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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/08/10 07:56:41 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@fae79bbc3e499f3b9f26c9a13743896f948b723d)
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 0575fb8f6 deploying docs (apache/tvm@fae79bbc3e499f3b9f26c9a13743896f948b723d)
0575fb8f6 is described below
commit 0575fb8f665ca724a8b560060835e5c859a1141e
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Aug 10 07:56:33 2022 +0000
deploying docs (apache/tvm@fae79bbc3e499f3b9f26c9a13743896f948b723d)
---
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290 files changed, 14240 insertions(+), 12233 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 8b4ea614b..140f55eba 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -317,7 +317,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 3.064 seconds)
+ **Total running time of the script:** ( 1 minutes 6.369 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 1aad04f34..e0d77995c 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -115,7 +115,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip871e6cb1-0d61-4c9d-8c70-7d6ec9f7c77e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipa6e16c62-fe36-48cd-900e-5d1b1515a350 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 1737a16f5..36109ba12 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -113,7 +113,7 @@ 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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51%|##### | 21.0M/41.5M [00:00<00:00, 52.7MB/s]
63%|######3 | 26.3M/41.5M [00:00<00:00, 38.4MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 37.3MB/s]
86%|########6 | 35.9M/41.5M [00:00<00:00, 36.6MB/s]
95%|#########5| 39.5M/41.5M [00:01<00:00, 25.6MB/s]
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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 af7c9a839..699fe9cf9 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -94,7 +94,7 @@ Load a pretrained PyTorch model
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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38%|###8 | 17.2M/44.7M [00:00<00:00, 174MB/s]
93%|#########2| 41.4M/44.7M [00:00<00:00, 221MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 217MB/s]
+
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39%|###9 | 17.5M/44.7M [00:00<00:00, 184MB/s]
99%|#########8| 44.2M/44.7M [00:00<00:00, 240MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 232MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 312ff75c0..1dc96b849 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -423,7 +423,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.560 seconds)
+ **Total running time of the script:** ( 1 minutes 3.600 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 eea49fec4..ef64be4db 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
=================
-**05:02.073** total execution time for **how_to_compile_models** files:
+**05:11.601** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:04.560 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:06.369 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:03.064 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:03.600 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:38.556 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:40.470 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:27.544 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:28.855 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:25.525 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:26.353 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:24.550 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:25.223 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:21.921 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:23.214 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:19.342 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:19.775 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:14.368 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:15.301 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.644 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.442 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
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 a32e85d8b..2b3ceaed3 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
@@ -441,7 +441,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7443 15.7269 15.8897 15.6568 0.0714
+ 16.2295 16.1601 16.6853 16.0297 0.1966
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 2d6851c85..1eb953f22 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
@@ -123,7 +123,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
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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60%|###### | 102M/170M [00:00<00:00, 215MB/s]
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90%|########9 | 153M/170M [00:00<00:00, 242MB/s]
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+
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s]
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]
92%|#########1| 156M/170M [00:04<00:00, 34.7MB/s]
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/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: 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).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 56.242 seconds)
+ **Total running time of the script:** ( 3 minutes 5.418 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 bd7e4496c..cb3014dde 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -232,7 +232,7 @@ training. Other models require a full post training calibration.
.. code-block:: none
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 172MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
40%|#### | 5.44M/13.6M [00:00<00:00, 56.5MB/s]
81%|########1 | 11.0M/13.6M [00:00<00:00, 56.5MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 44.9MB/s]
@@ -412,7 +412,7 @@ 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)
- 90.2299 90.1862 90.9239 90.0643 0.1529
+ 90.5174 90.4569 93.0623 90.1768 0.3247
@@ -461,7 +461,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 8.639 seconds)
+ **Total running time of the script:** ( 1 minutes 10.339 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 5eb011a9d..0d3bb77aa 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
@@ -439,7 +439,7 @@ 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)
- 118.7039 118.6372 124.3227 118.0358 0.6183
+ 120.3960 120.1338 126.8454 119.1645 0.9981
@@ -476,7 +476,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 52.409 seconds)
+ **Total running time of the script:** ( 1 minutes 59.298 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
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 e8e9533cf..a5b54db28 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -255,7 +255,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.130 seconds)
+ **Total running time of the script:** ( 1 minutes 42.744 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
index 70bf04d0e..ae7dd4621 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -158,7 +158,7 @@ Convert and compile model for CPU.
data: None
input_sym_arg_type = in_param.infer_type()[0]
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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86%|########6 |
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@@ -241,7 +241,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 35.727 seconds)
+ **Total running time of the script:** ( 2 minutes 38.356 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.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 e824aa4d8..a44caeadb 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**11:23.717** total execution time for **how_to_deploy_models** files:
+**11:54.912** 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``) | 02:56.242 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:05.418 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:35.727 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:38.356 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 01:52.409 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 01:59.298 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:36.130 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:42.744 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:08.639 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:10.339 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:30.998 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:32.060 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:21.923 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:23.615 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:21.643 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:23.075 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 755410a44..9711bba44 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
@@ -476,7 +476,7 @@ 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.zip82f9de2d-d806-40fb-8202-e068dcbb339b from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip4391d886-7d94-4143-80a2-9022470c245b from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
@@ -590,7 +590,7 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
+ Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
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 a1c278a2c..897a7c5e6 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
=================
-**00:40.750** total execution time for **how_to_extend_tvm** files:
+**00:42.016** 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``) | 00:37.616 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:38.731 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.207 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.299 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:00.920 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:00.978 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 684826994..939bb7531 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
@@ -216,10 +216,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 6496us [6496us] (45.60%; 45.60%)
- FoldScaleAxis: 7749us [6us] (54.40%; 54.40%)
- FoldConstant: 7743us [1602us] (54.36%; 99.92%)
- InferType: 6141us [6141us] (43.11%; 79.31%)
+ InferType: 6412us [6412us] (45.21%; 45.21%)
+ FoldScaleAxis: 7772us [5us] (54.79%; 54.79%)
+ FoldConstant: 7767us [1603us] (54.76%; 99.93%)
+ InferType: 6164us [6164us] (43.46%; 79.36%)
@@ -258,10 +258,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 6143us [6143us] (44.63%; 44.63%)
- FoldScaleAxis: 7621us [5us] (55.37%; 55.37%)
- FoldConstant: 7616us [1574us] (55.33%; 99.94%)
- InferType: 6042us [6042us] (43.90%; 79.33%)
+ InferType: 6172us [6172us] (44.59%; 44.59%)
+ FoldScaleAxis: 7671us [5us] (55.41%; 55.41%)
+ FoldConstant: 7666us [1595us] (55.38%; 99.94%)
+ InferType: 6071us [6071us] (43.86%; 79.20%)
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 70a6f20ea..085557492 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
@@ -340,7 +340,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 52.768086 ms
+ Convolution: 36.492911 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 9063f8e48..6431db4cd 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
@@ -671,7 +671,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.608071 ms
+ conv2d with tensor core: 8.869483 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 b10281fb4..32e083d16 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -143,8 +143,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018873
- Baseline: 3.255020
+ Numpy running time: 0.018670
+ Baseline: 3.236111
@@ -239,7 +239,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.303365
+ Opt1: 0.315768
@@ -342,7 +342,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.336829
+ Opt2: 0.342657
@@ -438,7 +438,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.115725
+ Opt3: 0.120931
@@ -563,7 +563,7 @@ flattening.
.. code-block:: none
- Opt4: 0.110672
+ Opt4: 0.111174
@@ -685,7 +685,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111722
+ Opt5: 0.111934
@@ -810,7 +810,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.145089
+ Opt6: 0.145182
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 02d2fab67..d70911faa 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
=================
-**00:33.919** total execution time for **how_to_optimize_operators** files:
+**00:34.201** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.802 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:32.013 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.165 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.212 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:00.953 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:00.976 | 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 e7c7f5dcd..015fc47b4 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
=================
-**06:13.024** total execution time for **how_to_tune_with_autoscheduler** files:
+**06:17.476** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 03:26.920 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 03:28.355 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:22.161 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:23.413 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 00:46.145 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 00:46.102 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:20.098 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:21.874 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:09.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:08.897 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:08.669 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:08.835 | 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 c7a295c5c..50b1d23d1 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
@@ -771,7 +771,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.364 ms
+ Execution time of this operator: 0.353 ms
@@ -1378,7 +1378,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 26.920 seconds)
+ **Total running time of the script:** ( 3 minutes 28.355 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py:
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 f2b5421eb..b9ec04a5b 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)
- 9.9184 9.9224 9.9384 9.8945 0.0181
+ 9.7941 9.8211 9.8446 9.7168 0.0555
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 028bb1074..f3cc935f4 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)
- 751.9575 751.7686 752.3663 751.7377 0.2893
+ 754.4837 752.7454 761.3244 749.3813 5.0283
@@ -694,7 +694,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 22.161 seconds)
+ **Total running time of the script:** ( 1 minutes 23.413 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
index 19e420d46..579facdf3 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -397,143 +397,80 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_16: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_19: Buffer(placeholder_11, float32, [4916, 16, 1], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 2048) "parallel" {
- let cse_var_1: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 512), 16)
- allocate(compute_4: Pointer(global float32), float32, [32]), storage_scope = global {
- compute_5: Buffer(compute_4, float32, [32], [])[0] = 0f32
- compute_5[1] = 0f32
- compute_5[2] = 0f32
- compute_5[3] = 0f32
- compute_5[4] = 0f32
- compute_5[5] = 0f32
- compute_5[6] = 0f32
- compute_5[7] = 0f32
- compute_5[8] = 0f32
- compute_5[9] = 0f32
- compute_5[10] = 0f32
- compute_5[11] = 0f32
- compute_5[12] = 0f32
- compute_5[13] = 0f32
- compute_5[14] = 0f32
- compute_5[15] = 0f32
- compute_5[16] = 0f32
- compute_5[17] = 0f32
- compute_5[18] = 0f32
- compute_5[19] = 0f32
- compute_5[20] = 0f32
- compute_5[21] = 0f32
- compute_5[22] = 0f32
- compute_5[23] = 0f32
- compute_5[24] = 0f32
- compute_5[25] = 0f32
- compute_5[26] = 0f32
- compute_5[27] = 0f32
- compute_5[28] = 0f32
- compute_5[29] = 0f32
- compute_5[30] = 0f32
- compute_5[31] = 0f32
- for (elem_idx: int32, 0, (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[0] = (compute_5[0] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[1] = (compute_5[1] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[2] = (compute_5[2] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[3] = (compute_5[3] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[4] = (compute_5[4] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[5] = (compute_5[5] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[6] = (compute_5[6] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[7] = (compute_5[7] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[8] = (compute_5[8] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2048)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[9] = (compute_5[9] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2304)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[10] = (compute_5[10] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2560)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[11] = (compute_5[11] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2816)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[12] = (compute_5[12] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3072)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[13] = (compute_5[13] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3328)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[14] = (compute_5[14] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3584)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[15] = (compute_5[15] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3840)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[16] = (compute_5[16] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4096)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[17] = (compute_5[17] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4352)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[18] = (compute_5[18] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4608)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[19] = (compute_5[19] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4864)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[20] = (compute_5[20] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5120)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[21] = (compute_5[21] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5376)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[22] = (compute_5[22] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5632)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[23] = (compute_5[23] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5888)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[24] = (compute_5[24] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6144)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[25] = (compute_5[25] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6400)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[26] = (compute_5[26] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6656)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[27] = (compute_5[27] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6912)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[28] = (compute_5[28] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7168)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[29] = (compute_5[29] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7424)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[30] = (compute_5[30] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7680)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[31] = (compute_5[31] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7936)], 0f32)))
+ preflattened_buffer_map = {placeholder_5: placeholder_15: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_16: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [4096]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 32) {
+ let cse_var_1: int32 = (((i.outer.inner*1024) + (i.inner.init*32)) + (nb_j.inner*16))
+ {
+ compute_5: Buffer(compute_4, float32, [4096], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ }
+ }
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 32) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner)
+ let cse_var_19: int32 = ((i.outer.inner*8192) + (i.inner*256))
+ let cse_var_18: int32 = (((i.outer.inner*1024) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_17: int32 = (cse_var_18 + 9)
+ let cse_var_16: int32 = (cse_var_18 + 8)
+ let cse_var_15: int32 = (cse_var_18 + 7)
+ let cse_var_14: int32 = (cse_var_18 + 6)
+ let cse_var_13: int32 = (cse_var_18 + 5)
+ let cse_var_12: int32 = (cse_var_18 + 4)
+ let cse_var_11: int32 = (cse_var_18 + 3)
+ let cse_var_10: int32 = (cse_var_18 + 2)
+ let cse_var_9: int32 = (cse_var_18 + 15)
+ let cse_var_8: int32 = (cse_var_18 + 14)
+ let cse_var_7: int32 = (cse_var_18 + 13)
+ let cse_var_6: int32 = (cse_var_18 + 12)
+ let cse_var_5: int32 = (cse_var_18 + 11)
+ let cse_var_4: int32 = (cse_var_18 + 10)
+ let cse_var_3: int32 = (cse_var_18 + 1)
+ {
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
+ }
+ }
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_2: int32 = (((floordiv(i0.outer.i1.outer.fused, 512)*16384) + (i0.inner*512)) + floormod(i0.outer.i1.outer.fused, 512))
- compute[cse_var_2] = max((compute_5[i0.inner] + placeholder_4[cse_var_2]), 0f32)
+ for (i0.inner: int32, 0, 128) {
+ for (i1.inner: int32, 0, 32) {
+ let cse_var_22: int32 = (((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)
+ compute[cse_var_22] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_22]), 0f32)
+ }
}
}
}
@@ -589,7 +526,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 3.759 ms
+ Execution time of this operator: 1.730 ms
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 80c695710..8fea3dcf8 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:45.391** total execution time for **how_to_tune_with_autotvm** files:
+**00:46.187** 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``) | 00:45.357 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:46.151 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.019 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.020 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
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 dab1c6549..1f401ee73 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
@@ -1156,8 +1156,8 @@ for this template
TimeoutError
[('tile_f', [-1, 2, 1, 64]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4909501
- No: 9 GFLOPS: 80.78/80.78 result: MeasureResult(costs=(0.0028658760000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9257969856262207, timestamp=1660112412.9944103) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
- No: 10 GFLOPS: 0.00/80.78 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 80.79/80.79 result: MeasureResult(costs=(0.0028654588000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9271838665008545, timestamp=1660114072.9038901) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
+ No: 10 GFLOPS: 0.00/80.79 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1280,8 +1280,8 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5092711
- No: 11 GFLOPS: 260.36/260.36 result: MeasureResult(costs=(0.0008891516795580111,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6766939163208008, timestamp=1660112413.8546999) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
- No: 12 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 261.20/261.20 result: MeasureResult(costs=(0.0008862881491712708,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6869051456451416, timestamp=1660114073.8029814) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
+ No: 12 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1404,7 +1404,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 128, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,183542
- No: 13 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1527,7 +1527,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 8, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2482196
- No: 14 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1650,9 +1650,9 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10306226
- No: 15 GFLOPS: 5.29/260.36 result: MeasureResult(costs=(0.04379221625,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8522651195526123, timestamp=1660112418.4552326) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
- No: 16 GFLOPS: 3.34/260.36 result: MeasureResult(costs=(0.06938179675,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.596736669540405, timestamp=1660112419.6940444) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
- No: 17 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 5.31/261.20 result: MeasureResult(costs=(0.04355937375,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8288960456848145, timestamp=1660114078.374904) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
+ No: 16 GFLOPS: 3.36/261.20 result: MeasureResult(costs=(0.06893701575,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.56779932975769, timestamp=1660114079.6050844) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
+ No: 17 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1670,8 +1670,8 @@ for this template
TimeoutError
[('tile_f', [-1, 2, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 16]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10195251
- No: 18 GFLOPS: 28.02/260.36 result: MeasureResult(costs=(0.008261535357142857,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3037467002868652, timestamp=1660112430.7455037) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
- No: 19 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 28.17/261.20 result: MeasureResult(costs=(0.008219214357142858,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2799923419952393, timestamp=1660114090.6589699) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
+ No: 19 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1794,7 +1794,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 16, 4, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6956993
- No: 20 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+ No: 20 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1973,7 +1973,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
Finish loading 20 records
- Time cost of this operator: 0.001253
+ Time cost of this operator: 0.001254
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 791b7ebee..bc46c141b 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
@@ -329,10 +329,10 @@ 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 311.0 98.713 (1, 2, 10, 10, 3) 2 1 [311.0]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.098 0.983 (1, 6, 10, 10) 1 1 [3.098]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.959 0.304 (1, 1, 10, 10, 3) 1 1 [0.959]
- Total_time - 315.056 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 312.7 98.727 (1, 2, 10, 10, 3) 2 1 [312.7]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.968 (1, 6, 10, 10) 1 1 [3.067]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.963 0.304 (1, 1, 10, 10, 3) 1 1 [0.963]
+ Total_time - 316.731 - - - - -
@@ -398,10 +398,10 @@ 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 195.9 98.664 (1, 6, 10, 10, 1) 2 1 [195.9]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.791 0.902 (1, 6, 10, 10) 1 1 [1.791]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.862 0.434 (1, 3, 10, 10, 1) 1 1 [0.862]
- Total_time - 198.552 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 79.312 96.587 (1, 6, 10, 10, 1) 2 1 [79.312]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.84 2.241 (1, 6, 10, 10) 1 1 [1.84]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 1.172 (1, 1, 10, 10, 3) 1 1 [0.962]
+ Total_time - 82.115 - - - - -
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 cf92ba6ec..81d41d4a6 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
@@ -225,7 +225,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmpb3abcwff/images/random'
+ '/tmp/tmpkk78cada/images/random'
@@ -325,8 +325,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpb3abcwff/images/target contains 8144 images
- /tmp/tmpb3abcwff/images/random contains 5000 images
+ /tmp/tmpkk78cada/images/target contains 8144 images
+ /tmp/tmpkk78cada/images/random contains 5000 images
@@ -501,13 +501,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 55s - loss: 0.2212 - accuracy: 0.9220 - val_loss: 0.1603 - val_accuracy: 0.9524
+ 328/328 - 55s - loss: 0.2200 - accuracy: 0.9264 - val_loss: 0.1369 - val_accuracy: 0.9581
Epoch 2/3
- 328/328 - 52s - loss: 0.1008 - accuracy: 0.9626 - val_loss: 0.1076 - val_accuracy: 0.9675
+ 328/328 - 52s - loss: 0.1032 - accuracy: 0.9616 - val_loss: 0.1184 - val_accuracy: 0.9615
Epoch 3/3
- 328/328 - 52s - loss: 0.0645 - accuracy: 0.9774 - val_loss: 0.1079 - val_accuracy: 0.9656
+ 328/328 - 52s - loss: 0.0660 - accuracy: 0.9759 - val_loss: 0.1038 - val_accuracy: 0.9649
- <keras.callbacks.History object at 0x7f7e70c0c590>
+ <keras.callbacks.History object at 0x7f4225688510>
@@ -864,7 +864,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 5 minutes 23.893 seconds)
+ **Total running time of the script:** ( 5 minutes 8.196 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 aeddc533d..05b52f4d4 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
@@ -5,16 +5,16 @@
Computation times
=================
-**06:16.595** total execution time for **how_to_work_with_microtvm** files:
+**06:04.416** total execution time for **how_to_work_with_microtvm** files:
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:23.893 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:08.196 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:41.390 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:44.338 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.111 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.533 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.199 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.346 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``) | 00:00.001 | 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 5ef8ffd09..ad6078829 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
=================
-**00:36.005** total execution time for **how_to_work_with_relay** files:
+**00:42.949** 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``) | 00:29.266 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:31.415 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:05.173 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:09.945 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.559 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.583 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.007 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
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 9c05de3b2..706bf4147 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
@@ -261,7 +261,7 @@ The following example customizes CUDA lowering rule for :code:`exp`.
.. code-block:: none
- <function my_cuda_math_rule at 0x7f7deb41e680>
+ <function my_cuda_math_rule at 0x7f4188dc1050>
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 742c13e2f..51ae52b11 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
=================
-**00:03.792** total execution time for **how_to_work_with_schedules** files:
+**00:04.178** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:01.728 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:01.901 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:00.919 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.019 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.490 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.545 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.472 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.529 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.101 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.099 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.040 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.041 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.027 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.015 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.016 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 49f1d351b..eb9b43819 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -347,7 +347,7 @@ The importing needs to happen before the tensorized GEMV being executed.
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [1024, 64], []), B_1: B_3: Buffer(B_2, float32, [512, 64], []), C_1: C_3: Buffer(C_2, float32, [1024, 512], [])} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp1xnag1lc/input0.cc'\nsource_filename = \"/tmp/tmp1xnag1lc/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpav3gf_o6/input0.cc'\nsource_filename = \"/tmp/tmpav3gf_o6/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
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 1d58cda96..100ecb414 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
=================
-**00:21.432** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:22.139** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:21.425 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:22.133 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index d0a93f77a..bdf2ad699 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -291,7 +291,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: 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.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
- resnet18_v1 inference graph built in 23.64s!
+ resnet18_v1 inference graph built in 24.03s!
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 fb53f8f20..71b1e6e39 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -335,7 +335,7 @@ The compilation steps are:
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py:411: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 16.07s!
+ yolov3-tiny inference graph built in 16.81s!
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 5ce2f5643..76211bf6e 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
=================
-**01:33.082** total execution time for **topic_vta_tutorials_frontend** files:
+**01:33.979** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:49.330 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:49.739 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:43.751 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:44.240 | 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 fa3bb0cd8..3f1f51329 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
=================
-**00:03.167** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.300** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.807 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.884 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.360 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.415 | 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 f58701f20..8b0611380 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
=================
-**00:00.619** total execution time for **topic_vta_tutorials** files:
+**00:00.738** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.321 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.400 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.298 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.338 | 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 f4f1cca55..11baf8a7d 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -205,6 +205,13 @@ trials, we can load the best schedule from the log file and apply it.
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ *E
+
+
@@ -328,7 +335,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.635 ms
+ Execution time of this operator: 94.385 ms
@@ -428,7 +435,7 @@ resume the status and do more 5 trials.
