliteral">"const_name_to_constant"</span>;</div>
-<div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  </div>
-<div class="line"><a name="l00516"></a><span class="lineno"> 516</span> } <span class="comment">// namespace attr</span></div>
-<div class="line"><a name="l00517"></a><span class="lineno"> 517</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00518"></a><span class="lineno"> 518</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_IR_MODULE_H_</span></div>
+<div class="line"><a name="l00533"></a><span class="lineno"><a class="line" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603"> 533</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">kSystemLibPrefix</a> = <span class="stringliteral">"system_lib_prefix"</span>;</div>
+<div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  </div>
+<div class="line"><a name="l00543"></a><span class="lineno"><a class="line" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f"> 543</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">kConstNameToConstant</a> = <span class="stringliteral">"const_name_to_constant"</span>;</div>
+<div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  </div>
+<div class="line"><a name="l00545"></a><span class="lineno"> 545</span> } <span class="comment">// namespace attr</span></div>
+<div class="line"><a name="l00546"></a><span class="lineno"> 546</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00547"></a><span class="lineno"> 547</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_IR_MODULE_H_</span></div>
<div class="ttc" id="aarray_8h_html"><div class="ttname"><a href="array_8h.html">array.h</a></div><div class="ttdoc">Runtime Array container types.</div></div>
<div class="ttc" id="aclasstvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
<div class="ttc" id="aclasstvm_1_1BaseFunc_html"><div class="ttname"><a href="classtvm_1_1BaseFunc.html">tvm::BaseFunc</a></div><div class="ttdoc">Managed reference to BaseFuncNode.</div><div class="ttdef"><b>Definition:</b> function.h:143</div></div>
@@ -367,10 +369,11 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1attr_html_a0baac5edeb8047d3aea7fc8f19850f40"><div class="ttname"><a href="namespacetvm_1_1attr.html#a0baac5edeb8047d3aea7fc8f19850f40">tvm::attr::kConstantMemoryPools</a></div><div class="ttdeci">constexpr const char * kConstantMemoryPools</div><div class="ttdoc">constant memory pools of the module</div><div class="ttdef"><b>Definition:</b> module.h:487</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_a1dea1ab8e50c1fb0e456d5ab6bd903d0"><div class="ttname"><a href="namespacetvm_1_1attr.html#a1dea1ab8e50c1fb0e456d5ab6bd903d0">tvm::attr::kConstants</a></div><div class="ttdeci">constexpr const char * kConstants</div><div class="ttdef"><b>Definition:</b> module.h:496</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_a32466fc61e01e9fccbf0c50a6d307930"><div class="ttname"><a href="namespacetvm_1_1attr.html#a32466fc61e01e9fccbf0c50a6d307930">tvm::attr::kWorkspaceMemoryPools</a></div><div class="ttdeci">constexpr const char * kWorkspaceMemoryPools</div><div class="ttdoc">workspace memory pools of the module</div><div class="ttdef"><b>Definition:</b> module.h:478</div></div>
-<div class="ttc" id="anamespacetvm_1_1attr_html_a38f40a3fc908b9359c4ca46bdd18811f"><div class="ttname"><a href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">tvm::attr::kConstNameToConstant</a></div><div class="ttdeci">constexpr const char * kConstNameToConstant</div><div class="ttdoc">All the named runtime::NDArrays accumulated during compilation by external codegen....</div><div class="ttdef"><b>Definition:</b> module.h:514</div></div>
+<div class="ttc" id="anamespacetvm_1_1attr_html_a38f40a3fc908b9359c4ca46bdd18811f"><div class="ttname"><a href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">tvm::attr::kConstNameToConstant</a></div><div class="ttdeci">constexpr const char * kConstNameToConstant</div><div class="ttdoc">All the named runtime::NDArrays accumulated during compilation by external codegen....</div><div class="ttdef"><b>Definition:</b> module.h:543</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_a5a2d23031351ba22f800d41a0e06d562"><div class="ttname"><a href="namespacetvm_1_1attr.html#a5a2d23031351ba22f800d41a0e06d562">tvm::attr::kModuleName</a></div><div class="ttdeci">constexpr const char * kModuleName</div><div class="ttdoc">Name of the module.</div><div class="ttdef"><b>Definition:</b> module.h:451</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_a688274c1d1bdf64c3a44c373c58ab06d"><div class="ttname"><a href="namespacetvm_1_1attr.html#a688274c1d1bdf64c3a44c373c58ab06d">tvm::attr::kExecutor</a></div><div class="ttdeci">constexpr const char * kExecutor</div><div class="ttdoc">Executor targeted by the module.</div><div class="ttdef"><b>Definition:</b> module.h:460</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_a8604338aa4e9293d10d427a00a2c9f13"><div class="ttname"><a href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">tvm::attr::kExternalMods</a></div><div class="ttdeci">constexpr const char * kExternalMods</div><div class="ttdoc">All the runtime::Modules accumulated during compilation by external codegen. These modules must be ei...</div><div class="ttdef"><b>Definition:</b> module.h:504</div></div>
+<div class="ttc" id="anamespacetvm_1_1attr_html_aae652055bd020f8aa1cc32436e155603"><div class="ttname"><a href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">tvm::attr::kSystemLibPrefix</a></div><div class="ttdeci">constexpr const char * kSystemLibPrefix</div><div class="ttdoc">A prefix for generating C symbols system lib creation.</div><div class="ttdef"><b>Definition:</b> module.h:533</div></div>
<div class="ttc" id="anamespacetvm_1_1attr_html_aef97167cb20d9c7d9f7df38fab897984"><div class="ttname"><a href="namespacetvm_1_1attr.html#aef97167cb20d9c7d9f7df38fab897984">tvm::attr::kRuntime</a></div><div class="ttdeci">constexpr const char * kRuntime</div><div class="ttdoc">Runtime target of the module.</div><div class="ttdef"><b>Definition:</b> module.h:469</div></div>
