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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/11/11 19:22:26 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@88979834842115ef9ea8487d9a631dc3275f7a7d)
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 73481ea60a deploying docs (apache/tvm@88979834842115ef9ea8487d9a631dc3275f7a7d)
73481ea60a is described below
commit 73481ea60ad0422402616758ead15d53254a8206
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Fri Nov 11 19:22:18 2022 +0000
deploying docs (apache/tvm@88979834842115ef9ea8487d9a631dc3275f7a7d)
---
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_keras.rst.txt | 2 +-
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 22 +-
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 4 +-
.../how_to/deploy_models/deploy_quantized.rst.txt | 2 +-
.../deploy_models/deploy_ssd_gluoncv.rst.txt | 4 +-
.../deploy_models/sg_execution_times.rst.txt | 20 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 2 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 10 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 14 +-
.../tune_conv2d_layer_cuda.rst.txt | 4 +-
.../tune_network_cuda.rst.txt | 4 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 88 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 10 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 1053 +++++++-
.../work_with_microtvm/micro_autotune.rst.txt | 16 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 12 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../how_to/work_with_schedules/intrin_math.rst.txt | 2 +-
.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../how_to/work_with_schedules/tensorize.rst.txt | 2 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 4 +-
.../frontend/deploy_classification.rst.txt | 2 +-
.../tutorials/frontend/deploy_detection.rst.txt | 2 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 4 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 60 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 24 +-
.../tutorial/tensor_expr_get_started.rst.txt | 47 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_keras.html | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 15 +-
docs/how_to/compile_models/from_pytorch.html | 11 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 22 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 35 +-
docs/how_to/deploy_models/deploy_prequantized.html | 8 +-
.../deploy_models/deploy_prequantized_tflite.html | 4 +-
docs/how_to/deploy_models/deploy_quantized.html | 2 +-
docs/how_to/deploy_models/deploy_ssd_gluoncv.html | 34 +-
docs/how_to/deploy_models/sg_execution_times.html | 20 +-
.../extend_tvm/bring_your_own_datatypes.html | 2 +-
docs/how_to/extend_tvm/sg_execution_times.html | 10 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 4 +-
.../tune_with_autoscheduler/tune_network_cuda.html | 4 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autoscheduler/tune_sparse_x86.html | 88 +-
.../tune_with_autotvm/sg_execution_times.html | 10 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 1053 +++++++-
docs/how_to/work_with_microtvm/micro_autotune.html | 16 +-
docs/how_to/work_with_microtvm/micro_pytorch.html | 5 +-
docs/how_to/work_with_microtvm/micro_train.html | 16 +-
.../work_with_microtvm/sg_execution_times.html | 12 +-
.../how_to/work_with_relay/sg_execution_times.html | 8 +-
docs/how_to/work_with_schedules/intrin_math.html | 2 +-
.../work_with_schedules/sg_execution_times.html | 18 +-
docs/how_to/work_with_schedules/tensorize.html | 2 +-
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docs/tutorial/sg_execution_times.html | 24 +-
docs/tutorial/tensor_expr_get_started.html | 43 +-
192 files changed, 6870 insertions(+), 2496 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 bf9e397f45..9ef9c562a8 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -315,7 +315,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 11.692 seconds)
+ **Total running time of the script:** ( 1 minutes 11.033 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index bec8ffffdf..f0a4b73d2a 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -228,7 +228,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
-
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 1s 941ms/step
+
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 1s 932ms/step
Keras top-1 id: 285, class name: Egyptian cat
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 8dddb7c90e..fb3857aa46 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.zip0f22659a-1eda-4207-9f61-6f443972fb41 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipa3da7928-338f-43fc-aa87-db1e4a282088 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 611a0125a5..718349e7bb 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -116,7 +116,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
0%| | 0.00/41.5M [00:00<?, ?B/s]
19%|#9 | 7.99M/41.5M [00:00<00:00, 39.6MB/s]
35%|###4 | 14.3M/41.5M [00:00<00:00, 39.2MB/s]
44%|####3 | 18.1M/41.5M [00:00<00:00, 37.7MB/s]
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77%|#######7 | 32.0M/41.5M [00:00<00:00, 47.6MB/s]
92%|#########2| 38.3M/41.5M [00:00<00:00, 43.7MB/s]
100%|##########| 41.5M/41.5M [00:01<00:00, 42.9MB/s]
+
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15%|#5 | 6.33M/41.5M [00:00<00:00, 62.5MB/s]
30%|##9 | 12.3M/41.5M [00:00<00:00, 47.8MB/s]
41%|####1 | 17.0M/41.5M [00:00<00:00, 40.9MB/s]
57%|#####6 | 23.6M/41.5M [00:00<00:00, 49.9MB/s]
69%|######9 | 28.7M/41.5M [00:00<00:00, 47.4MB/s]
80%|######## | 33.4M/41.5M [00:00<00:00, 44.5MB/s]
92%|#########2| 38.3M/41.5M [00:00<00:00, 37.7MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 43.6MB/s]
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 74983493b0..5daa00413d 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -98,7 +98,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
0%| | 0.00/44.7M [00:00<?, ?B/s]
18%|#7 | 7.99M/44.7M [00:00<00:00, 69.1MB/s]
39%|###9 | 17.5M/44.7M [00:00<00:00, 85.6MB/s]
63%|######2 | 28.0M/44.7M [00:00<00:00, 96.2MB/s]
83%|########3 | 37.3M/44.7M [00:00<00:00, 90.0MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 64.0MB/s]
+
0%| | 0.00/44.7M [00:00<?, ?B/s]
18%|#7 | 7.99M/44.7M [00:00<00:00, 74.8MB/s]
34%|###3 | 15.1M/44.7M [00:00<00:00, 72.9MB/s]
54%|#####3 | 24.0M/44.7M [00:00<00:00, 79.6MB/s]
72%|#######1 | 32.0M/44.7M [00:00<00:00, 81.1MB/s]
89%|########8 | 39.7M/44.7M [00:00<00:00, 64.5MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 63.6MB/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 b58795dcb9..3460f8a4e2 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -416,7 +416,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 10.815 seconds)
+ **Total running time of the script:** ( 1 minutes 10.478 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 4f7e7eec0f..523dbe3e51 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
=================
-**05:43.394** total execution time for **how_to_compile_models** files:
+**05:42.185** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:11.692 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:11.033 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:10.815 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:10.478 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:45.624 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:44.838 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:32.269 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:31.834 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:30.052 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:29.933 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:26.208 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:26.842 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:24.609 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:25.061 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:22.229 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:22.159 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:17.477 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:17.668 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.418 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.340 | 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 8340a6b2ae..6f01f9cf57 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
@@ -434,7 +434,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.1948 16.1111 16.6856 15.7359 0.3016
+ 15.6913 15.6844 15.7736 15.6278 0.0415
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 0e7c58553c..d8cd275627 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
@@ -127,7 +127,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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1
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/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/nn/functional.py:3897: 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)
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/anchor_utils.py:124: 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').
@@ -296,7 +296,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 10.309 seconds)
+ **Total running time of the script:** ( 3 minutes 8.915 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 1860b98df7..aa75bf4398 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -236,7 +236,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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59%|#####8 | 7.99M/13.6M [00:00<00:00, 61.5MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 78.8MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
59%|#####8 | 7.99M/13.6M [00:00<00:00, 78.1MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 106MB/s]
@@ -418,7 +418,7 @@ 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)
- 90.1874 90.0828 96.1832 89.8562 0.6344
+ 88.8378 88.7331 90.5060 88.5198 0.3382
@@ -467,7 +467,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.760 seconds)
+ **Total running time of the script:** ( 1 minutes 4.035 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 82555f4262..3a5e2877f2 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
@@ -432,7 +432,7 @@ 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)
- 119.3842 119.2682 123.6073 118.4121 0.5972
+ 119.3821 119.3645 122.1992 118.4341 0.5097
@@ -469,7 +469,7 @@ 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:** ( 2 minutes 25.582 seconds)
+ **Total running time of the script:** ( 2 minutes 20.507 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 3deb1b8d78..471aec1200 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -253,7 +253,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 34.704 seconds)
+ **Total running time of the script:** ( 1 minutes 28.804 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 eab2c26a93..4aed35f879 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
@@ -166,7 +166,7 @@ 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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@@ -242,7 +242,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 58.518 seconds)
+ **Total running time of the script:** ( 2 minutes 57.052 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 0eba16b854..58d024b94d 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
=================
-**12:39.242** total execution time for **how_to_deploy_models** files:
+**12:23.496** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:10.309 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:08.915 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:58.518 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:57.052 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 02:25.582 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 02:20.507 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:34.704 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:28.804 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:04.760 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:04.035 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:35.298 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:35.251 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:25.280 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:24.783 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:24.785 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:24.142 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index bb9d7a05cb..c897b48eac 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
@@ -472,7 +472,7 @@ 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.zipd49fbfe6-2e6c-4236-a41d-2df55e0b26ec from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip2d9dfe66-c60a-4fb6-a94b-adc6a22ba651 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index e92ebe1bd1..769bab0eab 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
=================
-**00:46.883** total execution time for **how_to_extend_tvm** files:
+**00:46.625** 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``) | 00:43.483 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:43.262 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.381 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.358 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.011 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:00.996 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index aa401cbf19..e468e9e4d1 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: 7361us [7361us] (46.78%; 46.78%)
- FoldScaleAxis: 8375us [7us] (53.22%; 53.22%)
- FoldConstant: 8368us [1729us] (53.17%; 99.91%)
- InferType: 6639us [6639us] (42.19%; 79.34%)
+ InferType: 7197us [7197us] (46.17%; 46.17%)
+ FoldScaleAxis: 8390us [7us] (53.83%; 53.83%)
+ FoldConstant: 8383us [1714us] (53.78%; 99.92%)
+ InferType: 6669us [6669us] (42.79%; 79.55%)
@@ -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: 6741us [6741us] (44.97%; 44.97%)
- FoldScaleAxis: 8249us [5us] (55.03%; 55.03%)
- FoldConstant: 8244us [1736us] (54.99%; 99.94%)
- InferType: 6508us [6508us] (43.42%; 78.95%)
+ InferType: 6694us [6694us] (45.10%; 45.10%)
+ FoldScaleAxis: 8147us [5us] (54.90%; 54.90%)
+ FoldConstant: 8142us [1653us] (54.86%; 99.94%)
+ InferType: 6489us [6489us] (43.72%; 79.70%)
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 6c6a65d155..0985d3f03b 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: 34.181152 ms
+ Convolution: 38.561569 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 1927444be3..626750af5e 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
@@ -659,7 +659,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.399268 ms
+ conv2d with tensor core: 11.910179 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 282c8d4516..4cb0a0d562 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: 0.018167
- Baseline: 3.189108
+ Numpy running time: 0.018436
+ Baseline: 3.462091
@@ -239,7 +239,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.297096
+ Opt1: 0.294242
@@ -342,7 +342,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.331047
+ Opt2: 0.333649
@@ -438,7 +438,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.114364
+ Opt3: 0.115591
@@ -563,7 +563,7 @@ flattening.
.. code-block:: none
- Opt4: 0.109365
+ Opt4: 0.108762
@@ -685,7 +685,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.112397
+ Opt5: 0.110485
@@ -810,7 +810,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.147102
+ Opt6: 0.145104
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 aa1171e9e8..b46ffb6ddc 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.060** total execution time for **how_to_optimize_operators** files:
+**00:34.657** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.494 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:32.209 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.489 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.406 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.078 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.042 | 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 9d635bf9c4..b38e3af6d3 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
=================
-**09:01.419** total execution time for **how_to_tune_with_autoscheduler** files:
+**09:11.381** 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``) | 05:35.870 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 05:47.500 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:31.708 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:30.348 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:02.650 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:02.030 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:28.544 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:28.897 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:11.709 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:11.726 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:10.939 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:10.880 | 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 49bb8439a7..f64208e5a4 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: 0.349 ms
+ Execution time of this operator: 0.348 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:** ( 5 minutes 35.870 seconds)
+ **Total running time of the script:** ( 5 minutes 47.500 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 605aaae849..bb765478e0 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
@@ -643,7 +643,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 8.2247 8.2274 8.2284 8.2184 0.0045
+ 8.1995 8.1982 8.2087 8.1915 0.0071
@@ -671,7 +671,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.650 seconds)
+ **Total running time of the script:** ( 1 minutes 2.030 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index d75b33ee34..bb3e31173a 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
@@ -662,7 +662,7 @@ 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)
- 752.6515 752.4521 754.0442 751.4582 1.0651
+ 744.6625 744.5877 744.9302 744.4696 0.1953
@@ -690,7 +690,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 31.708 seconds)
+ **Total running time of the script:** ( 1 minutes 30.348 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 4969dea201..4ba71ac55b 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
@@ -386,30 +386,78 @@ 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_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 16) {
- for (j.init: int32, 0, 16) {
- compute_5: Buffer(compute_4, float32, [512], [])[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32
+ preflattened_buffer_map = {placeholder_6: placeholder_15: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 64) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [1024]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 2) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 16) {
+ let cse_var_1: int32 = (((i.outer.inner*512) + (i.inner.init*32)) + (nb_j.inner*16))
+ {
+ compute_5: Buffer(compute_4, float32, [1024], [])[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_1: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- for (i.inner: int32, 0, 16) {
- for (j: int32, 0, 16) {
- let cse_var_3: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_2: int32 = (((i.inner*32) + (nb_j.inner*16)) + j)
- compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 16) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_19: int32 = (((i.outer.inner*512) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_18: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*8192) + (i.outer.inner*4096)) + (i.inner*256))
+ let cse_var_17: int32 = (cse_var_19 + 9)
+ let cse_var_16: int32 = (cse_var_19 + 8)
+ let cse_var_15: int32 = (cse_var_19 + 7)
+ let cse_var_14: int32 = (cse_var_19 + 6)
+ let cse_var_13: int32 = (cse_var_19 + 5)
+ let cse_var_12: int32 = (cse_var_19 + 4)
+ let cse_var_11: int32 = (cse_var_19 + 3)
+ let cse_var_10: int32 = (cse_var_19 + 2)
+ let cse_var_9: int32 = (cse_var_19 + 15)
+ let cse_var_8: int32 = (cse_var_19 + 14)
+ let cse_var_7: int32 = (cse_var_19 + 13)
+ let cse_var_6: int32 = (cse_var_19 + 12)
+ let cse_var_5: int32 = (cse_var_19 + 11)
+ let cse_var_4: int32 = (cse_var_19 + 10)
+ let cse_var_3: int32 = (cse_var_19 + 1)
+ {
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 16) {
- for (i1.inner: int32, 0, 32) {
- let cse_var_4: int32 = ((((floordiv(i0.outer.i1.outer.fused, 16)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)) + i1.inner)
- compute[cse_var_4] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_4]), 0f32)
- }
+ for (i0.inner: int32, 0, 32) {
+ let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+ compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
}
}
}
@@ -465,7 +513,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.504 ms
+ Execution time of this operator: 1.690 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 02b92f93bd..2782a3020a 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**00:48.744** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.563** 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``) | 00:48.707 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:23.528 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.022 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.021 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
-+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
++--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 9f23052786..222535641a 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
@@ -265,9 +265,7 @@ for this template
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 167.60/167.60 result: MeasureResult(costs=(0.001381298825,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.522179365158081, timestamp=1668157477.0085385) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4360472
- No: 2 GFLOPS: 8.79/167.60 result: MeasureResult(costs=(0.02632410425,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.8582940101623535, timestamp=1668157477.7304428) [('tile_f', [-1, 4, 4, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2141377
- No: 3 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ No: 1 GFLOPS: 0.00/0.00 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
@@ -389,9 +387,8 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 32, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7926252
- No: 4 GFLOPS: 12.79/167.60 result: MeasureResult(costs=(0.018099557166666665,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.841151237487793, timestamp=1668157480.3641567) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2974401
- No: 5 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2347210
+ No: 2 GFLOPS: 0.00/0.00 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
@@ -513,8 +510,9 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 64]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9264785
- No: 6 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1335762
+ No: 3 GFLOPS: 179.68/179.68 result: MeasureResult(costs=(0.0012884258333333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4960997104644775, timestamp=1668189427.0684822) [('tile_f', [-1, 1, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,781010
+ No: 4 GFLOPS: 0.00/179.68 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
@@ -636,8 +634,8 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 64, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 128, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,447135
- No: 7 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4232533
+ No: 5 GFLOPS: 0.00/179.68 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
@@ -759,9 +757,9 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 256]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,575092
- No: 8 GFLOPS: 5.27/167.60 result: MeasureResult(costs=(0.04390903075,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5156841278076172, timestamp=1668157482.9872196) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,959201
- No: 9 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 256]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4625497
+ No: 6 GFLOPS: 54.70/179.68 result: MeasureResult(costs=(0.00423223296,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.938089370727539, timestamp=1668189430.1134493) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,1818418
+ No: 7 GFLOPS: 0.00/179.68 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
@@ -883,27 +881,8 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 8, 32, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 64, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4377213
- No: 10 GFLOPS: 31.11/167.60 result: MeasureResult(costs=(0.007442182789473684,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3957109451293945, timestamp=1668157494.0435655) [('tile_f', [-1, 1, 8, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,415387
- No: 11 GFLOPS: 0.00/167.60 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
- return self.__get_result()
- File "/usr/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
- raise self._exception
- File "/usr/lib/python3.7/concurrent/futures/thread.py", line 57, in run
- result = self.fn(*self.args, **self.kwargs)
- File "/workspace/python/tvm/contrib/popen_pool.py", line 432, in <lambda>
- worker = lambda *args: self._worker_run(*args)
- File "/workspace/python/tvm/contrib/popen_pool.py", line 401, in _worker_run
- return proc.recv()
- File "/workspace/python/tvm/contrib/popen_pool.py", line 309, in recv
- raise TimeoutError()
- TimeoutError
-
- [('tile_f', [-1, 8, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8771723
- No: 12 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 16, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 128, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4285319
+ No: 8 GFLOPS: 0.00/179.68 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
@@ -1025,13 +1004,8 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 32, 2, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 64, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7638909
- No: 13 GFLOPS: 53.11/167.60 result: MeasureResult(costs=(0.0043591832608695655,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.326113224029541, timestamp=1668157499.8068411) [('tile_f', [-1, 8, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,121735
- No: 14 GFLOPS: 77.04/167.60 result: MeasureResult(costs=(0.0030047811470588237,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2728245258331299, timestamp=1668157500.4617536) [('tile_f', [-1, 4, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6022337
- No: 15 GFLOPS: 908.00/908.00 result: MeasureResult(costs=(0.00025495840031897925,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.206448793411255, timestamp=1668157501.4171813) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10164732
- No: 16 GFLOPS: 6.49/908.00 result: MeasureResult(costs=(0.035650565999999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.15035080909729, timestamp=1668157502.193695) [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 32, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7247781
- No: 17 GFLOPS: 10.62/908.00 result: MeasureResult(costs=(0.0217960255,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.662975549697876, timestamp=1668157504.025463) [('tile_f', [-1, 8, 1, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,626699
- No: 18 GFLOPS: 0.00/908.00 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 8, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 128, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5676155
+ No: 9 GFLOPS: 0.00/179.68 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
@@ -1153,9 +1127,8 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 16, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4794620
- No: 19 GFLOPS: 57.42/908.00 result: MeasureResult(costs=(0.004031399519999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.133490800857544, timestamp=1668157504.7228327) [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2715288
- No: 20 GFLOPS: 0.00/908.00 result: Traceback (most recent call last):
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 1, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6772703
+ No: 10 GFLOPS: 0.00/179.68 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
@@ -1277,7 +1250,993 @@ for this template
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2962976
+ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 128, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6265651
+ No: 11 GFLOPS: 47.27/179.68 result: MeasureResult(costs=(0.004897892318181819,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4505457878112793, timestamp=1668189432.7280188) [('tile_f', [-1, 1, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,100140
+ No: 12 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 2, 8, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 32, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,55858
+ No: 13 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 32, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3972921
+ No: 14 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 8, 1, 32]), ('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', 0), ('unroll_explicit', 1)],None,6835148
+ No: 15 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2194924
+ No: 16 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 32, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 16]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7491060
+ No: 17 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7249783
+ No: 18 GFLOPS: 6.69/179.68 result: MeasureResult(costs=(0.034608097,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.389497995376587, timestamp=1668189434.490282) [('tile_f', [-1, 2, 8, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,1937695
+ No: 19 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 256, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4555812
+ No: 20 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+ tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+ Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1261416
@@ -1332,9 +2291,9 @@ and measure running time.