Resume search:
/usr/local/lib/python3.7/dist-packages/xgboost/training.py:17: UserWarning: Old style callback is deprecated. See: https://xgboost.readthedocs.io/en/latest/python/callbacks.html
warnings.warn(f'Old style callback is deprecated. See: {link}', UserWarning)
- *E
+
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index 185a32b94..87a1e4240 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -462,16 +462,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 10.60/10.60 result: MeasureResult(costs=(0.025320319,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5414156913757324, timestamp=1660111207.5470946) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
- No: 2 GFLOPS: 2.94/10.60 result: MeasureResult(costs=(0.09125585180000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6053290367126465, timestamp=1660111209.1699479) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
- No: 3 GFLOPS: 11.84/11.84 result: MeasureResult(costs=(0.0226737416,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.562427282333374, timestamp=1660111210.2256548) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
- No: 4 GFLOPS: 1.86/11.84 result: MeasureResult(costs=(0.14393653420000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4279561042785645, timestamp=1660111213.220523) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 5 GFLOPS: 3.62/11.84 result: MeasureResult(costs=(0.0741092016,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.331512212753296, timestamp=1660111214.6794274) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
- No: 6 GFLOPS: 1.73/11.84 result: MeasureResult(costs=(0.1547912886,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.635258436203003, timestamp=1660111217.3525927) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
- No: 7 GFLOPS: 0.87/11.84 result: MeasureResult(costs=(0.3087904848,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.066814661026001, timestamp=1660111222.9974334) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 8 GFLOPS: 10.72/11.84 result: MeasureResult(costs=(0.0250475276,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5554065704345703, timestamp=1660111223.5608907) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
- No: 9 GFLOPS: 1.91/11.84 result: MeasureResult(costs=(0.14051162639999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3484604358673096, timestamp=1660111226.0294333) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
- No: 10 GFLOPS: 2.79/11.84 result: MeasureResult(costs=(0.0961280754,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6432266235351562, timestamp=1660111227.7303085) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
+ No: 1 GFLOPS: 10.68/10.68 result: MeasureResult(costs=(0.025131534,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5366127490997314, timestamp=1660112814.8317795) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
+ No: 2 GFLOPS: 2.85/10.68 result: MeasureResult(costs=(0.094106117,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6591527462005615, timestamp=1660112816.5074644) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
+ No: 3 GFLOPS: 11.89/11.89 result: MeasureResult(costs=(0.022569936999999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5576667785644531, timestamp=1660112817.5565057) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+ No: 4 GFLOPS: 1.82/11.89 result: MeasureResult(costs=(0.14712051539999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.487107276916504, timestamp=1660112820.6377022) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
+ No: 5 GFLOPS: 3.62/11.89 result: MeasureResult(costs=(0.074219207,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3120856285095215, timestamp=1660112822.0785651) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
+ No: 6 GFLOPS: 1.74/11.89 result: MeasureResult(costs=(0.1545908622,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.6289732456207275, timestamp=1660112824.7518473) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
+ No: 7 GFLOPS: 0.87/11.89 result: MeasureResult(costs=(0.30805183080000004,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.058203935623169, timestamp=1660112830.37719) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 8 GFLOPS: 10.79/11.89 result: MeasureResult(costs=(0.024882658199999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.540186882019043, timestamp=1660112830.9381738) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
+ No: 9 GFLOPS: 1.92/11.89 result: MeasureResult(costs=(0.14005611759999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3483951091766357, timestamp=1660112833.4065702) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+ No: 10 GFLOPS: 2.78/11.89 result: MeasureResult(costs=(0.09641162480000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6572589874267578, timestamp=1660112835.1177318) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index a47fa5167..00bd99450 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -327,7 +327,7 @@ standard deviation.
.. code-block:: none
- {'mean': 494.4935347899991, 'median': 494.2289559499841, 'std': 1.2781998650481616}
+ {'mean': 498.93778268000005, 'median': 498.86395675000017, 'std': 1.4324142228454322}
@@ -563,30 +563,30 @@ the tuning data to.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.55/ 17.55 GFLOPS | Progress: (4/20) | 6.34 s
[Task 1/25] Current/Best: 6.16/ 17.55 GFLOPS | Progress: (8/20) | 9.34 s
[Task 1/25] Current/Best: 11.53/ 22.74 GFLOPS | Progress: (12/20) | 11.74 s
[Task 1/25] Current/Best: 16.77/ 22.85 GFLOPS | Progress: (16/20) | 13.43 s
[Task 1/25] Current/Best: 11.60/ 23.81 GFLOPS | Progress: (20/20) | 15.16 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.34/ 13.35 GFLOPS | Progress: (4/20) | 3.78 s
[Task 2/25] Current/Best: 14.21/ 18.28 GFLOPS | Progress: (8/20) | 5.07 s
[Task 2/25] Current/Best: 20.85/ 20.85 GFLOPS | Progress: (12/20) | 6.39 s
[Task 2/25] Current/Best: 12.57/ 20.85 GFLOPS | Progress: (16/20) | 7.67 s
[Task 2/25] Current/Best: 20.14/ 20.85 GFLOPS | Progress: (20/20) | 9.27 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.55 GFLOPS | Progress: (4/20) | 5.87 s
[Task 3/25] Current/Best: 15.55/ 16.89 GFLOPS | Progress: (8/20) | 7.79 s
[Task 3/25] Current/Best: 14.93/ 16.89 GFLOPS | Progress: (12/20) | 9.51 s
[Task 3/25] Current/Best: 7.22/ 23.87 GFLOPS | Progress: (16/20) | 11.45 s
[Task 3/25] Current/Best: 12.04/ 23.87 GFLOPS | Progress: (20/20) | 16.02 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.54/ 20.56 GFLOPS | Progress: (4/20) | 2.38 s
[Task 4/25] Current/Best: 6.85/ 20.56 GFLOPS | Progress: (8/20) | 6.68 s
[Task 4/25] Current/Best: 22.41/ 22.41 GFLOPS | Progress: (12/20) | 11.15 s
[Task 4/25] Current/Best: 16.94/ 22.41 GFLOPS | Progress: (16/20) | 13.40 s
[Task 4/25] Current/Best: 13.45/ 22.41 GFLOPS | Progress: (20/20) | 15.40 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.91/ 10.44 GFLOPS | Progress: (4/20) | 2.59 s
[Task 5/25] Current/Best: 11.86/ 12.70 GFLOPS | Progress: (8/20) | 4.63 s
[Task 5/25] Current/Best: 11.73/ 18.16 GFLOPS | Progress: (12/20) | 7.56 s
[Task 5/25] Current/Best: 11.94/ 22.82 GFLOPS | Progress: (16/20) | 8.96 s
[Task 5/25] Current/Best: 12.10/ 22.82 GFLOPS | Progress: (20/20) | 10.83 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.21/ 20.68 GFLOPS | Progress: (4/20) | 3.93 s
[Task 6/25] Current/Best: 18.99/ 20.68 GFLOPS | Progress: (8/20) | 5.69 s
[Task 6/25] Current/Best: 13.27/ 20.68 GFLOPS | Progress: (12/20) | 7.60 s
[Task 6/25] Current/Best: 19.98/ 20.68 GFLOPS | Progress: (16/20) | 9.82 s
[Task 6/25] Current/Best: 3.74/ 20.68 GFLOPS | Progress: (20/20) | 12.36 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.22/ 12.81 GFLOPS | Progress: (4/20) | 3.62 s
[Task 7/25] Current/Best: 20.30/ 21.16 GFLOPS | Progress: (8/20) | 5.14 s
[Task 7/25] Current/Best: 12.63/ 21.16 GFLOPS | Progress: (12/20) | 7.10 s
[Task 7/25] Current/Best: 12.28/ 21.16 GFLOPS | Progress: (16/20) | 9.13 s
[Task 7/25] Current/Best: 6.37/ 21.85 GFLOPS | Progress: (20/20) | 11.57 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.09/ 13.94 GFLOPS | Progress: (4/20) | 2.89 s
[Task 8/25] Current/Best: 9.57/ 13.94 GFLOPS | Progress: (8/20) | 7.56 s
[Task 8/25] Current/Best: 12.71/ 13.94 GFLOPS | Progress: (12/20) | 13.62 s
[Task 8/25] Current/Best: 19.03/ 19.03 GFLOPS | Progress: (16/20) | 15.71 s
[Task 8/25] Current/Best: 20.11/ 20.11 GFLOPS | Progress: (20/20) | 22.15 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.37/ 15.63 GFLOPS | Progress: (4/20) | 11.95 s
[Task 9/25] Current/Best: 23.54/ 23.54 GFLOPS | Progress: (8/20) | 13.75 s
[Task 9/25] Current/Best: 8.29/ 23.54 GFLOPS | Progress: (12/20) | 16.11 s
[Task 9/25] Current/Best: 17.96/ 23.54 GFLOPS | Progress: (16/20) | 18.75 s
[Task 9/25] Current/Best: 9.19/ 23.54 GFLOPS | Progress: (20/20) | 26.38 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.18/ 18.18 GFLOPS | Progress: (4/20) | 2.57 s
[Task 10/25] Current/Best: 15.58/ 18.18 GFLOPS | Progress: (8/20) | 4.14 s
[Task 10/25] Current/Best: 12.85/ 19.02 GFLOPS | Progress: (12/20) | 5.67 s
[Task 10/25] Current/Best: 19.22/ 20.41 GFLOPS | Progress: (16/20) | 6.77 s
[Task 10/25] Current/Best: 8.88/ 20.41 GFLOPS | Progress: (20/20
) | 8.29 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.30/ 18.12 GFLOPS | Progress: (4/20) | 3.30 s
[Task 11/25] Current/Best: 16.85/ 18.12 GFLOPS | Progress: (8/20) | 6.01 s
[Task 11/25] Current/Best: 17.63/ 18.12 GFLOPS | Progress: (12/20) | 8.05 s
[Task 11/25] Current/Best: 13.48/ 21.13 GFLOPS | Progress: (16/20) | 10.77 s
[Task 11/25] Current/Best: 19.45/ 21.60 GFLOPS | Progress: (20/20) | 12.76 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.78/ 18.09 GFLOPS | Progress: (4/20) | 5.25 s
[Task 12/25] Current/Best: 5.30/ 18.09 GFLOPS | Progress: (8/20) | 8.92 s
[Task 12/25] Current/Best: 18.78/ 19.00 GFLOPS | Progress: (12/20) | 10.90 s
[Task 12/25] Current/Best: 15.43/ 19.00 GFLOPS | Progress: (16/20) | 13.67 s
[Task 12/25] Current/Best: 15.17/ 19.00 GFLOPS | Progress: (20/20) | 15.62 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.52/ 17.25 GFLOPS | Progress: (4/20) | 3.67 s
[Task 13/25] Current/Best: 15.83/ 20.85 GFLOPS | Progress: (8/20) | 6.09 s
[Task 13/25] Current/Best: 19.62/ 21.46 GFLOPS | Progress: (12/20) | 8.99 s
[Task 13/25] Current/Best: 12.24/ 21.46 GFLOPS | Progress: (16/20) | 12.35 s
[Task 13/25] Current/Best: 18.88/ 21.46 GFLOPS | Progress: (20/20) | 14.59 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.75/ 13.75 GFLOPS | Progress: (4/20) | 3.35 s
[Task 14/25] Current/Best: 6.08/ 13.75 GFLOPS | Progress: (8/20) | 5.50 s
[Task 14/25] Current/Best: 21.07/ 21.07 GFLOPS | Progress: (12/20) | 8.03 s
[Task 14/25] Current/Best: 16.68/ 21.07 GFLOPS | Progress: (16/20) | 9.68 s Done.
-
[Task 14/25] Current/Best: 17.01/ 21.07 GFLOPS | Progress: (20/20) | 11.44 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.17/ 17.60 GFLOPS | Progress: (4/20) | 2.71 s
[Task 15/25] Current/Best: 14.30/ 17.97 GFLOPS | Progress: (8/20) | 4.04 s
[Task 15/25] Current/Best: 10.31/ 22.34 GFLOPS | Progress: (12/20) | 6.09 s
[Task 15/25] Current/Best: 20.40/ 22.34 GFLOPS | Progress: (16/20) | 9.01 s
[Task 15/25] Current/Best: 9.71/ 22.34 GFLOPS | Progress: (20/20) | 9.97 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.85/ 20.85 GFLOPS | Progress: (4/20) | 2.93 s
[Task 16/25] Current/Best: 2.98/ 20.85 GFLOPS | Progress: (8/20) | 4.56 s
[Task 16/25] Current/Best: 19.67/ 20.85 GFLOPS | Progress: (12/20) | 5.79 s
[Task 16/25] Current/Best: 17.82/ 20.85 GFLOPS | Progress: (16/20) |
7.13 s
[Task 16/25] Current/Best: 10.01/ 22.57 GFLOPS | Progress: (20/20) | 9.15 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.58/ 18.81 GFLOPS | Progress: (4/20) | 4.70 s
[Task 17/25] Current/Best: 14.06/ 23.26 GFLOPS | Progress: (8/20) | 7.55 s
[Task 17/25] Current/Best: 17.00/ 23.26 GFLOPS | Progress: (12/20) | 9.60 s
[Task 17/25] Current/Best: 17.08/ 23.26 GFLOPS | Progress: (16/20) | 11.73 s
[Task 17/25] Current/Best: 10.04/ 23.26 GFLOPS | Progress: (20/20) | 13.84 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.25/ 16.75 GFLOPS | Progress: (4/20) | 3.68 s
[Task 18/25] Current/Best: 10.52/ 16.75 GFLOPS | Progress: (8/20) | 7.12 s
[Task 18/25] Current/Best: 18.83/ 18.83 GFLOPS | Progress: (12/20) | 9.03 s
[Task 18/25] Current/Best: 10.09/ 18.83 GFLOPS | Progress: (16/20) | 12.59 s
[Task 18/25] Current/Best: 20.72/ 20.72 GFLOPS | Progress: (20/20) | 14.08 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.10/ 20.38 GFLOPS | Progress: (4/20) | 6.00 s
[Task 19/25] Current/Best: 2.60/ 20.38 GFLOPS | Progress: (8/20) | 9.31 s
[Task 19/25] Current/Best: 19.86/ 21.48 GFLOPS | Progress: (12/20) | 12.04 s
[Task 19/25] Current/Best: 15.15/ 21.48 GFLOPS | Progress: (16/20) | 14.80 s
[Task 19/25] Current/Best: 2.69/ 23.02 GFLOPS | Progress: (20/20) | 17.58 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.85/ 15.03 GFLOPS | Progress: (4/20) | 3.33 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.51/ 17.51 GFLOPS | Progress: (4/20) | 6.46 s
[Task 1/25] Current/Best: 6.15/ 17.51 GFLOPS | Progress: (8/20) | 9.42 s
[Task 1/25] Current/Best: 11.51/ 22.88 GFLOPS | Progress: (12/20) | 11.92 s
[Task 1/25] Current/Best: 16.63/ 22.89 GFLOPS | Progress: (16/20) | 13.61 s
[Task 1/25] Current/Best: 11.60/ 23.64 GFLOPS | Progress: (20/20) | 15.36 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.34/ 12.87 GFLOPS | Progress: (4/20) | 3.97 s
[Task 2/25] Current/Best: 14.24/ 18.69 GFLOPS | Progress: (8/20) | 5.28 s
[Task 2/25] Current/Best: 20.71/ 20.71 GFLOPS | Progress: (12/20) | 6.60 s
[Task 2/25] Current/Best: 12.96/ 20.71 GFLOPS | Progress: (16/20) | 7.86 s
[Task 2/25] Current/Best: 18.90/ 20.71 GFLOPS | Progress: (20/20) | 9.48 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.49 GFLOPS | Progress: (4/20) | 5.89 s
[Task 3/25] Current/Best: 15.52/ 16.91 GFLOPS | Progress: (8/20) | 7.84 s
[Task 3/25] Current/Best: 14.88/ 16.91 GFLOPS | Progress: (12/20) | 9.58 s
[Task 3/25] Current/Best: 7.22/ 23.76 GFLOPS | Progress: (16/20) | 11.54 s
[Task 3/25] Current/Best: 12.64/ 23.76 GFLOPS | Progress: (20/20) | 16.16 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.49/ 20.41 GFLOPS | Progress: (4/20) | 2.43 s
[Task 4/25] Current/Best: 6.67/ 20.41 GFLOPS | Progress: (8/20) | 7.17 s
[Task 4/25] Current/Best: 20.84/ 20.84 GFLOPS | Progress: (12/20) | 12.11 s
[Task 4/25] Current/Best: 16.13/ 20.99 GFLOPS | Progress: (16/20) | 14.51 s
[Task 4/25] Current/Best: 13.48/ 20.99 GFLOPS | Progress: (20/20) | 16.51 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.85/ 10.41 GFLOPS | Progress: (4/20) | 2.62 s
[Task 5/25] Current/Best: 11.91/ 12.78 GFLOPS | Progress: (8/20) | 4.74 s
[Task 5/25] Current/Best: 11.76/ 17.55 GFLOPS | Progress: (12/20) | 7.95 s
[Task 5/25] Current/Best: 11.87/ 22.59 GFLOPS | Progress: (16/20) | 9.39 s
[Task 5/25] Current/Best: 11.47/ 22.59 GFLOPS | Progress: (20/20) | 11.36 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.25/ 20.56 GFLOPS | Progress: (4/20) | 4.13 s
[Task 6/25] Current/Best: 18.94/ 20.56 GFLOPS | Progress: (8/20) | 5.89 s
[Task 6/25] Current/Best: 12.78/ 20.56 GFLOPS | Progress: (12/20) | 7.85 s
[Task 6/25] Current/Best: 19.95/ 20.56 GFLOPS | Progress: (16/20) | 10.10 s
[Task 6/25] Current/Best: 3.57/ 20.56 GFLOPS | Progress: (20/20) | 12.67 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.83/ 12.99 GFLOPS | Progress: (4/20) | 3.59 s
[Task 7/25] Current/Best: 20.21/ 20.67 GFLOPS | Progress: (8/20) | 5.13 s
[Task 7/25] Current/Best: 15.94/ 20.81 GFLOPS | Progress: (12/20) | 7.07 s
[Task 7/25] Current/Best: 12.17/ 20.81 GFLOPS | Progress: (16/20) | 9.15 s
[Task 7/25] Current/Best: 6.39/ 21.72 GFLOPS | Progress: (20/20) | 11.61 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.30/ 14.42 GFLOPS | Progress: (4/20) | 2.96 s
[Task 8/25] Current/Best: 10.30/ 14.42 GFLOPS | Progress: (8/20) | 8.06 s
[Task 8/25] Current/Best: 12.90/ 14.42 GFLOPS | Progress: (12/20) | 14.67 s
[Task 8/25] Current/Best: 18.60/ 18.60 GFLOPS | Progress: (16/20) | 16.74 s
[Task 8/25] Current/Best: 20.29/ 20.29 GFLOPS | Progress: (20/20) | 23.92 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.11/ 15.47 GFLOPS | Progress: (4/20) | 12.02 s
[Task 9/25] Current/Best: 23.40/ 23.40 GFLOPS | Progress: (8/20) | 13.83 s
[Task 9/25] Current/Best: 8.24/ 23.40 GFLOPS | Progress: (12/20) | 16.41 s
[Task 9/25] Current/Best: 17.95/ 23.40 GFLOPS | Progress: (16/20) | 19.20 s
[Task 9/25] Current/Best: 9.04/ 23.40 GFLOPS | Progress: (20/20) | 27.91 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.22/ 18.22 GFLOPS | Progress: (4/20) | 2.62 s
[Task 10/25] Current/Best: 15.25/ 18.22 GFLOPS | Progress: (8/20) | 4.26 s
[Task 10/25] Current/Best: 12.36/ 19.10 GFLOPS | Progress: (12/20) | 5.82 s
[Task 10/25] Current/Best: 19.14/ 20.51 GFLOPS | Progress: (16/20) | 6.95 s
[Task 10/25] Current/Best: 8.90/ 20.51 GFLOPS | Progress: (20/20
) | 8.49 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.34/ 18.12 GFLOPS | Progress: (4/20) | 3.36 s
[Task 11/25] Current/Best: 15.64/ 18.12 GFLOPS | Progress: (8/20) | 6.21 s
[Task 11/25] Current/Best: 18.14/ 18.14 GFLOPS | Progress: (12/20) | 8.26 s
[Task 11/25] Current/Best: 13.50/ 21.20 GFLOPS | Progress: (16/20) | 11.18 s
[Task 11/25] Current/Best: 19.42/ 21.59 GFLOPS | Progress: (20/20) | 13.28 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.83/ 18.06 GFLOPS | Progress: (4/20) | 5.88 s
[Task 12/25] Current/Best: 4.96/ 18.06 GFLOPS | Progress: (8/20) | 9.92 s
[Task 12/25] Current/Best: 18.82/ 18.92 GFLOPS | Progress: (12/20) | 11.93 s
[Task 12/25] Current/Best: 15.40/ 18.92 GFLOPS | Progress: (16/20) | 14.93 s
[Task 12/25] Current/Best: 15.12/ 18.92 GFLOPS | Progress: (20/20) | 16.86 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.80/ 17.32 GFLOPS | Progress: (4/20) | 3.82 s
[Task 13/25] Current/Best: 16.12/ 21.04 GFLOPS | Progress: (8/20) | 6.44 s
[Task 13/25] Current/Best: 19.61/ 21.38 GFLOPS | Progress: (12/20) | 9.60 s
[Task 13/25] Current/Best: 12.25/ 21.38 GFLOPS | Progress: (16/20) | 13.09 s
[Task 13/25] Current/Best: 18.77/ 21.38 GFLOPS | Progress: (20/20) | 15.45 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.73/ 13.73 GFLOPS | Progress: (4/20) | 3.38 s
[Task 14/25] Current/Best: 6.12/ 13.73 GFLOPS | Progress: (8/20) | 5.56 s
[Task 14/25] Current/Best: 21.23/ 21.23 GFLOPS | Progress: (12/20) | 8.21 s
[Task 14/25] Current/Best: 16.90/ 21.23 GFLOPS | Progress: (16/20) | 9.86 s Done.
+
[Task 14/25] Current/Best: 17.23/ 21.23 GFLOPS | Progress: (20/20) | 11.64 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.21/ 17.57 GFLOPS | Progress: (4/20) | 2.76 s
[Task 15/25] Current/Best: 14.29/ 17.82 GFLOPS | Progress: (8/20) | 4.12 s
[Task 15/25] Current/Best: 10.35/ 22.18 GFLOPS | Progress: (12/20) | 6.38 s
[Task 15/25] Current/Best: 20.15/ 22.18 GFLOPS | Progress: (16/20) | 9.56 s
[Task 15/25] Current/Best: 9.72/ 22.18 GFLOPS | Progress: (20/20) | 10.58 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.83/ 20.83 GFLOPS | Progress: (4/20) | 3.00 s
[Task 16/25] Current/Best: 3.02/ 20.83 GFLOPS | Progress: (8/20) | 4.61 s
[Task 16/25] Current/Best: 19.58/ 20.83 GFLOPS | Progress: (12/20) | 5.83 s
[Task 16/25] Current/Best: 17.89/ 20.83 GFLOPS | Progress: (16/20) |
7.20 s
[Task 16/25] Current/Best: 10.06/ 22.42 GFLOPS | Progress: (20/20) | 9.40 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 12.00/ 18.79 GFLOPS | Progress: (4/20) | 4.93 s
[Task 17/25] Current/Best: 14.45/ 23.07 GFLOPS | Progress: (8/20) | 7.84 s
[Task 17/25] Current/Best: 16.94/ 23.07 GFLOPS | Progress: (12/20) | 9.93 s
[Task 17/25] Current/Best: 16.42/ 23.07 GFLOPS | Progress: (16/20) | 12.14 s
[Task 17/25] Current/Best: 10.04/ 23.07 GFLOPS | Progress: (20/20) | 14.30 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.73/ 18.07 GFLOPS | Progress: (4/20) | 3.86 s
[Task 18/25] Current/Best: 10.57/ 18.07 GFLOPS | Progress: (8/20) | 7.62 s
[Task 18/25] Current/Best: 18.97/ 18.97 GFLOPS | Progress: (12/20) | 9.57 s
[Task 18/25] Current/Best: 10.08/ 18.97 GFLOPS | Progress: (16/20) | 13.44 s
[Task 18/25] Current/Best: 20.75/ 20.75 GFLOPS | Progress: (20/20) | 14.96 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.20/ 20.50 GFLOPS | Progress: (4/20) | 6.23 s
[Task 19/25] Current/Best: 2.60/ 20.50 GFLOPS | Progress: (8/20) | 9.60 s
[Task 19/25] Current/Best: 20.12/ 21.44 GFLOPS | Progress: (12/20) | 12.60 s
[Task 19/25] Current/Best: 15.37/ 21.44 GFLOPS | Progress: (16/20) | 15.63 s
[Task 19/25] Current/Best: 2.69/ 23.77 GFLOPS | Progress: (20/20) | 18.40 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.16/ 15.03 GFLOPS | Progress: (4/20) | 3.38 s Done.