<div class="ttc" id="anamespacetvm_1_1script_1_1ir__builder_1_1ir_html_a8dd2baa6a2608b6ea12f5455971dacc5"><div class="ttname"><a href="namespacetvm_1_1script_1_1ir__builder_1_1ir.html#a8dd2baa6a2608b6ea12f5455971dacc5">tvm::script::ir_builder::ir::IRModule</a></div><div class="ttdeci">IRModuleFrame IRModule()</div><div class="ttdoc">The IRModule declaration statement.</div><div class="ttdef"><b>Definition:</b> module.h:341</div></div>
<div class="ttc" id="anamespacetvm_1_1te_html_ae0c71f84710b436cbe0b32289d0838f4"><div class="ttname"><a href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">tvm::te::var</a></div><div class="ttdeci">Var var(std::string name_hint, DataType t=DataType::Int(32))</div><div class="ttdoc">Construct a new Var expression.</div></div>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_p.html b/docs/reference/api/doxygen/namespacemembers_func_p.html
index 6ecf974901..086af205a8 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_p.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_p.html
@@ -65,10 +65,10 @@ $(function() {
<h3><a id="index_p"></a>- p -</h3><ul>
<li>PackImportsToC()
-: <a class="el" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">tvm::codegen</a>
+: <a class="el" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">tvm::codegen</a>
</li>
<li>PackImportsToLLVM()
-: <a class="el" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">tvm::codegen</a>
+: <a class="el" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">tvm::codegen</a>
</li>
<li>pad()
: <a class="el" href="namespacetvm_1_1topi.html#a3305d377f96cd20c23032eeada2756d5">tvm::topi</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_k.html b/docs/reference/api/doxygen/namespacemembers_k.html
index c0b91a579b..fe8de8dffa 100644
--- a/docs/reference/api/doxygen/namespacemembers_k.html
+++ b/docs/reference/api/doxygen/namespacemembers_k.html
@@ -323,7 +323,7 @@ $(function() {
</li>
<li>kOpaque
: <a class="el" href="namespacetvm_1_1relay.html#ab5f4d382bf1bee69c3e484ea6c837578a3cb3cec00829ebd525feba875f2d6ac1">tvm::relay</a>
-, <a class="el" href="namespacetvm_1_1tir.html#add7d0a6b1dd91f0c3c5dd2f4cf64358eaf324873e6195114a186db7f910559b2c">tvm::tir</a>
+, <a class="el" href="namespacetvm_1_1tir.html#add7d0a6b1dd91f0c3c5dd2f4cf64358ea338caebfc6f31586a1bdd09e3a67c9b5">tvm::tir</a>
</li>
<li>kOrdered
: <a class="el" href="namespacetvm_1_1tir.html#add7d0a6b1dd91f0c3c5dd2f4cf64358eaba48cf32065b1bf9086138313912f64b">tvm::tir</a>
@@ -382,6 +382,9 @@ $(function() {
<li>kString
: <a class="el" href="namespacetvm_1_1runtime_1_1metadata.html#a6edfc2b47c55d18f94867a18a7b02fb7a6bc1719d53714867a67daaecca418348">tvm::runtime::metadata</a>
</li>
+<li>kSystemLibPrefix
+: <a class="el" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">tvm::attr</a>
+</li>
<li>kTarget
: <a class="el" href="namespacetvm_1_1attr.html#a580ae92d00e34f0a8f0d40395b8d972c">tvm::attr</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_p.html b/docs/reference/api/doxygen/namespacemembers_p.html
index 66495f32e1..e6aba9f2e2 100644
--- a/docs/reference/api/doxygen/namespacemembers_p.html
+++ b/docs/reference/api/doxygen/namespacemembers_p.html
@@ -65,10 +65,10 @@ $(function() {
<h3><a id="index_p"></a>- p -</h3><ul>
<li>PackImportsToC()
-: <a class="el" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">tvm::codegen</a>
+: <a class="el" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">tvm::codegen</a>
</li>
<li>PackImportsToLLVM()
-: <a class="el" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">tvm::codegen</a>
+: <a class="el" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">tvm::codegen</a>
</li>
<li>pad()
: <a class="el" href="namespacetvm_1_1topi.html#a3305d377f96cd20c23032eeada2756d5">tvm::topi</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_t.html b/docs/reference/api/doxygen/namespacemembers_t.html
index 24d73fe0b5..1d611031ae 100644
--- a/docs/reference/api/doxygen/namespacemembers_t.html
+++ b/docs/reference/api/doxygen/namespacemembers_t.html
@@ -233,9 +233,6 @@ $(function() {
<li>tvm_dev_mblob
: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm::runtime::symbol</a>
</li>
-<li>tvm_dev_mblob_nbytes
-: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm::runtime::symbol</a>
-</li>
<li>tvm_entrypoint_suffix
: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d">tvm::runtime::symbol</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_vars.html b/docs/reference/api/doxygen/namespacemembers_vars.html
index c61d3d833e..f5f42800d8 100644
--- a/docs/reference/api/doxygen/namespacemembers_vars.html
+++ b/docs/reference/api/doxygen/namespacemembers_vars.html
@@ -330,6 +330,9 @@ $(function() {
<li>kSkipOptimization
: <a class="el" href="namespacetvm_1_1relay_1_1attr.html#aed3671df7e12d228b8070806d07f7c62">tvm::relay::attr</a>
</li>
+<li>kSystemLibPrefix
+: <a class="el" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">tvm::attr</a>
+</li>
<li>kTarget
: <a class="el" href="namespacetvm_1_1attr.html#a580ae92d00e34f0a8f0d40395b8d972c">tvm::attr</a>
</li>
@@ -523,9 +526,6 @@ $(function() {
<li>tvm_dev_mblob
: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm::runtime::symbol</a>
</li>
-<li>tvm_dev_mblob_nbytes
-: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm::runtime::symbol</a>
-</li>
<li>tvm_entrypoint_suffix
: <a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d">tvm::runtime::symbol</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1attr.html b/docs/reference/api/doxygen/namespacetvm_1_1attr.html
index a32eed9ebc..5ea43e500e 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1attr.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1attr.html
@@ -106,6 +106,9 @@ Variables</h2></td></tr>
<tr class="memitem:a8604338aa4e9293d10d427a00a2c9f13"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">kExternalMods</a> = "external_mods"</td></tr>
<tr class="memdesc:a8604338aa4e9293d10d427a00a2c9f13"><td class="mdescLeft"> </td><td class="mdescRight">All the runtime::Modules accumulated during compilation by external codegen. These modules must be either directly linked or captured in the final compilation artifact. <a href="namespacetvm_1_1attr.html#a8604338aa4e9293d10d427a00a2c9f13">More...</a><br /></td></tr>