Finish loading 20 records
Best config:
- [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10164732
+ [('tile_f', [-1, 1, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,781010
Finish loading 20 records
- Time cost of this operator: 0.000635
+ Time cost of this operator: 0.000934
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 097ba81005..59f4dd059a 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
@@ -327,10 +327,10 @@ 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 310.6 98.635 (1, 2, 10, 10, 3) 2 1 [310.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.328 1.057 (1, 6, 10, 10) 1 1 [3.328]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.97 0.308 (1, 1, 10, 10, 3) 1 1 [0.97]
- Total_time - 314.899 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.9 98.714 (1, 2, 10, 10, 3) 2 1 [308.9]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.078 0.984 (1, 6, 10, 10) 1 1 [3.078]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.946 0.302 (1, 1, 10, 10, 3) 1 1 [0.946]
+ Total_time - 312.924 - - - - -
@@ -394,10 +394,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 102.6 97.474 (1, 6, 10, 10, 1) 2 1 [102.6]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.806 1.715 (1, 6, 10, 10) 1 1 [1.806]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.853 0.81 (1, 3, 10, 10, 1) 1 1 [0.853]
- Total_time - 105.259 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 137.3 98.139 (1, 6, 10, 10, 1) 2 1 [137.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.773 1.267 (1, 6, 10, 10) 1 1 [1.773]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.831 0.594 (1, 3, 10, 10, 1) 1 1 [0.831]
+ Total_time - 139.904 - - - - -
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
index 083bc4bb70..84d2830677 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -109,7 +109,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/ao/quantization/utils.py:281: UserWarning: must run observer before calling calculate_qparams. Returning default values.
"must run observer before calling calculate_qparams. " +
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
-
0%| | 0.00/3.42M [00:00<?, ?B/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 98.4MB/s]
+
0%| | 0.00/3.42M [00:00<?, ?B/s]
66%|######5 | 2.26M/3.42M [00:00<00:00, 23.6MB/s]
100%|##########| 3.42M/3.42M [00:00<00:00, 26.3MB/s]
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -314,7 +314,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 1.171 seconds)
+ **Total running time of the script:** ( 1 minutes 0.935 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_pytorch.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
index 06b87abfa6..3efdf7e4d7 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/tmp_3wrvtfh/images/random'
+ '/tmp/tmpvckjxel4/images/random'
@@ -325,8 +325,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmp_3wrvtfh/images/target contains 8144 images
- /tmp/tmp_3wrvtfh/images/random contains 5000 images
+ /tmp/tmpvckjxel4/images/target contains 8144 images
+ /tmp/tmpvckjxel4/images/random contains 5000 images
@@ -501,13 +501,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 46s - loss: 0.2525 - accuracy: 0.9177 - val_loss: 0.1238 - val_accuracy: 0.9558 - 46s/epoch - 140ms/step
+ 328/328 - 46s - loss: 0.2440 - accuracy: 0.9146 - val_loss: 0.1043 - val_accuracy: 0.9622 - 46s/epoch - 140ms/step
Epoch 2/3
- 328/328 - 43s - loss: 0.1037 - accuracy: 0.9607 - val_loss: 0.1002 - val_accuracy: 0.9656 - 43s/epoch - 130ms/step
+ 328/328 - 42s - loss: 0.1057 - accuracy: 0.9600 - val_loss: 0.1039 - val_accuracy: 0.9649 - 42s/epoch - 129ms/step
Epoch 3/3
- 328/328 - 43s - loss: 0.0639 - accuracy: 0.9752 - val_loss: 0.1019 - val_accuracy: 0.9698 - 43s/epoch - 130ms/step
+ 328/328 - 42s - loss: 0.0645 - accuracy: 0.9755 - val_loss: 0.0914 - val_accuracy: 0.9690 - 42s/epoch - 129ms/step
- <keras.callbacks.History object at 0x7f3591b42650>
+ <keras.callbacks.History object at 0x7f2f286eb150>
@@ -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:** ( 4 minutes 40.078 seconds)
+ **Total running time of the script:** ( 4 minutes 36.438 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 3743eb1ff5..18744e8fe9 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
@@ -5,18 +5,18 @@
Computation times
=================
-**06:41.660** total execution time for **how_to_work_with_microtvm** files:
+**06:37.498** total execution time for **how_to_work_with_microtvm** files:
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:40.078 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:36.438 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:01.171 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:00.935 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:48.438 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:48.282 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.256 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.161 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.715 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.680 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.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 d627d0a31e..0008e72c6a 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:42.1000** total execution time for **how_to_work_with_relay** files:
+**00:43.266** 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``) | 00:31.269 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:31.714 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:10.163 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:09.930 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.561 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.615 | 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 c8a41bdfcb..e241820d8c 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 0x7f344fc00950>
+ <function my_cuda_math_rule at 0x7f2f9f779320>
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 e53830dc59..f2461b7922 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
Computation times
=================
-**00:07.765** total execution time for **how_to_work_with_schedules** files:
+**00:07.993** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.441 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:05.653 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.012 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.011 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.558 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.567 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.541 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.549 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.113 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.115 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.049 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.050 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.034 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.029 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.019 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.020 | 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 7fa6f9a371..e4324ad459 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/tmpolf_aqlv/input0.cc'\nsource_filename = \"/tmp/tmpolf_aqlv/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/tmpe_3c4rc7/input0.cc'\nsource_filename = \"/tmp/tmpe_3c4rc7/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 843ddf8062..a66a0b9b54 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
=================
-**00:25.667** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:25.543** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:25.661 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:25.537 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.006 | 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 cd97c10465..24908f1222 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -289,7 +289,7 @@ 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 28.25s!
+ resnet18_v1 inference graph built in 28.71s!
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 209f42dd2f..d191e0e4fb 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -333,7 +333,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 19.11s!
+ yolov3-tiny inference graph built in 19.37s!
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 d9d8f52438..71dd14617c 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
=================
-**01:39.842** total execution time for **topic_vta_tutorials_frontend** files:
+**01:39.789** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:51.654 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:51.187 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:48.187 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:48.602 | 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 154a6c9226..740bfeb2e5 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
=================
-**00:03.223** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.067** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.789 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.626 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.434 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.440 | 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 490b3572b9..5218bc26cf 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
=================
-**00:00.770** total execution time for **topic_vta_tutorials** files:
+**00:00.773** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.412 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.410 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.358 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.363 | 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 a15c8104d0..4472a4de5c 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -326,7 +326,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 96.346 ms
+ Execution time of this operator: 95.445 ms
@@ -444,7 +444,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.784 seconds)
+ **Total running time of the script:** ( 1 minutes 25.052 seconds)
.. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index 07349e2fe4..b0daa7fdd8 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -450,16 +450,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 10.08/10.08 result: MeasureResult(costs=(0.0266248904,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5757319927215576, timestamp=1668156115.6507032) [('tile_y', [-1, 2]), ('tile_x', [-1, 32])],None,51
- No: 2 GFLOPS: 0.51/10.08 result: MeasureResult(costs=(0.5259885548000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=8.59346628189087, timestamp=1668156124.2711923) [('tile_y', [-1, 256]), ('tile_x', [-1, 1])],None,8
- No: 3 GFLOPS: 1.52/10.08 result: MeasureResult(costs=(0.1760899008,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.9477429389953613, timestamp=1668156127.9811218) [('tile_y', [-1, 1]), ('tile_x', [-1, 1])],None,0
- No: 4 GFLOPS: 8.83/10.08 result: MeasureResult(costs=(0.030416350199999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7780344486236572, timestamp=1668156129.3570764) [('tile_y', [-1, 16]), ('tile_x', [-1, 64])],None,64
- No: 5 GFLOPS: 8.44/10.08 result: MeasureResult(costs=(0.0318113476,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.8947675228118896, timestamp=1668156130.4624736) [('tile_y', [-1, 2]), ('tile_x', [-1, 128])],None,71
- No: 6 GFLOPS: 1.51/10.08 result: MeasureResult(costs=(0.17751757419999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.9860386848449707, timestamp=1668156133.4751816) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
- No: 7 GFLOPS: 9.86/10.08 result: MeasureResult(costs=(0.0272273636,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9148175716400146, timestamp=1668156134.8040752) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 8 GFLOPS: 2.42/10.08 result: MeasureResult(costs=(0.1107797798,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9805409908294678, timestamp=1668156136.7837281) [('tile_y', [-1, 2]), ('tile_x', [-1, 16])],None,41
- No: 9 GFLOPS: 9.11/10.08 result: MeasureResult(costs=(0.0294727768,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.587029218673706, timestamp=1668156137.5103405) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
- No: 10 GFLOPS: 9.41/10.08 result: MeasureResult(costs=(0.028520014400000005,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.589123010635376, timestamp=1668156138.1292331) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
+ No: 1 GFLOPS: 2.24/2.24 result: MeasureResult(costs=(0.11992156139999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.069396734237671, timestamp=1668188088.1763022) [('tile_y', [-1, 4]), ('tile_x', [-1, 2])],None,12
+ No: 2 GFLOPS: 3.87/3.87 result: MeasureResult(costs=(0.06937556339999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3113415241241455, timestamp=1668188089.490357) [('tile_y', [-1, 32]), ('tile_x', [-1, 16])],None,45
+ No: 3 GFLOPS: 2.74/3.87 result: MeasureResult(costs=(0.0979683338,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7150208950042725, timestamp=1668188091.9737027) [('tile_y', [-1, 16]), ('tile_x', [-1, 4])],None,24
+ No: 4 GFLOPS: 3.23/3.87 result: MeasureResult(costs=(0.0829796676,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.468566656112671, timestamp=1668188094.2138343) [('tile_y', [-1, 4]), ('tile_x', [-1, 16])],None,42
+ No: 5 GFLOPS: 13.08/13.08 result: MeasureResult(costs=(0.020529707,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.4877355098724365, timestamp=1668188094.828068) [('tile_y', [-1, 8]), ('tile_x', [-1, 512])],None,93
+ No: 6 GFLOPS: 4.01/13.08 result: MeasureResult(costs=(0.06692205,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2299630641937256, timestamp=1668188096.061663) [('tile_y', [-1, 8]), ('tile_x', [-1, 16])],None,43
+ No: 7 GFLOPS: 2.45/13.08 result: MeasureResult(costs=(0.10968753059999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.906531810760498, timestamp=1668188098.7266843) [('tile_y', [-1, 1]), ('tile_x', [-1, 16])],None,40
+ No: 8 GFLOPS: 1.57/13.08 result: MeasureResult(costs=(0.1711916372,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.919497489929199, timestamp=1668188101.6711657) [('tile_y', [-1, 8]), ('tile_x', [-1, 1])],None,3
+ No: 9 GFLOPS: 12.44/13.08 result: MeasureResult(costs=(0.021585056,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5558176040649414, timestamp=1668188102.3406963) [('tile_y', [-1, 256]), ('tile_x', [-1, 128])],None,78
+ No: 10 GFLOPS: 1.14/13.08 result: MeasureResult(costs=(0.23470468259999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.8662045001983643, timestamp=1668188106.262973) [('tile_y', [-1, 1]), ('tile_x', [-1, 2])],None,10
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 3e208108e6..746b4f85b2 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -320,7 +320,7 @@ standard deviation.