Done.
-
[Task 20/25] Current/Best: 9.94/ 15.03 GFLOPS | Progress: (8/20) | 6.76 s
[Task 20/25] Current/Best: 2.32/ 16.78 GFLOPS | Progress: (12/20) | 10.81 s
[Task 20/25] Current/Best: 12.43/ 16.78 GFLOPS | Progress: (16/20) | 14.53 s
[Task 20/25] Current/Best: 12.62/ 22.17 GFLOPS | Progress: (20/20) | 16.63 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.40/ 17.68 GFLOPS | Progress: (4/20) | 3.23 s
[Task 21/25] Current/Best: 14.60/ 17.68 GFLOPS | Progress: (8/20) | 4.83 s
[Task 21/25] Current/Best: 1.61/ 17.68 GFLOPS | Progress: (12/20) | 6.97 s
[Task 21/25] Current/Best: 18.02/ 18.02 GFLOPS | Progress: (16/20) | 10.40 s
[Task 21/25] Current/Best: 4.46/ 18.02 GFLOPS | Progress: (20/20) | 17.46 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 17.02 GFLOPS | Progress: (4/20
) | 2.68 s
[Task 22/25] Current/Best: 8.72/ 21.87 GFLOPS | Progress: (8/20) | 4.65 s
[Task 22/25] Current/Best: 19.93/ 21.87 GFLOPS | Progress: (12/20) | 6.94 s
[Task 22/25] Current/Best: 15.34/ 21.87 GFLOPS | Progress: (16/20) | 8.97 s
[Task 22/25] Current/Best: 13.81/ 21.87 GFLOPS | Progress: (20/20) | 10.62 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.42/ 20.65 GFLOPS | Progress: (4/20) | 3.24 s
[Task 23/25] Current/Best: 15.11/ 20.65 GFLOPS | Progress: (8/20) | 6.61 s
[Task 23/25] Current/Best: 20.83/ 21.51 GFLOPS | Progress: (12/20) | 8.40 s
[Task 23/25] Current/Best: 6.48/ 21.51 GFLOPS | Progress: (16/20) | 15.35 s
[Task 23/25] Current/Best: 7.96/ 21.51 GFLOPS | Progress: (20/20) | 19.52 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.45/ 8.45 GFLOPS | Progress: (4/20) | 11.82 s
[Task 24/25] Current/Best: 3.45/ 8.45 GFLOPS | Progress: (8/20) | 23.06 s
[Task 24/25] Current/Best: 4.60/ 8.45 GFLOPS | Progress: (12/20) | 33.82 s Done.
-
[Task 24/25] Current/Best: 6.95/ 8.80 GFLOPS | Progress: (16/20) | 39.14 s
[Task 24/25] Current/Best: 3.35/ 8.80 GFLOPS | Progress: (20/20) | 44.95 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.80 GFLOPS | Progress: (4/20) | 11.59 s
[Task 25/25] Current/Best: 5.70/ 7.93 GFLOPS | Progress: (8/20) | 22.91 s
[Task 25/25] Current/Best: 5.93/ 7.93 GFLOPS | Progress: (12/20) | 34.37 s
[Task 25/25] Current/Best: 5.80/ 8.48 GFLOPS | Progress: (16/20) | 36.21 s
[Task 25/25] Current/Best: 2.86/ 8.63 GFLOPS | Progress: (20/20) | 46.86 s
+
[Task 20/25] Current/Best: 9.73/ 15.03 GFLOPS | Progress: (8/20) | 6.98 s
[Task 20/25] Current/Best: 2.32/ 16.76 GFLOPS | Progress: (12/20) | 11.06 s
[Task 20/25] Current/Best: 12.41/ 16.76 GFLOPS | Progress: (16/20) | 14.86 s
[Task 20/25] Current/Best: 12.64/ 21.57 GFLOPS | Progress: (20/20) | 16.99 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.37/ 17.53 GFLOPS | Progress: (4/20) | 3.32 s
[Task 21/25] Current/Best: 14.46/ 17.53 GFLOPS | Progress: (8/20) | 4.95 s
[Task 21/25] Current/Best: 1.61/ 17.53 GFLOPS | Progress: (12/20) | 7.14 s
[Task 21/25] Current/Best: 17.84/ 17.84 GFLOPS | Progress: (16/20) | 10.73 s
[Task 21/25] Current/Best: 4.41/ 17.84 GFLOPS | Progress: (20/20) | 18.13 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 16.96 GFLOPS | Progress: (4/20
) | 2.75 s
[Task 22/25] Current/Best: 8.71/ 22.04 GFLOPS | Progress: (8/20) | 4.76 s
[Task 22/25] Current/Best: 20.05/ 22.04 GFLOPS | Progress: (12/20) | 7.19 s
[Task 22/25] Current/Best: 15.58/ 22.04 GFLOPS | Progress: (16/20) | 9.32 s
[Task 22/25] Current/Best: 15.00/ 22.04 GFLOPS | Progress: (20/20) | 11.09 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.26/ 20.41 GFLOPS | Progress: (4/20) | 3.36 s
[Task 23/25] Current/Best: 15.87/ 20.41 GFLOPS | Progress: (8/20) | 6.78 s
[Task 23/25] Current/Best: 20.78/ 21.20 GFLOPS | Progress: (12/20) | 8.62 s
[Task 23/25] Current/Best: 6.00/ 21.20 GFLOPS | Progress: (16/20) | 16.00 s
[Task 23/25] Current/Best: 6.52/ 21.20 GFLOPS | Progress: (20/20) | 20.35 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.61/ 8.61 GFLOPS | Progress: (4/20) | 11.83 s
[Task 24/25] Current/Best: 2.11/ 8.61 GFLOPS | Progress: (8/20) | 22.87 s
[Task 24/25] Current/Best: 4.68/ 8.61 GFLOPS | Progress: (12/20) | 34.45 s Done.
+
[Task 24/25] Current/Best: 6.80/ 8.89 GFLOPS | Progress: (16/20) | 40.22 s
[Task 24/25] Current/Best: 2.92/ 8.89 GFLOPS | Progress: (20/20) | 46.45 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.82 GFLOPS | Progress: (4/20) | 11.63 s
[Task 25/25] Current/Best: 5.77/ 8.07 GFLOPS | Progress: (8/20) | 22.96 s
[Task 25/25] Current/Best: 5.07/ 8.07 GFLOPS | Progress: (12/20) | 34.43 s
[Task 25/25] Current/Best: 5.15/ 9.52 GFLOPS | Progress: (16/20) | 36.20 s
[Task 25/25] Current/Best: 2.61/ 9.52 GFLOPS | Progress: (20/20) | 46.90 s
@@ -690,8 +690,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621105
- class='n02123159 tiger cat' with probability=0.356377
+ class='n02123045 tabby, tabby cat' with probability=0.621104
+ class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -748,8 +748,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 407.8879050999967, 'median': 407.60160954996536, 'std': 0.9046787250814441}
- unoptimized: {'mean': 494.4935347899991, 'median': 494.2289559499841, 'std': 1.2781998650481616}
+ optimized: {'mean': 415.4607261000024, 'median': 414.8889504000067, 'std': 1.500784521798114}
+ unoptimized: {'mean': 498.93778268000005, 'median': 498.86395675000017, 'std': 1.4324142228454322}
@@ -772,7 +772,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 10 minutes 14.239 seconds)
+ **Total running time of the script:** ( 10 minutes 34.426 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 ad86d39b5..1e852b726 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -282,7 +282,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.282e-07 secs/op
+ 1.278e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 0307a7bbe..bbd73206e 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -263,7 +263,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x21b7b300)), stage(b, placeholder(b, 0xdc73dc0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
+ [stage(a, placeholder(a, 0xba02050)), stage(b, placeholder(b, 0x22c41590)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 78bc4b509..dbf1c635c 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
=================
-**13:06.738** total execution time for **tutorial** files:
+**13:29.985** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 10:14.239 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 10:34.426 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.936 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 00:59.907 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 00:58.253 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.839 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:30.303 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:30.881 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:23.636 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:23.788 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.701 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.254 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.506 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.720 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.156 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.159 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.005 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.002 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.001 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.001 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.py``) | 00:00.001 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.001 | 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 f86b80eae..7a60d60a9 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -403,7 +403,7 @@ compile and run this new schedule with the parallel operation applied:
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- parallel: 0.000007
+ parallel: 0.000006
@@ -512,10 +512,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.26022000183002e-06 1.0
- naive 6.650599999999999e-06 0.805135940510856
- parallel 7.2073e-06 0.8725312398947304
- vector 2.45597e-05 2.9732501064812915
+ numpy 8.249059999343444e-06 1.0
+ naive 6.691099999999999e-06 0.8111348445195641
+ parallel 6.0724000000000005e-06 0.7361323593819554
+ vector 2.4622400000000003e-05 2.9848734282402773
@@ -936,7 +936,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018325
+ Numpy running time: 0.018434
@@ -996,7 +996,7 @@ optimizations.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- none: 3.255054
+ none: 3.232418
@@ -1101,7 +1101,7 @@ schedule.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- blocking: 0.305791
+ blocking: 0.308073
@@ -1199,7 +1199,7 @@ already cache friendly from our previous optimizations.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- vectorization: 0.339108
+ vectorization: 0.340009
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1275,7 +1275,7 @@ more cache friendly.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- loop permutation: 0.117431
+ loop permutation: 0.117527
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1376,7 +1376,7 @@ optimized schedule.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- array packing: 0.110354
+ array packing: 0.110379
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1471,7 +1471,7 @@ to `C` when all the block results are ready.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- block caching: 0.111313
+ block caching: 0.110959
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1559,7 +1559,7 @@ of thread-level parallelization.
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- parallelization: 0.144285
+ parallelization: 0.145257
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1640,13 +1640,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.2550544737000005 1.0
- blocking 0.3057912927 0.09394352542199053
- vectorization 0.3391082254 0.10417897093271615
- loop permutation 0.1174309573 0.03607649526261751
- array packing 0.1103538458 0.033902303845183106
- block caching 0.1113132894 0.034197058850898694
- parallelization 0.14428522230000002 0.04432651541342467
+ none 3.2324178162000003 1.0
+ blocking 0.30807275230000003 0.09530721887375544
+ vectorization 0.3400090568 0.10518722397085145
+ loop permutation 0.11752707310000002 0.03635887431104551
+ array packing 0.11037899209999999 0.03414750146061269
+ block caching 0.1109585756 0.034326804859169426
+ parallelization 0.1452566378 0.04493745736458114
diff --git a/docs/commit_hash b/docs/commit_hash
index 59f7541c3..e6042899d 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-1a98ea2e4faf791eb8ec239209904ff96c016052
+fae79bbc3e499f3b9f26c9a13743896f948b723d
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 3be5d8697..8bf007a33 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -574,7 +574,7 @@ 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 3.064 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 6.369 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index ebf79e1b8..99fe65ec2 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -427,7 +427,7 @@ to download the full example code</p>
<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.zip871e6cb1-0d61-4c9d-8c70-7d6ec9f7c77e 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.zipa6e16c62-fe36-48cd-900e-5d1b1515a350 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 673f99615..33eee2e8b 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -432,14 +432,14 @@ python3 -m pip install -f https://release.oneflow.info <span class="nv">oneflow<
<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, 52.1MB/s]
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- 51%|#####1 | 21.2M/41.5M [00:00<00:00, 41.6MB/s]
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- 97%|#########6| 40.1M/41.5M [00:01<00:00, 28.9MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 36.3MB/s]
+ 19%|#9 | 7.99M/41.5M [00:00<00:00, 39.2MB/s]
+ 38%|###7 | 15.7M/41.5M [00:00<00:00, 51.5MB/s]
+ 51%|##### | 21.0M/41.5M [00:00<00:00, 52.7MB/s]
+ 63%|######3 | 26.3M/41.5M [00:00<00:00, 38.4MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 37.3MB/s]
+ 86%|########6 | 35.9M/41.5M [00:00<00:00, 36.6MB/s]
+ 95%|#########5| 39.5M/41.5M [00:01<00:00, 25.6MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 33.4MB/s]
</pre></div>
</div>
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diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 159425b9b..0ccc93929 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -414,9 +414,9 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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+100%|##########| 44.7M/44.7M [00:00<00:00, 232MB/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 002be2a03..93e4818a3 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -636,7 +636,7 @@ 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 4.560 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.600 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 f49c0cced..ef9df175b 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>05:02.073</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:11.601</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -335,44 +335,44 @@
<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>01:04.560</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>01:06.369</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>01:03.064</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>01:03.600</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>00:38.556</p></td>
+<td><p>00:40.470</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>00:27.544</p></td>
+<td><p>00:28.855</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>00:25.525</p></td>
+<td><p>00:26.353</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>00:24.550</p></td>
+<td><p>00:25.223</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>00:21.921</p></td>
+<td><p>00:23.214</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:19.342</p></td>
+<td><p>00:19.775</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:14.368</p></td>
+<td><p>00:15.301</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>00:02.644</p></td>
+<td><p>00:02.442</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
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 bc26b60f0..1bbdae2c7 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -653,7 +653,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7443 15.7269 15.8897 15.6568 0.0714
+ 16.2295 16.1601 16.6853 16.0297 0.1966
</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 aaaad0d4e..d5a3f78a3 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -436,14 +436,45 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>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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/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: 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).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -538,7 +569,7 @@ 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> ( 2 minutes 56.242 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 5.418 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 247e363fd..6d3001624 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -480,7 +480,9 @@ training. Other models require a full post training calibration.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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+ 81%|########1 | 11.0M/13.6M [00:00<00:00, 56.5MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 44.9MB/s]
</pre></div>
</div>
</div>
@@ -569,7 +571,7 @@ 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)
- 90.2299 90.1862 90.9239 90.0643 0.1529
+ 90.5174 90.4569 93.0623 90.1768 0.3247
</pre></div>
</div>
<div class="admonition note">
@@ -608,7 +610,7 @@ 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 8.639 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 10.339 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 d21e93422..0e7c7441b 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -573,7 +573,7 @@ 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)
- 118.7039 118.6372 124.3227 118.0358 0.6183
+ 120.3960 120.1338 126.8454 119.1645 0.9981
</pre></div>
</div>
<div class="admonition note">
@@ -601,7 +601,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 52.409 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 59.298 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index d02148db0..1a2920f56 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -509,7 +509,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.130 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.744 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/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index 6c542a9f3..2bab33285 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -441,23 +441,25 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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+ 3%|2 | 3547/132723 [00:00<00:03, 35466.31KB/s]
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+ 55%|#####4 | 72851/132723 [00:01<00:00, 74063.22KB/s]
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+ 86%|########6 | 114394/132723 [00:01<00:00, 81690.33KB/s]
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+100%|##########| 132723/132723 [00:01<00:00, 72790.74KB/s]
</pre></div>
</div>
<p>Create TVM runtime and do inference
@@ -500,7 +502,7 @@ Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from h
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" srcset="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" alt="deploy ssd gluoncv" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 35.727 seconds)</p>
+<img src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" srcset="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" alt="deploy ssd gluoncv" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 38.356 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.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_ssd_gluoncv.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 40aa95bc5..948b7c6a5 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>11:23.717</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:54.912</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -336,35 +336,35 @@
</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>02:56.242</p></td>
+<td><p>03:05.418</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></td>
-<td><p>02:35.727</p></td>
+<td><p>02:38.356</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>01:52.409</p></td>
+<td><p>01:59.298</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>01:36.130</p></td>
+<td><p>01:42.744</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>01:08.639</p></td>
+<td><p>01:10.339</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>00:30.998</p></td>
+<td><p>00:32.060</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:21.923</p></td>
+<td><p>00:23.615</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:21.643</p></td>
+<td><p>00:23.075</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
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 f3766ead8..48cf9a42e 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -612,7 +612,7 @@ 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.zip82f9de2d-d806-40fb-8202-e068dcbb339b 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.zip4391d886-7d94-4143-80a2-9022470c245b 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>
@@ -676,7 +676,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
+ Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
</pre></div>
</div>
<p>When we attempt to run the model, we get a familiar error telling us that more functions need to be registered for myfloat.</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 6ef214df9..173b8b44f 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>00:40.750</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:42.016</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,15 +336,15 @@
</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>00:37.616</p></td>
+<td><p>00:38.731</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>00:02.207</p></td>
+<td><p>00:02.299</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>00:00.920</p></td>
+<td><p>00:00.978</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 3e89254b9..0cb92d390 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -512,10 +512,10 @@ 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: 6496us [6496us] (45.60%; 45.60%)
-FoldScaleAxis: 7749us [6us] (54.40%; 54.40%)
- FoldConstant: 7743us [1602us] (54.36%; 99.92%)
- InferType: 6141us [6141us] (43.11%; 79.31%)
+InferType: 6412us [6412us] (45.21%; 45.21%)
+FoldScaleAxis: 7772us [5us] (54.79%; 54.79%)
+ FoldConstant: 7767us [1603us] (54.76%; 99.93%)
+ InferType: 6164us [6164us] (43.46%; 79.36%)
</pre></div>
</div>
</div>
@@ -537,10 +537,10 @@ 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: 6143us [6143us] (44.63%; 44.63%)
-FoldScaleAxis: 7621us [5us] (55.37%; 55.37%)
- FoldConstant: 7616us [1574us] (55.33%; 99.94%)
- InferType: 6042us [6042us] (43.90%; 79.33%)
+InferType: 6172us [6172us] (44.59%; 44.59%)
+FoldScaleAxis: 7671us [5us] (55.41%; 55.41%)
+ FoldConstant: 7666us [1595us] (55.38%; 99.94%)
+ InferType: 6071us [6071us] (43.86%; 79.20%)
</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 ec1e7f7af..4d3258d80 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -564,7 +564,7 @@ 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: 52.768086 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 36.492911 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 c1adc3795..1c129ef23 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -906,7 +906,7 @@ 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: 6.608071 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 8.869483 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 889e59c36..ce3ca3821 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -461,8 +461,8 @@ 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: 0.018873
-Baseline: 3.255020
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018670
+Baseline: 3.236111
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -522,7 +522,7 @@ 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: 0.303365
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.315768
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -589,7 +589,7 @@ 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: 0.336829
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.342657
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -650,7 +650,7 @@ 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: 0.115725
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.120931
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -733,7 +733,7 @@ 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: 0.110672
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.111174
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -819,7 +819,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111722
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111934
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -909,7 +909,7 @@ write to C when all the block results are ready.</p>
<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: 0.145089
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145182
</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 6a5641508..0200f970a 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>00:33.919</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.201</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -336,15 +336,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:31.802</p></td>
+<td><p>00:32.013</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>00:01.165</p></td>
+<td><p>00:01.212</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>00:00.953</p></td>
+<td><p>00:00.976</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 a175940b9..04f1d354e 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>06:13.024</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>06:17.476</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -336,27 +336,27 @@
</colgroup>
<tbody>
<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>03:26.920</p></td>
+<td><p>03:28.355</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>01:22.161</p></td>
+<td><p>01:23.413</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:46.145</p></td>
+<td><p>00:46.102</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:20.098</p></td>
+<td><p>00:21.874</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>00:09.032</p></td>
+<td><p>00:08.897</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>00:08.669</p></td>
+<td><p>00:08.835</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index 2e2f6e196..65328da1e 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
@@ -1004,7 +1004,7 @@ cooperative fetching, unrolling and operator fusion.</p>
<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: 0.364 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.353 ms
</pre></div>
</div>
</div>
@@ -1567,7 +1567,7 @@ In the example below we resume the status and do more 5 trials.</p>
Get devices for measurement successfully!