<tr class="separator:a8604338aa4e9293d10d427a00a2c9f13"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aae652055bd020f8aa1cc32436e155603"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">kSystemLibPrefix</a> = "system_lib_prefix"</td></tr>
+<tr class="memdesc:aae652055bd020f8aa1cc32436e155603"><td class="mdescLeft"> </td><td class="mdescRight">A prefix for generating C symbols system lib creation. <a href="namespacetvm_1_1attr.html#aae652055bd020f8aa1cc32436e155603">More...</a><br /></td></tr>
+<tr class="separator:aae652055bd020f8aa1cc32436e155603"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a38f40a3fc908b9359c4ca46bdd18811f"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1attr.html#a38f40a3fc908b9359c4ca46bdd18811f">kConstNameToConstant</a> = "const_name_to_constant"</td></tr>
<tr class="memdesc:a38f40a3fc908b9359c4ca46bdd18811f"><td class="mdescLeft"> </td><td class="mdescRight">All the named runtime::NDArrays accumulated during compilation by external codegen. Generally the associated <a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM.">runtime::Module</a> will indicate it requires bindings for these names, and during module initialization these bindings will be recovered from a ConstLoaderModule. See also kConstantsA [...]
<tr class="separator:a38f40a3fc908b9359c4ca46bdd18811f"><td class="memSeparator" colspan="2"> </td></tr>
@@ -404,6 +407,37 @@ Variables</h2></td></tr>
<p><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode.">Type</a>: Runtime</p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1relay_1_1Runtime.html" title="Managed reference class to RuntimeNode.">tvm::relay::Runtime</a> </dd></dl>
+</div>
+</div>
+<a id="aae652055bd020f8aa1cc32436e155603"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aae652055bd020f8aa1cc32436e155603">◆ </a></span>kSystemLibPrefix</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">constexpr const char* tvm::attr::kSystemLibPrefix = "system_lib_prefix"</td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">constexpr</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>A prefix for generating C symbols system lib creation. </p>
+<p>This prefix guides passes that creates global_symbol for internal functions that may have c linkage (e.g. TIR functions and some BYOC functions). It also affects the symbol of the fat bin blob during module export.</p>
+<p>This attribute is used to avoid symbol conflict when we generate and combine multiple system libs that get linked into one.</p>
+<p>Rationale: mechanisms like BYOC rely on the common global symbol and each external compiler also has its own mechanism of mangling. As a result, we cannot rely on other mechanisms on setting a global_symbol and then renaming, because the external compiler already agreed on the name.</p>
+<p>system_lib_prefix provides a way to hint at the passes to allow names to avoid name conflict at the beginning.</p>
+<p>Note that users can still directly specify global symbols that may conflict. It is up to the downstream toolchain to manage those external-facing functions.</p>
+<p>This does not affect non-C linkage functions it is less of an issue because they will be embedded into fatbin that in different symbols, The system lib loader can pick the right prefix for a given prefix.</p>
+<p>Having this attribute implies system lib generation linkage. </p>
+
</div>
</div>
<a id="a580ae92d00e34f0a8f0d40395b8d972c"></a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1codegen.html b/docs/reference/api/doxygen/namespacetvm_1_1codegen.html
index 945b2e15ae..94cb26b2a1 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1codegen.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1codegen.html
@@ -80,12 +80,12 @@ Functions</h2></td></tr>
<tr class="memitem:a0d6322c2dda54a66a3b82022f5f3632c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">Build</a> (<a class="el" href="classtvm_1_1IRModule.html">IRModule</a> mod, <a class="el" href="classtvm_1_1Target.html">Target</a> target)</td></tr>
<tr class="memdesc:a0d6322c2dda54a66a3b82022f5f3632c"><td class="mdescLeft"> </td><td class="mdescRight">Build a module from array of lowered function. <a href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">More...</a><br /></td></tr>
<tr class="separator:a0d6322c2dda54a66a3b82022f5f3632c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abf02059ebadcdb8bbbe5c840b646d67b"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">PackImportsToC</a> (const <a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> &m, bool system_lib)</td></tr>
-<tr class="memdesc:abf02059ebadcdb8bbbe5c840b646d67b"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a C file. Compile the C file and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#abf02059ebadcdb8bbbe5c840b646d67b">More...</a><br /></td></tr>
-<tr class="separator:abf02059ebadcdb8bbbe5c840b646d67b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">PackImportsToLLVM</a> (const <a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> &m, bool system_lib, const std::string &target_triple)</td></tr>
-<tr class="memdesc:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a LLVM module. Compile the LLVM module and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#ab2cd2a65bac4b26427a8ca0abe4e0bd6">More...</a><br /></td></tr>
-<tr class="separator:ab2cd2a65bac4b26427a8ca0abe4e0bd6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8b151920f583ba2612f4020879afb844"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">PackImportsToC</a> (const <a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> &m, bool system_lib, const std::string &c_symbol_prefix="")</td></tr>
+<tr class="memdesc:a8b151920f583ba2612f4020879afb844"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a C file. Compile the C file and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844">More...</a><br /></td></tr>
+<tr class="separator:a8b151920f583ba2612f4020879afb844"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a08cb4d3bf9e767ce04908b843132aec8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">PackImportsToLLVM</a> (const <a class="el" href="classtvm_1_1runtime_1_1Module.html">runtime::Module</a> &m, bool system_lib, const std::string &target_triple, const st [...]