.. code-block:: none
- {'mean': 509.437529610002, 'median': 508.79846905000363, 'std': 1.9956144726592564}
+ {'mean': 507.7278030899982, 'median': 508.08670225000014, 'std': 1.1649449954831121}
@@ -554,31 +554,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 16.50/ 22.59 GFLOPS | Progress: (4/20) | 6.75 s
[Task 1/25] Current/Best: 11.26/ 22.64 GFLOPS | Progress: (8/20) | 10.11 s
[Task 1/25] Current/Best: 9.01/ 22.64 GFLOPS | Progress: (12/20) | 12.23 s
[Task 1/25] Current/Best: 22.48/ 22.64 GFLOPS | Progress: (16/20) | 14.76 s
[Task 1/25] Current/Best: 23.83/ 23.83 GFLOPS | Progress: (20/20) | 16.58 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 6.24/ 16.85 GFLOPS | Progress: (4/20) | 3.11 s
[Task 2/25] Current/Best: 8.15/ 20.72 GFLOPS | Progress: (8/20) | 4.36 s
[Task 2/25] Current/Best: 6.42/ 20.72 GFLOPS | Progress: (12/20) | 6.44 s
[Task 2/25] Current/Best: 8.20/ 20.72 GFLOPS | Progress: (16/20) | 7.85 s
[Task 2/25] Current/Best: 15.77/ 20.72 GFLOPS | Progress: (20/20) | 9.17 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 16.84/ 16.90 GFLOPS | Progress: (4/20) | 3.31 s
[Task 3/25] Current/Best: 20.07/ 20.07 GFLOPS | Progress: (8/20) | 5.49 s
[Task 3/25] Current/Best: 9.93/ 23.12 GFLOPS | Progress: (12/20) | 7.27 s
[Task 3/25] Current/Best: 15.01/ 23.12 GFLOPS | Progress: (16/20) | 9.40 s
[Task 3/25] Current/Best: 10.27/ 23.36 GFLOPS | Progress: (20/20) | 11.31 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 4.65/ 15.98 GFLOPS | Progress: (4/20) | 8.55 s
[Task 4/25] Current/Best: 20.84/ 20.84 GFLOPS | Progress: (8/20) | 13.06 s
[Task 4/25] Current/Best: 6.88/ 20.84 GFLOPS | Progress: (12/20) | 19.35 s
[Task 4/25] Current/Best: 11.49/ 20.84 GFLOPS | Progress: (16/20) | 21.06 s
[Task 4/25] Current/Best: 18.10/ 20.84 GFLOPS | Progress: (20/20) | 23.65 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 16.17/ 16.17 GFLOPS | Progress: (4/20) | 3.08 s
[Task 5/25] Current/Best: 16.80/ 20.60 GFLOPS | Progress: (8/20) | 4.88 s
[Task 5/25] Current/Best: 18.00/ 20.60 GFLOPS | Progress: (12/20) | 6.34 s
[Task 5/25] Current/Best: 18.16/ 20.60 GFLOPS | Progress: (16/20) | 8.18 s
[Task 5/25] Current/Best: 20.91/ 20.91 GFLOPS | Progress: (20/20) | 9.90 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 17.90/ 18.49 GFLOPS | Progress: (4/20) | 3.27 s
[Task 6/25] Current/Best: 23.43/ 23.43 GFLOPS | Progress: (8/20) | 6.45 s
[Task 6/25] Current/Best: 12.76/ 23.43 GFLOPS | Progress: (12/20) | 10.05 s
[Task 6/25] Current/Best: 3.32/ 23.43 GFLOPS | Progress: (16/20) | 13.68 s
[Task 6/25] Current/Best: 18.53/ 23.43 GFLOPS | Progress: (20/20) | 16.92 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 16.83/ 16.83 GFLOPS | Progress: (4/20) | 4.90 s
[Task 7/25] Current/Best: 8.66/ 20.75 GFLOPS | Progress: (8/20) | 6.67 s
[Task 7/25] Current/Best: 8.50/ 20.75 GFLOPS | Progress: (12/20) | 9.75 s
[Task 7/25] Current/Best: 8.86/ 20.75 GFLOPS | Progress: (16/20) | 12.31 s
[Task 7/25] Current/Best: 14.37/ 20.75 GFLOPS | Progress: (20/20) | 15.02 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 4.48/ 18.57 GFLOPS | Progress: (4/20) | 3.74 s
[Task 8/25] Current/Best: 11.67/ 18.57 GFLOPS | Progress: (8/20) | 11.51 s
[Task 8/25] Current/Best: 11.63/ 18.57 GFLOPS | Progress: (12/20) | 15.23 s
[Task 8/25] Current/Best: 4.21/ 18.57 GFLOPS | Progress: (16/20) | 25.90 s
[Task 8/25] Current/Best: 11.63/ 18.57 GFLOPS | Progress: (20/20) | 37.17 s
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 7.39/ 23.41 GFLOPS | Progress: (4/20) | 3.59 s
[Task 9/25] Current/Best: 14.33/ 23.41 GFLOPS | Progress: (8/20) | 5.34 s
[Task 9/25] Current/Best: 8.14/ 23.41 GFLOPS | Progress: (12/20) | 11.60 s
[Task 9/25] Current/Best: 5.20/ 23.41 GFLOPS | Progress: (16/20) | 16.37 s
[Task 9/25] Current/Best: 21.24/ 23.41 GFLOPS | Progress: (20/2
0) | 17.82 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 15.02/ 18.72 GFLOPS | Progress: (4/20) | 4.48 s
[Task 10/25] Current/Best: 13.92/ 18.72 GFLOPS | Progress: (8/20) | 5.97 s
[Task 10/25] Current/Best: 13.31/ 18.72 GFLOPS | Progress: (12/20) | 7.53 s
[Task 10/25] Current/Best: 14.13/ 18.72 GFLOPS | Progress: (16/20) | 9.38 s
[Task 10/25] Current/Best: 10.32/ 18.72 GFLOPS | Progress: (20/20) | 11.20 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/ 19.13 GFLOPS | Progress: (4/20) | 3.83 s
[Task 11/25] Current/Best: 13.44/ 19.13 GFLOPS | Progress: (8/20) | 6.38 s
[Task 11/25] Current/Best: 13.61/ 19.13 GFLOPS | Progress: (12/20) | 10.27 s
[Task 11/25] Current/Best: 6.26/ 22.81 GFLOPS | Progress: (16/20) | 12.58 s
[Task 11/25] Current/Best: 7.11/ 22.81 GFLOPS | Progress: (20/20) | 14.73 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 19.68/ 19.68 GFLOPS | Progress: (4/20) | 3.41 s
[Task 12/25] Current/Best: 15.87/ 19.68 GFLOPS | Progress: (8/20) | 6.37 s
[Task 12/25] Current/Best: 20.01/ 20.01 GFLOPS | Progress: (12/20) | 8.08 s
[Task 12/25] Current/Best: 14.93/ 20.01 GFLOPS | Progress: (16/20) | 11.43 s
[Task 12/25] Current/Best: 10.51/ 20.01 GFLOPS | Progress: (20/20) | 14.22 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 10.21/ 12.21 GFLOPS | Progress: (4/20) | 4.66 s
[Task 13/25] Current/Best: 7.87/ 18.97 GFLOPS | Progress: (8/20) | 8.37 s
[Task 13/25] Current/Best: 6.14/ 21.78 GFLOPS | Progress: (12/20) | 10.91 s
[Task 13/25] Current/Best: 12.50/ 21.78 GFLOPS | Progress: (16/20) | 14.06 s
[Task 13/25] Current/Best: 11.82/ 21.78 GFLOPS | Progress: (20/20) | 16.42 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 5.38/ 14.97 GFLOPS | Progress: (4/20) | 4.32 s
[Task 14/25] Current/Best: 6.30/ 14.97 GFLOPS | Progress: (8/20) | 11.78 s
[Task 14/25] Current/Best: 3.15/ 14.97 GFLOPS | Progress: (12/20) | 14.90 s
[Task 14/25] Current/Best: 8.57/ 15.64 GFLOPS | Progress: (16/20) | 20.68 s Done.
-
[Task 14/25] Current/Best: 11.80/ 16.64 GFLOPS | Progress: (20/20) | 24.56 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 12.99/ 20.80 GFLOPS | Progress: (4/20) | 2.65 s
[Task 15/25] Current/Best: 18.92/ 20.80 GFLOPS | Progress: (8/20) | 4.05 s
[Task 15/25] Current/Best: 21.04/ 21.04 GFLOPS | Progress: (12/20) | 6.01 s
[Task 15/25] Current/Best: 7.71/ 22.08 GFLOPS | Progress: (16/20) | 7.40 s
[Task 15/25] Current/Best: 9.62/ 22.08 GFLOPS | Progress: (20/20) | 10.41 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 1.58/ 13.09 GFLOPS | Progress: (4/20) | 6.38 s
[Task 16/25] Current/Best: 15.51/ 23.06 GFLOPS | Progress: (8/20) | 7.92 s
[Task 16/25] Current/Best: 11.85/ 23.06 GFLOPS | Progress: (12/20) | 9.38 s
[Task 16/25] Current/Best: 11.54/ 23.06 GFLOPS | Progress: (16/20) | 12.36 s
[Task 16/25] Current/Best: 15.23/ 23.06 GFLOPS | Progress: (20/20) |
14.23 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 10.22/ 13.76 GFLOPS | Progress: (4/20) | 3.74 s
[Task 17/25] Current/Best: 12.07/ 22.36 GFLOPS | Progress: (8/20) | 5.81 s
[Task 17/25] Current/Best: 8.68/ 22.53 GFLOPS | Progress: (12/20) | 8.06 s
[Task 17/25] Current/Best: 20.25/ 22.53 GFLOPS | Progress: (16/20) | 10.03 s
[Task 17/25] Current/Best: 14.15/ 22.53 GFLOPS | Progress: (20/20) | 13.99 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 6.38/ 20.75 GFLOPS | Progress: (4/20) | 3.14 s
[Task 18/25] Current/Best: 5.68/ 20.75 GFLOPS | Progress: (8/20) | 6.53 s
[Task 18/25] Current/Best: 13.40/ 20.75 GFLOPS | Progress: (12/20) | 9.88 s
[Task 18/25] Current/Best: 4.54/ 20.75 GFLOPS | Progress: (16/20) | 13.35 s
[Task 18/25] Current/Best: 12.07/ 20.75 GFLOPS | Progress: (20/20) | 15.96 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 6.65/ 21.86 GFLOPS | Progress: (4/20) | 4.59 s
[Task 19/25] Current/Best: 6.43/ 21.86 GFLOPS | Progress: (8/20) | 8.37 s
[Task 19/25] Current/Best: 6.48/ 21.86 GFLOPS | Progress: (12/20) | 10.95 s
[Task 19/25] Current/Best: 11.20/ 21.86 GFLOPS | Progress: (16/20) | 14.45 s
[Task 19/25] Current/Best: 10.65/ 21.86 GFLOPS | Progress: (20/20) | 17.32 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 7.88/ 15.19 GFLOPS | Progress: (4/20) | 4.03 s
[Task 20/25] Current/Best: 19.05/ 19.05 GFLOPS | Progress: (8/20) | 5.67 s
[Task 20/25] Current/Best: 17.90/ 19.05 GFLOPS | Progress: (12/20) | 8.16 s
[Task 20/25] Current/Best: 11.15/ 19.05 GFLOPS | Progress: (16/20) | 11.68 s
[Task 20/25] Current/Best: 10.22/ 19.05 GFLOPS | Progress: (20/20) | 14.48 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.96/ 17.96 GFLOPS | Progress: (4/20) | 6.75 s
[Task 1/25] Current/Best: 9.58/ 18.17 GFLOPS | Progress: (8/20) | 10.07 s
[Task 1/25] Current/Best: 1.92/ 18.17 GFLOPS | Progress: (12/20) | 13.81 s
[Task 1/25] Current/Best: 6.30/ 18.17 GFLOPS | Progress: (16/20) | 19.72 s
[Task 1/25] Current/Best: 10.72/ 21.94 GFLOPS | Progress: (20/20) | 22.15 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 14.52/ 14.52 GFLOPS | Progress: (4/20) | 3.35 s
[Task 2/25] Current/Best: 10.02/ 14.52 GFLOPS | Progress: (8/20) | 4.74 s
[Task 2/25] Current/Best: 14.40/ 18.63 GFLOPS | Progress: (12/20) | 5.77 s
[Task 2/25] Current/Best: 14.85/ 21.55 GFLOPS | Progress: (16/20) | 7.52 s
[Task 2/25] Current/Best: 12.57/ 21.55 GFLOPS | Progress: (20/20) | 8.85 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 11.67/ 15.09 GFLOPS | Progress: (4/20) | 3.87 s
[Task 3/25] Current/Best: 15.37/ 22.64 GFLOPS | Progress: (8/20) | 5.82 s
[Task 3/25] Current/Best: 6.44/ 22.64 GFLOPS | Progress: (12/20) | 8.52 s
[Task 3/25] Current/Best: 13.36/ 22.64 GFLOPS | Progress: (16/20) | 11.26 s
[Task 3/25] Current/Best: 14.77/ 22.64 GFLOPS | Progress: (20/20) | 13.49 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 7.99/ 19.11 GFLOPS | Progress: (4/20) | 3.52 s
[Task 4/25] Current/Best: 15.07/ 19.11 GFLOPS | Progress: (8/20) | 8.15 s
[Task 4/25] Current/Best: 16.07/ 19.11 GFLOPS | Progress: (12/20) | 10.04 s
[Task 4/25] Current/Best: 14.15/ 19.11 GFLOPS | Progress: (16/20) | 12.35 s
[Task 4/25] Current/Best: 12.74/ 19.11 GFLOPS | Progress: (20/20) | 14.95 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 5.15/ 13.66 GFLOPS | Progress: (4/20) | 3.40 s
[Task 5/25] Current/Best: 14.74/ 14.74 GFLOPS | Progress: (8/20) | 5.64 s
[Task 5/25] Current/Best: 11.18/ 14.74 GFLOPS | Progress: (12/20) | 7.49 s
[Task 5/25] Current/Best: 11.58/ 15.54 GFLOPS | Progress: (16/20) | 10.52 s
[Task 5/25] Current/Best: 1.63/ 15.54 GFLOPS | Progress: (20/20) | 13.41 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 17.64/ 17.64 GFLOPS | Progress: (4/20) | 3.93 s
[Task 6/25] Current/Best: 5.68/ 17.64 GFLOPS | Progress: (8/20) | 6.36 s
[Task 6/25] Current/Best: 13.51/ 22.04 GFLOPS | Progress: (12/20) | 8.73 s
[Task 6/25] Current/Best: 11.03/ 22.04 GFLOPS | Progress: (16/20) | 10.73 s
[Task 6/25] Current/Best: 5.63/ 22.04 GFLOPS | Progress: (20/20) | 13.21 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 21.41/ 21.41 GFLOPS | Progress: (4/20) | 3.76 s
[Task 7/25] Current/Best: 8.30/ 21.41 GFLOPS | Progress: (8/20) | 7.34 s
[Task 7/25] Current/Best: 15.94/ 21.41 GFLOPS | Progress: (12/20) | 9.43 s
[Task 7/25] Current/Best: 9.67/ 21.41 GFLOPS | Progress: (16/20) | 11.52 s
[Task 7/25] Current/Best: 11.70/ 21.41 GFLOPS | Progress: (20/20) | 14.53 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 11.50/ 12.98 GFLOPS | Progress: (4/20) | 5.91 s
[Task 8/25] Current/Best: 11.51/ 14.78 GFLOPS | Progress: (8/20) | 12.70 s
[Task 8/25] Current/Best: 15.60/ 15.60 GFLOPS | Progress: (12/20) | 16.47 s
[Task 8/25] Current/Best: 9.20/ 22.10 GFLOPS | Progress: (16/20) | 20.04 s
[Task 8/25] Current/Best: 12.78/ 22.10 GFLOPS | Progress: (20/20) | 22.03 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 16.35/ 16.54 GFLOPS | Progress: (4/20) | 3.68 s
[Task 9/25] Current/Best: 6.17/ 22.74 GFLOPS | Progress: (8/20) | 6.09 s
[Task 9/25] Current/Best: 1.89/ 22.74 GFLOPS | Progress: (12/20) | 10.00 s
[Task 9/25] Current/Best: 11.75/ 22.74 GFLOPS | Progress: (16/20) | 14.57 s
[Task 9/25] Current/Best: 14.40/ 22.74 GFLOPS | Progress: (20/20) | 19.12 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 15.89/ 20.82 GFLOPS | Progress: (4/20) | 2.95 s
[Task 10/25] Current/Best: 14.93/ 20.82 GFLOPS | Progress: (8/20) | 4.28 s
[Task 10/25] Current/Best: 9.47/ 20.82 GFLOPS | Progress: (12/20) | 6.05 s
[Task 10/25] Current/Best: 16.09/ 20.82 GFLOPS | Progress: (16/20) | 7.54 s
[Task 10/25] Current/Best: 5.39/ 20.82 GFLOPS | Progress: (20/20) | 9.36 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 21.32/ 21.32 GFLOPS | Progress: (4/20) | 3.47 s
[Task 11/25] Current/Best: 12.38/ 21.32 GFLOPS | Progress: (8/20) | 5.67 s
[Task 11/25] Current/Best: 10.40/ 21.32 GFLOPS | Progress: (12/20) | 8.12 s
[Task 11/25] Current/Best: 6.24/ 21.32 GFLOPS | Progress: (16/20) | 10.58 s
[Task 11/25] Current/Best: 18.92/ 21.32 GFLOPS | Progress: (20/20) | 13.27 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 16.97/ 16.97 GFLOPS | Progress: (4/20) | 4.02 s
[Task 12/25] Current/Best: 6.75/ 17.67 GFLOPS | Progress: (8/20) | 6.61 s
[Task 12/25] Current/Best: 11.02/ 18.16 GFLOPS | Progress: (12/20) | 12.36 s
[Task 12/25] Current/Best: 14.44/ 18.16 GFLOPS | Progress: (16/20) | 14.95 s
[Task 12/25] Current/Best: 4.38/ 18.16 GFLOPS | Progress: (20/20) | 17.57 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 10.00/ 22.33 GFLOPS | Progress: (4/20) | 4.12 s
[Task 13/25] Current/Best: 9.86/ 22.33 GFLOPS | Progress: (8/20) | 6.02 s
[Task 13/25] Current/Best: 12.04/ 22.33 GFLOPS | Progress: (12/20) | 9.55 s
[Task 13/25] Current/Best: 4.94/ 22.33 GFLOPS | Progress: (16/20) | 12.21 s
[Task 13/25] Current/Best: 10.36/ 22.33 GFLOPS | Progress: (20/20) | 15.02 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 8.44/ 17.32 GFLOPS | Progress: (4/20) | 5.03 s
[Task 14/25] Current/Best: 6.30/ 17.32 GFLOPS | Progress: (8/20) | 7.31 s
[Task 14/25] Current/Best: 3.10/ 17.61 GFLOPS | Progress: (12/20) | 12.69 s
[Task 14/25] Current/Best: 10.78/ 17.61 GFLOPS | Progress: (16/20) | 17.80 s
[Task 14/25] Current/Best: 14.41/ 17.61 GFLOPS | Progress: (20/20) | 19.69 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 15.23/ 17.42 GFLOPS | Progress: (4/20) | 3.53 s
[Task 15/25] Current/Best: 16.10/ 17.42 GFLOPS | Progress: (8/20) | 4.80 s
[Task 15/25] Current/Best: 13.88/ 20.03 GFLOPS | Progress: (12/20) | 6.51 s
[Task 15/25] Current/Best: 11.35/ 20.03 GFLOPS | Progress: (16/20) | 8.19 s
[Task 15/25] Current/Best: 12.42/ 20.03 GFLOPS | Progress: (20/20)
| 11.11 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 10.61/ 17.91 GFLOPS | Progress: (4/20) | 3.45 s Done.