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 26.920 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 28.355 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_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_conv2d_layer_cuda.py</span></code></a></p>
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 2ae0ebd89..74166c6ee 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -906,7 +906,7 @@ 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)
- 9.9184 9.9224 9.9384 9.8945 0.0181
+ 9.7941 9.8211 9.8446 9.7168 0.0555
</pre></div>
</div>
</div>
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 5bfcc4d9c..f2e34c79f 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -925,7 +925,7 @@ 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)
- 751.9575 751.7686 752.3663 751.7377 0.2893
+ 754.4837 752.7454 761.3244 749.3813 5.0283
</pre></div>
</div>
</div>
@@ -947,7 +947,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.161 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 23.413 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
index 65740b0ba..78cd6ef9e 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -625,143 +625,80 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_8: placeholder_15: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_16: Buffer(placeholder_14, float32, [128, 512], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_19: Buffer(placeholder_11, float32, [4916, 16, 1], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 2048) "parallel" {
- let cse_var_1: int32 = floordiv(floormod(i0.outer.i1.outer.fused, 512), 16)
- allocate(compute_4: Pointer(global float32), float32, [32]), storage_scope = global {
- compute_5: Buffer(compute_4, float32, [32], [])[0] = 0f32
- compute_5[1] = 0f32
- compute_5[2] = 0f32
- compute_5[3] = 0f32
- compute_5[4] = 0f32
- compute_5[5] = 0f32
- compute_5[6] = 0f32
- compute_5[7] = 0f32
- compute_5[8] = 0f32
- compute_5[9] = 0f32
- compute_5[10] = 0f32
- compute_5[11] = 0f32
- compute_5[12] = 0f32
- compute_5[13] = 0f32
- compute_5[14] = 0f32
- compute_5[15] = 0f32
- compute_5[16] = 0f32
- compute_5[17] = 0f32
- compute_5[18] = 0f32
- compute_5[19] = 0f32
- compute_5[20] = 0f32
- compute_5[21] = 0f32
- compute_5[22] = 0f32
- compute_5[23] = 0f32
- compute_5[24] = 0f32
- compute_5[25] = 0f32
- compute_5[26] = 0f32
- compute_5[27] = 0f32
- compute_5[28] = 0f32
- compute_5[29] = 0f32
- compute_5[30] = 0f32
- compute_5[31] = 0f32
- for (elem_idx: int32, 0, (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[0] = (compute_5[0] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[1] = (compute_5[1] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[2] = (compute_5[2] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[3] = (compute_5[3] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[4] = (compute_5[4] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[5] = (compute_5[5] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[6] = (compute_5[6] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[7] = (compute_5[7] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[8] = (compute_5[8] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2048)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[9] = (compute_5[9] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2304)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[10] = (compute_5[10] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2560)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[11] = (compute_5[11] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 2816)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[12] = (compute_5[12] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3072)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[13] = (compute_5[13] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3328)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[14] = (compute_5[14] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3584)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[15] = (compute_5[15] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 3840)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[16] = (compute_5[16] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4096)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[17] = (compute_5[17] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4352)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[18] = (compute_5[18] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4608)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[19] = (compute_5[19] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 4864)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[20] = (compute_5[20] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5120)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[21] = (compute_5[21] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5376)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[22] = (compute_5[22] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5632)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[23] = (compute_5[23] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 5888)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[24] = (compute_5[24] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6144)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[25] = (compute_5[25] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6400)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[26] = (compute_5[26] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6656)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[27] = (compute_5[27] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 6912)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[28] = (compute_5[28] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7168)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[29] = (compute_5[29] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7424)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[30] = (compute_5[30] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7680)], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])), dtype=bool) {
- compute_5[31] = (compute_5[31] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + (elem_idx*16)) + floormod(i0.outer.i1.outer.fused, 16))]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 512)*8192) + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 7936)], 0f32)))
+ preflattened_buffer_map = {placeholder_5: placeholder_15: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_16: Buffer(placeholder_12, int32, [4916], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [4096]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 32) {
+ let cse_var_1: int32 = (((i.outer.inner*1024) + (i.inner.init*32)) + (nb_j.inner*16))
+ {
+ compute_5: Buffer(compute_4, float32, [4096], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ }
+ }
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 32) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((i0.outer.i1.outer.fused*2) + nb_j.inner)
+ let cse_var_19: int32 = ((i.outer.inner*8192) + (i.inner*256))
+ let cse_var_18: int32 = (((i.outer.inner*1024) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_17: int32 = (cse_var_18 + 9)
+ let cse_var_16: int32 = (cse_var_18 + 8)
+ let cse_var_15: int32 = (cse_var_18 + 7)
+ let cse_var_14: int32 = (cse_var_18 + 6)
+ let cse_var_13: int32 = (cse_var_18 + 5)
+ let cse_var_12: int32 = (cse_var_18 + 4)
+ let cse_var_11: int32 = (cse_var_18 + 3)
+ let cse_var_10: int32 = (cse_var_18 + 2)
+ let cse_var_9: int32 = (cse_var_18 + 15)
+ let cse_var_8: int32 = (cse_var_18 + 14)
+ let cse_var_7: int32 = (cse_var_18 + 13)
+ let cse_var_6: int32 = (cse_var_18 + 12)
+ let cse_var_5: int32 = (cse_var_18 + 11)
+ let cse_var_4: int32 = (cse_var_18 + 10)
+ let cse_var_3: int32 = (cse_var_18 + 1)
+ {
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
+ }
+ }
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_2: int32 = (((floordiv(i0.outer.i1.outer.fused, 512)*16384) + (i0.inner*512)) + floormod(i0.outer.i1.outer.fused, 512))
- compute[cse_var_2] = max((compute_5[i0.inner] + placeholder_4[cse_var_2]), 0f32)
+ for (i0.inner: int32, 0, 128) {
+ for (i1.inner: int32, 0, 32) {
+ let cse_var_22: int32 = (((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)
+ compute[cse_var_22] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_22]), 0f32)
+ }
}
}
}
@@ -799,7 +736,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
<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: 3.759 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.730 ms
</pre></div>
</div>
<div class="admonition note">
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 80cb44437..653f25766 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>00:45.391</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:46.187</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,15 +336,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:45.357</p></td>
+<td><p>00:46.151</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.019</p></td>
+<td><p>00:00.020</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
-<td><p>00:00.005</p></td>
+<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
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 70a75137a..0774747d7 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -1436,8 +1436,8 @@ No: 8 GFLOPS: 0.00/0.00 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 2, 1, 64]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4909501
-No: 9 GFLOPS: 80.78/80.78 result: MeasureResult(costs=(0.0028658760000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9257969856262207, timestamp=1660112412.9944103) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
-No: 10 GFLOPS: 0.00/80.78 result: Traceback (most recent call last):
+No: 9 GFLOPS: 80.79/80.79 result: MeasureResult(costs=(0.0028654588000000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9271838665008545, timestamp=1660114072.9038901) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
+No: 10 GFLOPS: 0.00/80.79 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1560,8 +1560,8 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5092711
-No: 11 GFLOPS: 260.36/260.36 result: MeasureResult(costs=(0.0008891516795580111,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6766939163208008, timestamp=1660112413.8546999) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
-No: 12 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 11 GFLOPS: 261.20/261.20 result: MeasureResult(costs=(0.0008862881491712708,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6869051456451416, timestamp=1660114073.8029814) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
+No: 12 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1684,7 +1684,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 128, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,183542
-No: 13 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1807,7 +1807,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 8, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2482196
-No: 14 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1930,9 +1930,9 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10306226
-No: 15 GFLOPS: 5.29/260.36 result: MeasureResult(costs=(0.04379221625,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8522651195526123, timestamp=1660112418.4552326) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
-No: 16 GFLOPS: 3.34/260.36 result: MeasureResult(costs=(0.06938179675,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.596736669540405, timestamp=1660112419.6940444) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
-No: 17 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 15 GFLOPS: 5.31/261.20 result: MeasureResult(costs=(0.04355937375,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8288960456848145, timestamp=1660114078.374904) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
+No: 16 GFLOPS: 3.36/261.20 result: MeasureResult(costs=(0.06893701575,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.56779932975769, timestamp=1660114079.6050844) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
+No: 17 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1950,8 +1950,8 @@ No: 17 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 2, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 16]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10195251
-No: 18 GFLOPS: 28.02/260.36 result: MeasureResult(costs=(0.008261535357142857,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3037467002868652, timestamp=1660112430.7455037) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
-No: 19 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 18 GFLOPS: 28.17/261.20 result: MeasureResult(costs=(0.008219214357142858,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2799923419952393, timestamp=1660114090.6589699) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
+No: 19 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -2074,7 +2074,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 16, 4, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6956993
-No: 20 GFLOPS: 0.00/260.36 result: Traceback (most recent call last):
+No: 20 GFLOPS: 0.00/261.20 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -2237,7 +2237,7 @@ and measure running time.</p>
Best config:
[('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
Finish loading 20 records
-Time cost of this operator: 0.001253
+Time cost of this operator: 0.001254
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 6c36d03d5..b07301338 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -584,10 +584,10 @@ the tuned operator.</p>
########## 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 311.0 98.713 (1, 2, 10, 10, 3) 2 1 [311.0]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.098 0.983 (1, 6, 10, 10) 1 1 [3.098]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.959 0.304 (1, 1, 10, 10, 3) 1 1 [0.959]
-Total_time - 315.056 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 312.7 98.727 (1, 2, 10, 10, 3) 2 1 [312.7]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.968 (1, 6, 10, 10) 1 1 [3.067]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.963 0.304 (1, 1, 10, 10, 3) 1 1 [0.963]
+Total_time - 316.731 - - - - -
</pre></div>
</div>
</div>
@@ -640,10 +640,10 @@ Total_time -
########## 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 195.9 98.664 (1, 6, 10, 10, 1) 2 1 [195.9]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.791 0.902 (1, 6, 10, 10) 1 1 [1.791]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.862 0.434 (1, 3, 10, 10, 1) 1 1 [0.862]
-Total_time - 198.552 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 79.312 96.587 (1, 6, 10, 10, 1) 2 1 [79.312]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.84 2.241 (1, 6, 10, 10) 1 1 [1.84]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.962 1.172 (1, 1, 10, 10, 3) 1 1 [0.962]
+Total_time - 82.115 - - - - -
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index b128c1f42..208c01c61 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -516,7 +516,7 @@ 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/tmpb3abcwff/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpkk78cada/images/random'
</pre></div>
</div>
</div>
@@ -576,8 +576,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpb3abcwff/images/target contains 8144 images
-/tmp/tmpb3abcwff/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], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpkk78cada/images/target contains 8144 images
+/tmp/tmpkk78cada/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -689,13 +689,13 @@ 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 - 55s - loss: 0.2212 - accuracy: 0.9220 - val_loss: 0.1603 - val_accuracy: 0.9524
+328/328 - 55s - loss: 0.2200 - accuracy: 0.9264 - val_loss: 0.1369 - val_accuracy: 0.9581
Epoch 2/3
-328/328 - 52s - loss: 0.1008 - accuracy: 0.9626 - val_loss: 0.1076 - val_accuracy: 0.9675
+328/328 - 52s - loss: 0.1032 - accuracy: 0.9616 - val_loss: 0.1184 - val_accuracy: 0.9615
Epoch 3/3
-328/328 - 52s - loss: 0.0645 - accuracy: 0.9774 - val_loss: 0.1079 - val_accuracy: 0.9656
+328/328 - 52s - loss: 0.0660 - accuracy: 0.9759 - val_loss: 0.1038 - val_accuracy: 0.9649
-<keras.callbacks.History object at 0x7f7e70c0c590>
+<keras.callbacks.History object at 0x7f4225688510>
</pre></div>
</div>
</div>
@@ -957,7 +957,7 @@ 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> ( 5 minutes 23.893 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 8.196 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 26d18305d..8516ed9e0 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>06:16.595</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>06:04.416</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -336,19 +336,19 @@
</colgroup>
<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">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
-<td><p>05:23.893</p></td>
+<td><p>05:08.196</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>00:41.390</p></td>
+<td><p>00:44.338</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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">microTVM Host-Driven AoT</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:08.111</p></td>
+<td><p>00:08.533</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:03.199</p></td>
+<td><p>00:03.346</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">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>
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<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<col style="width: 84%" />
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<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
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index c830fab00..17d1544c6 100644
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+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -522,7 +522,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
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</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f7deb41e680>
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</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
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<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<tr class="row-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>
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<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
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diff --git a/docs/install/nnpack.html b/docs/install/nnpack.html
index 3153785d7..aa2238b85 100644
--- a/docs/install/nnpack.html
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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Installing TVM</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="from_source.html">Install from Source</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="from_source.html">Install from Source</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#developers-get-source-from-github">Developers: Get Source from Github</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#build-the-shared-library">Build the Shared Library</a></li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#python-package-installation">Python Package Installation</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="from_source.html#install-contrib-libraries">Install Contrib Libraries</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a><ul>
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<title>Node131</title>
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+<title>Node191->Node192</title>
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+<title>Node194->Node191</title>
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+<title>Node195->Node163</title>
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<g id="edge97" class="edge">
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+<path fill="none" stroke="#191970" d="M1918.1312,-610.9136C2010.1549,-576.4926 2183,-493.5347 2183,-356 2183,-356 2183,-356 2183,-283.5 2183,-213.0048 2205.8468,-130.9849 2216.11,-97.6964"/>
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+<!-- Node198->Node180 -->
+<g id="edge75" class="edge">
+<title>Node198->Node180</title>
+<path fill="none" stroke="#191970" d="M1846.2868,-605.966C1833.2378,-587.8868 1813.2618,-563.3266 1791,-547 1764.3091,-527.4252 1729.0318,-513.4272 1703.6433,-505.0118"/>
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-<!-- Node197->Node34 -->
+<!-- Node198->Node131 -->
<g id="edge74" class="edge">
-<title>Node197->Node34</title>
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-<polygon fill="#191970" stroke="#191970" points="1880.5892,-612.9085 1870.7383,-616.8082 1880.8711,-619.9028 1880.5892,-612.9085"/>
+<title>Node198->Node131</title>
+<path fill="none" stroke="#191970" d="M1874.0996,-604.8173C1882.1938,-588.3891 1893.1308,-566.3506 1903,-547 1921.9885,-509.7691 1944.9263,-466.3095 1956.872,-443.7868"/>
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-<!-- Node197->Node179 -->
+<!-- Node198->Node200 -->
<g id="edge76" class="edge">
-<title>Node197->Node179</title>
-<path fill="none" stroke="#191970" d="M1829.334,-594.8129C1839.5196,-577.3528 1848.4233,-553.6671 1836.4535,-536 1821.4396,-513.84 1795.1931,-501.2147 1770.2775,-494.0218"/>
-<polygon fill="#191970" stroke="#191970" points="1826.3511,-592.9816 1823.9945,-603.311 1832.2782,-596.7057 1826.3511,-592.9816"/>
+<title>Node198->Node200</title>
+<path fill="none" stroke="#191970" d="M1930.377,-623.0597C1996.2174,-615.81 2098.1715,-601.8156 2184,-578 2219.5484,-568.1361 2225.4451,-556.8406 2261,-547 2359.4486,-519.7523 2387.7334,-530.5238 2488,-511 2490.9298,-510.4295 2493.9263,-509.8226 2496.9485,-509.1919"/>
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-<title>Node197->Node131</title>
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+<!-- Node198->Node203 -->
+<g id="edge78" class="edge">
+<title>Node198->Node203</title>
+<path fill="none" stroke="#191970" d="M1929.033,-611.8874C1969.6051,-601.2273 2020.8078,-587.774 2059.5234,-577.6017"/>
+<polygon fill="#191970" stroke="#191970" points="1928.0093,-608.5375 1919.227,-614.4639 1929.7882,-615.3077 1928.0093,-608.5375"/>
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-<!-- Node197->Node199 -->
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-<title>Node197->Node199</title>
-<path fill="none" stroke="#191970" d="M1880.8411,-615.4124C2018.4873,-608.7639 2324.5149,-591.6716 2426.4535,-567 2490.1571,-551.5822 2559.9416,-518.8509 2597.9181,-499.5729"/>
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+<!-- Node198->Node209 -->
+<g id="edge91" class="edge">
+<title>Node198->Node209</title>
+<path fill="none" stroke="#191970" d="M1808.3182,-610.865C1780.6025,-600.3773 1747.7367,-587.4515 1723.7946,-577.6017"/>
+<polygon fill="#191970" stroke="#191970" points="1807.2917,-614.2183 1817.8837,-614.4639 1809.7567,-607.6666 1807.2917,-614.2183"/>
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-<!-- Node197->Node202 -->
-<g id="edge79" class="edge">
-<title>Node197->Node202</title>
-<path fill="none" stroke="#191970" d="M1880.5956,-603.6894C1881.8914,-603.4536 1883.1783,-603.2235 1884.4535,-603 2001.528,-582.4774 2031.77,-583.6792 2149.4535,-567 2164.6876,-564.8409 2181.1545,-562.4412 2196.4294,-560.1877"/>
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+<!-- Node198->Node210 -->
+<g id="edge93" class="edge">
+<title>Node198->Node210</title>
+<path fill="none" stroke="#191970" d="M1930.4951,-619.8589C2020.7685,-607.1523 2178.6775,-584.9256 2269.1352,-572.1932"/>
+<polygon fill="#191970" stroke="#191970" points="1929.7815,-616.4247 1920.3669,-621.2845 1930.7572,-623.3564 1929.7815,-616.4247"/>
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-<!-- Node197->Node208 -->
-<g id="edge92" class="edge">
-<title>Node197->Node208</title>
-<path fill="none" stroke="#191970" d="M1781.4524,-596.7808C1769.6175,-586.9383 1756.8928,-575.541 1747.8773,-566.6432"/>
-<polygon fill="#191970" stroke="#191970" points="1779.4843,-599.692 1789.4471,-603.2967 1783.9067,-594.2659 1779.4843,-599.692"/>
+<!-- Node200->Node201 -->
+<g id="edge77" class="edge">
+<title>Node200->Node201</title>
+<path fill="none" stroke="#191970" d="M2558.3827,-470.3179C2559.5885,-461.3414 2560.9085,-451.5143 2561.9658,-443.6432"/>
+<polygon fill="#191970" stroke="#191970" points="2554.9048,-469.9197 2557.0422,-480.2967 2561.8425,-470.8517 2554.9048,-469.9197"/>
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-<!-- Node197->Node209 -->
-<g id="edge94" class="edge">
-<title>Node197->Node209</title>
-<path fill="none" stroke="#191970" d="M1880.2768,-600.955C1921.5265,-590.2842 1973.6692,-576.7955 2013.0753,-566.6017"/>
-<polygon fill="#191970" stroke="#191970" points="1879.3829,-597.5709 1870.5782,-603.4639 1881.1361,-604.3478 1879.3829,-597.5709"/>
+<!-- Node203->Node6 -->
+<g id="edge79" class="edge">
+<title>Node203->Node6</title>
+<path fill="none" stroke="#191970" d="M2048.7476,-559.4435C1915.6456,-553.1345 1612.1795,-537.0411 1358,-511 1140.2165,-488.6877 1085.0359,-485.074 870,-444 760.9268,-423.1659 635.261,-390.1513 565.5932,-371.036"/>
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-<!-- Node199->Node200 -->
-<g id="edge78" class="edge">
-<title>Node199->Node200</title>
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-<polygon fill="#191970" stroke="#191970" points="2630.126,-458.7211 2630.7649,-469.2967 2636.8604,-460.6309 2630.126,-458.7211"/>
+<!-- Node203->Node38 -->
+<g id="edge83" class="edge">
+<title>Node203->Node38</title>
+<path fill="none" stroke="#191970" d="M2123.6876,-537.4095C2125.6866,-529.0622 2127.6918,-519.6808 2129,-511 2144.104,-410.7708 2145,-384.8609 2145,-283.5 2145,-283.5 2145,-283.5 2145,-216.5 2145,-166.0882 1999.7426,-120.2348 1913.7797,-97.5127"/>
+<polygon fill="#191970" stroke="#191970" points="2120.2452,-536.7486 2121.2118,-547.2992 2127.0356,-538.4485 2120.2452,-536.7486"/>
</g>
-<!-- Node202->Node6 -->
+<!-- Node203->Node33 -->
<g id="edge80" class="edge">
-<title>Node202->Node6</title>
-<path fill="none" stroke="#191970" d="M2186.1225,-540.6178C2173.9319,-538.9035 2161.3335,-537.2783 2149.4535,-536 1914.7102,-510.7409 1854.6697,-520.3904 1619.4535,-500 1336.725,-475.4909 1265.1765,-474.602 984.4535,-433 855.7732,-413.9301 707.0078,-383.5967 622.1975,-365.5089"/>
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-<!-- Node202->Node38 -->
-<g id="edge84" class="edge">
-<title>Node202->Node38</title>
-<path fill="none" stroke="#191970" d="M2246.625,-526.76C2244.2598,-518.3486 2241.9071,-508.8398 2240.4535,-500 2213.3403,-335.1175 2283.5931,-240.0394 2154.4535,-134 2119.939,-105.6593 2002.2863,-92.4668 1922.798,-86.6628"/>
-<polygon fill="#191970" stroke="#191970" points="2243.2698,-527.7565 2249.4567,-536.3572 2249.9836,-525.7754 2243.2698,-527.7565"/>
+<title>Node203->Node33</title>
+<path fill="none" stroke="#191970" d="M2169.6956,-543.4356C2188.0785,-535.2204 2208.0744,-524.3855 2224,-511 2288.8634,-456.4821 2335,-440.7317 2335,-356 2335,-356 2335,-356 2335,-149.5 2335,-110.634 2335.9893,-91.7227 2306,-67 2265.4217,-33.5479 2122.512,-21.8055 2032.3374,-17.6969"/>
+<polygon fill="#191970" stroke="#191970" points="2168.0641,-540.3272 2160.2597,-547.4925 2170.829,-546.758 2168.0641,-540.3272"/>
</g>
-<!-- Node202->Node33 -->
+<!-- Node203->Node34 -->
<g id="edge81" class="edge">
-<title>Node202->Node33</title>
-<path fill="none" stroke="#191970" d="M2262.2458,-526.68C2273.3648,-489.0031 2292.4535,-414.9371 2292.4535,-350.5 2292.4535,-350.5 2292.4535,-350.5 2292.4535,-283.5 2292.4535,-112.9936 2049.9998,-48.2279 1922.6169,-25.967"/>
-<polygon fill="#191970" stroke="#191970" points="2258.88,-525.7189 2259.3436,-536.3036 2265.5819,-527.7401 2258.88,-525.7189"/>
+<title>Node203->Node34</title>
+<path fill="none" stroke="#191970" d="M2156.5688,-542.2337C2170.4565,-533.8464 2185.4568,-523.2089 2197,-511 2247.9737,-457.0864 2259,-430.1956 2259,-356 2259,-356 2259,-356 2259,-216.5 2259,-172.0065 2239.3691,-122.2478 2228.3268,-97.778"/>
+<polygon fill="#191970" stroke="#191970" points="2154.5395,-539.3648 2147.6507,-547.4143 2158.0557,-545.4176 2154.5395,-539.3648"/>
</g>
-<!-- Node202->Node34 -->
+<!-- Node203->Node129 -->
<g id="edge82" class="edge">
-<title>Node202->Node34</title>
-<path fill="none" stroke="#191970" d="M2270.2599,-527.1425C2292.5288,-490.416 2330.4535,-417.9659 2330.4535,-350.5 2330.4535,-350.5 2330.4535,-350.5 2330.4535,-216.5 2330.4535,-163.8917 2381.2168,-119.6583 2412.1506,-97.5874"/>
-<polygon fill="#191970" stroke="#191970" points="2266.9987,-525.7603 2264.7032,-536.1035 2272.9478,-529.4493 2266.9987,-525.7603"/>
+<title>Node203->Node129</title>
+<path fill="none" stroke="#191970" d="M2118.2568,-537.117C2118.4708,-504.4024 2113.8787,-447.6249 2083,-413 2041.0124,-365.9185 1869.1641,-321.1083 1770.5185,-298.5916"/>
+<polygon fill="#191970" stroke="#191970" points="2114.7553,-537.1465 2118.031,-547.2222 2121.7536,-537.303 2114.7553,-537.1465"/>
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-<!-- Node202->Node35 -->
+<!-- Node203->Node179 -->
+<g id="edge90" class="edge">
+<title>Node203->Node179</title>
+<path fill="none" stroke="#191970" d="M2048.7459,-559.3728C1946.7524,-553.9219 1748.8109,-540.3358 1583,-511 1466.3417,-490.3604 1397.4824,-537.7095 1325,-444 1316.5705,-433.1018 1317.2553,-424.395 1325,-413 1335.0507,-398.2122 1373.5255,-383.4048 1408.1772,-372.5896"/>
+<polygon fill="#191970" stroke="#191970" points="2048.74,-562.8772 2058.9096,-559.9059 2049.1067,-555.8868 2048.74,-562.8772"/>
+</g>
+<!-- Node203->Node180 -->
+<g id="edge85" class="edge">
+<title>Node203->Node180</title>
+<path fill="none" stroke="#191970" d="M2048.5574,-552.2183C1962.9448,-539.3572 1817.5233,-517.5114 1734.3608,-505.0183"/>
+<polygon fill="#191970" stroke="#191970" points="2048.2843,-555.7164 2058.6933,-553.7409 2049.3242,-548.7941 2048.2843,-555.7164"/>
+</g>
+<!-- Node203->Node131 -->
+<g id="edge84" class="edge">
+<title>Node203->Node131</title>
+<path fill="none" stroke="#191970" d="M2092.1052,-540.5533C2061.1792,-513.2895 2009.2158,-467.4797 1982.3114,-443.7614"/>
+<polygon fill="#191970" stroke="#191970" points="2090.0433,-543.4014 2099.8591,-547.389 2094.6724,-538.1505 2090.0433,-543.4014"/>
+</g>
+<!-- Node203->Node200 -->
<g id="edge86" class="edge">
-<title>Node202->Node35</title>
-<path fill="none" stroke="#191970" d="M2276.2641,-528.8501C2293.7676,-511.3516 2319.4621,-487.1393 2344.4535,-469 2517.9138,-343.0989 2656.3727,-417.0894 2764.4535,-232 2805.4327,-161.8227 2730.8994,-67.2028 2697.957,-30.7196"/>
-<polygon fill="#191970" stroke="#191970" points="2273.3705,-526.7988 2268.8303,-536.3716 2278.3492,-531.7195 2273.3705,-526.7988"/>
+<title>Node203->Node200</title>
+<path fill="none" stroke="#191970" d="M2185.1375,-547.6626C2186.4347,-547.435 2187.7232,-547.2138 2189,-547 2321.0102,-524.8919 2356.1628,-534.1175 2488,-511 2490.9399,-510.4845 2493.9446,-509.9218 2496.9734,-509.3262"/>