+<tr class="memdesc:a08cb4d3bf9e767ce04908b843132aec8"><td class="mdescLeft"> </td><td class="mdescRight">Pack imported device library to a LLVM module. Compile the LLVM module and link with the host library will allow the DSO loader to automatically discover and import the dependency from the shared library. <a href="namespacetvm_1_1codegen.html#a08cb4d3bf9e767ce04908b843132aec8">More...</a><br /></td></tr>
+<tr class="separator:a08cb4d3bf9e767ce04908b843132aec8"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>namespace for target translation and codegen. </p>
@@ -128,8 +128,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="abf02059ebadcdb8bbbe5c840b646d67b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abf02059ebadcdb8bbbe5c840b646d67b">◆ </a></span>PackImportsToC()</h2>
+<a id="a8b151920f583ba2612f4020879afb844"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a8b151920f583ba2612f4020879afb844">◆ </a></span>PackImportsToC()</h2>
<div class="memitem">
<div class="memproto">
@@ -144,7 +144,13 @@ Functions</h2></td></tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool </td>
- <td class="paramname"><em>system_lib</em> </td>
+ <td class="paramname"><em>system_lib</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::string & </td>
+ <td class="paramname"><em>c_symbol_prefix</em> = <code>""</code> </td>
</tr>
<tr>
<td></td>
@@ -159,6 +165,7 @@ Functions</h2></td></tr>
<table class="params">
<tr><td class="paramname">m</td><td>The host module with the imports. </td></tr>
<tr><td class="paramname">system_lib</td><td>Whether expose as system library. </td></tr>
+ <tr><td class="paramname">c_symbol_prefix</td><td>Optional symbol prefix of the blob symbol. </td></tr>
</table>
</dd>
</dl>
@@ -166,8 +173,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="ab2cd2a65bac4b26427a8ca0abe4e0bd6"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab2cd2a65bac4b26427a8ca0abe4e0bd6">◆ </a></span>PackImportsToLLVM()</h2>
+<a id="a08cb4d3bf9e767ce04908b843132aec8"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a08cb4d3bf9e767ce04908b843132aec8">◆ </a></span>PackImportsToLLVM()</h2>
<div class="memitem">
<div class="memproto">
@@ -188,7 +195,13 @@ Functions</h2></td></tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::string & </td>
- <td class="paramname"><em>target_triple</em> </td>
+ <td class="paramname"><em>target_triple</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::string & </td>
+ <td class="paramname"><em>c_symbol_prefix</em> = <code>""</code> </td>
</tr>
<tr>
<td></td>
@@ -204,6 +217,7 @@ Functions</h2></td></tr>
<tr><td class="paramname">m</td><td>The host module with the imports. </td></tr>
<tr><td class="paramname">system_lib</td><td>Whether expose as system library. </td></tr>
<tr><td class="paramname">target_triple</td><td>LLVM target triple </td></tr>
+ <tr><td class="paramname">c_symbol_prefix</td><td>Optional symbol prefix of the blob symbol.</td></tr>
</table>
</dd>
</dl>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1runtime_1_1symbol.html b/docs/reference/api/doxygen/namespacetvm_1_1runtime_1_1symbol.html
index bcdeb89d13..7cea506cdd 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1runtime_1_1symbol.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1runtime_1_1symbol.html
@@ -86,9 +86,6 @@ Variables</h2></td></tr>
<tr class="memitem:a316b327224938ffb2a73a9c4432d1da9"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm_dev_mblob</a> = "__tvm_dev_mblob"</td></tr>
<tr class="memdesc:a316b327224938ffb2a73a9c4432d1da9"><td class="mdescLeft"> </td><td class="mdescRight">Global variable to store device module blob. <a href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">More...</a><br /></td></tr>
<tr class="separator:a316b327224938ffb2a73a9c4432d1da9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aba35391edf7c3a25628bf99ed42cdca1"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm_dev_mblob_nbytes</a> = "__tvm_dev_mblob_nbytes"</td></tr>
-<tr class="memdesc:aba35391edf7c3a25628bf99ed42cdca1"><td class="mdescLeft"> </td><td class="mdescRight">Number of bytes of device module blob. <a href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">More...</a><br /></td></tr>
-<tr class="separator:aba35391edf7c3a25628bf99ed42cdca1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm_set_device</a> = "__tvm_set_device"</td></tr>
<tr class="memdesc:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="mdescLeft"> </td><td class="mdescRight">global function to set device <a href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">More...</a><br /></td></tr>
<tr class="separator:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="memSeparator" colspan="2"> </td></tr>
@@ -136,30 +133,6 @@ Variables</h2></td></tr>
<p>Global variable to store device module blob. </p>
-</div>
-</div>
-<a id="aba35391edf7c3a25628bf99ed42cdca1"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aba35391edf7c3a25628bf99ed42cdca1">◆ </a></span>tvm_dev_mblob_nbytes</h2>
-
-<div class="memitem">
-<div class="memproto">
-<table class="mlabels">
- <tr>
- <td class="mlabels-left">
- <table class="memname">
- <tr>
- <td class="memname">constexpr const char* tvm::runtime::symbol::tvm_dev_mblob_nbytes = "__tvm_dev_mblob_nbytes"</td>
- </tr>
- </table>
- </td>
- <td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">constexpr</span></span> </td>
- </tr>
-</table>
-</div><div class="memdoc">
-
-<p>Number of bytes of device module blob. </p>
-
</div>
</div>
<a id="a00aad7edbef6fd3a1e2f6f94582fea8d"></a>
diff --git a/docs/reference/api/doxygen/runtime_2module_8h.html b/docs/reference/api/doxygen/runtime_2module_8h.html
index 72b4cc39c6..bedb1ab608 100644
--- a/docs/reference/api/doxygen/runtime_2module_8h.html
+++ b/docs/reference/api/doxygen/runtime_2module_8h.html
@@ -152,9 +152,6 @@ Variables</h2></td></tr>
<tr class="memitem:a316b327224938ffb2a73a9c4432d1da9"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm::runtime::symbol::tvm_dev_mblob</a> = "__tvm_dev_mblob"</td></tr>
<tr class="memdesc:a316b327224938ffb2a73a9c4432d1da9"><td class="mdescLeft"> </td><td class="mdescRight">Global variable to store device module blob. <a href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">More...</a><br /></td></tr>