Done.
-
[Task 21/25] Current/Best: 14.94/ 14.98 GFLOPS | Progress: (4/20) | 3.45 s
[Task 21/25] Current/Best: 9.69/ 14.98 GFLOPS | Progress: (8/20) | 6.20 s
[Task 21/25] Current/Best: 19.57/ 19.57 GFLOPS | Progress: (12/20) | 8.08 s
[Task 21/25] Current/Best: 13.73/ 19.57 GFLOPS | Progress: (16/20) | 10.32 s
[Task 21/25] Current/Best: 8.45/ 19.57 GFLOPS | Progress: (20/20) | 12.75 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 1.56/ 16.46 GFLOPS | Progress: (4/20) | 5.35 s
[Task 22/25] Current/Best: 16.44/ 20.74 GFLOPS | Progress: (8/20) | 6.60 s
[Task 22/25] Current/Best: 8.23/ 20.74 GFLOPS | Progress: (12/20) | 9.07 s
[Task 22/25] Current/Best: 8.94/ 20.74 GFLOPS | Progress: (16/20) | 12.03 s
[Task 22/25] Current/Best: 3.09/ 20.94 GFLOPS | Progress: (20/20) | 13.84 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 6.17/ 9.78 GFLOPS | Progress: (4/20) | 5.15 s
[Task 23/25] Current/Best: 12.19/ 22.76 GFLOPS | Progress: (8/20) | 7.84 s
[Task 23/25] Current/Best: 12.38/ 22.76 GFLOPS | Progress: (12/20) | 10.70 s
[Task 23/25] Current/Best: 12.62/ 22.76 GFLOPS | Progress: (16/20) | 15.13 s
[Task 23/25] Current/Best: 20.57/ 22.76 GFLOPS | Progress: (20/20) | 18.20 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 2.31/ 3.60 GFLOPS | Progress: (4/20) | 12.22 s
[Task 24/25] Current/Best: 2.89/ 9.13 GFLOPS | Progress: (8/20) | 19.57 s
[Task 24/25] Current/Best: 4.16/ 9.13 GFLOPS | Progress: (12/20) | 30.30 s
[Task 24/25] Current/Best: 4.60/ 9.13 GFLOPS | Progress: (16/20) | 32.49 s Done.
-
[Task 24/25] Current/Best: 3.45/ 9.13 GFLOPS | Progress: (20/20) | 43.21 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 6.73/ 7.72 GFLOPS | Progress: (4/20) | 12.18 s
[Task 25/25] Current/Best: 5.57/ 8.37 GFLOPS | Progress: (8/20) | 23.64 s
[Task 25/25] Current/Best: 9.02/ 9.02 GFLOPS | Progress: (12/20) | 34.93 s
[Task 25/25] Current/Best: 8.56/ 9.02 GFLOPS | Progress: (16/20) | 45.58 s
[Task 25/25] Current/Best: 1.54/ 9.76 GFLOPS | Progress: (20/20) | 47.33 s Done.
-
+
[Task 16/25] Current/Best: 16.34/ 20.74 GFLOPS | Progress: (8/20) | 5.39 s
[Task 16/25] Current/Best: 9.99/ 20.74 GFLOPS | Progress: (12/20) | 8.33 s
[Task 16/25] Current/Best: 22.24/ 22.24 GFLOPS | Progress: (16/20) | 10.16 s
[Task 16/25] Current/Best: 12.80/ 22.24 GFLOPS | Progress: (20/20) | 12.67 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 16.86/ 18.83 GFLOPS | Progress: (4/20) | 3.29 s
[Task 17/25] Current/Best: 12.37/ 22.77 GFLOPS | Progress: (8/20) | 5.89 s
[Task 17/25] Current/Best: 15.34/ 22.77 GFLOPS | Progress: (12/20) | 8.35 s
[Task 17/25] Current/Best: 3.09/ 22.77 GFLOPS | Progress: (16/20) | 11.41 s
[Task 17/25] Current/Best: 8.13/ 22.77 GFLOPS | Progress: (20/20) | 14.34 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 18.34/ 20.40 GFLOPS | Progress: (4/20) | 3.27 s
[Task 18/25] Current/Best: 13.10/ 20.40 GFLOPS | Progress: (8/20) | 10.99 s
[Task 18/25] Current/Best: 14.97/ 20.40 GFLOPS | Progress: (12/20) | 14.09 s
[Task 18/25] Current/Best: 10.99/ 20.40 GFLOPS | Progress: (16/20) | 16.49 s
[Task 18/25] Current/Best: 18.85/ 20.40 GFLOPS | Progress: (20/20) | 18.28 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 18.09/ 20.13 GFLOPS | Progress: (4/20) | 3.92 s
[Task 19/25] Current/Best: 9.71/ 20.13 GFLOPS | Progress: (8/20) | 7.02 s
[Task 19/25] Current/Best: 9.76/ 20.13 GFLOPS | Progress: (12/20) | 11.17 s
[Task 19/25] Current/Best: 12.84/ 20.13 GFLOPS | Progress: (16/20) | 14.97 s
[Task 19/25] Current/Best: 11.12/ 20.13 GFLOPS | Progress: (20/20) | 21.60 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 3.10/ 6.49 GFLOPS | Progress: (4/20) | 4.70 s
[Task 20/25] Current/Best: 20.67/ 20.67 GFLOPS | Progress: (8/20) | 7.30 s
[Task 20/25] Current/Best: 6.23/ 20.67 GFLOPS | Progress: (12/20) | 9.12 s
[Task 20/25] Current/Best: 13.43/ 20.67 GFLOPS | Progress: (16/20) | 12.04 s
[Task 20/25] Current/Best: 11.07/ 20.67 GFLOPS | Progress: (20/20) | 15.83 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 17.67/ 18.63 GFLOPS | Progress: (4/20) | 3.19 s
[Task 21/25] Current/Best: 14.35/ 18.63 GFLOPS | Progress: (8/20) | 4.55 s
[Task 21/25] Current/Best: 12.22/ 18.63 GFLOPS | Progress: (12/20) | 7.04 s Done.
+
[Task 21/25] Current/Best: 14.25/ 18.70 GFLOPS | Progress: (16/20) | 8.96 s
[Task 21/25] Current/Best: 10.66/ 18.70 GFLOPS | Progress: (20/20) | 10.07 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 11.72/ 17.52 GFLOPS | Progress: (4/20) | 4.36 s
[Task 22/25] Current/Best: 11.73/ 17.52 GFLOPS | Progress: (8/20) | 6.30 s
[Task 22/25] Current/Best: 7.51/ 17.52 GFLOPS | Progress: (12/20) | 9.00 s
[Task 22/25] Current/Best: 7.43/ 17.52 GFLOPS | Progress: (16/20) | 11.86 s
[Task 22/25] Current/Best: 18.64/ 18.64 GFLOPS | Progress: (20/20) | 13.93 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 20.01/ 20.11 GFLOPS | Progress: (4/20) | 3.76 s
[Task 23/25] Current/Best: 6.86/ 20.11 GFLOPS | Progress: (8/20) | 6.27 s
[Task 23/25] Current/Best: 18.03/ 20.11 GFLOPS | Progress: (12/20) | 9.23 s
[Task 23/25] Current/Best: 8.23/ 20.11 GFLOPS | Progress: (16/20) | 12.89 s
[Task 23/25] Current/Best: 12.96/ 20.11 GFLOPS | Progress: (20/20) | 15.59 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 9.99/ 9.99 GFLOPS | Progress: (4/20) | 2.83 s
[Task 24/25] Current/Best: 0.88/ 9.99 GFLOPS | Progress: (8/20) | 13.54 s
[Task 24/25] Current/Best: 3.27/ 9.99 GFLOPS | Progress: (12/20) | 21.66 s
[Task 24/25] Current/Best: 5.07/ 9.99 GFLOPS | Progress: (16/20) | 32.16 s
[Task 24/25] Current/Best: 9.35/ 9.99 GFLOPS | Progress: (20/20) | 42.66 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
+ Done.
+
[Task 25/25] Current/Best: 1.54/ 5.94 GFLOPS | Progress: (4/20) | 13.04 s
[Task 25/25] Current/Best: 5.81/ 8.43 GFLOPS | Progress: (8/20) | 14.82 s
[Task 25/25] Current/Best: 4.29/ 8.43 GFLOPS | Progress: (12/20) | 19.41 s
[Task 25/25] Current/Best: 7.51/ 8.43 GFLOPS | Progress: (16/20) | 20.57 s
[Task 25/25] Current/Best: 5.74/ 8.43 GFLOPS | Progress: (20/20) | 31.08 s
@@ -674,8 +674,8 @@ 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
@@ -732,8 +732,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 407.5403767800026, 'median': 406.85900895000486, 'std': 1.8852394637939685}
- unoptimized: {'mean': 509.437529610002, 'median': 508.79846905000363, 'std': 1.9956144726592564}
+ optimized: {'mean': 414.2405844899986, 'median': 414.3426308500011, 'std': 1.0258821366311048}
+ unoptimized: {'mean': 507.7278030899982, 'median': 508.08670225000014, 'std': 1.1649449954831121}
@@ -756,7 +756,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 11 minutes 4.887 seconds)
+ **Total running time of the script:** ( 10 minutes 31.316 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 496d9491a9..c092bc944a 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -270,7 +270,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.29e-07 secs/op
+ 1.248e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 3c53c5d854..f46f1d955b 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, 0x2316e4b0)), stage(b, placeholder(b, 0xdec6340)), 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, 0xe9fa350)), stage(b, placeholder(b, 0x16788d00)), 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 a49efc4b03..9de1bf93f0 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
=================
-**14:33.083** total execution time for **tutorial** files:
+**13:59.850** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 11:04.887 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 10:31.316 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:25.784 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:25.052 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:57.939 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.501 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:35.517 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:35.789 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:26.946 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:23.994 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.073 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.269 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.766 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.767 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.162 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.154 | 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_uma.py` (``uma.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_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.001 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_install.py` (``install.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 784ce26ae6..aa3eb99a92 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -294,7 +294,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000008
+ Numpy running time: 0.000007
naive: 0.000007
@@ -394,7 +394,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000014
+ parallel: 0.000007
@@ -501,10 +501,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.789060000504832e-06 1.0
- naive 6.8521e-06 0.8797082060679845
- parallel 1.3775999999999998e-05 1.768634469256513
- vector 2.4737e-05 3.175864609901159
+ numpy 7.159299998420465e-06 1.0
+ naive 6.6387e-06 0.9272833938324524
+ parallel 6.928900000000001e-06 0.9678180829869825
+ vector 2.45793e-05 3.433198777174145
@@ -925,7 +925,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018086
+ Numpy running time: 0.019143
@@ -983,7 +983,7 @@ optimizations.
.. code-block:: none
- none: 3.194429
+ none: 3.444761
@@ -1086,7 +1086,7 @@ schedule.