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-<!-- Node202->Node129 -->
-<g id="edge83" class="edge">
-<title>Node202->Node129</title>
-<path fill="none" stroke="#191970" d="M2228.4612,-529.0715C2219.526,-520.5465 2209.906,-510.3765 2202.4535,-500 2154.213,-432.8318 2168.194,-399.5108 2116.4535,-335 2105.5224,-321.3709 2090.8528,-308.2048 2079.1332,-298.5966"/>
-<polygon fill="#191970" stroke="#191970" points="2226.3472,-531.8838 2236.0654,-536.1036 2231.0998,-526.7444 2226.3472,-531.8838"/>
+<!-- Node203->Node201 -->
+<g id="edge89" class="edge">
+<title>Node203->Node201</title>
+<path fill="none" stroke="#191970" d="M2184.9376,-547.2328C2231.4688,-536.2528 2288.6516,-521.5774 2311,-511 2332.295,-500.9211 2332.6102,-489.8762 2354,-480 2402.736,-457.4972 2462.5504,-444.0649 2505.8596,-436.5911"/>
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-<!-- Node202->Node179 -->
+<!-- Node203->Node204 -->
<g id="edge87" class="edge">
-<title>Node202->Node179</title>
-<path fill="none" stroke="#191970" d="M2186.0652,-541.0958C2173.8807,-539.3303 2161.2993,-537.5641 2149.4535,-536 2022.8877,-519.2888 1875.5476,-502.6929 1786.9907,-493.0268"/>
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-<g id="edge85" class="edge">
-<title>Node202->Node131</title>
-<path fill="none" stroke="#191970" d="M2218.2469,-531.3579C2201.7238,-522.0189 2181.9765,-510.6504 2164.4535,-500 2127.1761,-477.3429 2084.7777,-449.62 2059.3455,-432.7737"/>
-<polygon fill="#191970" stroke="#191970" points="2216.6292,-534.4637 2227.0601,-536.3201 2220.0635,-528.3641 2216.6292,-534.4637"/>
+<title>Node203->Node204</title>
+<path fill="none" stroke="#191970" d="M2185.1961,-546.1717C2238.6951,-534.0882 2312.2679,-517.9195 2362.9764,-507.1288"/>
+<polygon fill="#191970" stroke="#191970" points="2184.0941,-542.8326 2175.1139,-548.4546 2185.64,-549.6598 2184.0941,-542.8326"/>
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-<!-- Node202->Node199 -->
+<!-- Node204->Node203 -->
<g id="edge88" class="edge">
-<title>Node202->Node199</title>
-<path fill="none" stroke="#191970" d="M2322.6891,-539.2103C2393.1661,-526.5168 2501.961,-506.922 2568.4307,-494.9504"/>
-<polygon fill="#191970" stroke="#191970" points="2321.7997,-535.814 2312.5785,-541.0313 2323.0406,-542.7032 2321.7997,-535.814"/>
+<title>Node204->Node203</title>
+<path fill="none" stroke="#191970" d="M2352.8776,-511.8116C2299.4413,-523.8814 2225.9448,-540.0339 2175.2196,-550.8295"/>
+<polygon fill="#191970" stroke="#191970" points="2353.9678,-515.1534 2362.9479,-509.5314 2352.4218,-508.3263 2353.9678,-515.1534"/>
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-<!-- Node202->Node200 -->
-<g id="edge91" class="edge">
-<title>Node202->Node200</title>
-<path fill="none" stroke="#191970" d="M2291.2909,-531.5706C2307.7898,-522.3526 2327.3417,-511.0211 2344.4535,-500 2364.2606,-487.243 2365.8542,-478.409 2387.4535,-469 2452.4639,-440.6803 2533.51,-427.7689 2587.233,-421.9924"/>
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+<!-- Node209->Node198 -->
+<g id="edge92" class="edge">
+<title>Node209->Node198</title>
+<path fill="none" stroke="#191970" d="M1744.8665,-581.2049C1772.605,-591.7034 1805.4624,-604.6278 1829.3648,-614.4639"/>
+<polygon fill="#191970" stroke="#191970" points="1745.8836,-577.8481 1735.2916,-577.6017 1743.4181,-584.3995 1745.8836,-577.8481"/>
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-<!-- Node202->Node203 -->
-<g id="edge89" class="edge">
-<title>Node202->Node203</title>
-<path fill="none" stroke="#191970" d="M2303.1661,-533.1392C2333.6406,-522.6033 2372.759,-509.5375 2403.6253,-499.6017"/>
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+<!-- Node212->Node162 -->
+<g id="edge101" class="edge">
+<title>Node212->Node162</title>
+<path fill="none" stroke="#191970" d="M2852.9417,-671.6103C2850.3739,-662.5553 2847.5507,-652.5998 2845.2943,-644.6432"/>
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-<!-- Node203->Node202 -->
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-<title>Node203->Node202</title>
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+<!-- Node214->Node13 -->
+<g id="edge116" class="edge">
+<title>Node214->Node13</title>
+<path fill="none" stroke="#191970" d="M1886.4364,-695.5571C1744.5072,-692.8559 1423.236,-682.6489 1321,-645 1298.8917,-636.8585 1299.671,-623.2427 1278,-614 1196.532,-579.2537 1159.3414,-621.1585 1082,-578 1018.1754,-542.3842 968.5843,-465.2777 952.5264,-438.1657"/>
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-<!-- Node208->Node197 -->
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-<title>Node208->Node197</title>
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+<title>Node214->Node16</title>
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+<g id="edge106" class="edge">
+<title>Node214->Node139</title>
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+<title>Node214->Node38</title>
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+<title>Node214->Node33</title>
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+<title>Node214->Node34</title>
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+<!-- Node214->Node40 -->
<g id="edge114" class="edge">
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+<title>Node214->Node40</title>
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+<!-- Node214->Node176 -->
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+<title>Node214->Node176</title>
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+<!-- Node214->Node177 -->
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+<title>Node214->Node177</title>
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+<!-- Node214->Node128 -->
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+<title>Node214->Node128</title>
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-<!-- Node213->Node34 -->
+<!-- Node214->Node129 -->
<g id="edge112" class="edge">
-<title>Node213->Node34</title>
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+<title>Node214->Node129</title>
+<path fill="none" stroke="#191970" d="M1886.6235,-690.8036C1827.9826,-684.3552 1741.975,-671.1359 1671,-645 1504.1127,-583.5452 1413.7152,-598.1352 1325,-444 1318.1271,-432.0589 1315.9875,-423.4213 1325,-413 1361.2839,-371.0444 1520.6503,-397.976 1572,-377 1617.602,-358.3719 1662.0125,-320.0812 1684.6027,-298.7323"/>
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+<!-- Node214->Node180 -->
<g id="edge117" class="edge">
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+<title>Node214->Node180</title>
+<path fill="none" stroke="#191970" d="M1887.0507,-682.0041C1799.3156,-661.7272 1654.2645,-622.3627 1624,-578 1606.814,-552.8082 1639.012,-520.9423 1658.1938,-505.2044"/>
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-<!-- Node213->Node40 -->
+<!-- Node214->Node131 -->
<g id="edge115" class="edge">
-<title>Node213->Node40</title>
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+<title>Node214->Node131</title>
+<path fill="none" stroke="#191970" d="M1955.9496,-671.0511C1958.0026,-616.0306 1962.8012,-487.4277 1964.4399,-443.5103"/>
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+<!-- Node214->Node186 -->
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+<title>Node214->Node186</title>
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+<title>Node214->Node198</title>
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+<title>Node214->Node201</title>
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+<title>Node214->Node203</title>
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-<!-- Node213->Node185 -->
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<g id="edge120" class="edge">
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-<title>Node213->Node202</title>
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diff --git a/docs/reference/api/doxygen/classes.html b/docs/reference/api/doxygen/classes.html
index 76bf11b57..1d33b4e39 100644
--- a/docs/reference/api/doxygen/classes.html
+++ b/docs/reference/api/doxygen/classes.html
@@ -125,13 +125,13 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1AssignDoc.html">AssignDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html">Dilation2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html">L [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1AssignDocNode.html">AssignDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Div.html">Div</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoopRV.html">LoopRV</a> (<a class="el" href="namespacetvm_ [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html">AttachMap</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DivNode.html">DivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoopRVNode.html">LoopRVNode</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html">AttachMapNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1Doc.html">Doc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAt [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1AttrAccessDoc.html">AttrAccessDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1DocNode.html">DocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">L [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html">AttachMapNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1Doc.html">Doc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAt [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1AttrAccessDoc.html">AttrAccessDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1DocNode.html">DocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">L [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1AttrAccessDocNode.html">AttrAccessDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html">DominatorPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LTNode.html">LTNod [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html">AttrDocEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html">DominatorPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_m"></a><table border="0" cellspacing="0" cellpadding="0"><t [...]
</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1TensorInfoNode.html">TensorInfoNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.html">tvm::runtime::metadata</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html">RecClosureObj</a> (<a class="el" href="n [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DurationNode.html">DurationNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runtime::profiling</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html">RecClosureObj</a> (<a class="el" href="n [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DurationNode.html">DurationNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runtime::profiling</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a> (<a class="el" href="namespacetvm_1 [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrExistVisitor.html">AttrExistVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DynExpandDimsAttrs.html">DynExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">MapNode</a> (<a class="el" h [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfo.html">AttrFieldInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_e"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  e  </div></td></tr></table>
</td><td valign="top"><a class="el" href="classtvm_1_1MapValuePath.html">MapValuePath</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html">RecordToFile</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> (<a class="el" [...]
@@ -149,162 +149,162 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Executor.html">Executor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm:: [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html">ExecutorNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html">MeasureCallback</a> (<a class="e [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExecutorRegEntry.html">ExecutorRegEntry</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html">MeasureCallback</a> (<a class="el" href="namespacetvm_1_1auto__sche [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html">MeasureCallbackNode</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallbackNode.html">MeasureCallbackNode< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html">MeasureCallbackNode</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallbackNode.html">MeasureCallbackNode< [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprDoc.html">ExprDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprDocNode.html">ExprDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html">MeasureCandida [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html">Measu [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1M [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </ [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfo.html">MemoryInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>) [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm. [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1MemoryManager.html">MemoryManager</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html">Measu [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1M [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfo.html">MemoryInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>) [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm. [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1MemoryManager.html">MemoryManager</a> (<a class="el" href [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_b"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  b  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structMemoryManagerInterface.html">MemoryManagerInterface</a>   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1ReportNode.html">ReportNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runti [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ReprPrinter.html">ReprPrinter</a> (<a class="el" href="namespacetvm.html"> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprStmtDoc.html">ExprStmtDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html">Metadata</a> (<a class="el" hr [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprStmtDocNode.html">ExprStmtDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArrayNode.html">MetadataArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1m [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html">MetadataBase</a> (<a class="el" href="namespacetvm_1_1runtime_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBaseNode.html">MetadataBaseNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.htm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFuncNode.html">BaseFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataNode.html">MetadataNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1meta [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html">ExtractedTask</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MetaScheduleLayoutTransformAttrs.html">MetaScheduleLayoutTra [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html">ExtractedTaskNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html">MetricCo [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncObj_1_1Extractor.html">PackedFuncObj::Extractor</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html">MetricCollector [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structMemoryManagerInterface.html">MemoryManagerInterface</a>   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1ReportNode.html">ReportNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runti [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ReprPrinter.html">ReprPrinter</a> (<a class="el" href="namespacetvm.html"> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprStmtDoc.html">ExprStmtDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html">Metadata</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ExprStmtDocNode.html">ExprStmtDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArrayNode.html">MetadataArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1m [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html">MetadataBase</a> (<a class="el" href="namespacetvm_1_1runtime_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBaseNode.html">MetadataBaseNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFuncNode.html">BaseFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataNode.html">MetadataNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1meta [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html">ExtractedTask</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MetaScheduleLayoutTransformAttrs.html">MetaScheduleLayoutTra [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html">ExtractedTaskNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html">MetricCo [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncObj_1_1Extractor.html">PackedFuncObj::Extractor</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html">MetricCollector [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueHash.html">BaseValueHash</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_f"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  f  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html">ReverseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>) [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchMatmulAttrs.html">BatchMatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html">ReverseIterAdapter</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html">FeatureExtractor</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorP [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html">BatchToSpaceNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html">FeatureExtractorNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingArrayElementP [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingArrayElementPathNode.html">MissingArrayElementPathNode</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingMapEntryPath.html">MissingMapEntryPath</a> (<a class="el" href= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html">FixedPointMultiplyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingMapEntryPathNode.html">MissingMapEntryP [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a c [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td> [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Block.html">Block</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1BlockInfo.html">BlockInfo</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.ht [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockNode.html">BlockNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> (<a class="el" href="namespacetvm_1_1arith.htm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html">BlockRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html">FollowFusedSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">Module</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html">BlockRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html">FollowFusedSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode. [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRV.html">BlockRV</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html">BlockRVNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">MultiBoxPriorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm: [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html">BlockScopeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ForDoc.html">ForDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBoxTransfo [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ForDocNode.html">ForDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultinomialAttrs.html">MultinomialAttrs</a> (<a class="el" href="namespacetvm_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html">Mutator</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1qnn_1_1BroadcastAttrs.html">BroadcastAttrs</a> (<a class="el" href="namespacetvm_1_1relay_1_1qnn.html">tvm::relay::qnn</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html">FrameBuffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__sche [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html">ReverseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">TuningRecordNode</a> (<a class="el" href="namespacetvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchMatmulAttrs.html">BatchMatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html">ReverseIterAdapter</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html">FeatureExtractor</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorP [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html">BatchToSpaceNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html">FeatureExtractorNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingArrayElementP [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingArrayElementPathNode.html">MissingArrayElementPathNode</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingMapEntryPath.html">MissingMapEntryPath</a> (<a class="el" href= [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html">FixedPointMultiplyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MissingMapEntryPathNode.html">MissingMapEntryP [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Block.html">Block</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1BlockInfo.html">BlockInfo</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockNode.html">BlockNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> (<a class="el" href="namespacetvm_1_1arith.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html">BlockRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html">FollowFusedSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">Module</a> (<a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html">BlockRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html">FollowFusedSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRV.html">BlockRV</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html">BlockRVNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">MultiBoxPriorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html">BlockScopeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ForDoc.html">ForDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBoxTransfo [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ForDocNode.html">ForDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultinomialAttrs.html">MultinomialAttrs</a> (<a class="el" href="namespacetvm_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html">Mutator</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1qnn_1_1BroadcastAttrs.html">BroadcastAttrs</a> (<a class="el" href="namespacetvm_1_1relay_1_1qnn.html">tvm::relay::qnn</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html">FrameBuffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__sche [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html">BroadcastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Framer.html">Framer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_n"></a><table border="0" cellspacing="0" cellpadd [...]
</td><td rowspan="2" valign="bottom"><a name="letter_s"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  s  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="structTVMGraphExecutorGraphAttr.html">TVMGraphExecutorGraphAttr</a>   </td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj_1_1FromStd.html">ShapeTupleObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structTVMMetadata.html">TVMMetadata</a>   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespacetvm_1_1r [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html">BufferInfo</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1NDArrayContainerTrait.html">NDArrayContainerTrait</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html">BufferInfoAnalysis</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1FunctionDoc.html">FunctionDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Ndarray [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysisNode.html">BufferInfoAnalysisNode</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1FunctionDocNode.html">FunctionDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1BufferInfoNode.html">BufferInfoNode</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html">FunctionPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NLLLossAttrs.html">NLLLossAttrs</a> (<a class="el" href="namespacetvm_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPatternNode.html">FunctionPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectRef &n, Args... [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAttrs</a> (<a class="el" href="namespacetv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NormalAttrs.html">NormalAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm:: [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html">BufferRegion</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html">BufferRegionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html">FuseStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNode.html">NotNode</a> (<a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class= [...]
+</td><td valign="top"><a class="el" href="structTVMFuncRegistry.html">TVMFuncRegistry</a>   </td></tr>
+<tr><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structTVMGraphExecutorGraphAttr.html">TVMGraphExecutorGrap [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ShapeTupleObj_1_1FromStd.html">ShapeTupleObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespace [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html">BufferInfo</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1NDArrayContainerTrait.html">NDArrayContainerTrait</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html">BufferInfoAnalysis</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1FunctionDoc.html">FunctionDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Ndarray [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysisNode.html">BufferInfoAnalysisNode</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1FunctionDocNode.html">FunctionDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1BufferInfoNode.html">BufferInfoNode</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp.html">tvm::tir::usmp</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html">FunctionPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NLLLossAttrs.html">NLLLossAttrs</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPatternNode.html">FunctionPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectRef &n, Args... [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAttrs</a> (<a class="el" href="namespacetv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NormalAttrs.html">NormalAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm:: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html">BufferRegion</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html">BufferRegionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html">FuseStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNode.html">NotNode</a> (<a class="el" href=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class= [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
</td><td rowspan="2" valign="bottom"><a name="letter_o"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  o  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html">ScheduleStateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeCallNode.html">TypeCallNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html">Builder</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ScopeDoc.html">ScopeDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeConstraint.html">TypeConstraint [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html">BuilderInput</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html">BuilderInputNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a c [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html">BuilderNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html">BuilderResult</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html">BuilderResultNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPath.html">ObjectPath</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html">BuildResult</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathNode.html">ObjectPathNode</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html">BuildResultNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathPair.html">Object [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html">ScheduleStateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeCall.html">TypeCall</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html">Builder</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ScopeDoc.html">ScopeDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeCallNode.html">TypeCallNode</a> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html">BuilderInput</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html">BuilderInputNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html">BuilderNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html">BuilderResult</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html">BuilderResultNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPath.html">ObjectPath</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html">BuildResult</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathNode.html">ObjectPathNode</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html">BuildResultNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathPair.html">Object [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_c"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  c  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathPairNode.html">ObjectPathPairNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SearchSortedAttrs.html">SearchSortedAttrs</a> (<a class="el" href="n [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html">SearchStrategy</a> (<a class="el" href="namespacetvm_1_1meta_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html">CacheReadStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html">CacheReadStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html">CacheWriteStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="names [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html">CacheWriteStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html">GreedyBase</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp_1_1algo.html">tvm::tir::usmp::algo</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1r [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Array< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html">ObjectTypeChecker< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1CallDoc.html">CallDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1CallDocNode.html">CallDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href [...]