<tr class="separator:a316b327224938ffb2a73a9c4432d1da9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aba35391edf7c3a25628bf99ed42cdca1"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm::runtime::symbol::tvm_dev_mblob_nbytes</a> = "__tvm_dev_mblob_nbytes"</td></tr>
-<tr class="memdesc:aba35391edf7c3a25628bf99ed42cdca1"><td class="mdescLeft"> </td><td class="mdescRight">Number of bytes of device module blob. <a href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">More...</a><br /></td></tr>
-<tr class="separator:aba35391edf7c3a25628bf99ed42cdca1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="memItemLeft" align="right" valign="top">constexpr const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm::runtime::symbol::tvm_set_device</a> = "__tvm_set_device"</td></tr>
<tr class="memdesc:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="mdescLeft"> </td><td class="mdescRight">global function to set device <a href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">More...</a><br /></td></tr>
<tr class="separator:aaad9cf8f51a55b2ec5b19854d692bd21"><td class="memSeparator" colspan="2"> </td></tr>
diff --git a/docs/reference/api/doxygen/runtime_2module_8h_source.html b/docs/reference/api/doxygen/runtime_2module_8h_source.html
index 9cfa770f67..76f1e154c3 100644
--- a/docs/reference/api/doxygen/runtime_2module_8h_source.html
+++ b/docs/reference/api/doxygen/runtime_2module_8h_source.html
@@ -177,39 +177,38 @@ $(function() {
<div class="line"><a name="l00276"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#aff7a6c169b32757c856f6fc505316361"> 276</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#aff7a6c169b32757c856f6fc505316361">tvm_get_c_metadata</a> = <span class="stringliteral">"get_c_metadata"</span>;</div>
<div class="line"><a name="l00278"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#aeebcec34a8e6cc24c495710aedea5de9"> 278</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#aeebcec34a8e6cc24c495710aedea5de9">tvm_module_ctx</a> = <span class="stringliteral">"__tvm_module_ctx"</span>;</div>
<div class="line"><a name="l00280"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9"> 280</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm_dev_mblob</a> = <span class="stringliteral">"__tvm_dev_mblob"</span>;</div>
-<div class="line"><a name="l00282"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1"> 282</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm_dev_mblob_nbytes</a> = <span class="stringliteral">"__tvm_dev_mblob_nbytes"</span>;</div>
-<div class="line"><a name="l00284"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21"> 284</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm_set_device</a> = <span class="stringliteral">"__tvm_set_device"</span>;</div>
-<div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#ac21d436bd65464b49858ba4bc55fb3ec"> 286</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#ac21d436bd65464b49858ba4bc55fb3ec">tvm_global_barrier_state</a> = <span class="stringliteral">"__tvm_global_barrier_state"</span>;</div>
-<div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5"> 288</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5">tvm_prepare_global_barrier</a> = <span class="stringliteral">"__tvm_prepare_global_barrier"</span>;</div>
-<div class="line"><a name="l00290"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4"> 290</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4">tvm_module_main</a> = <span class="stringliteral">"__tvm_main__"</span>;</div>
-<div class="line"><a name="l00292"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#ae37724090c492bb8cdf40f967eef111c"> 292</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#ae37724090c492bb8cdf40f967eef111c">tvm_param_prefix</a> = <span class="stringliteral">"__tvm_param__"</span>;</div>
-<div class="line"><a name="l00294"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#a209395ae8f4fe3b2a46e2b4a205e4772"> 294</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#a209395ae8f4fe3b2a46e2b4a205e4772">tvm_lookup_linked_param</a> = <span class="stringliteral">"_lookup_linked_param"</span>;</div>
-<div class="line"><a name="l00296"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d"> 296</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d">tvm_entrypoint_suffix</a> = <span class="stringliteral">"run"</span>;</div>
-<div class="line"><a name="l00297"></a><span class="lineno"> 297</span> } <span class="comment">// namespace symbol</span></div>
+<div class="line"><a name="l00282"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21"> 282</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm_set_device</a> = <span class="stringliteral">"__tvm_set_device"</span>;</div>
+<div class="line"><a name="l00284"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#ac21d436bd65464b49858ba4bc55fb3ec"> 284</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#ac21d436bd65464b49858ba4bc55fb3ec">tvm_global_barrier_state</a> = <span class="stringliteral">"__tvm_global_barrier_state"</span>;</div>
+<div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5"> 286</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5">tvm_prepare_global_barrier</a> = <span class="stringliteral">"__tvm_prepare_global_barrier"</span>;</div>
+<div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4"> 288</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4">tvm_module_main</a> = <span class="stringliteral">"__tvm_main__"</span>;</div>
+<div class="line"><a name="l00290"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#ae37724090c492bb8cdf40f967eef111c"> 290</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#ae37724090c492bb8cdf40f967eef111c">tvm_param_prefix</a> = <span class="stringliteral">"__tvm_param__"</span>;</div>
+<div class="line"><a name="l00292"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#a209395ae8f4fe3b2a46e2b4a205e4772"> 292</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#a209395ae8f4fe3b2a46e2b4a205e4772">tvm_lookup_linked_param</a> = <span class="stringliteral">"_lookup_linked_param"</span>;</div>
+<div class="line"><a name="l00294"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d"> 294</a></span> constexpr <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d">tvm_entrypoint_suffix</a> = <span class="stringliteral">"run"</span>;</div>