.. code-block:: none
- blocking: 0.291321
+ blocking: 0.305775
@@ -1182,7 +1182,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.329895
+ vectorization: 0.335886
@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], []),
@@ -1256,7 +1256,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.115137
+ loop permutation: 0.117990
@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], []),
@@ -1355,7 +1355,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.109133
+ array packing: 0.108471
@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], []),
@@ -1448,7 +1448,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.110608
+ block caching: 0.112193
@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], []),
@@ -1534,7 +1534,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.146229
+ parallelization: 0.147401
@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], []),
@@ -1615,13 +1615,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.1944290215000004 1.0
- blocking 0.29132084450000006 0.09119653075378248
- vectorization 0.3298950855 0.10327200362870857
- loop permutation 0.11513653099999999 0.03604291415620047
- array packing 0.1091326201 0.034163419930599945
- block caching 0.11060836059999998 0.03462539310016094
- parallelization 0.1462290094 0.04577625873538289
+ none 3.4447611562999993 1.0
+ blocking 0.3057749593 0.08876521344325403
+ vectorization 0.3358855503 0.09750619420615303
+ loop permutation 0.11799022210000001 0.034252076340390666
+ array packing 0.1084708411 0.03148863917651347
+ block caching 0.11219302439999998 0.03256917368416507
+ parallelization 0.1474014388 0.04279003161958641
@@ -1661,6 +1661,11 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.501 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index 9d016e1ec5..df59605ddf 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-5ffcfd9327e24129b50ebb3c8c0456961e444999
+88979834842115ef9ea8487d9a631dc3275f7a7d
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index a7aa97be8c..6826941b52 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -585,7 +585,7 @@ 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 11.692 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 11.033 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
index c42b429ef8..5d61951c91 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -506,7 +506,7 @@ pip install -U tensorflow --user
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 941ms/step
+1/1 [==============================] - 1s 932ms/step
Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index a1b751c67f..aa3341af4c 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -440,7 +440,7 @@ 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.zip0f22659a-1eda-4207-9f61-6f443972fb41 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.zipa3da7928-338f-43fc-aa87-db1e4a282088 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 0e91504a61..9a5c28223a 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -448,13 +448,14 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
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- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 47.6MB/s]
- 92%|#########2| 38.3M/41.5M [00:00<00:00, 43.7MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 42.9MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 62.5MB/s]
+ 30%|##9 | 12.3M/41.5M [00:00<00:00, 47.8MB/s]
+ 41%|####1 | 17.0M/41.5M [00:00<00:00, 40.9MB/s]
+ 57%|#####6 | 23.6M/41.5M [00:00<00:00, 49.9MB/s]
+ 69%|######9 | 28.7M/41.5M [00:00<00:00, 47.4MB/s]
+ 80%|######## | 33.4M/41.5M [00:00<00:00, 44.5MB/s]
+ 92%|#########2| 38.3M/41.5M [00:00<00:00, 37.7MB/s]
+100%|##########| 41.5M/41.5M [00:00<00:00, 43.6MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 54ba101fd0..3c0a8cb6aa 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -431,11 +431,12 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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- 63%|######2 | 28.0M/44.7M [00:00<00:00, 96.2MB/s]
- 83%|########3 | 37.3M/44.7M [00:00<00:00, 90.0MB/s]
-100%|##########| 44.7M/44.7M [00:00<00:00, 64.0MB/s]
+ 18%|#7 | 7.99M/44.7M [00:00<00:00, 74.8MB/s]
+ 34%|###3 | 15.1M/44.7M [00:00<00:00, 72.9MB/s]
+ 54%|#####3 | 24.0M/44.7M [00:00<00:00, 79.6MB/s]
+ 72%|#######1 | 32.0M/44.7M [00:00<00:00, 81.1MB/s]
+ 89%|########8 | 39.7M/44.7M [00:00<00:00, 64.5MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 63.6MB/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 f7959c7d99..458f557eb0 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -645,7 +645,7 @@ 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 10.815 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 10.478 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 309f6078a8..6390720c70 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>05:43.394</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:42.185</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -349,43 +349,43 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:11.692</p></td>
+<td><p>01:11.033</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>01:10.815</p></td>
+<td><p>01:10.478</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>00:45.624</p></td>
+<td><p>00:44.838</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>00:32.269</p></td>
+<td><p>00:31.834</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:30.052</p></td>
+<td><p>00:29.933</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><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>00:26.208</p></td>
+<td><p>00:26.842</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>00:24.609</p></td>
+<td><p>00:25.061</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>00:22.229</p></td>
+<td><p>00:22.159</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>00:17.477</p></td>
+<td><p>00:17.668</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>00:02.418</p></td>
+<td><p>00:02.340</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 6be858d8ad..1cf46f5632 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -662,7 +662,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.1948 16.1111 16.6856 15.7359 0.3016
+ 15.6913 15.6844 15.7736 15.6278 0.0415
</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 bb7c754512..04d2843ad2 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -453,24 +453,21 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/nn/functional.py:3897: 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)
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/anchor_utils.py:124: 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=& [...]
@@ -568,7 +565,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 10.309 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 8.915 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 277bdfc3fb..bae481ad4f 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -497,8 +497,8 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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-100%|##########| 13.6M/13.6M [00:00<00:00, 78.8MB/s]
+ 59%|#####8 | 7.99M/13.6M [00:00<00:00, 78.1MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 106MB/s]
</pre></div>
</div>
</div>
@@ -589,7 +589,7 @@ 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)
- 90.1874 90.0828 96.1832 89.8562 0.6344
+ 88.8378 88.7331 90.5060 88.5198 0.3382
</pre></div>
</div>
<div class="admonition note">
@@ -628,7 +628,7 @@ 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 4.760 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.035 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 5c732d7a47..8a14bcd93e 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -582,7 +582,7 @@ 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)
- 119.3842 119.2682 123.6073 118.4121 0.5972
+ 119.3821 119.3645 122.1992 118.4341 0.5097
</pre></div>
</div>
<div class="admonition note">
@@ -610,7 +610,7 @@ 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> ( 2 minutes 25.582 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 20.507 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 431f85af1b..edcdbd458a 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -520,7 +520,7 @@ 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 34.704 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 28.804 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 786a4a9f66..00202b1d1c 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -462,22 +462,22 @@ 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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</pre></div>
</div>
<p>Create TVM runtime and do inference
@@ -516,7 +516,7 @@ 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 58.518 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 57.052 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 0f985b0565..ee6ead5e52 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>12:39.242</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>12:23.496</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -349,39 +349,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:10.309</p></td>
+<td><p>03:08.915</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>02:58.518</p></td>
+<td><p>02:57.052</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>02:25.582</p></td>
+<td><p>02:20.507</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>01:34.704</p></td>
+<td><p>01:28.804</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>01:04.760</p></td>
+<td><p>01:04.035</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>00:35.298</p></td>
+<td><p>00:35.251</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>00:25.280</p></td>
+<td><p>00:24.783</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>00:24.785</p></td>
+<td><p>00:24.142</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index ede779b9dd..24318e8954 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -621,7 +621,7 @@ 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.zipd49fbfe6-2e6c-4236-a41d-2df55e0b26ec 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.zip2d9dfe66-c60a-4fb6-a94b-adc6a22ba651 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 4616acbdb8..e9cd9efbc5 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>00:46.883</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:46.625</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -349,19 +349,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:43.483</p></td>
+<td><p>00:43.262</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>00:02.381</p></td>
+<td><p>00:02.358</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></td>
-<td><p>00:01.011</p></td>
+<td><p>00:00.996</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
-<td><p>00:00.008</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 79dcb7cee2..02a3cd3c1e 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -525,10 +525,10 @@ 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: 7361us [7361us] (46.78%; 46.78%)
-FoldScaleAxis: 8375us [7us] (53.22%; 53.22%)
- FoldConstant: 8368us [1729us] (53.17%; 99.91%)
- InferType: 6639us [6639us] (42.19%; 79.34%)
+InferType: 7197us [7197us] (46.17%; 46.17%)
+FoldScaleAxis: 8390us [7us] (53.83%; 53.83%)
+ FoldConstant: 8383us [1714us] (53.78%; 99.92%)
+ InferType: 6669us [6669us] (42.79%; 79.55%)
</pre></div>
</div>
</div>
@@ -550,10 +550,10 @@ 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: 6741us [6741us] (44.97%; 44.97%)
-FoldScaleAxis: 8249us [5us] (55.03%; 55.03%)
- FoldConstant: 8244us [1736us] (54.99%; 99.94%)
- InferType: 6508us [6508us] (43.42%; 78.95%)
+InferType: 6694us [6694us] (45.10%; 45.10%)
+FoldScaleAxis: 8147us [5us] (54.90%; 54.90%)
+ FoldConstant: 8142us [1653us] (54.86%; 99.94%)
+ InferType: 6489us [6489us] (43.72%; 79.70%)
</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 4f6a1ff67b..9f54e4c754 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -577,7 +577,7 @@ 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: 34.181152 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 38.561569 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 1d03d39cdf..601942d68a 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -916,7 +916,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.399268 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.910179 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 4c96f46d21..29fed0da90 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -474,8 +474,8 @@ 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: 0.018167
-Baseline: 3.189108
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018436
+Baseline: 3.462091
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -535,7 +535,7 @@ 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: 0.297096
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.294242
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -602,7 +602,7 @@ 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: 0.331047
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.333649
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -663,7 +663,7 @@ 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: 0.114364
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.115591
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -746,7 +746,7 @@ 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: 0.109365
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.108762
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -832,7 +832,7 @@ 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: 0.112397
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110485
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -922,7 +922,7 @@ 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: 0.147102
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145104
</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 58551f90d8..5463016158 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -340,7 +340,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.060</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.657</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -349,15 +349,15 @@
</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>00:31.494</p></td>
+<td><p>00:32.209</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>00:01.489</p></td>
+<td><p>00:01.406</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.078</p></td>
+<td><p>00:01.042</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 400b51e7c4..d7f7f3496c 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>09:01.419</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>09:11.381</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -349,27 +349,27 @@
</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>05:35.870</p></td>
+<td><p>05:47.500</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>01:31.708</p></td>
+<td><p>01:30.348</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>01:02.650</p></td>
+<td><p>01:02.030</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>00:28.544</p></td>
+<td><p>00:28.897</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:11.709</p></td>
+<td><p>00:11.726</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:10.939</p></td>
+<td><p>00:10.880</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 9a9e1f8c58..4f0d92b60c 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
@@ -1017,7 +1017,7 @@ 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: 0.349 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.348 ms
</pre></div>
</div>
</div>
@@ -1580,7 +1580,7 @@ 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> ( 5 minutes 35.870 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 47.500 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 06a252f035..8a70d3ece2 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -915,7 +915,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 8.2247 8.2274 8.2284 8.2184 0.0045
+ 8.1995 8.1982 8.2087 8.1915 0.0071
</pre></div>
</div>
</div>
@@ -937,7 +937,7 @@ 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 2.650 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.030 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index e93787cd7b..8898903b20 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -934,7 +934,7 @@ 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)
- 752.6515 752.4521 754.0442 751.4582 1.0651
+ 744.6625 744.5877 744.9302 744.4696 0.1953
</pre></div>
</div>
</div>
@@ -956,7 +956,7 @@ 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 31.708 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.348 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 3d822cdd8d..4fe4795903 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -632,30 +632,78 @@ 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_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_17: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 16) {
- for (j.init: int32, 0, 16) {
- compute_5: Buffer(compute_4, float32, [512], [])[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32
+ preflattened_buffer_map = {placeholder_6: placeholder_15: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_8: placeholder_18: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 64) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [1024]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 2) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 16) {
+ let cse_var_1: int32 = (((i.outer.inner*512) + (i.inner.init*32)) + (nb_j.inner*16))
+ {
+ compute_5: Buffer(compute_4, float32, [1024], [])[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_1: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
- for (i.inner: int32, 0, 16) {
- for (j: int32, 0, 16) {
- let cse_var_3: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_2: int32 = (((i.inner*32) + (nb_j.inner*16)) + j)
- compute_5[cse_var_2] = (compute_5[cse_var_2] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + j)]*max(placeholder[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 16) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_19: int32 = (((i.outer.inner*512) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_18: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*8192) + (i.outer.inner*4096)) + (i.inner*256))
+ let cse_var_17: int32 = (cse_var_19 + 9)
+ let cse_var_16: int32 = (cse_var_19 + 8)
+ let cse_var_15: int32 = (cse_var_19 + 7)
+ let cse_var_14: int32 = (cse_var_19 + 6)
+ let cse_var_13: int32 = (cse_var_19 + 5)
+ let cse_var_12: int32 = (cse_var_19 + 4)
+ let cse_var_11: int32 = (cse_var_19 + 3)
+ let cse_var_10: int32 = (cse_var_19 + 2)
+ let cse_var_9: int32 = (cse_var_19 + 15)
+ let cse_var_8: int32 = (cse_var_19 + 14)
+ let cse_var_7: int32 = (cse_var_19 + 13)
+ let cse_var_6: int32 = (cse_var_19 + 12)
+ let cse_var_5: int32 = (cse_var_19 + 11)
+ let cse_var_4: int32 = (cse_var_19 + 10)
+ let cse_var_3: int32 = (cse_var_19 + 1)
+ {
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + 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_18 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 16) {
- for (i1.inner: int32, 0, 32) {
- let cse_var_4: int32 = ((((floordiv(i0.outer.i1.outer.fused, 16)*8192) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)) + i1.inner)
- compute[cse_var_4] = max((compute_5[((i0.inner*32) + i1.inner)] + placeholder_4[cse_var_4]), 0f32)
- }
+ for (i0.inner: int32, 0, 32) {
+ let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+ compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
}
}
}
@@ -693,7 +741,7 @@ 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: 1.504 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.690 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 537b3f8bdd..be5ed3ac03 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>00:48.744</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.563</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -349,22 +349,22 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:48.707</p></td>
+<td><p>00:23.528</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>00:00.022</p></td>
+<td><p>00:00.021</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 582386bb83..069f6fb247 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -567,9 +567,7 @@ for this template</p>
waiting for device...
device available
Get devices for measurement successfully!