+</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ObjectPathPairNode.html">ObjectPathPairNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SearchSortedAttrs.html">SearchSortedAttrs</a> (<a class="el" href="n [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html">SearchStrategy</a> (<a class="el" href="namespacetvm_1_1meta_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html">CacheReadStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html">CacheReadStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html">CacheWriteStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="names [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html">CacheWriteStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html">GreedyBase</a> (<a class="el" href="namespacetvm_1_1tir_1_1usmp_1_1algo.html">tvm::tir::usmp::algo</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1r [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Array< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html">ObjectTypeChecker< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1CallDoc.html">CallDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1CallDocNode.html">CallDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1profiling_1_1CallFrame.html">CallFrame</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runtime::profiling</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_h"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  h  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectSHashReduce_3_01T_00_01TraitName_00_01false_01_4.html">SelectSHashReduce< T, TraitName, false ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01uint64__t_01_4.ht [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CallLoweredAttrs.html">CallLoweredAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectVisitAttrs.html">SelectVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail. [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" hr [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDoc.html">OperationDoc</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDocNode.html">OperationDocNode</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.ht [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDevice_01_4.html">Handler< DLDevice ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImpleme [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html">HardwareParams</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html">OpImplementationNode</a> (<a c [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html">HardwareParamsNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" hr [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecialization</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectSHashReduce_3_01T_00_01TraitName_00_01false_01_4.html">SelectSHashReduce< T, TraitName, false ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int64__t_01_4.htm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CallLoweredAttrs.html">CallLoweredAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectVisitAttrs.html">SelectVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDoc.html">OperationDoc</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDocNode.html">OperationDocNode</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDevice_01_4.html">Handler< DLDevice ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImpleme [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html">HardwareParams</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html">OpImplementationNode</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html">HardwareParamsNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecialization</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ClassDoc.html">ClassDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_i"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  i  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html">OpSpecializationNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html">ShapeFuncAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeVisitor.html">TypeVisitor</a> (<a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ClassDocNode.html">ClassDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html">ShapeOfAttrs</a> ( [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html">OpSpecializationNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html">ShapeFuncAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeVarNode.html">TypeVarNode</a> (<a class="el" href=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1ClassDocNode.html">ClassDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html">ShapeOfAttrs</a> ( [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay< [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IdDoc.html">IdDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="n [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IdDocNode.html">IdDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&# [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IdDoc.html">IdDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="n [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IdDocNode.html">IdDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&# [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ClosureObj.html">ClosureObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1If.html">If</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_p"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  p&# [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer.html">SHashReducer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1UnknownAttributeAccessPath.html">UnknownAttributeAccessPath</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IfDoc.html">IfDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html">Shuffle</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IfDocNode.html">IfDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html">PackedFuncObj</a> (<a class="el" href="namespacetvm_1_1run [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1CompilationConfig.html">CompilationConfig</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfPattern.html">IfPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFuncSubObj.html">PackedFuncSubObj</a> (<a class="el" href="namespacetvm_1_1runtim [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer.html">SHashReducer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1UniqueAttrs.html">UniqueAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IfDoc.html">IfDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html">Shuffle</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IfDocNode.html">IfDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFuncObj.html">PackedFuncObj</a> (<a class="el" href="namespacetvm_1_1run [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1CompilationConfig.html">CompilationConfig</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfPattern.html">IfPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFuncSubObj.html">PackedFuncSubObj</a> (<a class="el" href="namespacetvm_1_1runtim [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1CompilationConfigNode.html">CompilationConfigNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfPatternNode.html">IfPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">PackedFuncValueConverter</a> (<a class= [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1CompilationConfigNode.html">CompilationConfigNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfPatternNode.html">IfPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">PackedFuncValueConverter</a> (<a class= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1CompileError.html">CompileError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html">PackedFuncValueConverter< Optional< [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html">PackedFuncValue [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html">ComputeAtStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConvert [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadAt [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" hr [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" href="namespacetvm_1_1transform.html">tvm::transfor [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class="el" href="namespacetvm_1_1trans [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html">ComputeRootStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IndexDoc.html">IndexDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pas [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html">ComputeRootStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IndexDocNode.html">IndexDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMap.html">IndexMap</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html">PassInstrument</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html">IndexMapNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html">PassInstrumentNode</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantInfo.html">ConstantInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> (<a class="el" href="namespacetvm_1_1transform.html">tvm::tran [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadata.html">ConstantInfoMetadata</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.html">tvm::runtime::metadata</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadataNode.html">ConstantInfoMetadataNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.html">tvm::runtime::metadata</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1re [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantInfoNode.html">ConstantInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html">PatternConstructorNode</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantMemoryPools.html">ConstantMemoryPools</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html">PatternFunctor</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1CompileError.html">CompileError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html">PackedFuncValueConverter< Optional< [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html">PackedFuncValue [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html">ComputeAtStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConvert [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_1_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadAt [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" href="namespacetvm_1_1transform.html">tvm::transfor [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class="el" href="namespacetvm_1_1trans [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html">ComputeRootStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IndexDoc.html">IndexDoc</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pas [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html">ComputeRootStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1IndexDocNode.html">IndexDocNode</a> (<a class="el" href="namespacetvm_1_1script_1_1printer.html">tvm::script::printer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMap.html">IndexMap</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html">PassInstrument</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html">IndexMapNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html">PassInstrumentNode</a> (<a class="el" href="namespac [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantInfo.html">ConstantInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> (<a class="el" href="namespacetvm_1_1transform.html">tvm::tran [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadata.html">ConstantInfoMetadata</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.html">tvm::runtime::metadata</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ConstantInfoMetadataNode.html">ConstantInfoMetadataNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1metadata.html">tvm::runtime::metadata</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1re [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantInfoNode.html">ConstantInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html">PatternConstructorNode</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantMemoryPools.html">ConstantMemoryPools</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html">PatternFunctor</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantMemoryPoolsNode.html">ConstantMemoryPoolsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html">InstructionKind</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantMemoryPoolsNode.html">ConstantMemoryPoolsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html">InstructionKind</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html">InstructionKindNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html">PatternMutator</a> (<a class="el" href= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKindRegEntry.html">InstructionKindRegEntry</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">PatternNode</a> (<a class="e [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionNode.html">InstructionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html">PatternTuple</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantPoolInfo.html">ConstantPoolInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html">PatternTupleNode</a> (<a class="el" href="namespacetvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantPoolInfoNode.html">ConstantPoolInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> (<a class="el" href="namespacetv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> (<a class="el" href="namespacetvm_1_1relay.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a class="el" href="namespacetvm_1_1te.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a c [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerType.html">PointerType</a> (<a class="el" href="namespacetvm.html">tvm</a>)    [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerTypeNode.html">PointerTypeNode</a> (<a class="el" href="namespacetvm.html">tvm< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html">PoolAllocation</a> (<a cl [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1PoolAllocationNode.html">PoolAllocationNode</a> (<a class= [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PoolInfo.html">PoolInfo</a> (<a class="el" href="namespacetvm.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html">InstructionKindNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html">PatternMutator</a> (<a class="el" href= [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionKindRegEntry.html">InstructionKindRegEntry</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">PatternNode</a> (<a class="e [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1InstructionNode.html">InstructionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html">PatternTuple</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstantPoolInfo.html">ConstantPoolInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html">PatternTupleNode</a> (<a class="el" href="namespacetvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1ConstantPoolInfoNode.html">ConstantPoolInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> (<a class="el" href="namespacetv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> (<a class="el" href="namespacetvm_1_1relay.html">tv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a class="el" href="namespacetvm_1_1te.html">tv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerType.html">PointerType</a> (<a class="el" href="namespacetvm.html">tvm</a>)    [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerTypeNode.html">PointerTypeNode</a> (<a class="el" href="namespacetvm.html">tvm< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html">PoolAllocation</a> (<a cl [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1usmp_1_1PoolAllocationNode.html">PoolAllocationNode</a> (<a class= [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PoolInfo.html">PoolInfo</a> (<a class="el" href="namespacetvm.ht [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1PoolInfoNode.html">PoolInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1PoolInfoNode.html">PoolInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PoolInfoProperties.html">PoolInfoProperties</a> (<a class="el" href="namespacetvm.htm [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1is__specialized.html">is_specialized</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1PoolInfoPropertiesNode.html">PoolInfoPropertiesNode< [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PoolInfoProperties.html">PoolInfoProperties</a> (<a class="el" href="namespacetvm.htm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1is__specialized.html">is_specialized</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1PoolInfoPropertiesNode.html">PoolInfoPropertiesNode< [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html">Conv2DWinogradNNPACKWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html">is_specialized< Container< Args... >, Container ></a> (<a class="el" href="namespacetvm_1_1detail.html [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html">Conv3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">IterAdapter</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1PostprocNode.html">PostprocNode</a> (<a class="el" href= [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html">Conv3DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html">MapNode::iterator</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html">Pragma [...]
diff --git a/docs/reference/api/doxygen/classtvm_1_1TracedObject-members.html b/docs/reference/api/doxygen/classtvm_1_1TracedObject-members.html
index 25725eafe..6f4dc6f75 100644
--- a/docs/reference/api/doxygen/classtvm_1_1TracedObject-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1TracedObject-members.html
@@ -75,9 +75,10 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#acecefa7675d26f36f6265ff44e0aee8c">GetAttr</a>(T BaseType::*member_ptr) const</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a40abb28dd63fd42a015aa2bf907bac98">GetPath</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a4b14c15accff657a8b09ca9ed00b0125">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#ad3f916ea73842f1311171576b63f3ae9">TracedObject</a>(const RefT &object_ref, ObjectPath path)</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a3fa2cb293f3afab0566d68ceb6c506e0">TracedObject</a>(const TracedObject< DerivedRef > &derived)</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a1f8bbeb719ce563a5cb4d9ea36e125d2">TryDowncast</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#acb3c3ecfc22420f670914e7a55de40fc">ObjectRefType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#ad3f916ea73842f1311171576b63f3ae9">TracedObject</a>(const RefT &object_ref, ObjectPath path)</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a3fa2cb293f3afab0566d68ceb6c506e0">TracedObject</a>(const TracedObject< DerivedRef > &derived)</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html#a1f8bbeb719ce563a5cb4d9ea36e125d2">TryDowncast</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject< RefT ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1TracedObject.html b/docs/reference/api/doxygen/classtvm_1_1TracedObject.html
index 4ede76b46..835d3b7c8 100644
--- a/docs/reference/api/doxygen/classtvm_1_1TracedObject.html
+++ b/docs/reference/api/doxygen/classtvm_1_1TracedObject.html
@@ -63,6 +63,7 @@ $(function() {
</div><!-- top -->
<div class="header">
<div class="summary">
+<a href="#pub-types">Public Types</a> |
<a href="#pub-methods">Public Member Functions</a> |
<a href="classtvm_1_1TracedObject-members.html">List of all members</a> </div>
<div class="headertitle">
@@ -81,6 +82,11 @@ Collaboration diagram for tvm::TracedObject< RefT >:</div>
</div>
</div>
<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
+Public Types</h2></td></tr>
+<tr class="memitem:acb3c3ecfc22420f670914e7a55de40fc"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1TracedObject.html#acb3c3ecfc22420f670914e7a55de40fc">ObjectRefType</a> = RefT</td></tr>
+<tr class="separator:acb3c3ecfc22420f670914e7a55de40fc"><td class="memSeparator" colspan="2"> </td></tr>
+</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:ad3f916ea73842f1311171576b63f3ae9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1TracedObject.html#ad3f916ea73842f1311171576b63f3ae9">TracedObject</a> (const RefT &object_ref, <a class="el" href="classtvm_1_1ObjectPath.html">ObjectPath</a> path)</td></tr>
@@ -119,7 +125,24 @@ Public Member Functions</h2></td></tr>
class tvm::TracedObject< RefT ></h3>
<p>Traced wrapper for regular (non-container) TVM objects. </p>
-</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
+</div><h2 class="groupheader">Member Typedef Documentation</h2>
+<a id="acb3c3ecfc22420f670914e7a55de40fc"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#acb3c3ecfc22420f670914e7a55de40fc">◆ </a></span>ObjectRefType</h2>
+
+<div class="memitem">
+<div class="memproto">
+<div class="memtemplate">
+template<typename RefT> </div>
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="classtvm_1_1TracedObject.html">tvm::TracedObject</a>< RefT >::<a class="el" href="classtvm_1_1TracedObject.html#acb3c3ecfc22420f670914e7a55de40fc">ObjectRefType</a> = RefT</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<h2 class="groupheader">Constructor & Destructor Documentation</h2>
<a id="ad3f916ea73842f1311171576b63f3ae9"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad3f916ea73842f1311171576b63f3ae9">◆ </a></span>TracedObject() <span class="overload">[1/2]</span></h2>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor-members.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor-members.html
new file mode 100644
index 000000000..33baab3c1
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+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor-members.html
@@ -0,0 +1,84 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.13"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>tvm: Member List</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+ <td id="projectalign" style="padding-left: 0.5em;">
+ <div id="projectname">tvm
+ </div>
+ </td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.13 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+<script type="text/javascript" src="menudata.js"></script>
+<script type="text/javascript" src="menu.js"></script>
+<script type="text/javascript">
+$(function() {
+ initMenu('',true,false,'search.php','Search');
+ $(document).ready(function() { init_search(); });
+});
+</script>
+<div id="main-nav"></div>
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0"
+ name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div id="nav-path" class="navpath">
+ <ul>
+<li class="navelem"><a class="el" href="namespacetvm.html">tvm</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1script.html">script</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1script_1_1printer.html">printer</a></li><li class="navelem"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TracedObjectFunctor</a></li> </ul>
+</div>
+</div><!-- top -->
+<div class="header">
+ <div class="headertitle">
+<div class="title">tvm::script::printer::TracedObjectFunctor< R, Args > Member List</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p>This is the complete list of members for <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor< R, Args ></a>, including all inherited members.</p>
+<table class="directory">
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a1309ef39f5afbe12204f17fb4e939a99">operator()</a>(const String &token, TracedObject< TObjectRef > traced_object, Args... args) const</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor< R, Args ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a39e23af093ba0ee9dab17de86b6fa58e">set_dispatch</a>(String token, uint32_t type_index, runtime::PackedFunc f)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor< R, Args ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a6245531472de83df80ff595f3a4438de">set_dispatch</a>(String token, TCallable f)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor< R, Args ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a456a9e3ef4c5864ce42a1bc68e2679b6">set_dispatch</a>(TCallable &&f)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor< R, Args ></a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+</table></div><!-- contents -->
+<!-- start footer part -->
+<hr class="footer"/><address class="footer"><small>
+Generated by  <a href="http://www.doxygen.org/index.html">
+<img class="footer" src="doxygen.png" alt="doxygen"/>
+</a> 1.8.13
+</small></address>
+</body>
+</html>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html
new file mode 100644
index 000000000..fabd7c475
--- /dev/null
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html
@@ -0,0 +1,323 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.13"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>tvm: tvm::script::printer::TracedObjectFunctor< R, Args > Class Template Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+ <td id="projectalign" style="padding-left: 0.5em;">
+ <div id="projectname">tvm
+ </div>
+ </td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.13 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+<script type="text/javascript" src="menudata.js"></script>
+<script type="text/javascript" src="menu.js"></script>
+<script type="text/javascript">
+$(function() {
+ initMenu('',true,false,'search.php','Search');
+ $(document).ready(function() { init_search(); });
+});
+</script>
+<div id="main-nav"></div>
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0"
+ name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div id="nav-path" class="navpath">
+ <ul>
+<li class="navelem"><a class="el" href="namespacetvm.html">tvm</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1script.html">script</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1script_1_1printer.html">printer</a></li><li class="navelem"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TracedObjectFunctor</a></li> </ul>
+</div>
+</div><!-- top -->
+<div class="header">
+ <div class="summary">
+<a href="#pub-methods">Public Member Functions</a> |
+<a href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor-members.html">List of all members</a> </div>
+ <div class="headertitle">
+<div class="title">tvm::script::printer::TracedObjectFunctor< R, Args > Class Template Reference</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Dynamic dispatch functor based on <a class="el" href="classtvm_1_1TracedObject.html" title="Traced wrapper for regular (non-container) TVM objects. ">TracedObject</a>.
+ <a href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#details">More...</a></p>
+
+<p><code>#include <<a class="el" href="traced__object__functor_8h_source.html">traced_object_functor.h</a>></code></p>
+<div class="dynheader">
+Collaboration diagram for tvm::script::printer::TracedObjectFunctor< R, Args >:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor__coll__graph.svg" width="172" height="162"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+</div>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
+Public Member Functions</h2></td></tr>
+<tr class="memitem:a1309ef39f5afbe12204f17fb4e939a99"><td class="memTemplParams" colspan="2">template<class TObjectRef > </td></tr>
+<tr class="memitem:a1309ef39f5afbe12204f17fb4e939a99"><td class="memTemplItemLeft" align="right" valign="top">R </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a1309ef39f5afbe12204f17fb4e939a99">operator()</a> (const <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> &token, <a class="el" href="classtvm_1_1TracedObject.html">TracedObject</a>< TObjectRef > traced_object, Args... [...]
+<tr class="memdesc:a1309ef39f5afbe12204f17fb4e939a99"><td class="mdescLeft"> </td><td class="mdescRight">Call the dispatch function. <a href="#a1309ef39f5afbe12204f17fb4e939a99">More...</a><br /></td></tr>
+<tr class="separator:a1309ef39f5afbe12204f17fb4e939a99"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a39e23af093ba0ee9dab17de86b6fa58e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a39e23af093ba0ee9dab17de86b6fa58e">set_dispatch</a> (<a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> token, uint32_t type_index, <a class="el" h [...]
+<tr class="memdesc:a39e23af093ba0ee9dab17de86b6fa58e"><td class="mdescLeft"> </td><td class="mdescRight">Set the dispatch function. <a href="#a39e23af093ba0ee9dab17de86b6fa58e">More...</a><br /></td></tr>
+<tr class="separator:a39e23af093ba0ee9dab17de86b6fa58e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6245531472de83df80ff595f3a4438de"><td class="memTemplParams" colspan="2">template<typename TCallable , typename TObjectRef = typename detail::FirstArgType<TCallable>::ObjectRefType, typename = std::enable_if_t<IsDispatchFunction<TObjectRef, TCallable>::value>> </td></tr>
+<tr class="memitem:a6245531472de83df80ff595f3a4438de"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a> & </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a6245531472de83df80ff595f3a4438de">set_dispatch</a> (<a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> token, TCallable f)</td></tr>
+<tr class="memdesc:a6245531472de83df80ff595f3a4438de"><td class="mdescLeft"> </td><td class="mdescRight">Set the dispatch function. <a href="#a6245531472de83df80ff595f3a4438de">More...</a><br /></td></tr>
+<tr class="separator:a6245531472de83df80ff595f3a4438de"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a456a9e3ef4c5864ce42a1bc68e2679b6"><td class="memTemplParams" colspan="2">template<typename TCallable > </td></tr>
+<tr class="memitem:a456a9e3ef4c5864ce42a1bc68e2679b6"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a> & </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a456a9e3ef4c5864ce42a1bc68e2679b6">set_dispatch</a> (TCallable &&f)</td></tr>
+<tr class="memdesc:a456a9e3ef4c5864ce42a1bc68e2679b6"><td class="mdescLeft"> </td><td class="mdescRight">Set the default dispatch function. <a href="#a456a9e3ef4c5864ce42a1bc68e2679b6">More...</a><br /></td></tr>
+<tr class="separator:a456a9e3ef4c5864ce42a1bc68e2679b6"><td class="memSeparator" colspan="2"> </td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><h3>template<typename R, typename... Args><br />
+class tvm::script::printer::TracedObjectFunctor< R, Args ></h3>
+
+<p>Dynamic dispatch functor based on <a class="el" href="classtvm_1_1TracedObject.html" title="Traced wrapper for regular (non-container) TVM objects. ">TracedObject</a>. </p>
+<p>This functor dispatches based on the type of object ref inside the input <a class="el" href="classtvm_1_1TracedObject.html" title="Traced wrapper for regular (non-container) TVM objects. ">TracedObject</a>, and the input dispatch token. </p>
+</div><h2 class="groupheader">Member Function Documentation</h2>
+<a id="a1309ef39f5afbe12204f17fb4e939a99"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1309ef39f5afbe12204f17fb4e939a99">◆ </a></span>operator()()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<div class="memtemplate">
+template<typename R , typename... Args> </div>
+<div class="memtemplate">
+template<class TObjectRef > </div>
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">R <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor</a>< R, Args >::operator() </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> & </td>
+ <td class="paramname"><em>token</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1TracedObject.html">TracedObject</a>< TObjectRef > </td>
+ <td class="paramname"><em>traced_object</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">Args... </td>
+ <td class="paramname"><em>args</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td> const</td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Call the dispatch function. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">token</td><td>The dispatch token. </td></tr>
+ <tr><td class="paramname">traced_object</td><td>The traced object. </td></tr>
+ <tr><td class="paramname">args</td><td>Other args.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The return value of the dispatch function</dd></dl>
+<p>If the TObjectRef isn't registered with the token, it will try to find dispatch function for TObjectRef with kDefaultDispatchToken. </p>
+
+</div>
+</div>
+<a id="a39e23af093ba0ee9dab17de86b6fa58e"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a39e23af093ba0ee9dab17de86b6fa58e">◆ </a></span>set_dispatch() <span class="overload">[1/3]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+<div class="memtemplate">
+template<typename R , typename... Args> </div>