+<div class="line"><a name="l00295"></a><span class="lineno"> 295</span> } <span class="comment">// namespace symbol</span></div>
+<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  </div>
+<div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="comment">// implementations of inline functions.</span></div>
<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  </div>
-<div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="comment">// implementations of inline functions.</span></div>
+<div class="line"><a name="l00299"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260"> 299</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260">Module::Import</a>(<a class="code" href="classtvm_1_1runtime_1_1Module.html">Module</a> other) { <span class="keywordflow">return</span> (*this)->Imp [...]
<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  </div>
-<div class="line"><a name="l00301"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260"> 301</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260">Module::Import</a>(<a class="code" href="classtvm_1_1runtime_1_1Module.html">Module</a> other) { <span class="keywordflow">return</span> (*this)->Imp [...]
+<div class="line"><a name="l00301"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354"> 301</a></span> <span class="keyword">inline</span> <a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>* <a class="code" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">Module::operator-></a>() { <span class="keywordflow">return</span> <span class="keyword">static_cast<</spa [...]
<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  </div>
-<div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354"> 303</a></span> <span class="keyword">inline</span> <a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>* <a class="code" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">Module::operator-></a>() { <span class="keywordflow">return</span> <span class="keyword">static_cast<</spa [...]
-<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  </div>
-<div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#ab6d988e0c9c19eaec2970a0222cc4fc5"> 305</a></span> <span class="keyword">inline</span> <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>* <a class="code" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">Module::operator-></a>()<span class="keyword"> const </span>{</div>
-<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>*<span class="keyword">></span>(<a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html#aadbc0886ffa80162ff31eefd0431ba09">get</a>());</div>
-<div class="line"><a name="l00307"></a><span class="lineno"> 307</span> }</div>
-<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  </div>
-<div class="line"><a name="l00309"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#ac75e2cf6722f7bb4a9dab69e45054d8f"> 309</a></span> <span class="keyword">inline</span> std::ostream& <a class="code" href="namespacetvm_1_1runtime.html#af22b89284299c81d0c1802199af446d7">operator<<</a>(std::ostream& out, <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">Module</a>& module) {</div>
-<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  out << <span class="stringliteral">"Module(type_key= "</span>;</div>
-<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  out << module-><a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html#a5834549180e1c668e7126562cea9a557">type_key</a>();</div>
-<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  out << <span class="stringliteral">")"</span>;</div>
-<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  </div>
-<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">return</span> out;</div>
-<div class="line"><a name="l00315"></a><span class="lineno"> 315</span> }</div>
-<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  </div>
-<div class="line"><a name="l00317"></a><span class="lineno"> 317</span> } <span class="comment">// namespace runtime</span></div>
-<div class="line"><a name="l00318"></a><span class="lineno"> 318</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  </div>
-<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="preprocessor">#include <<a class="code" href="packed__func_8h.html">tvm/runtime/packed_func.h</a>></span> <span class="comment">// NOLINT(*)</span></div>
-<div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_MODULE_H_</span></div>
+<div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="classtvm_1_1runtime_1_1Module.html#ab6d988e0c9c19eaec2970a0222cc4fc5"> 303</a></span> <span class="keyword">inline</span> <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>* <a class="code" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">Module::operator-></a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a>*<span class="keyword">></span>(<a class="code" href="classtvm_1_1runtime_1_1ObjectRef.html#aadbc0886ffa80162ff31eefd0431ba09">get</a>());</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span> }</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  </div>
+<div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="namespacetvm_1_1runtime.html#ac75e2cf6722f7bb4a9dab69e45054d8f"> 307</a></span> <span class="keyword">inline</span> std::ostream& <a class="code" href="namespacetvm_1_1runtime.html#af22b89284299c81d0c1802199af446d7">operator<<</a>(std::ostream& out, <span class="keyword">const</span> <a class="code" href="classtvm_1_1runtime_1_1Module.html">Module</a>& module) {</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  out << <span class="stringliteral">"Module(type_key= "</span>;</div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  out << module-><a class="code" href="classtvm_1_1runtime_1_1ModuleNode.html#a5834549180e1c668e7126562cea9a557">type_key</a>();</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  out << <span class="stringliteral">")"</span>;</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  </div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">return</span> out;</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> }</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  </div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span> } <span class="comment">// namespace runtime</span></div>
+<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  </div>