-No: 1 GFLOPS: 167.60/167.60 result: MeasureResult(costs=(0.001381298825,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.522179365158081, timestamp=1668157477.0085385) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4360472
-No: 2 GFLOPS: 8.79/167.60 result: MeasureResult(costs=(0.02632410425,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.8582940101623535, timestamp=1668157477.7304428) [('tile_f', [-1, 4, 4, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2141377
-No: 3 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+No: 1 GFLOPS: 0.00/0.00 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
@@ -691,9 +689,8 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 32, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7926252
-No: 4 GFLOPS: 12.79/167.60 result: MeasureResult(costs=(0.018099557166666665,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.841151237487793, timestamp=1668157480.3641567) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2974401
-No: 5 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2347210
+No: 2 GFLOPS: 0.00/0.00 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
@@ -815,8 +812,9 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 64]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9264785
-No: 6 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1335762
+No: 3 GFLOPS: 179.68/179.68 result: MeasureResult(costs=(0.0012884258333333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4960997104644775, timestamp=1668189427.0684822) [('tile_f', [-1, 1, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,781010
+No: 4 GFLOPS: 0.00/179.68 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
@@ -938,8 +936,8 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 64, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 128, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,447135
-No: 7 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4232533
+No: 5 GFLOPS: 0.00/179.68 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
@@ -1061,9 +1059,9 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 256]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,575092
-No: 8 GFLOPS: 5.27/167.60 result: MeasureResult(costs=(0.04390903075,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5156841278076172, timestamp=1668157482.9872196) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,959201
-No: 9 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 256]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4625497
+No: 6 GFLOPS: 54.70/179.68 result: MeasureResult(costs=(0.00423223296,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.938089370727539, timestamp=1668189430.1134493) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,1818418
+No: 7 GFLOPS: 0.00/179.68 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
@@ -1185,27 +1183,8 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 8, 32, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 64, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4377213
-No: 10 GFLOPS: 31.11/167.60 result: MeasureResult(costs=(0.007442182789473684,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3957109451293945, timestamp=1668157494.0435655) [('tile_f', [-1, 1, 8, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,415387
-No: 11 GFLOPS: 0.00/167.60 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
- return self.__get_result()
- File "/usr/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
- raise self._exception
- File "/usr/lib/python3.7/concurrent/futures/thread.py", line 57, in run
- result = self.fn(*self.args, **self.kwargs)
- File "/workspace/python/tvm/contrib/popen_pool.py", line 432, in <lambda>
- worker = lambda *args: self._worker_run(*args)
- File "/workspace/python/tvm/contrib/popen_pool.py", line 401, in _worker_run
- return proc.recv()
- File "/workspace/python/tvm/contrib/popen_pool.py", line 309, in recv
- raise TimeoutError()
-TimeoutError
-
- [('tile_f', [-1, 8, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8771723
-No: 12 GFLOPS: 0.00/167.60 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 16, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 128, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4285319
+No: 8 GFLOPS: 0.00/179.68 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
@@ -1327,13 +1306,8 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 32, 2, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 64, 4]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7638909
-No: 13 GFLOPS: 53.11/167.60 result: MeasureResult(costs=(0.0043591832608695655,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.326113224029541, timestamp=1668157499.8068411) [('tile_f', [-1, 8, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,121735
-No: 14 GFLOPS: 77.04/167.60 result: MeasureResult(costs=(0.0030047811470588237,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2728245258331299, timestamp=1668157500.4617536) [('tile_f', [-1, 4, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6022337
-No: 15 GFLOPS: 908.00/908.00 result: MeasureResult(costs=(0.00025495840031897925,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.206448793411255, timestamp=1668157501.4171813) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10164732
-No: 16 GFLOPS: 6.49/908.00 result: MeasureResult(costs=(0.035650565999999995,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.15035080909729, timestamp=1668157502.193695) [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 32, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7247781
-No: 17 GFLOPS: 10.62/908.00 result: MeasureResult(costs=(0.0217960255,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.662975549697876, timestamp=1668157504.025463) [('tile_f', [-1, 8, 1, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,626699
-No: 18 GFLOPS: 0.00/908.00 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 8, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 128, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5676155
+No: 9 GFLOPS: 0.00/179.68 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
@@ -1455,9 +1429,8 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 16, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4794620
-No: 19 GFLOPS: 57.42/908.00 result: MeasureResult(costs=(0.004031399519999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.133490800857544, timestamp=1668157504.7228327) [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2715288
-No: 20 GFLOPS: 0.00/908.00 result: Traceback (most recent call last):
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 1, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6772703
+No: 10 GFLOPS: 0.00/179.68 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
@@ -1579,7 +1552,993 @@ Traceback (most recent call last):
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
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, 2, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2962976
+tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 128, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6265651
+No: 11 GFLOPS: 47.27/179.68 result: MeasureResult(costs=(0.004897892318181819,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4505457878112793, timestamp=1668189432.7280188) [('tile_f', [-1, 1, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,100140
+No: 12 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 2, 8, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 32, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,55858
+No: 13 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 32, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3972921
+No: 14 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 8, 1, 32]), ('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', 0), ('unroll_explicit', 1)],None,6835148
+No: 15 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2194924
+No: 16 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 32, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 16]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7491060
+No: 17 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 4]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7249783
+No: 18 GFLOPS: 6.69/179.68 result: MeasureResult(costs=(0.034608097,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.389497995376587, timestamp=1668189434.490282) [('tile_f', [-1, 2, 8, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,1937695
+No: 19 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 1, 256, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4555812
+No: 20 GFLOPS: 0.00/179.68 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
+ func = build(s, args, target_host=task.target_host, runtime=runtime)
+ File "/workspace/python/tvm/driver/build_module.py", line 227, in build
+ input_mod = lower(inputs, args, name=name, binds=binds)
+ File "/workspace/python/tvm/driver/build_module.py", line 134, in lower
+ return ffi.lower_schedule(inp, args, name, binds, simple_mode)
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
+ File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
+tvm._ffi.base.TVMError: Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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
+
+Traceback (most recent call last):
+ 24: TVMFuncCall
+ at ../src/runtime/c_runtime_api.cc:477
+ 23: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 22: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 21: operator()
+ at ../include/tvm/runtime/packed_func.h:1731
+ 20: unpack_call<tvm::IRModule, 5, tvm::<lambda(tvm::te::Schedule, const tvm::runtime::Array<tvm::runtime::ObjectRef>&, const tvm::runtime::String&, const tvm::runtime::Map<tvm::te::Tensor, tvm::tir::Buffer>&, bool)> >
+ at ../include/tvm/runtime/packed_func.h:1671
+ 19: run<>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 18: run<tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 17: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 16: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 15: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1631
+ 14: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
+ at ../include/tvm/runtime/packed_func.h:1646
+ 13: operator()
+ at ../src/driver/driver_api.cc:388
+ 12: tvm::LowerSchedule(tvm::te::Schedule, tvm::runtime::Array<tvm::runtime::ObjectRef, void> const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::unordered_map<tvm::te::Tensor, tvm::tir::Buffer, std::hash<tvm::te::Tensor>, std::equal_to<tvm::te::Tensor>, std::allocator<std::pair<tvm::te::Tensor const, tvm::tir::Buffer> > > const&, tvm::GlobalVarSupply, bool)
+ at ../src/driver/driver_api.cc:374
+ 11: tvm::LowerWithPassList(tvm::IRModule, tvm::runtime::Array<tvm::transform::Pass, void>)
+ at ../src/driver/driver_api.cc:269
+ 10: tvm::transform::Pass::operator()(tvm::IRModule) const
+ at ../src/ir/transform.cc:258
+ 9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 8: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:453
+ 7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/ir/transform.cc:274
+ 6: tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
+ at ../src/tir/ir/transform.cc:100
+ 5: tvm::runtime::TypedPackedFunc<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext) const
+ at ../include/tvm/runtime/packed_func.h:1750
+ 4: tvm::tir::PrimFunc tvm::runtime::detail::typed_packed_call_dispatcher<tvm::tir::PrimFunc>::run<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
+ at ../include/tvm/runtime/packed_func.h:1694
+ 3: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext>(tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&) const
+ at ../include/tvm/runtime/packed_func.h:1618
+ 2: tvm::runtime::PackedFuncObj::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
+ at ../include/tvm/runtime/packed_func.h:1217
+ 1: Call
+ at ../include/tvm/runtime/packed_func.h:1213
+ 0: operator()
+ at ../src/runtime/c_runtime_api.cc:534
+ File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
+ 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, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1261416
</pre></div>
</div>
<p>Finally we can inspect the best config from log file, check correctness,
@@ -1618,9 +2577,9 @@ and measure running time.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Finish loading 20 records
Best config:
-[('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10164732
+[('tile_f', [-1, 1, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,781010
Finish loading 20 records
-Time cost of this operator: 0.000635
+Time cost of this operator: 0.000934
</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 d2724d37be..f36e868718 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -596,10 +596,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 310.6 98.635 (1, 2, 10, 10, 3) 2 1 [310.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.328 1.057 (1, 6, 10, 10) 1 1 [3.328]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.97 0.308 (1, 1, 10, 10, 3) 1 1 [0.97]
-Total_time - 314.899 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.9 98.714 (1, 2, 10, 10, 3) 2 1 [308.9]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.078 0.984 (1, 6, 10, 10) 1 1 [3.078]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.946 0.302 (1, 1, 10, 10, 3) 1 1 [0.946]
+Total_time - 312.924 - - - - -
</pre></div>
</div>
</div>
@@ -650,10 +650,10 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 102.6 97.474 (1, 6, 10, 10, 1) 2 1 [102.6]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.806 1.715 (1, 6, 10, 10) 1 1 [1.806]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.853 0.81 (1, 3, 10, 10, 1) 1 1 [0.853]
-Total_time - 105.259 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 137.3 98.139 (1, 6, 10, 10, 1) 2 1 [137.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.773 1.267 (1, 6, 10, 10) 1 1 [1.773]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.831 0.594 (1, 3, 10, 10, 1) 1 1 [0.831]
+Total_time - 139.904 - - - - -
</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_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index 550aa74856..3029c8402e 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -440,7 +440,8 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 98.4MB/s]
+ 66%|######5 | 2.26M/3.42M [00:00<00:00, 23.6MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 26.3MB/s]
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
@@ -564,7 +565,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.171 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 0.935 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index af2519486d..a33b00fbbe 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -530,7 +530,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp_3wrvtfh/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpvckjxel4/images/random'
</pre></div>
</div>
</div>
@@ -590,8 +590,8 @@ 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="[0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmp_3wrvtfh/images/target contains 8144 images
-/tmp/tmp_3wrvtfh/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpvckjxel4/images/target contains 8144 images
+/tmp/tmpvckjxel4/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -703,13 +703,13 @@ 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 - 46s - loss: 0.2525 - accuracy: 0.9177 - val_loss: 0.1238 - val_accuracy: 0.9558 - 46s/epoch - 140ms/step
+328/328 - 46s - loss: 0.2440 - accuracy: 0.9146 - val_loss: 0.1043 - val_accuracy: 0.9622 - 46s/epoch - 140ms/step
Epoch 2/3
-328/328 - 43s - loss: 0.1037 - accuracy: 0.9607 - val_loss: 0.1002 - val_accuracy: 0.9656 - 43s/epoch - 130ms/step
+328/328 - 42s - loss: 0.1057 - accuracy: 0.9600 - val_loss: 0.1039 - val_accuracy: 0.9649 - 42s/epoch - 129ms/step
Epoch 3/3
-328/328 - 43s - loss: 0.0639 - accuracy: 0.9752 - val_loss: 0.1019 - val_accuracy: 0.9698 - 43s/epoch - 130ms/step
+328/328 - 42s - loss: 0.0645 - accuracy: 0.9755 - val_loss: 0.0914 - val_accuracy: 0.9690 - 42s/epoch - 129ms/step
-<keras.callbacks.History object at 0x7f3591b42650>
+<keras.callbacks.History object at 0x7f2f286eb150>
</pre></div>
</div>
</div>
@@ -971,7 +971,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 40.078 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 36.438 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 1177449faa..11c52c4fc1 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -340,7 +340,7 @@
<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>06:41.660</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>06:37.498</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -349,23 +349,23 @@
</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>04:40.078</p></td>
+<td><p>04:36.438</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_pytorch.html#sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">microTVM PyTorch Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_pytorch.py</span></code>)</p></td>
-<td><p>01:01.171</p></td>
+<td><p>01:00.935</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>00:48.438</p></td>
+<td><p>00:48.282</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">microTVM Host-Driven AoT</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:08.256</p></td>
+<td><p>00:08.161</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:03.715</p></td>
+<td><p>00:03.680</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index a6b64d48fb..73e61dfe93 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -340,7 +340,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:42.1000</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:43.266</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -349,15 +349,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="using_pipeline_executor.html#sphx-glr-how-to-work-with-relay-using-pipeline-executor-py"><span class="std std-ref">Using Pipeline Executor in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_pipeline_executor.py</span></code>)</p></td>
-<td><p>00:31.269</p></td>
+<td><p>00:31.714</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></td>
-<td><p>00:10.163</p></td>
+<td><p>00:09.930</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></td>
-<td><p>00:01.561</p></td>
+<td><p>00:01.615</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 4fd8ec88ec..be0bf1e7ba 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -535,7 +535,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<a href="../../reference/api/python/ir.html#tvm.ir.register_intrin_lowering" title="tvm.ir.register_intrin_lowering" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-function"><span class="n">register_intrin_lowering</span></a><span class="p">(</span><span class="s2">"tir.exp"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">f</span><span class="o">= [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f344fc00950>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7f2f9f779320>
</pre></div>
</div>
<p>Register the rule to TVM with override option to override existing rule.
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index f1d36b9d9f..4de5309028 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -340,7 +340,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:07.765</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:07.993</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -349,35 +349,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></td>
-<td><p>00:05.441</p></td>
+<td><p>00:05.653</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></td>
-<td><p>00:01.012</p></td>
+<td><p>00:01.011</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></td>
-<td><p>00:00.558</p></td>
+<td><p>00:00.567</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></td>
-<td><p>00:00.541</p></td>
+<td><p>00:00.549</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></td>
-<td><p>00:00.113</p></td>
+<td><p>00:00.115</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></td>
-<td><p>00:00.049</p></td>
+<td><p>00:00.050</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></td>
-<td><p>00:00.034</p></td>
+<td><p>00:00.029</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
-<td><p>00:00.019</p></td>
+<td><p>00:00.020</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 2a6a16a9a9..5919a1f9a0 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -590,7 +590,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
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/tmpolf_aqlv/input0.cc'\nsource_filename = \"/tmp/tmpolf_aqlv/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 = allo [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpe_3c4rc7/input0.cc'\nsource_filename = \"/tmp/tmpe_3c4rc7/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 = allo [...]
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/install/nnpack.html b/docs/install/nnpack.html
index 1ef28de467..23d2181e9d 100644
--- a/docs/install/nnpack.html
+++ b/docs/install/nnpack.html
@@ -229,7 +229,17 @@
<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>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="from_source.html#enable-c-tests">Enable C++ Tests</a></li>
+</ul>
+</li>
<li class="toctree-l2"><a class="reference internal" href="docker.html">Docker Images</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">NNPACK Contrib Installation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#conditions">Conditions</a></li>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg
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+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode__inherit__graph.svg
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<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ annotations</text>
<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
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<text text-anchor="start" x="235" y="-57" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
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-<text text-anchor="start" x="1370" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_FINAL_OBJECT_INFO()</text>
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+<text text-anchor="start" x="1370" y="-57" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode-members.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode-members.html
index 45601dddd7..207d388ff2 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode-members.html
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<tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a08e6e65098b35e2908e774de3ab970f3">AddCallback</a>(runtime::TypedPackedFunc< void()> callback)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
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- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a9334403129924bc1ed84d8753dd9f672">buffer</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html">tvm::script::ir_builder::tir::AllocateConstFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4b062ec7c2f519922051de6cfffd9762">buffer_var</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html">tvm::script::ir_builder::tir::AllocateConstFrameNode</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a620cb147179d7c6893d1ffd8b306f516">data</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html">tvm::script::ir_builder::tir::AllocateConstFrameNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html
index c36d7d3f40..5cb4900e01 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html
@@ -85,7 +85,7 @@ Inheritance diagram for tvm::script::ir_builder::tir::AllocateConstFrameNode:</d
<div class="dynheader">
Collaboration diagram for tvm::script::ir_builder::tir::AllocateConstFrameNode:</div>
<div class="dyncontent">
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<table class="memberdecls">
@@ -151,9 +151,9 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a620cb147179d7c6893d1ffd8b306f516"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">tvm::runtime::NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a620cb147179d7c6893d1ffd8b306f516">data</a></td></tr>
<tr class="memdesc:a620cb147179d7c6893d1ffd8b306f516"><td class="mdescLeft"> </td><td class="mdescRight">The data associated with the constant. <a href="#a620cb147179d7c6893d1ffd8b306f516">More...</a><br /></td></tr>
<tr class="separator:a620cb147179d7c6893d1ffd8b306f516"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9334403129924bc1ed84d8753dd9f672"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">tvm::tir::Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a9334403129924bc1ed84d8753dd9f672">buffer</a></td></tr>
-<tr class="memdesc:a9334403129924bc1ed84d8753dd9f672"><td class="mdescLeft"> </td><td class="mdescRight">The buffer. <a href="#a9334403129924bc1ed84d8753dd9f672">More...</a><br /></td></tr>
-<tr class="separator:a9334403129924bc1ed84d8753dd9f672"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4b062ec7c2f519922051de6cfffd9762"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4b062ec7c2f519922051de6cfffd9762">buffer_var</a></td></tr>
+<tr class="memdesc:a4b062ec7c2f519922051de6cfffd9762"><td class="mdescLeft"> </td><td class="mdescRight">The buffer var. <a href="#a4b062ec7c2f519922051de6cfffd9762">More...</a><br /></td></tr>
+<tr class="separator:a4b062ec7c2f519922051de6cfffd9762"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4d617420058fe33bc87a4cf74e773d88"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4d617420058fe33bc87a4cf74e773d88 [...]