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a>& <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor</a>< R, Args >::set_dispatch </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
+ <td class="paramname"><em>token</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">uint32_t </td>
+ <td class="paramname"><em>type_index</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">runtime::PackedFunc</a> </td>
+ <td class="paramname"><em>f</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Set the dispatch function. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">token</td><td>The dispatch token. </td></tr>
+ <tr><td class="paramname">type_index</td><td>The TVM object type index for this dispatch function. </td></tr>
+ <tr><td class="paramname">f</td><td>The dispatch function.</td></tr>
+ </table>
+ </dd>
+</dl>
+<p>This takes a type-erased packed function as input. It should be used through FFI boundary, for example, registering dispatch function from Python. </p>
+
+</div>
+</div>
+<a id="a6245531472de83df80ff595f3a4438de"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a6245531472de83df80ff595f3a4438de">◆ </a></span>set_dispatch() <span class="overload">[2/3]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+<div class="memtemplate">
+template<typename R , typename... Args> </div>
+<div class="memtemplate">
+template<typename TCallable , typename TObjectRef = typename detail::FirstArgType<TCallable>::ObjectRefType, typename = std::enable_if_t<IsDispatchFunction<TObjectRef, TCallable>::value>> </div>
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a>& <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor</a>< R, Args >::set_dispatch </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
+ <td class="paramname"><em>token</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">TCallable </td>
+ <td class="paramname"><em>f</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Set the dispatch function. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">token</td><td>The dispatch token. </td></tr>
+ <tr><td class="paramname">f</td><td>The dispatch function.</td></tr>
+ </table>
+ </dd>
+</dl>
+<p>The diaptch function should have signature <code>R(<a class="el" href="classtvm_1_1TracedObject.html" title="Traced wrapper for regular (non-container) TVM objects. ">TracedObject</a><TObjectRef>, Args...)</code>. </p>
+
+</div>
+</div>
+<a id="a456a9e3ef4c5864ce42a1bc68e2679b6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a456a9e3ef4c5864ce42a1bc68e2679b6">◆ </a></span>set_dispatch() <span class="overload">[3/3]</span></h2>
+
+<div class="memitem">
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+template<typename R , typename... Args> </div>
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+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">TSelf</a>& <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html">tvm::script::printer::TracedObjectFunctor</a>< R, Args >::set_dispatch </td>
+ <td>(</td>
+ <td class="paramtype">TCallable && </td>
+ <td class="paramname"><em>f</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Set the default dispatch function. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">f</td><td>The dispatch function.</td></tr>
+ </table>
+ </dd>
+</dl>
+<p>Default dispatch function will be used if there is no function registered with the requested dispatch token.</p>
+<p>Default dispatch function has an empty string as dispatch token. </p>
+
+</div>
+</div>
+<hr/>The documentation for this class was generated from the following file:<ul>
+<li>include/tvm/script/printer/<a class="el" href="traced__object__functor_8h_source.html">traced_object_functor.h</a></li>
+</ul>
+</div><!-- contents -->
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<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
</li>
<li>Move()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a162dc8d73dc2306f066c3ee013ff096f">tvm::runtime::vm::Instruction</a>
diff --git a/docs/reference/api/doxygen/functions_func_o.html b/docs/reference/api/doxygen/functions_func_o.html
index 8886b3911..525ecf576 100644
--- a/docs/reference/api/doxygen/functions_func_o.html
+++ b/docs/reference/api/doxygen/functions_func_o.html
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, <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html#a71c712200306656ffdca92629b8172bc">tvm::runtime::PackedFunc</a>
, <a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a533c034c471f3c09341dcfbfcbb20d9a">tvm::runtime::TVMArgsSetter</a>
, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a987beda43b26ffc3665f1c448bf83e8c">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+, <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a1309ef39f5afbe12204f17fb4e939a99">tvm::script::printer::TracedObjectFunctor< R, Args ></a>
, <a class="el" href="classtvm_1_1SEqualReducer.html#ab8192a058d6e7e19b39685be482a0bc2">tvm::SEqualReducer</a>
, <a class="el" href="classtvm_1_1SHashReducer.html#a259dd594f7eb56fef692f38668b0ecb1">tvm::SHashReducer</a>
, <a class="el" href="classtvm_1_1StructuralEqual.html#a1a8f9e17db557ecb536eaa41d3fd2465">tvm::StructuralEqual</a>
@@ -265,14 +266,14 @@ $(function() {
, <a class="el" href="classtvm_1_1TracedArrayIterator.html#a7511fac25e2b3ff75f1767ac7e190531">tvm::TracedArrayIterator< T ></a>
</li>
<li>operator++()
-: <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#a76971f210bcd72c05992ae57b2ac8b2e">tvm::runtime::IterAdapter< Converter, TIter ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#a40f67dd792619d7d41ac45fc0f52a0c4">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a8052ae36e24a4973c1a123c99cf5152c">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#aa080c358ffab71cff472538a435eb615">tvm::runtime::MapNode::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayIterator.html#a7e37edb0b1ba2d20e9d50af007d67bef">tvm::runtime::metadata::ArrayIterator< C, Ref ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#aacad35edbbab947e69a85d31d2fe417d">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#a362bd28f025d28551159e46c415309bd">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1support_1_1Span_1_1iterator__base.html#a526da770183e25f30104efb0ba4e62d5">tvm::support::Span< T, W >::iterator_base< W1 ></a>
, <a class="el" href="classtvm_1_1TracedArrayIterator.html#aa26da863ccd52bfaa81a1124c91a4bcd">tvm::TracedArrayIterator< T ></a>
-, <a class="el" href="classtvm_1_1TracedMapIterator.html#af37d544e301dd262e0d574bc568b7bb2">tvm::TracedMapIterator< K, V ></a>
+, <a class="el" href="classtvm_1_1TracedMapIterator.html#a8a0c3235d895aeb419dbf23c0b1dd9f3">tvm::TracedMapIterator< K, V ></a>
</li>
<li>operator+=()
: <a class="el" href="classtvm_1_1relay_1_1FeatureSet.html#a73e4e0de2e58ef67a88d27a48b3c3fc0">tvm::relay::FeatureSet</a>
@@ -282,12 +283,12 @@ $(function() {
, <a class="el" href="classtvm_1_1relay_1_1FeatureSet.html#ad0412921b6fe93a2f51b3c72c03c4095">tvm::relay::FeatureSet</a>
, <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#ab5671b2b9d56c2abcfb8d3f03cf6fd9e">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#a67f728a692ed84a78a20a2095815f260">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
-, <a class="el" href="classtvm_1_1TracedArrayIterator.html#aabf6b2121e29b8e4b2e13bbb945d7421">tvm::TracedArrayIterator< T ></a>
+, <a class="el" href="classtvm_1_1TracedArrayIterator.html#aad1f3796e892a744a54685f435e63f97">tvm::TracedArrayIterator< T ></a>
</li>
<li>operator--()
: <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#acb643cc554062422a4fd00bbfb3b0a6d">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a08393c19a1c8b1c4057a33832cd48662">tvm::runtime::MapNode::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#a3a04e74706f1a844a7347d9073494f62">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#afa7ed1b09064bb3d6dea7ad886145346">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1TracedArrayIterator.html#a955f12369599be874e249db22fb8b33a">tvm::TracedArrayIterator< T ></a>
</li>
<li>operator-=()
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, <a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html#a106ab6e70f61976d2a7d5ca37ae26f51">tvm::relay::PatternWildcard</a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a3779f858a014a0feea9d9e0c5d027d73">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
, <a class="el" href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#adf85d43ef116b85c8aa2a25599bc5584">tvm::runtime::MapNode::iterator</a>
-, <a class="el" href="classtvm_1_1runtime_1_1Module.html#a87bcc010c62887a453d49d46854fa354">tvm::runtime::Module</a>
+, <a class="el" href="classtvm_1_1runtime_1_1Module.html#ab6d988e0c9c19eaec2970a0222cc4fc5">tvm::runtime::Module</a>
, <a class="el" href="classtvm_1_1runtime_1_1NDArray.html#ad57933f49a9fd51d7f996e1b16ffd2e0">tvm::runtime::NDArray</a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a42a4f53709a0c071904c98e715ee7140">tvm::runtime::ObjectPtr< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a3deeeac5827a88f375b8c6ae1039c219">tvm::runtime::ObjectRef</a>
@@ -321,11 +322,11 @@ $(function() {
, <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#ada44b624c1397c943ad9a0427378b048">tvm::tir::SizeVar</a>
, <a class="el" href="classtvm_1_1tir_1_1Var.html#a6a5db358f4fd7a9005a2d7a5f0fbe501">tvm::tir::Var</a>
, <a class="el" href="classtvm_1_1TracedMapIterator.html#a47366afaaedf33630bb24f6c2fbb8a79">tvm::TracedMapIterator< K, V ></a>
-, <a class="el" href="classtvm_1_1transform_1_1PassContext.html#a2d1a6fffe70703812245b8d834da9a44">tvm::transform::PassContext</a>
+, <a class="el" href="classtvm_1_1transform_1_1PassContext.html#ab10baf6f7bcbd6e0e4b295399bc9f3a9">tvm::transform::PassContext</a>
, <a class="el" href="classtvm_1_1transform_1_1Sequential.html#a326b2bc7ce7710e22a91265b3aff37a0">tvm::transform::Sequential</a>
, <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#ab0177fd981b51149651e8912e6bf1b68">tvm::TypedEnvFunc< R(Args...)></a>
, <a class="el" href="classtvm_1_1TypeReporter.html#a64f4543a37a87db2fc57039a4ee6d3af">tvm::TypeReporter</a>
-, <a class="el" href="classtvm_1_1With.html#ad09ae9255869b0273672f44f23944bc3">tvm::With< ContextType ></a>
+, <a class="el" href="classtvm_1_1With.html#a51c3e4f1087edf083a7ddccfdbc96d55">tvm::With< ContextType ></a>
</li>
<li>operator/()
: <a class="el" href="classtvm_1_1relay_1_1DFPattern.html#ad0839b39558941ac374c213f78ca2c81">tvm::relay::DFPattern</a>
@@ -340,20 +341,20 @@ $(function() {
<li>operator=()
: <a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9dccc7d98b8b9465390e10436d3a9178">tvm::arith::Analyzer</a>
, <a class="el" href="classtvm_1_1Integer.html#ad538a2ae6f636b3ce38fb4162b1c2549">tvm::Integer</a>
-, <a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html#a98237b6a6448ba4ed10f64c3016eb4e2">tvm::relay::PatternWildcard</a>
-, <a class="el" href="classtvm_1_1runtime_1_1Array.html#a5bc26222aa0257ece4c3a9d4d70fa1d8">tvm::runtime::Array< T, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1Map.html#ac62909410a98a078ff01f688cdf70ffe">tvm::runtime::Map< K, V, typename, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">tvm::runtime::Object</a>
+, <a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html#aef430b36a4be504969829f800e126245">tvm::relay::PatternWildcard</a>
+, <a class="el" href="classtvm_1_1runtime_1_1Array.html#a8f52fa8c97344586060ca735a3b91b39">tvm::runtime::Array< T, typename ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a6b398835e5160e792634c8ee0783f284">tvm::runtime::Map< K, V, typename, typename ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">tvm::runtime::Object</a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#afda3c65f41e83e1c87145a216f8c846d">tvm::runtime::ObjectPtr< T ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#af840ea1549dbe89d1680025c803e02e1">tvm::runtime::Optional< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#ab0f16ec4c71f65eec4a2ea9a83711a73">tvm::runtime::Optional< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaf3615a763a98184d648c976565f6893">tvm::runtime::String</a>
-, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#a3eaf85a64828b05e95d57e958089a34d">tvm::runtime::TVMRetValue</a>
-, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#aa590b3e712e06867805b41aaf17019ed">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#ac87c22e4d49e79ce5b36b3aead667fcb">tvm::runtime::TVMRetValue</a>
+, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a8dd1fbae84cb9597c52977b0e8db64dc">tvm::runtime::TypedPackedFunc< R(Args...)></a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a69c12a9470f67ea26e53172f5c9220a1">tvm::runtime::vm::Instruction</a>
, <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#aab332907b9f98876f441f6403b801187">tvm::TypedEnvFunc< R(Args...)></a>
</li>
<li>operator==()
-: <a class="el" href="classtvm_1_1Integer.html#ad2291d037ff36f5371f6381478b3eeaf">tvm::Integer</a>
+: <a class="el" href="classtvm_1_1Integer.html#a5b9ad6d47c7c6df5a066d58f6ba65f8e">tvm::Integer</a>
, <a class="el" href="classtvm_1_1runtime_1_1DataType.html#a806810d87352f5c2bac1ad4e8d086a01">tvm::runtime::DataType</a>
, <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#ac4066242008f1e60533099615a20fdbd">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ade3c126684dcdc6ed432f3bb7eb62099">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
@@ -376,7 +377,7 @@ $(function() {
, <a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html#a713c3d1884423e3e67e3fdaef2566925">tvm::AttrRegistryMapContainerMap< KeyType ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ADT.html#a88cf389012aad0e129c84c056ae546fc">tvm::runtime::ADT</a>
, <a class="el" href="classtvm_1_1runtime_1_1Array.html#ac84683f61368eb10e26e72a0097cccd7">tvm::runtime::Array< T, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html#a45c91b04c230f7400727b1bc02a93f02">tvm::runtime::InplaceArrayBase< ArrayType, ElemType ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html#a4b37b5fcd2285b818e8f623da8b326b2">tvm::runtime::InplaceArrayBase< ArrayType, ElemType ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a865a58097e473b532b1373bd15a1e91f">tvm::runtime::Map< K, V, typename, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html#a65fcb8067aa37421bb244573a07bb0b7">tvm::runtime::metadata::ArrayAccessor< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html#ab5e946c37ae641eb9b541df79a54c3e5">tvm::runtime::metadata::ArrayAccessor< const char *, ::tvm::runtime::String ></a>
@@ -384,7 +385,7 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#a6d950e33524258ba832ab87622183038">tvm::runtime::TVMArgs</a>
, <a class="el" href="classtvm_1_1script_1_1printer_1_1ExprDocNode.html#ac353b9e318287f71827131a2c29a91e3">tvm::script::printer::ExprDocNode</a>
, <a class="el" href="classtvm_1_1support_1_1Span.html#ac9f8667c4b7baa4b2dba95d70f0245c0">tvm::support::Span< T, W ></a>
-, <a class="el" href="classtvm_1_1te_1_1Schedule.html#a2040189df3b89304a12acce3efff04a6">tvm::te::Schedule</a>
+, <a class="el" href="classtvm_1_1te_1_1Schedule.html#a3f63b27dcbddd06c550cc1a5a6562717">tvm::te::Schedule</a>
, <a class="el" href="classtvm_1_1te_1_1Tensor.html#a4cf42a49cf51e5aa8d33c32885a7129e">tvm::te::Tensor</a>
, <a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html#a1a00e3ec5f80973c337b0e7ab9c0974d">tvm::te::Tensor::Slice</a>
, <a class="el" href="classtvm_1_1tir_1_1Layout.html#ab80c1b1c7c763b5609cddb0fb613dc5c">tvm::tir::Layout</a>
@@ -403,7 +404,7 @@ $(function() {
</li>
<li>Optional()
: <a class="el" href="classtvm_1_1relay_1_1DFPattern.html#aa573fa65bf9c4f0ba090225105233136">tvm::relay::DFPattern</a>
-, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#a4474559794818ad1257b70e681929938">tvm::runtime::Optional< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#aa07cc5bd536a18454ed737fb42b80be2">tvm::runtime::Optional< T ></a>
</li>
<li>Or()
: <a class="el" href="classtvm_1_1tir_1_1Or.html#ab0b68884ed84d03d3af7c95da2fcf81e">tvm::tir::Or</a>
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index ad55dc277..20934df2c 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
@@ -330,6 +330,7 @@ $(function() {
</li>
<li>set_dispatch()
: <a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html#a2fcc19e5151e9b9e56cafc76231b29fd">tvm::NodeFunctor< R(const ObjectRef &n, Args...)></a>
+, <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a39e23af093ba0ee9dab17de86b6fa58e">tvm::script::printer::TracedObjectFunctor< R, Args ></a>
</li>
<li>set_is_pure()
: <a class="el" href="classtvm_1_1tir_1_1InstructionKindRegEntry.html#ade332453b008e4fce49a3e3ebb4721c5">tvm::tir::InstructionKindRegEntry</a>
@@ -431,7 +432,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relay_1_1ShapePattern.html#a853201a9d5df8de96713a58c292e8698">tvm::relay::ShapePattern</a>
</li>
<li>ShapeTuple()
-: <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a7015b56c1a918607b1e220b43fc86d10">tvm::runtime::ShapeTuple</a>
+: <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a08d3ed30940fe54e2380eb933401cd05">tvm::runtime::ShapeTuple</a>
</li>
<li>SHashReduce()
: <a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html#add6e128afc66485e71854a8800c90c7a">tvm::arith::IntConstraintsNode</a>
@@ -625,7 +626,7 @@ $(function() {
: <a class="el" href="classtvm_1_1parser_1_1Source.html#a0ef9f726abcc6c4c9e81b3a257055df8">tvm::parser::Source</a>
</li>
<li>SourceMap()
-: <a class="el" href="classtvm_1_1parser_1_1SourceMap.html#afc48463cc0967ab79876178613a5aff2">tvm::parser::SourceMap</a>
+: <a class="el" href="classtvm_1_1parser_1_1SourceMap.html#a5fd3cc7160f57f7adcf1a0633603c72e">tvm::parser::SourceMap</a>
</li>
<li>SpaceGenerator()
: <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html#a4f11a841acdf7a66893eeefc1feb04ae">tvm::meta_schedule::SpaceGenerator</a>
@@ -635,7 +636,7 @@ $(function() {
</li>
<li>Span()
: <a class="el" href="classtvm_1_1Span.html#a5216631b639e8c802263d87d3fe9e5f6">tvm::Span</a>
-, <a class="el" href="classtvm_1_1support_1_1Span.html#a3c22dd06856e7029e7107adf38eb72f5">tvm::support::Span< T, W ></a>
+, <a class="el" href="classtvm_1_1support_1_1Span.html#a77653730a2542edf93b7c4413a72f3ec">tvm::support::Span< T, W ></a>
</li>
<li>SpecializedCondition()
: <a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html#a48d119ee1c6033929a5592cfc2592e60">tvm::te::SpecializedCondition</a>
@@ -657,10 +658,10 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#a51432f38d9ec4792a2525023179ae604">tvm::te::Stage</a>
</li>
<li>SplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a184575a8029d77f7a3bee23d81141df5">tvm::auto_scheduler::SplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a64ed86582a56a2645b3e4eb44ecb31af">tvm::auto_scheduler::SplitStep</a>
</li>
<li>Stage()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html#af0643fe8c1298451c9a322f915c48843">tvm::auto_scheduler::Stage</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html#a39ffbb1b4e189180bc4067e74965f42b">tvm::auto_scheduler::Stage</a>
, <a class="el" href="classtvm_1_1te_1_1Stage.html#afec82602b9321c489b88632a005335f8">tvm::te::Stage</a>
</li>
<li>Start()
@@ -700,7 +701,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtDoc.html#adec8d59e41d8a4093fb310089bf2c3ba">tvm::script::printer::StmtDoc</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
</li>
<li>StmtSRef()
: <a class="el" href="classtvm_1_1tir_1_1StmtSRef.html#a31687ace5dc4fe487ffb87d658d86412">tvm::tir::StmtSRef</a>
@@ -739,7 +740,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html#ac29b9295c432a87658392872c644864f">tvm::runtime::DeviceAPI</a>
</li>
<li>String()
-: <a class="el" href="classtvm_1_1runtime_1_1String.html#a02fca36e3ff55cc1e83635b02a11fca3">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#a68df7bab89fca339e3918438dd80300d">tvm::runtime::String</a>
</li>
<li>StringImm()
: <a class="el" href="classtvm_1_1tir_1_1StringImm.html#a0f2830290e055f677c5d5dea98aab726">tvm::tir::StringImm</a>
diff --git a/docs/reference/api/doxygen/functions_o.html b/docs/reference/api/doxygen/functions_o.html
index d3c9dff68..e3161c728 100644
--- a/docs/reference/api/doxygen/functions_o.html
+++ b/docs/reference/api/doxygen/functions_o.html
@@ -100,6 +100,9 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a5d7d1b555fd908e511caa42bd7ccfc71">tvm::runtime::ObjectPtr< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#aa07c1f6d66a438ea950637d13ed09471">tvm::runtime::ObjectRef</a>
</li>
+<li>ObjectRefType
+: <a class="el" href="classtvm_1_1TracedObject.html#acb3c3ecfc22420f670914e7a55de40fc">tvm::TracedObject< RefT ></a>
+</li>
<li>offset
: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html#a364fe1c876d030fb049ec3bee88d0ab6">tvm::auto_scheduler::StorageAlignStepNode</a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a0a335ae91c7fd20f47bb6581dc3c620c">tvm::runtime::vm::Instruction</a>
@@ -285,6 +288,7 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html#a71c712200306656ffdca92629b8172bc">tvm::runtime::PackedFunc</a>
, <a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a533c034c471f3c09341dcfbfcbb20d9a">tvm::runtime::TVMArgsSetter</a>
, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a987beda43b26ffc3665f1c448bf83e8c">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+, <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a1309ef39f5afbe12204f17fb4e939a99">tvm::script::printer::TracedObjectFunctor< R, Args ></a>
, <a class="el" href="classtvm_1_1SEqualReducer.html#ab8192a058d6e7e19b39685be482a0bc2">tvm::SEqualReducer</a>
, <a class="el" href="classtvm_1_1SHashReducer.html#a259dd594f7eb56fef692f38668b0ecb1">tvm::SHashReducer</a>
, <a class="el" href="classtvm_1_1StructuralEqual.html#a1a8f9e17db557ecb536eaa41d3fd2465">tvm::StructuralEqual</a>
@@ -342,9 +346,9 @@ $(function() {
<li>operator-()
: <a class="el" href="classtvm_1_1relay_1_1DFPattern.html#ad638d57cae9db9a1d4cd8831ab60a18b">tvm::relay::DFPattern</a>
, <a class="el" href="classtvm_1_1relay_1_1FeatureSet.html#ad0412921b6fe93a2f51b3c72c03c4095">tvm::relay::FeatureSet</a>
-, <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#ab5671b2b9d56c2abcfb8d3f03cf6fd9e">tvm::runtime::IterAdapter< Converter, TIter ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#a5bbe56d308014b49fec6374315f8d43a">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#a67f728a692ed84a78a20a2095815f260">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
-, <a class="el" href="classtvm_1_1TracedArrayIterator.html#aabf6b2121e29b8e4b2e13bbb945d7421">tvm::TracedArrayIterator< T ></a>
+, <a class="el" href="classtvm_1_1TracedArrayIterator.html#aad1f3796e892a744a54685f435e63f97">tvm::TracedArrayIterator< T ></a>
</li>
<li>operator--()
: <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#acb643cc554062422a4fd00bbfb3b0a6d">tvm::runtime::IterAdapter< Converter, TIter ></a>
@@ -411,15 +415,15 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#ac62909410a98a078ff01f688cdf70ffe">tvm::runtime::Map< K, V, typename, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">tvm::runtime::Object</a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#afda3c65f41e83e1c87145a216f8c846d">tvm::runtime::ObjectPtr< T ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#a377e159dbff53b8544a1b3337afc379d">tvm::runtime::Optional< T ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1String.html#a3b3c8614af05adc454f47132e04552ed">tvm::runtime::String</a>
-, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#addc4ffa96d47397893755d5e7e016084">tvm::runtime::TVMRetValue</a>
-, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#aa590b3e712e06867805b41aaf17019ed">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#af840ea1549dbe89d1680025c803e02e1">tvm::runtime::Optional< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaf3615a763a98184d648c976565f6893">tvm::runtime::String</a>
+, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#a2c8860df3cbdc52104dca8274abfa219">tvm::runtime::TVMRetValue</a>
+, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a8dd1fbae84cb9597c52977b0e8db64dc">tvm::runtime::TypedPackedFunc< R(Args...)></a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a69c12a9470f67ea26e53172f5c9220a1">tvm::runtime::vm::Instruction</a>
, <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#aab332907b9f98876f441f6403b801187">tvm::TypedEnvFunc< R(Args...)></a>
</li>
<li>operator==()
-: <a class="el" href="classtvm_1_1Integer.html#a5b9ad6d47c7c6df5a066d58f6ba65f8e">tvm::Integer</a>
+: <a class="el" href="classtvm_1_1Integer.html#ad2291d037ff36f5371f6381478b3eeaf">tvm::Integer</a>
, <a class="el" href="classtvm_1_1runtime_1_1DataType.html#a806810d87352f5c2bac1ad4e8d086a01">tvm::runtime::DataType</a>
, <a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html#ac4066242008f1e60533099615a20fdbd">tvm::runtime::IterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ade3c126684dcdc6ed432f3bb7eb62099">tvm::runtime::Map< K, V, typename, typename >::iterator</a>
@@ -427,7 +431,7 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayIterator.html#a6941bd0a5aa77acb1069e099e08455ed">tvm::runtime::metadata::ArrayIterator< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#ab849841f9277ad9015515ed591bf79d3">tvm::runtime::ObjectPtr< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#affdf1b8cdb36e140de7b3ad7064e4617">tvm::runtime::ObjectRef</a>
-, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#abb41934cee66ce6f25eba0f6de68ff1f">tvm::runtime::Optional< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#ae162e2c75ca786277c01a0f91b157fee">tvm::runtime::Optional< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html#a5c4ed23ffcb6a1874bd6eceb87b40b25">tvm::runtime::PackedFunc</a>
, <a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html#ae71116873ea643de9f8ec1836cc116db">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a>
, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a0e4a4d01d86eca79c5d9e1e90322c5cb">tvm::runtime::TypedPackedFunc< R(Args...)></a>
@@ -442,7 +446,7 @@ $(function() {
, <a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html#a713c3d1884423e3e67e3fdaef2566925">tvm::AttrRegistryMapContainerMap< KeyType ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ADT.html#a88cf389012aad0e129c84c056ae546fc">tvm::runtime::ADT</a>
, <a class="el" href="classtvm_1_1runtime_1_1Array.html#ac84683f61368eb10e26e72a0097cccd7">tvm::runtime::Array< T, typename ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html#a45c91b04c230f7400727b1bc02a93f02">tvm::runtime::InplaceArrayBase< ArrayType, ElemType ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html#a4b37b5fcd2285b818e8f623da8b326b2">tvm::runtime::InplaceArrayBase< ArrayType, ElemType ></a>
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a865a58097e473b532b1373bd15a1e91f">tvm::runtime::Map< K, V, typename, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor.html#a65fcb8067aa37421bb244573a07bb0b7">tvm::runtime::metadata::ArrayAccessor< C, Ref ></a>
, <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1ArrayAccessor_3_01const_01char_01_5_00_01_1_1tvm_1_1runtime_1_1String_01_4.html#ab5e946c37ae641eb9b541df79a54c3e5">tvm::runtime::metadata::ArrayAccessor< const char *, ::tvm::runtime::String ></a>
@@ -483,7 +487,7 @@ $(function() {
</li>
<li>Optional()
: <a class="el" href="classtvm_1_1relay_1_1DFPattern.html#aa573fa65bf9c4f0ba090225105233136">tvm::relay::DFPattern</a>
-, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#a39892e077bef2ff9379bd97b2f3e1a69">tvm::runtime::Optional< T ></a>
+, <a class="el" href="classtvm_1_1runtime_1_1Optional.html#a4474559794818ad1257b70e681929938">tvm::runtime::Optional< T ></a>
</li>
<li>optional_homogeneous_target
: <a class="el" href="classtvm_1_1CompilationConfigNode.html#ac29775462c7f70a3711221871fbd3453">tvm::CompilationConfigNode</a>
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index dad620082..628a066bf 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -418,6 +418,7 @@ $(function() {
</li>
<li>set_dispatch()
: <a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html#a2fcc19e5151e9b9e56cafc76231b29fd">tvm::NodeFunctor< R(const ObjectRef &n, Args...)></a>
+, <a class="el" href="classtvm_1_1script_1_1printer_1_1TracedObjectFunctor.html#a39e23af093ba0ee9dab17de86b6fa58e">tvm::script::printer::TracedObjectFunctor< R, Args ></a>
</li>
<li>set_is_pure()
: <a class="el" href="classtvm_1_1tir_1_1InstructionKindRegEntry.html#ade332453b008e4fce49a3e3ebb4721c5">tvm::tir::InstructionKindRegEntry</a>
@@ -836,7 +837,7 @@ $(function() {
</li>
<li>Span()
: <a class="el" href="classtvm_1_1Span.html#a5216631b639e8c802263d87d3fe9e5f6">tvm::Span</a>
-, <a class="el" href="classtvm_1_1support_1_1Span.html#a3c22dd06856e7029e7107adf38eb72f5">tvm::support::Span< T, W ></a>
+, <a class="el" href="classtvm_1_1support_1_1Span.html#a77653730a2542edf93b7c4413a72f3ec">tvm::support::Span< T, W ></a>
</li>
<li>span
: <a class="el" href="classtvm_1_1tir_1_1BufferNode.html#a13fc164e1b65cee741b4895df6316a4a">tvm::tir::BufferNode</a>
@@ -910,7 +911,7 @@ $(function() {
</li>
<li>Stage()
: <a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html#af0643fe8c1298451c9a322f915c48843">tvm::auto_scheduler::Stage</a>
-, <a class="el" href="classtvm_1_1te_1_1Stage.html#afec82602b9321c489b88632a005335f8">tvm::te::Stage</a>
+, <a class="el" href="classtvm_1_1te_1_1Stage.html#a1ecdc9a000be62c9cc26a96d4c33e36e">tvm::te::Stage</a>
</li>
<li>stage_id
: <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html#afcc7aaf263348f66139307affbfcee09">tvm::auto_scheduler::StepNode</a>
@@ -993,7 +994,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtDoc.html#adec8d59e41d8a4093fb310089bf2c3ba">tvm::script::printer::StmtDoc</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
</li>
<li>stmts
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtBlockDocNode.html#a17862bcb50fd1ef49cd9a47f065e612c">tvm::script::printer::StmtBlockDocNode</a>
@@ -1100,7 +1101,7 @@ $(function() {
, <a class="el" href="classtvm_1_1tir_1_1BufferNode.html#ac18ddd10b79a30ae57d3a8283686259d">tvm::tir::BufferNode</a>
</li>
<li>String()
-: <a class="el" href="classtvm_1_1runtime_1_1String.html#a68df7bab89fca339e3918438dd80300d">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#a02fca36e3ff55cc1e83635b02a11fca3">tvm::runtime::String</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj::FromStd</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_type.html b/docs/reference/api/doxygen/functions_type.html
index e188c2d73..2f647affc 100644
--- a/docs/reference/api/doxygen/functions_type.html
+++ b/docs/reference/api/doxygen/functions_type.html
@@ -302,6 +302,9 @@ $(function() {
<h3><a id="index_o"></a>- o -</h3><ul>
+<li>ObjectRefType
+: <a class="el" href="classtvm_1_1TracedObject.html#acb3c3ecfc22420f670914e7a55de40fc">tvm::TracedObject< RefT ></a>
+</li>
<li>OperationMap
: <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html#ad1678244c48ee2916fdbf0c3d7132384">tvm::auto_scheduler::AccessAnalyzerNode</a>
</li>
diff --git a/docs/reference/api/doxygen/functor_8h.html b/docs/reference/api/doxygen/functor_8h.html
index bbfa1bc1c..a8585a5a3 100644
--- a/docs/reference/api/doxygen/functor_8h.html
+++ b/docs/reference/api/doxygen/functor_8h.html
@@ -87,7 +87,7 @@ Include dependency graph for functor.h:</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="functor_8h__dep__incl.svg" width="3215" height="1334"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="functor_8h__dep__incl.svg" width="3379" height="1259"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
</div>
diff --git a/docs/reference/api/doxygen/functor_8h__dep__incl.svg b/docs/reference/api/doxygen/functor_8h__dep__incl.svg
index ec20bfae3..a22e3379b 100644
--- a/docs/reference/api/doxygen/functor_8h__dep__incl.svg
+++ b/docs/reference/api/doxygen/functor_8h__dep__incl.svg
@@ -4,1177 +4,1167 @@
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+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-940 2529.6837,-940 2529.6837,4 -4,4"/>
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<g id="node1" class="node">
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+<polygon fill="#bfbfbf" stroke="#000000" points="1067.1837,-916.5 1067.1837,-935.5 1214.1837,-935.5 1214.1837,-916.5 1067.1837,-916.5"/>
+<text text-anchor="middle" x="1140.6837" y="-923.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">include/tvm/node/functor.h</text>
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diff --git a/docs/reference/api/doxygen/hierarchy.html b/docs/reference/api/doxygen/hierarchy.html
index 0826f0b90..f436f4ded 100644
--- a/docs/reference/api/doxygen/hierarchy.html
+++ b/docs/reference/api/doxygen/hierarchy.html
@@ -1332,8 +1332,8 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_122_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">tvm::relay::PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can use t [...]