+<div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="preprocessor">#include <<a class="code" href="packed__func_8h.html">tvm/runtime/packed_func.h</a>></span> <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RUNTIME_MODULE_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_1ModuleNode_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ModuleNode.html">tvm::runtime::ModuleNode</a></div><div class="ttdoc">Base container of module.</div><div class="ttdef"><b>Definition:</b> module.h:142</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1ModuleNode_html_a12d5336240ba02a581bbb8a628a8cdb9"><div class="ttname"><a href="classtvm_1_1runtime_1_1ModuleNode.html#a12d5336240ba02a581bbb8a628a8cdb9">tvm::runtime::ModuleNode::imports</a></div><div class="ttdeci">const std::vector< Module > & imports() const</div><div class="ttdef"><b>Definition:</b> module.h:222</div></div>
@@ -231,11 +230,11 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM.</div><div class="ttdef"><b>Definition:</b> module.h:79</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_a1233f7b896bb299ef07f9e41a4ffdc17"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#a1233f7b896bb299ef07f9e41a4ffdc17">tvm::runtime::Module::GetFunction</a></div><div class="ttdeci">PackedFunc GetFunction(const std::string &name, bool query_imports=false)</div><div class="ttdoc">Get packed function from current module by name.</div><div class="ttdef"><b>Definition:</b> packed_func.h:1945</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_a1927112ff253c1a6150238257cc9d5dd"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#a1927112ff253c1a6150238257cc9d5dd">tvm::runtime::Module::LoadFromFile</a></div><div class="ttdeci">static Module LoadFromFile(const std::string &file_name, const std::string &format="")</div><div class="ttdoc">Load a module from file.</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_a87bcc010c62887a453d49d46854fa354"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">tvm::runtime::Module::operator-></a></div><div class="ttdeci">ModuleNode * operator->()</div><div class="ttdef"><b>Definition:</b> module.h:303</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_a87bcc010c62887a453d49d46854fa354"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">tvm::runtime::Module::operator-></a></div><div class="ttdeci">ModuleNode * operator->()</div><div class="ttdef"><b>Definition:</b> module.h:301</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_a908d19538a4fcadd6e93f39e4aa5292b"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#a908d19538a4fcadd6e93f39e4aa5292b">tvm::runtime::Module::ModuleNode</a></div><div class="ttdeci">friend class ModuleNode</div><div class="ttdef"><b>Definition:</b> module.h:117</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_abd1380b3f813c2b6acefca3aaef425f4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module::Module</a></div><div class="ttdeci">Module(ObjectPtr< Object > n)</div><div class="ttdef"><b>Definition:</b> module.h:83</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_abfbc619b3b3166d63ec52e399c24bed9"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module::Module</a></div><div class="ttdeci">Module()</div><div class="ttdef"><b>Definition:</b> module.h:81</div></div>
-<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_afb5a6c9b0e0a541245958456ebe39260"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260">tvm::runtime::Module::Import</a></div><div class="ttdeci">void Import(Module other)</div><div class="ttdoc">Import another module into this module.</div><div class="ttdef"><b>Definition:</b> module.h:301</div></div>
+<div class="ttc" id="aclasstvm_1_1runtime_1_1Module_html_afb5a6c9b0e0a541245958456ebe39260"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html#afb5a6c9b0e0a541245958456ebe39260">tvm::runtime::Module::Import</a></div><div class="ttdeci">void Import(Module other)</div><div class="ttdoc">Import another module into this module.</div><div class="ttdef"><b>Definition:</b> module.h:299</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1ObjectPtr_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectPtr.html">tvm::runtime::ObjectPtr</a></div><div class="ttdoc">A custom smart pointer for Object.</div><div class="ttdef"><b>Definition:</b> object.h:358</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:515</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1ObjectRef_html_aadbc0886ffa80162ff31eefd0431ba09"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html#aadbc0886ffa80162ff31eefd0431ba09">tvm::runtime::ObjectRef::get</a></div><div class="ttdeci">const Object * get() const</div><div class="ttdef"><b>Definition:</b> object.h:550</div></div>
@@ -243,16 +242,15 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1runtime_1_1Object_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></div><div class="ttdoc">base class of all object containers.</div><div class="ttdef"><b>Definition:</b> object.h:167</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format.</div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1String_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1String.html">tvm::runtime::String</a></div><div class="ttdoc">Reference to string objects.</div><div class="ttdef"><b>Definition:</b> string.h:98</div></div>
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+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_a00aad7edbef6fd3a1e2f6f94582fea8d"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#a00aad7edbef6fd3a1e2f6f94582fea8d">tvm::runtime::symbol::tvm_entrypoint_suffix</a></div><div class="ttdeci">constexpr const char * tvm_entrypoint_suffix</div><div class="ttdoc">Model entrypoint generated as an interface to the AOT function outside of TIR.</div><div class="ttdef"><b>Definition:</b> module.h:294</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_a209395ae8f4fe3b2a46e2b4a205e4772"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#a209395ae8f4fe3b2a46e2b4a205e4772">tvm::runtime::symbol::tvm_lookup_linked_param</a></div><div class="ttdeci">constexpr const char * tvm_lookup_linked_param</div><div class="ttdoc">A PackedFunc that looks up linked parameters by storage_id.</div><div class="ttdef"><b>Definition:</b> module.h:292</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_a316b327224938ffb2a73a9c4432d1da9"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#a316b327224938ffb2a73a9c4432d1da9">tvm::runtime::symbol::tvm_dev_mblob</a></div><div class="ttdeci">constexpr const char * tvm_dev_mblob</div><div class="ttdoc">Global variable to store device module blob.</div><div class="ttdef"><b>Definition:</b> module.h:280</div></div>