<tr class="memdesc:a4d617420058fe33bc87a4cf74e773d88"><td class="mdescLeft"> </td><td class="mdescRight">Additional annotations about the allocation. <a href="#a4d617420058fe33bc87a4cf74e773d88">More...</a><br /></td></tr>
<tr class="separator:a4d617420058fe33bc87a4cf74e773d88"><td class="memSeparator" colspan="2"> </td></tr>
@@ -365,19 +365,19 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="a9334403129924bc1ed84d8753dd9f672"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a9334403129924bc1ed84d8753dd9f672">◆ </a></span>buffer</h2>
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+ <td class="memname"><a class="el" href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a> tvm::script::ir_builder::tir::AllocateConstFrameNode::buffer_var</td>
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-<p>The buffer. </p>
+<p>The buffer var. </p>
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-<text text-anchor="start" x="20" y="-931.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
+<polygon fill="#ffffff" stroke="#000000" points="12,-1241.5 12,-1628.5 195,-1628.5 195,-1241.5 12,-1241.5"/>
+<text text-anchor="middle" x="103.5" y="-1616.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
+<polyline fill="none" stroke="#000000" points="12,-1609.5 195,-1609.5 "/>
+<text text-anchor="start" x="20" y="-1597.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<text text-anchor="start" x="20" y="-1586.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
+<text text-anchor="start" x="20" y="-1575.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
+<text text-anchor="start" x="20" y="-1564.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
+<text text-anchor="start" x="20" y="-1553.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
+<text text-anchor="start" x="20" y="-1542.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
+<text text-anchor="start" x="20" y="-1531.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
+<text text-anchor="start" x="20" y="-1520.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="20" y="-1509.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-1498.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
+<text text-anchor="start" x="20" y="-1487.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-1476.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
+<text text-anchor="start" x="20" y="-1465.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
+<text text-anchor="start" x="20" y="-1454.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
+<polyline fill="none" stroke="#000000" points="12,-1447.5 195,-1447.5 "/>
+<text text-anchor="start" x="20" y="-1435.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
+<text text-anchor="start" x="20" y="-1424.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
+<text text-anchor="start" x="20" y="-1413.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="20" y="-1402.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
+<text text-anchor="start" x="20" y="-1391.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="20" y="-1380.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1369.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1358.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1347.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="20" y="-1336.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="20" y="-1325.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="20" y="-1314.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="20" y="-1303.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="20" y="-1292.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-1281.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-1270.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="20" y="-1259.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="20" y="-1248.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node7->Node6 -->
<g id="edge3" class="edge">
<title>Node7->Node6</title>
-<path fill="none" stroke="#191970" d="M103.5,-913.7972C103.5,-896.3163 103.5,-879.5917 103.5,-864.547"/>
-<polygon fill="none" stroke="#191970" points="100.0001,-914.1701 103.5,-924.1701 107.0001,-914.1701 100.0001,-914.1701"/>
+<path fill="none" stroke="#191970" d="M103.5,-1230.7972C103.5,-1213.3163 103.5,-1196.5917 103.5,-1181.547"/>
+<polygon fill="none" stroke="#191970" points="100.0001,-1231.1701 103.5,-1241.1701 107.0001,-1231.1701 100.0001,-1231.1701"/>
</g>
<!-- Node7->Node7 -->
<g id="edge4" class="edge">
<title>Node7->Node7</title>
-<path fill="none" stroke="#404040" d="M195.3625,-1151.9248C206.0482,-1145.6637 213,-1134.3555 213,-1118 213,-1107.0112 209.8618,-1098.3007 204.5615,-1091.8687"/>
-<polygon fill="none" stroke="#404040" points="204.5184,-1091.8322 197.3548,-1091.0056 195.3625,-1084.0752 202.5261,-1084.9017 204.5184,-1091.8322"/>
-<text text-anchor="middle" x="239" y="-1115.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
+<path fill="none" stroke="#404040" d="M195.3625,-1468.9248C206.0482,-1462.6637 213,-1451.3555 213,-1435 213,-1424.0112 209.8618,-1415.3007 204.5615,-1408.8687"/>
+<polygon fill="none" stroke="#404040" points="204.5184,-1408.8322 197.3548,-1408.0056 195.3625,-1401.0752 202.5261,-1401.9017 204.5184,-1408.8322"/>
+<text text-anchor="middle" x="239" y="-1432.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
</g>
<!-- Node8 -->
<g id="node5" class="node">
<title>Node8</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::tir::Stmt \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 25 more...\l}">
-<polygon fill="#ffffff" stroke="#000000" points="231,-708.5 231,-886.5 344,-886.5 344,-708.5 231,-708.5"/>
-<text text-anchor="start" x="239" y="-874.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="287.5" y="-863.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
-<polyline fill="none" stroke="#000000" points="231,-856.5 344,-856.5 "/>
-<text text-anchor="middle" x="287.5" y="-844.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="231,-837.5 344,-837.5 "/>
-<text text-anchor="start" x="239" y="-825.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-814.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="239" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="239" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="239" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
+<polygon fill="#ffffff" stroke="#000000" points="225,-1025.5 225,-1203.5 338,-1203.5 338,-1025.5 225,-1025.5"/>
+<text text-anchor="start" x="233" y="-1191.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="middle" x="281.5" y="-1180.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
+<polyline fill="none" stroke="#000000" points="225,-1173.5 338,-1173.5 "/>
+<text text-anchor="middle" x="281.5" y="-1161.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="225,-1154.5 338,-1154.5 "/>
+<text text-anchor="start" x="233" y="-1142.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1131.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1120.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1109.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1098.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1087.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1076.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1065.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1054.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="233" y="-1043.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="233" y="-1032.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
</a>
</g>
</g>
<!-- Node8->Node5 -->
<g id="edge5" class="edge">
<title>Node8->Node5</title>
-<path fill="none" stroke="#404040" d="M230.5994,-719.1497C225.8004,-715.0243 220.7592,-711.2494 215.5,-708 183.3885,-688.1596 156.1941,-719.3833 132.5,-690 109.6643,-661.6811 124.4792,-424.2928 132.4155,-317.5904"/>
-<polygon fill="none" stroke="#404040" points="132.4208,-317.5179 128.8832,-311.2341 133.323,-305.5518 136.8606,-311.8356 132.4208,-317.5179"/>
-<text text-anchor="middle" x="152.5" y="-548.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
+<path fill="none" stroke="#404040" d="M265.3973,-1025.2477C248.5197,-931.7007 222.5832,-787.9431 208.8229,-711.6737"/>
+<polygon fill="none" stroke="#404040" points="208.7723,-711.393 203.7705,-706.1986 206.6416,-699.5837 211.6434,-704.7781 208.7723,-711.393"/>
+<text text-anchor="middle" x="282.5" y="-865.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
</g>
<!-- Node9 -->
<g id="node6" class="node">
<title>Node9</title>
<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
-<polygon fill="#ffffff" stroke="#000000" points="359.5,-1007 359.5,-1229 493.5,-1229 493.5,-1007 359.5,-1007"/>
-<text text-anchor="middle" x="426.5" y="-1217" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="359.5,-1210 493.5,-1210 "/>
-<text text-anchor="start" x="367.5" y="-1198" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="359.5,-1191 493.5,-1191 "/>
-<text text-anchor="start" x="367.5" y="-1179" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="367.5" y="-1168" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="367.5" y="-1157" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="367.5" y="-1146" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="367.5" y="-1135" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="367.5" y="-1124" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="367.5" y="-1113" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="367.5" y="-1102" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="367.5" y="-1091" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="367.5" y="-1080" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="367.5" y="-1069" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="367.5" y="-1058" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
-<text text-anchor="start" x="367.5" y="-1047" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
-<text text-anchor="start" x="367.5" y="-1036" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="367.5" y="-1025" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="367.5" y="-1014" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
+<polygon fill="#ffffff" stroke="#000000" points="471.5,-1324 471.5,-1546 605.5,-1546 605.5,-1324 471.5,-1324"/>
+<text text-anchor="middle" x="538.5" y="-1534" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="471.5,-1527 605.5,-1527 "/>
+<text text-anchor="start" x="479.5" y="-1515" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="471.5,-1508 605.5,-1508 "/>
+<text text-anchor="start" x="479.5" y="-1496" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="479.5" y="-1485" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="479.5" y="-1474" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="479.5" y="-1463" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="479.5" y="-1452" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="479.5" y="-1441" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="479.5" y="-1430" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="479.5" y="-1419" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="479.5" y="-1408" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="479.5" y="-1397" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="479.5" y="-1386" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="479.5" y="-1375" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="479.5" y="-1364" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="479.5" y="-1353" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="479.5" y="-1342" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="479.5" y="-1331" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
</g>
</g>
<!-- Node9->Node8 -->
<g id="edge6" class="edge">
<title>Node9->Node8</title>
-<path fill="none" stroke="#191970" d="M374.2451,-997.513C358.277,-960.6944 341.0123,-920.8864 326.1215,-886.5518"/>
-<polygon fill="none" stroke="#191970" points="371.0847,-999.0224 378.2746,-1006.8041 377.5067,-996.2372 371.0847,-999.0224"/>
-</g>
-<!-- Node11 -->
-<g id="node8" class="node">
-<title>Node11</title>
-<g id="a_node8"><a xlink:href="classtvm_1_1runtime_1_1NDArray.html" target="_top" xlink:title="Managed NDArray. The array is backed by reference counted blocks. ">
-<polygon fill="#ffffff" stroke="#000000" points="182,-412.5 182,-689.5 365,-689.5 365,-412.5 182,-412.5"/>
-<text text-anchor="middle" x="273.5" y="-677.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::NDArray</text>
-<polyline fill="none" stroke="#000000" points="182,-670.5 365,-670.5 "/>
-<text text-anchor="middle" x="273.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="182,-651.5 365,-651.5 "/>
-<text text-anchor="start" x="190" y="-639.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ NDArray()</text>
-<text text-anchor="start" x="190" y="-628.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ NDArray()</text>
-<text text-anchor="start" x="190" y="-617.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ reset()</text>
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+<polygon fill="none" stroke="#191970" points="553.5841,-1313.1773 555.6276,-1323.5732 560.5131,-1314.172 553.5841,-1313.1773"/>
+</g>
+<!-- Node16 -->
+<g id="node13" class="node">
+<title>Node16</title>
+<g id="a_node13"><a xlink:href="classtvm_1_1runtime_1_1Map.html" target="_top" xlink:title="{tvm::runtime::Map\<\l tvm::runtime::String,\l tvm::runtime::ObjectRef \>\n||+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ Map()\l+ operator=()\l+ operator=()\l+ at()\land 12 more...\l}">
+<polygon fill="#ffffff" stroke="#000000" points="620.5,-773.5 620.5,-962.5 768.5,-962.5 768.5,-773.5 620.5,-773.5"/>
+<text text-anchor="start" x="628.5" y="-950.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Map<</text>
+<text text-anchor="start" x="628.5" y="-939.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> tvm::runtime::String,</text>
+<text text-anchor="middle" x="694.5" y="-928.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> tvm::runtime::ObjectRef ></text>
+<polyline fill="none" stroke="#000000" points="620.5,-921.5 768.5,-921.5 "/>
+<text text-anchor="middle" x="694.5" y="-909.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="620.5,-902.5 768.5,-902.5 "/>
+<text text-anchor="start" x="628.5" y="-890.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-879.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-868.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-857.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-846.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-835.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-824.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
+<text text-anchor="start" x="628.5" y="-813.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="628.5" y="-802.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="628.5" y="-791.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
+<text text-anchor="start" x="628.5" y="-780.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 12 more...</text>
+</a>
+</g>
+</g>
+<!-- Node9->Node16 -->
+<g id="edge17" class="edge">
+<title>Node9->Node16</title>
+<path fill="none" stroke="#191970" d="M588.3299,-1314.3789C599.5054,-1284.8006 610.6561,-1253.0105 619.5,-1223 645.0967,-1136.1416 665.5953,-1034.8947 678.6503,-962.8055"/>
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<g id="node7" class="node">
<title>Node10</title>
<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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-<text text-anchor="start" x="364.5" y="-1525.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
-<text text-anchor="middle" x="426.5" y="-1514.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Object ></text>
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-<text text-anchor="start" x="364.5" y="-1476.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1465.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1454.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1443.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1432.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1421.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="364.5" y="-1410.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
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-<text text-anchor="start" x="364.5" y="-1388.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="364.5" y="-1377.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="364.5" y="-1366.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
+<polygon fill="#ffffff" stroke="#000000" points="468.5,-1676.5 468.5,-1854.5 608.5,-1854.5 608.5,-1676.5 468.5,-1676.5"/>
+<text text-anchor="start" x="476.5" y="-1842.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
+<text text-anchor="middle" x="538.5" y="-1831.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Object ></text>
+<polyline fill="none" stroke="#000000" points="468.5,-1824.5 608.5,-1824.5 "/>
+<text text-anchor="middle" x="538.5" y="-1812.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="468.5,-1805.5 608.5,-1805.5 "/>
+<text text-anchor="start" x="476.5" y="-1793.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1782.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1771.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1760.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1749.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1738.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1727.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
+<text text-anchor="start" x="476.5" y="-1716.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
+<text text-anchor="start" x="476.5" y="-1705.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="476.5" y="-1694.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="476.5" y="-1683.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
</a>
</g>
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<title>Node10->Node9</title>
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-<text text-anchor="middle" x="446" y="-1333" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
+<path fill="none" stroke="#404040" d="M538.5,-1676.4973C538.5,-1640.1115 538.5,-1597.409 538.5,-1558.1129"/>
+<polygon fill="none" stroke="#404040" points="538.5001,-1558.0376 534.5,-1552.0377 538.5,-1546.0376 542.5,-1552.0376 538.5001,-1558.0376"/>
+<text text-anchor="middle" x="558" y="-1650" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
+</g>
+<!-- Node11 -->
+<g id="node8" class="node">
+<title>Node11</title>
+<g id="a_node8"><a xlink:href="classtvm_1_1tir_1_1Var.html" target="_top" xlink:title="a named variable in TIR ">
+<polygon fill="#ffffff" stroke="#000000" points="348.5,-204.5 348.5,-327.5 466.5,-327.5 466.5,-204.5 348.5,-204.5"/>
+<text text-anchor="middle" x="407.5" y="-315.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::tir::Var</text>
+<polyline fill="none" stroke="#000000" points="348.5,-308.5 466.5,-308.5 "/>
+<text text-anchor="middle" x="407.5" y="-296.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="348.5,-289.5 466.5,-289.5 "/>
+<text text-anchor="start" x="356.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Var()</text>
+<text text-anchor="start" x="356.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Var()</text>
+<text text-anchor="start" x="356.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Var()</text>
+<text text-anchor="start" x="356.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ copy_with_suffix()</text>
+<text text-anchor="start" x="356.5" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ copy_with_dtype()</text>
+<text text-anchor="start" x="356.5" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="356.5" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+</a>
+</g>
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<title>Node11->Node4</title>
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+<path fill="none" stroke="#404040" d="M403.4132,-204.1323C404.2801,-173.2335 409.9842,-136.6472 428.5,-109 430.77,-105.6105 433.2864,-102.3793 436.0016,-99.3012"/>
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</g>
-<!-- Node12->Node4 -->
+<!-- Node12 -->
+<g id="node9" class="node">
+<title>Node12</title>
+<g id="a_node9"><a xlink:href="classtvm_1_1PrimExpr.html" target="_top" xlink:title="Reference to PrimExprNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="321.5,-609.5 321.5,-710.5 475.5,-710.5 475.5,-609.5 321.5,-609.5"/>
+<text text-anchor="middle" x="398.5" y="-698.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::PrimExpr</text>
+<polyline fill="none" stroke="#000000" points="321.5,-691.5 475.5,-691.5 "/>
+<text text-anchor="middle" x="398.5" y="-679.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="321.5,-672.5 475.5,-672.5 "/>
+<text text-anchor="start" x="329.5" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PrimExpr()</text>
+<text text-anchor="start" x="329.5" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ PrimExpr()</text>
+<text text-anchor="start" x="329.5" y="-638.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ dtype()</text>
+<text text-anchor="start" x="329.5" y="-627.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="329.5" y="-616.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+</a>
+</g>
+</g>
+<!-- Node12->Node11 -->
+<g id="edge9" class="edge">
+<title>Node12->Node11</title>
+<path fill="none" stroke="#191970" d="M399.8882,-599.2263C401.5741,-525.4242 404.4016,-401.6427 406.0949,-327.5101"/>
+<polygon fill="none" stroke="#191970" points="396.3875,-599.2206 399.6582,-609.298 403.3857,-599.3806 396.3875,-599.2206"/>
+</g>
+<!-- Node13->Node12 -->
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-<title>Node12->Node4</title>
-<path fill="none" stroke="#404040" d="M422.8798,-461.7225C421.0337,-377.6269 422.2375,-248.4184 440.5,-138 442.4304,-126.3283 445.4215,-114.0691 448.7425,-102.4314"/>
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-<text text-anchor="middle" x="465" y="-263.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +extents</text>