<tr id="row_123_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Profiler.html" target="_self">tvm::runtime::profiling::Profiler</a></td><td class="desc"></td></tr>
<tr id="row_124_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">tvm::ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
-<tr id="row_125_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">tvm::ReflectionVTable::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
-<tr id="row_126_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">tvm::runtime::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function. ">Registry</a> for global function </td></tr>
+<tr id="row_125_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Registry.html" target="_self">tvm::runtime::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Registry.html" title="Registry for global function. ">Registry</a> for global function </td></tr>
+<tr id="row_126_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">tvm::ReflectionVTable::Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
<tr id="row_127_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">tvm::ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
<tr id="row_128_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html" target="_self">tvm::runtime::ReverseIterAdapter< Converter, TIter ></a></td><td class="desc">Iterator adapter that adapts TIter to return another type </td></tr>
<tr id="row_129_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1RewriteSimplifier.html" target="_self">tvm::arith::RewriteSimplifier</a></td><td class="desc">Rewrite-rule based simplifier </td></tr>
@@ -1372,103 +1372,104 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_158_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TracedMap.html" target="_self">tvm::TracedMap< K, V ></a></td><td class="desc">Traced wrapper for Map objects </td></tr>
<tr id="row_159_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TracedMapIterator.html" target="_self">tvm::TracedMapIterator< K, V ></a></td><td class="desc">Iterator class for TracedMap<K, V> </td></tr>
<tr id="row_160_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TracedObject.html" target="_self">tvm::TracedObject< RefT ></a></td><td class="desc">Traced wrapper for regular (non-container) TVM objects </td></tr>
-<tr id="row_161_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector.html" target="_self">tvm::detail::TracedObjectWrapperSelector< T, IsObject ></a></td><td class="desc"></td></tr>
-<tr id="row_162_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01Array_3_01T_01_4_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< Array< T >, true ></a></td><td class="desc"></td></tr>
-<tr id="row_163_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01Map_3_01K_00_01V_01_4_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< Map< K, V >, true ></a></td><td class="desc"></td></tr>
-<tr id="row_164_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01Optional_3_01T_01_4_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< Optional< T >, true ></a></td><td class="desc"></td></tr>
-<tr id="row_165_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01T_00_01false_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< T, false ></a></td><td class="desc"></td></tr>
-<tr id="row_166_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01T_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< T, true ></a></td><td class="desc"></td></tr>
-<tr id="row_167_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TracedOptional.html" target="_self">tvm::TracedOptional< T ></a></td><td class="desc">Traced wrapper for Optional objects </td></tr>
-<tr id="row_168_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_168_" class="arrow" onclick="toggleFolder('168_')">►</span><span class="icona"><span class="icon">C</span></span><b>true_type</b></td><td class="desc"></td></tr>
-<tr id="row_168_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html" target="_self">tvm::detail::is_specialized< Container< Args... >, Container ></a></td><td class="desc"></td></tr>
-<tr id="row_169_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
-<tr id="row_170_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMAotExecutor.html" target="_self">TVMAotExecutor</a></td><td class="desc"></td></tr>
-<tr id="row_171_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
-<tr id="row_172_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
-<tr id="row_173_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">tvm::runtime::TVMArgsSetter</a></td><td class="desc"></td></tr>
-<tr id="row_174_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
-<tr id="row_175_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMConstantInfo.html" target="_self">TVMConstantInfo</a></td><td class="desc">Describes one constant argument to <code>run_model</code> </td></tr>
-<tr id="row_176_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
-<tr id="row_177_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphExecutorGraphAttr.html" target="_self">TVMGraphExecutorGraphAttr</a></td><td class="desc"></td></tr>
-<tr id="row_178_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMetadata.html" target="_self">TVMMetadata</a></td><td class="desc">Top-level metadata structure. Holds all other metadata types </td></tr>
-<tr id="row_179_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
-<tr id="row_180_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">tvm::runtime::TVMMovableArgValueWithContext_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>. ">TypedPack [...]
-<tr id="row_181_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name. ">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
-<tr id="row_182_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
-<tr id="row_183_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
-<tr id="row_184_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
-<tr id="row_185_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_185_" class="arrow" onclick="toggleFolder('185_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">tvm::runtime::TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
-<tr id="row_185_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">tvm::runtime::TVMArgValue</a></td><td class="desc">A single argument value to <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed format [...]
-<tr id="row_185_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">tvm::runtime::TVMMovableArgValue_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>. ">TypedPa [...]
-<tr id="row_185_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">tvm::runtime::TVMRetValue</a></td><td class="desc">Return Value container, Unlike <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" title="A single argument value to PackedFunc. Containing both type_code and TVMValue. [...]
-<tr id="row_186_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMTensorInfo.html" target="_self">TVMTensorInfo</a></td><td class="desc">Describes one tensor argument to <code>run_model</code>. NOTE: while TIR allows for other types of arguments, such as scalars, the AOT run_model function does not currently accept these. Therefore it's not possible to express tho [...]
-<tr id="row_187_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
-<tr id="row_188_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
-<tr id="row_189_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< bool(const Array< Type > &args, int num_inputs, const Attrs &attrs, const TypeReporter &reporter)></a></td><td class="desc"></td></tr>
-<tr id="row_190_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#TypedPackedFuncAnchor">TypedPackedFunc<R(Args..)></a> </td></tr>
-<tr id="row_191_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(ObjectRef json_attrs)></a></td><td class="desc"></td></tr>
-<tr id="row_192_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(Schedule sch, const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision)></a></td><td class="desc"></td></tr>
-<tr id="row_193_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< RunnerResult >(int)></a></td><td class="desc"></td></tr>
-<tr id="row_194_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< te::Tensor >(const Attrs &attrs, const Array< te::Tensor > &inputs, const Type &out_type)></a></td><td class="desc"></td></tr>
-<tr id="row_195_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tir::Schedule >(const IRModule &)></a></td><td class="desc"></td></tr>
-<tr id="row_196_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tir::Schedule >(const tir::Schedule &, const tir::BlockRV &)></a></td><td class="desc"></td></tr>
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+<tr id="row_165_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01Optional_3_01T_01_4_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< Optional< T >, true ></a></td><td class="desc"></td></tr>
+<tr id="row_166_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01T_00_01false_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< T, false ></a></td><td class="desc"></td></tr>
+<tr id="row_167_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TracedObjectWrapperSelector_3_01T_00_01true_01_4.html" target="_self">tvm::detail::TracedObjectWrapperSelector< T, true ></a></td><td class="desc"></td></tr>
+<tr id="row_168_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TracedOptional.html" target="_self">tvm::TracedOptional< T ></a></td><td class="desc">Traced wrapper for Optional objects </td></tr>
+<tr id="row_169_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_169_" class="arrow" onclick="toggleFolder('169_')">►</span><span class="icona"><span class="icon">C</span></span><b>true_type</b></td><td class="desc"></td></tr>
+<tr id="row_169_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html" target="_self">tvm::detail::is_specialized< Container< Args... >, Container ></a></td><td class="desc"></td></tr>
+<tr id="row_170_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
+<tr id="row_171_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMAotExecutor.html" target="_self">TVMAotExecutor</a></td><td class="desc"></td></tr>
+<tr id="row_172_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_173_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
+<tr id="row_174_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">tvm::runtime::TVMArgsSetter</a></td><td class="desc"></td></tr>
+<tr id="row_175_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
+<tr id="row_176_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMConstantInfo.html" target="_self">TVMConstantInfo</a></td><td class="desc">Describes one constant argument to <code>run_model</code> </td></tr>
+<tr id="row_177_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
+<tr id="row_178_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphExecutorGraphAttr.html" target="_self">TVMGraphExecutorGraphAttr</a></td><td class="desc"></td></tr>
+<tr id="row_179_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMetadata.html" target="_self">TVMMetadata</a></td><td class="desc">Top-level metadata structure. Holds all other metadata types </td></tr>
+<tr id="row_180_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
+<tr id="row_181_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValueWithContext__.html" target="_self">tvm::runtime::TVMMovableArgValueWithContext_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>. ">TypedPackedFunc</a> to [...]
+<tr id="row_182_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name. ">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
+<tr id="row_183_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
+<tr id="row_184_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
+<tr id="row_185_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
+<tr id="row_186_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_186_" class="arrow" onclick="toggleFolder('186_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html" target="_self">tvm::runtime::TVMPODValue_</a></td><td class="desc">Internal base class to handle conversion to POD values </td></tr>
+<tr id="row_186_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" target="_self">tvm::runtime::TVMArgValue</a></td><td class="desc">A single argument value to <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed format. ">PackedFun [...]
+<tr id="row_186_1_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMMovableArgValue__.html" target="_self">tvm::runtime::TVMMovableArgValue_</a></td><td class="desc">Internal auxiliary struct for <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" title="Please refer to TypedPackedFunc<R(Args..)>. ">TypedPackedFunc</a> [...]
+<tr id="row_186_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html" target="_self">tvm::runtime::TVMRetValue</a></td><td class="desc">Return Value container, Unlike <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html" title="A single argument value to PackedFunc. Containing both type_code and TVMValue. ">TVMArgValue [...]
+<tr id="row_187_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMTensorInfo.html" target="_self">TVMTensorInfo</a></td><td class="desc">Describes one tensor argument to <code>run_model</code>. NOTE: while TIR allows for other types of arguments, such as scalars, the AOT run_model function does not currently accept these. Therefore it's not possible to express those in this me [...]
+<tr id="row_188_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
+<tr id="row_189_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
+<tr id="row_190_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">tvm::TypedEnvFunc< bool(const Array< Type > &args, int num_inputs, const Attrs &attrs, const TypeReporter &reporter)></a></td><td class="desc"></td></tr>
+<tr id="row_191_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< FType ></a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#TypedPackedFuncAnchor">TypedPackedFunc<R(Args..)></a> </td></tr>
+<tr id="row_192_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(ObjectRef json_attrs)></a></td><td class="desc"></td></tr>
+<tr id="row_193_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< ObjectRef >(Schedule sch, const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision)></a></td><td class="desc"></td></tr>
+<tr id="row_194_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< RunnerResult >(int)></a></td><td class="desc"></td></tr>
+<tr id="row_195_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< te::Tensor >(const Attrs &attrs, const Array< te::Tensor > &inputs, const Type &out_type)></a></td><td class="desc"></td></tr>
+<tr id="row_196_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tir::Schedule >(const IRModule &)></a></td><td class="desc"></td></tr>
+<tr id="row_197_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tir::Schedule >(const tir::Schedule &, const tir::BlockRV &)></a></td><td class="desc"></td></tr>
+<tr id="row_198_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >()></a></td><td class="desc"></td></tr>
+<tr id="row_199_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< TuningRecord >(const Workload &, int)></a></td><td class="desc"></td></tr>
+<tr id="row_200_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Array< tvm::runtime::NDArray >(const TuneContext &context, const Array< MeasureCandidate > &candidates)></a></td><td class="desc"></td></tr>
+<tr id="row_201_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool()></a></td><td class="desc"></td></tr>
+<tr id="row_202_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool(const IRModule &)></a></td><td class="desc"></td></tr>
+<tr id="row_203_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< bool(const tir::Schedule &)></a></td><td class="desc"></td></tr>
+<tr id="row_204_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< int()></a></td><td class="desc"></td></tr>
+<tr id="row_205_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< int64_t()></a></td><td class="desc"></td></tr>
+<tr id="row_206_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< ObjectRef(Array< ObjectRef > attrs)></a></td><td class="desc"></td></tr>
+<tr id="row_207_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< Array< MeasureCandidate > >()></a></td><td class="desc"></td></tr>
+<tr id="row_208_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< tir::PrimFunc >(const Array< te::Tensor, void > &)></a></td><td class="desc"></td></tr>
+<tr id="row_209_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Optional< tir::Trace >(const tir::Trace &, support::LinearCongruentialEngine::TRandState rand_state)></a></td><td class="desc"></td></tr>
+<tr id="row_210_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::runtime::TypedPackedFunc< R(Args...)></a></td><td class="desc">A <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed form [...]
+<tr id="row_211_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< RunnerResult()></a></td><td class="desc"></td></tr>
+<tr id="row_212_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String()></a></td><td class="desc"></td></tr>
+<tr id="row_213_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< String(const Array< ObjectRef > &inputs, const Array< ObjectRef > &attrs, const Optional< ObjectRef > &decision, const Array< String > &outputs)></a></td><td class="desc"></td></tr>
+<tr id="row_214_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< TargetJSON(TargetJSON)></a></td><td class="desc"></td></tr>
+<tr id="row_215_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< te::Schedule(const Attrs &attrs, const Array< te::Tensor > &outs, const Target &target)></a></td><td class="desc"></td></tr>
+<tr id="row_216_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void()></a></td><td class="desc"></td></tr>
+<tr id="row_217_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const Array< MeasureCandidate > &, const Array< RunnerResult > &)></a></td><td class="desc"></td></tr>
+<tr id="row_218_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const Array< tir::Schedule > &, const Optional< Database > &, const Optional< CostModel > &)></a></td><td class="desc"></td></tr>
+<tr id="row_219_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TaskScheduler &task_scheduler, int task_id, const Array< MeasureCandidate > &measure_candidates, const Array< BuilderResult > &builds, const Array< RunnerResult > &results)></a></t [...]
+<tr id="row_220_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &)></a></td><td class="desc"></td></tr>
+<tr id="row_221_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &, const Array< MeasureCandidate > &, const Array< RunnerResult > &)></a></td><td class="desc"></td></tr>
+<tr id="row_222_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuneContext &, const Array< MeasureCandidate > &, void *p_addr)></a></td><td class="desc"></td></tr>
+<tr id="row_223_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(const TuningRecord &)></a></td><td class="desc"></td></tr>
+<tr id="row_224_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(int)></a></td><td class="desc"></td></tr>
+<tr id="row_225_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(size_t, void *)></a></td><td class="desc"></td></tr>
+<tr id="row_226_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(String)></a></td><td class="desc"></td></tr>
+<tr id="row_227_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< void(tvm::DiagnosticContext ctx)></a></td><td class="desc"></td></tr>
+<tr id="row_228_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc.html" target="_self">tvm::runtime::TypedPackedFunc< Workload(const IRModule &)></a></td><td class="desc"></td></tr>
+<tr id="row_229_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< FType ></a></td><td class="desc"></td></tr>
+<tr id="row_230_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">tvm::TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_231_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_231_" class="arrow" onclick="toggleFolder('231_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< Type(const Type &n)></a></td><td class="desc"></td></tr>
+<tr id="row_231_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">tvm::TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions. ">TypeMutator</a> that mutates expressions </td></tr>
+<tr id="row_232_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_232_" class="arrow" onclick="toggleFolder('232_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">tvm::TypeFunctor< void(const Type &n)></a></td><td class="desc"></td></tr>
+<tr id="row_232_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">tvm::TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
+<tr id="row_233_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">tvm::runtime::TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
+<tr id="row_234_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName.html" target="_self">tvm::detail::TypeName< T ></a></td><td class="desc">Helper struct to get the type name known to tvm </td></tr>
+<tr id="row_235_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01bool_01_4.html" target="_self">tvm::detail::TypeName< bool ></a></td><td class="desc"></td></tr>
+<tr id="row_236_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01DataType_01_4.html" target="_self">tvm::detail::TypeName< DataType ></a></td><td class="desc"></td></tr>
+<tr id="row_237_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01double_01_4.html" target="_self">tvm::detail::TypeName< double ></a></td><td class="desc"></td></tr>
+<tr id="row_238_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int_01_4.html" target="_self">tvm::detail::TypeName< int ></a></td><td class="desc"></td></tr>
+<tr id="row_239_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int64__t_01_4.html" target="_self">tvm::detail::TypeName< int64_t ></a></td><td class="desc"></td></tr>
+<tr id="row_240_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01uint64__t_01_4.html" target="_self">tvm::detail::TypeName< uint64_t ></a></td><td class="desc"></td></tr>
+<tr id="row_241_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01void_01_5_01_4.html" target="_self">tvm::detail::TypeName< void * ></a></td><td class="desc"></td></tr>
+<tr id="row_242_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html" target="_self">tvm::runtime::micro_rpc::Unframer</a></td><td class="desc"></td></tr>
+<tr id="row_243_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1v__info.html" target="_self">tvm::relay::v_info</a></td><td class="desc">A struct to keep info of traversed expr in ExpandDataflow function </td></tr>
+<tr id="row_244_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">tvm::runtime::Array< T, typename >::ValueConverter</a></td><td class="desc"></td></tr>
+<tr id="row_245_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1ValueTypeInfoMaker.html" target="_self">tvm::detail::ValueTypeInfoMaker< ValueType, IsArray, IsMap ></a></td><td class="desc"></td></tr>
+<tr id="row_246_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDeviceCache.html" target="_self">tvm::VirtualDeviceCache</a></td><td class="desc">A cache of <code>VirtualDevices</code>. This can be used: </td></tr>
+<tr id="row_247_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html" target="_self">tvm::runtime::vm::VMFrame</a></td><td class="desc">A representation of a stack frame </td></tr>
+<tr id="row_248_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">tvm::runtime::vm::VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
+<tr id="row_249_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">tvm::With< ContextType ></a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
+<tr id="row_250_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html" target="_self">tvm::meta_schedule::WorkloadEqual</a></td><td class="desc">The equality check for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
+<tr id="row_251_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadHash.html" target="_self">tvm::meta_schedule::WorkloadHash</a></td><td class="desc">The hash method for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
+<tr id="row_252_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1WriteStream.html" target="_self">tvm::runtime::micro_rpc::WriteStream</a></td><td class="desc"></td></tr>
</table>
</div><!-- directory -->
</div><!-- contents -->
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