-<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_aaad9cf8f51a55b2ec5b19854d692bd21"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm::runtime::symbol::tvm_set_device</a></div><div class="ttdeci">constexpr const char * tvm_set_device</div><div class="ttdoc">global function to set device</div><div class="ttdef"><b>Definition:</b> module.h:284</div></div>
-<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_aba35391edf7c3a25628bf99ed42cdca1"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#aba35391edf7c3a25628bf99ed42cdca1">tvm::runtime::symbol::tvm_dev_mblob_nbytes</a></div><div class="ttdeci">constexpr const char * tvm_dev_mblob_nbytes</div><div class="ttdoc">Number of bytes of device module blob.</div><div class="ttdef"><b>Definition:</b> module.h:282</div></div>
-<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_abfc81fd15b5d8087b956a8cef4152bf4"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4">tvm::runtime::symbol::tvm_module_main</a></div><div class="ttdeci">constexpr const char * tvm_module_main</div><div class="ttdoc">Placeholder for the module's entry function.</div><div class="ttdef"><b>Definition:</b> module.h:290</div></div>
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+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_aaad9cf8f51a55b2ec5b19854d692bd21"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#aaad9cf8f51a55b2ec5b19854d692bd21">tvm::runtime::symbol::tvm_set_device</a></div><div class="ttdeci">constexpr const char * tvm_set_device</div><div class="ttdoc">global function to set device</div><div class="ttdef"><b>Definition:</b> module.h:282</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_abfc81fd15b5d8087b956a8cef4152bf4"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#abfc81fd15b5d8087b956a8cef4152bf4">tvm::runtime::symbol::tvm_module_main</a></div><div class="ttdeci">constexpr const char * tvm_module_main</div><div class="ttdoc">Placeholder for the module's entry function.</div><div class="ttdef"><b>Definition:</b> module.h:288</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_ac21d436bd65464b49858ba4bc55fb3ec"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#ac21d436bd65464b49858ba4bc55fb3ec">tvm::runtime::symbol::tvm_global_barrier_state</a></div><div class="ttdeci">constexpr const char * tvm_global_barrier_state</div><div class="ttdoc">Auxiliary counter to global barrier.</div><div class="ttdef"><b>Definition:</b> module.h:284</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_ae37724090c492bb8cdf40f967eef111c"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#ae37724090c492bb8cdf40f967eef111c">tvm::runtime::symbol::tvm_param_prefix</a></div><div class="ttdeci">constexpr const char * tvm_param_prefix</div><div class="ttdoc">Prefix for parameter symbols emitted into the main program.</div><div class="ttdef"><b>Definition:</b> module.h:290</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_aeebcec34a8e6cc24c495710aedea5de9"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#aeebcec34a8e6cc24c495710aedea5de9">tvm::runtime::symbol::tvm_module_ctx</a></div><div class="ttdeci">constexpr const char * tvm_module_ctx</div><div class="ttdoc">Global variable to store module context.</div><div class="ttdef"><b>Definition:</b> module.h:278</div></div>
-<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_af4a5fe87c6e95d6b082a2be1960efbe5"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5">tvm::runtime::symbol::tvm_prepare_global_barrier</a></div><div class="ttdeci">constexpr const char * tvm_prepare_global_barrier</div><div class="ttdoc">Prepare the global barrier before kernels that uses global barrier.</div><div class="ttdef"><b>Definition:</b> module.h:288</div></div>
+<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_af4a5fe87c6e95d6b082a2be1960efbe5"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#af4a5fe87c6e95d6b082a2be1960efbe5">tvm::runtime::symbol::tvm_prepare_global_barrier</a></div><div class="ttdeci">constexpr const char * tvm_prepare_global_barrier</div><div class="ttdoc">Prepare the global barrier before kernels that uses global barrier.</div><div class="ttdef"><b>Definition:</b> module.h:286</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_1_1symbol_html_aff7a6c169b32757c856f6fc505316361"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1symbol.html#aff7a6c169b32757c856f6fc505316361">tvm::runtime::symbol::tvm_get_c_metadata</a></div><div class="ttdeci">constexpr const char * tvm_get_c_metadata</div><div class="ttdoc">A PackedFunc that retrieves exported metadata.</div><div class="ttdef"><b>Definition:</b> module.h:276</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_html_a6edb2235f96c34eaf0119834d293ca54"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a6edb2235f96c34eaf0119834d293ca54">tvm::runtime::ModulePropertyMask</a></div><div class="ttdeci">ModulePropertyMask</div><div class="ttdoc">Property of runtime module We classify the property of runtime module into the following categories.</div><div class="ttdef"><b>Definition:</b> module.h:48</div></div>
<div class="ttc" id="anamespacetvm_1_1runtime_html_a6edb2235f96c34eaf0119834d293ca54a6aa052b038d11007d0df028d29fcda2e"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a6edb2235f96c34eaf0119834d293ca54a6aa052b038d11007d0df028d29fcda2e">tvm::runtime::kRunnable</a></div><div class="ttdeci">@ kRunnable</div><div class="ttdoc">kRunnable we can run the module directly. LLVM/CUDA/JSON runtime, executors (e.g, virtual machine) ru...</div><div class="ttdef"><b>Definition:</b> module.h:6 [...]
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index 1f6d9186ed..51defd213f 100644
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+ ['packimportstoc_3283',['PackImportsToC',['../namespacetvm_1_1codegen.html#a8b151920f583ba2612f4020879afb844',1,'tvm::codegen']]],
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