+<title>Node13->Node12</title>
+<path fill="none" stroke="#191970" d="M419.5073,-1070.3592C414.0575,-1051.1089 408.4261,-1028.1486 405.5,-1007 391.0274,-902.399 393.0894,-778.3946 395.8302,-710.5727"/>
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</g>
-<!-- Node13->Node4 -->
+<!-- Node14->Node4 -->
<g id="edge12" class="edge">
-<title>Node13->Node4</title>
-<path fill="none" stroke="#404040" d="M535.6891,-456.1181C530.0332,-435.7702 524.6086,-414.2869 520.5,-394 497.7505,-281.6716 509.1016,-250.9203 489.5,-138 487.5142,-126.5601 485.0292,-114.405 482.4707,-102.7965"/>
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-<text text-anchor="middle" x="540.5" y="-263.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +buffer</text>
+<title>Node14->Node4</title>
+<path fill="none" stroke="#404040" d="M495.0745,-729.4537C493.7584,-639.0204 493.7366,-518.4224 499.5,-412 505.4532,-302.0722 521.0195,-174.366 530.5888,-102.4278"/>
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</g>
-<!-- Node14->Node4 -->
+<!-- Node15->Node4 -->
<g id="edge14" class="edge">
-<title>Node14->Node4</title>
-<path fill="none" stroke="#404040" d="M580.946,-171.4729C572.6473,-160.0021 564.0476,-148.602 555.5,-138 545.1448,-125.1561 533.4093,-111.9168 521.9852,-99.6108"/>
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-<text text-anchor="middle" x="575.5" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +annotations</text>
+<title>Node15->Node4</title>
+<path fill="none" stroke="#404040" d="M587.5938,-412.4961C575.3178,-320.7283 556.5433,-180.3813 546.1284,-102.5256"/>
+<polygon fill="none" stroke="#404040" points="546.1221,-102.4779 541.3619,-97.0612 544.5309,-90.5838 549.2912,-96.0004 546.1221,-102.4779"/>
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-<text text-anchor="start" x="743.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ DataType()</text>
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-<text text-anchor="start" x="743.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ DataType()</text>
-<text text-anchor="start" x="743.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ code()</text>
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-<text text-anchor="start" x="743.5" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Int()</text>
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-<text text-anchor="start" x="743.5" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Handle()</text>
-<text text-anchor="start" x="743.5" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Void()</text>
-<text text-anchor="start" x="743.5" y="-145.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ShapeIndex()</text>
+<!-- Node16->Node4 -->
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+<title>Node16->Node4</title>
+<path fill="none" stroke="#404040" d="M695.3261,-773.2367C695.4584,-601.8098 689.0954,-247.7837 636.5,-138 629.7337,-123.8766 619.8057,-110.8148 608.832,-99.2252"/>
+<polygon fill="none" stroke="#404040" points="608.7526,-99.1458 601.6822,-97.7281 600.2715,-90.6563 607.3419,-92.074 608.7526,-99.1458"/>
+<text text-anchor="middle" x="727.5" y="-499" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +annotations</text>
+</g>
+<!-- Node17 -->
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+<g id="a_node14"><a xlink:href="classtvm_1_1runtime_1_1DataType.html" target="_top" xlink:title="Runtime primitive data type. ">
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+<text text-anchor="middle" x="773.5" y="-381.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::DataType</text>
+<polyline fill="none" stroke="#000000" points="707.5,-374.5 839.5,-374.5 "/>
+<text text-anchor="middle" x="773.5" y="-362.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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+<text text-anchor="start" x="715.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ DataType()</text>
+<text text-anchor="start" x="715.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ DataType()</text>
+<text text-anchor="start" x="715.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ DataType()</text>
+<text text-anchor="start" x="715.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ code()</text>
+<text text-anchor="start" x="715.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ bits()</text>
+<text text-anchor="start" x="715.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ bytes()</text>
+<text text-anchor="start" x="715.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ lanes()</text>
+<text text-anchor="start" x="715.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ is_scalar()</text>
+<text text-anchor="start" x="715.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ is_bool()</text>
+<text text-anchor="start" x="715.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ is_float()</text>
+<text text-anchor="start" x="715.5" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
+<text text-anchor="start" x="715.5" y="-222.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Int()</text>
+<text text-anchor="start" x="715.5" y="-211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ UInt()</text>
+<text text-anchor="start" x="715.5" y="-200.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Float()</text>
+<text text-anchor="start" x="715.5" y="-189.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ BFloat()</text>
+<text text-anchor="start" x="715.5" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Bool()</text>
+<text text-anchor="start" x="715.5" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Handle()</text>
+<text text-anchor="start" x="715.5" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Void()</text>
+<text text-anchor="start" x="715.5" y="-145.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ShapeIndex()</text>
</a>
</g>
</g>
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-<title>Node15->Node4</title>
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-<text text-anchor="middle" x="715.5" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +dtype</text>
+<!-- Node17->Node4 -->
+<g id="edge18" class="edge">
+<title>Node17->Node4</title>
+<path fill="none" stroke="#404040" d="M718.7959,-138.1521C711.2531,-127.5368 702.8394,-117.5995 693.5,-109 681.993,-98.4046 668.4786,-89.4583 654.3085,-81.9358"/>
+<polygon fill="none" stroke="#404040" points="654.0971,-81.83 646.9412,-82.7203 643.3671,-76.4572 650.523,-75.5669 654.0971,-81.83"/>
+<text text-anchor="middle" x="722.5" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +dtype</text>
</g>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode__inherit__graph.svg
index bea0376e6b..1bb9e48bd3 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode__inherit__graph.svg
@@ -19,7 +19,7 @@
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<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ extents</text>
<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ data</text>
-<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
+<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer_var</text>
<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ annotations</text>
<text text-anchor="start" x="8" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
<polyline fill="none" stroke="#000000" points="0,-41.5 209,-41.5 "/>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode-members.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode-members.html
index bda70ec853..bf75964210 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode-members.html
@@ -80,7 +80,7 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a121a0372d334ad2ef396b980870d007d">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a08e6e65098b35e2908e774de3ab970f3">AddCallback</a>(runtime::TypedPackedFunc< void()> callback)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a272e14ad0ee3f8c0d75744faaee3f476">annotations</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ad7365c7b511a9f9dffc1aac531d046c2">buffer</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a22c42fc2abf33a4675850ae3ec826aec">buffer_var</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#aa21f554fdcf6ab78c55cb54cdc69c0c2">callbacks</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a5a6ff996869df4462d45ce04cd8efcbb">condition</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html
index b2dabeac5e..b84b465117 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html
@@ -85,7 +85,7 @@ Inheritance diagram for tvm::script::ir_builder::tir::AllocateFrameNode:</div>
<div class="dynheader">
Collaboration diagram for tvm::script::ir_builder::tir::AllocateFrameNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode__coll__graph.svg" width="1292" height="1944"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode__coll__graph.svg" width="1198" height="2382"><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">
@@ -157,9 +157,9 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a272e14ad0ee3f8c0d75744faaee3f476"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a272e14ad0ee3f8c0d75744faaee3f476">ann [...]
<tr class="memdesc:a272e14ad0ee3f8c0d75744faaee3f476"><td class="mdescLeft"> </td><td class="mdescRight">Additional annotation hints. <a href="#a272e14ad0ee3f8c0d75744faaee3f476">More...</a><br /></td></tr>
<tr class="separator:a272e14ad0ee3f8c0d75744faaee3f476"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad7365c7b511a9f9dffc1aac531d046c2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">tvm::tir::Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ad7365c7b511a9f9dffc1aac531d046c2">buffer</a></td></tr>
-<tr class="memdesc:ad7365c7b511a9f9dffc1aac531d046c2"><td class="mdescLeft"> </td><td class="mdescRight">The buffer. <a href="#ad7365c7b511a9f9dffc1aac531d046c2">More...</a><br /></td></tr>
-<tr class="separator:ad7365c7b511a9f9dffc1aac531d046c2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a22c42fc2abf33a4675850ae3ec826aec"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a22c42fc2abf33a4675850ae3ec826aec">buffer_var</a></td></tr>
+<tr class="memdesc:a22c42fc2abf33a4675850ae3ec826aec"><td class="mdescLeft"> </td><td class="mdescRight">The buffer var. <a href="#a22c42fc2abf33a4675850ae3ec826aec">More...</a><br /></td></tr>
+<tr class="separator:a22c42fc2abf33a4675850ae3ec826aec"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td colspan="2" onclick="javascript:toggleInherit('pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode')"><img src="closed.png" alt="-"/> Public Attributes inherited from <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td></tr>
<tr class="memitem:a13776bb5c2e5403138fbee06d4fdad40 inherit pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Stmt.html">tvm::tir::Stmt</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40" [...]
<tr class="memdesc:a13776bb5c2e5403138fbee06d4fdad40 inherit pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td class="mdescLeft"> </td><td class="mdescRight">The Stmt within in this frame. <a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">More...</a><br /></td></tr>
@@ -368,19 +368,19 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="ad7365c7b511a9f9dffc1aac531d046c2"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad7365c7b511a9f9dffc1aac531d046c2">◆ </a></span>buffer</h2>
+<a id="a22c42fc2abf33a4675850ae3ec826aec"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a22c42fc2abf33a4675850ae3ec826aec">◆ </a></span>buffer_var</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">tvm::tir::Buffer</a> tvm::script::ir_builder::tir::AllocateFrameNode::buffer</td>
+ <td class="memname"><a class="el" href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a> tvm::script::ir_builder::tir::AllocateFrameNode::buffer_var</td>
</tr>
</table>
</div><div class="memdoc">
-<p>The buffer. </p>
+<p>The buffer var. </p>
</div>
</div>
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--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode__coll__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode__coll__graph.svg
@@ -4,461 +4,455 @@
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+<svg width="898pt" height="1786pt"
+ viewBox="0.00 0.00 897.50 1786.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 1782)">
<title>tvm::script::ir_builder::tir::AllocateFrameNode</title>
-<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-1454 964.5,-1454 964.5,4 -4,4"/>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-1782 893.5,-1782 893.5,4 -4,4"/>
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<g id="node1" class="node">
<title>Node4</title>
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-<text text-anchor="middle" x="509.5" y="-67.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::AllocateFrameNode</text>
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-<text text-anchor="start" x="413" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="405,-41.5 614,-41.5 "/>
-<text text-anchor="start" x="413" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
-<text text-anchor="start" x="413" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_FINAL_OBJECT_INFO()</text>
-<text text-anchor="start" x="413" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
+<polygon fill="#bfbfbf" stroke="#000000" points="400,-.5 400,-90.5 609,-90.5 609,-.5 400,-.5"/>
+<text text-anchor="start" x="408" y="-78.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="504.5" y="-67.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::AllocateFrameNode</text>
+<polyline fill="none" stroke="#000000" points="400,-60.5 609,-60.5 "/>
+<text text-anchor="start" x="408" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="400,-41.5 609,-41.5 "/>
+<text text-anchor="start" x="408" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="408" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_FINAL_OBJECT_INFO()</text>
+<text text-anchor="start" x="408" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
</g>
<!-- Node5 -->
<g id="node2" class="node">
<title>Node5</title>
<g id="a_node2"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html" target="_top" xlink:title="A base frame that represents the TIR fame with body of statements. ">
-<polygon fill="#ffffff" stroke="#000000" points="46,-226.5 46,-305.5 253,-305.5 253,-226.5 46,-226.5"/>
-<text text-anchor="start" x="54" y="-293.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="149.5" y="-282.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::TIRFrameNode</text>
-<polyline fill="none" stroke="#000000" points="46,-275.5 253,-275.5 "/>
-<text text-anchor="start" x="54" y="-263.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="46,-256.5 253,-256.5 "/>
-<text text-anchor="start" x="54" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
-<text text-anchor="start" x="54" y="-233.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
+<polygon fill="#ffffff" stroke="#000000" points="97,-631.5 97,-710.5 304,-710.5 304,-631.5 97,-631.5"/>
+<text text-anchor="start" x="105" y="-698.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="200.5" y="-687.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::TIRFrameNode</text>
+<polyline fill="none" stroke="#000000" points="97,-680.5 304,-680.5 "/>
+<text text-anchor="start" x="105" y="-668.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="97,-661.5 304,-661.5 "/>
+<text text-anchor="start" x="105" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="105" y="-638.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
</a>
</g>
</g>
<!-- Node5->Node4 -->
<g id="edge1" class="edge">
<title>Node5->Node4</title>
-<path fill="none" stroke="#191970" d="M176.8732,-217.4973C199.1038,-182.3833 233.5859,-136.4455 275.5,-109 313.9736,-83.8073 362.226,-68.5522 404.904,-59.341"/>
-<polygon fill="none" stroke="#191970" points="173.7879,-215.8313 171.4882,-226.1735 179.7355,-219.5227 173.7879,-215.8313"/>
+<path fill="none" stroke="#191970" d="M190.6852,-621.5714C173.59,-521.0927 149.0688,-289.6915 249.5,-138 267.1135,-111.3965 337.5211,-87.0409 399.9007,-70.0068"/>
+<polygon fill="none" stroke="#191970" points="187.2429,-622.2053 192.4149,-631.4519 194.138,-620.9982 187.2429,-622.2053"/>
</g>
<!-- Node6 -->
<g id="node3" class="node">
<title>Node6</title>
<g id="a_node3"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html" target="_top" xlink:title="{tvm::script::ir_builder\l::IRBuilderFrameNode\n|+ callbacks\l+ _type_key\l|+ VisitAttrs()\l+ TVM_DECLARE_BASE_OBJECT_INFO()\l+ ~IRBuilderFrameNode()\l+ EnterWithScope()\l+ ExitWithScope()\l+ AddCallback()\l}">
-<polygon fill="#ffffff" stroke="#000000" points="0,-642.5 0,-776.5 207,-776.5 207,-642.5 0,-642.5"/>
-<text text-anchor="start" x="8" y="-764.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="103.5" y="-753.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::IRBuilderFrameNode</text>
-<polyline fill="none" stroke="#000000" points="0,-746.5 207,-746.5 "/>
-<text text-anchor="start" x="8" y="-734.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ callbacks</text>
-<text text-anchor="start" x="8" y="-723.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="0,-716.5 207,-716.5 "/>
-<text text-anchor="start" x="8" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
-<text text-anchor="start" x="8" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
-<text text-anchor="start" x="8" y="-682.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~IRBuilderFrameNode()</text>
-<text text-anchor="start" x="8" y="-671.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ EnterWithScope()</text>
-<text text-anchor="start" x="8" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
-<text text-anchor="start" x="8" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ AddCallback()</text>
+<polygon fill="#ffffff" stroke="#000000" points="0,-970.5 0,-1104.5 207,-1104.5 207,-970.5 0,-970.5"/>
+<text text-anchor="start" x="8" y="-1092.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="103.5" y="-1081.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::IRBuilderFrameNode</text>
+<polyline fill="none" stroke="#000000" points="0,-1074.5 207,-1074.5 "/>
+<text text-anchor="start" x="8" y="-1062.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ callbacks</text>
+<text text-anchor="start" x="8" y="-1051.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="0,-1044.5 207,-1044.5 "/>
+<text text-anchor="start" x="8" y="-1032.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="8" y="-1021.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
+<text text-anchor="start" x="8" y="-1010.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~IRBuilderFrameNode()</text>
+<text text-anchor="start" x="8" y="-999.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ EnterWithScope()</text>
+<text text-anchor="start" x="8" y="-988.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
+<text text-anchor="start" x="8" y="-977.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ AddCallback()</text>
</a>
</g>
</g>
<!-- Node6->Node5 -->
<g id="edge2" class="edge">
<title>Node6->Node5</title>
-<path fill="none" stroke="#191970" d="M111.5397,-631.9872C121.4187,-536.7407 137.8019,-378.7846 145.3985,-305.5435"/>
-<polygon fill="none" stroke="#191970" points="108.0368,-631.8341 110.4864,-642.1419 114.9995,-632.5564 108.0368,-631.8341"/>
+<path fill="none" stroke="#191970" d="M123.95,-960.2327C144.1494,-883.9123 174.24,-770.2196 190.004,-710.6574"/>
+<polygon fill="none" stroke="#191970" points="120.4664,-959.7156 121.2913,-970.2783 127.2334,-961.5066 120.4664,-959.7156"/>
</g>
<!-- Node7 -->
<g id="node4" class="node">
<title>Node7</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1Object.html" target="_top" xlink:title="base class of all object containers. ">
-<polygon fill="#ffffff" stroke="#000000" points="12,-836.5 12,-1223.5 195,-1223.5 195,-836.5 12,-836.5"/>
-<text text-anchor="middle" x="103.5" y="-1211.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
-<polyline fill="none" stroke="#000000" points="12,-1204.5 195,-1204.5 "/>
-<text text-anchor="start" x="20" y="-1192.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<text text-anchor="start" x="20" y="-1181.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
-<text text-anchor="start" x="20" y="-1170.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
-<text text-anchor="start" x="20" y="-1159.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
-<text text-anchor="start" x="20" y="-1148.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
-<text text-anchor="start" x="20" y="-1137.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
-<text text-anchor="start" x="20" y="-1126.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
-<text text-anchor="start" x="20" y="-1115.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
-<text text-anchor="start" x="20" y="-1104.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="20" y="-1093.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
-<text text-anchor="start" x="20" y="-1082.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="20" y="-1071.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
-<text text-anchor="start" x="20" y="-1060.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
-<text text-anchor="start" x="20" y="-1049.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
-<polyline fill="none" stroke="#000000" points="12,-1042.5 195,-1042.5 "/>
-<text text-anchor="start" x="20" y="-1030.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
-<text text-anchor="start" x="20" y="-1019.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
-<text text-anchor="start" x="20" y="-1008.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
-<text text-anchor="start" x="20" y="-997.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
-<text text-anchor="start" x="20" y="-986.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="20" y="-975.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
-<text text-anchor="start" x="20" y="-964.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
-<text text-anchor="start" x="20" y="-953.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
-<text text-anchor="start" x="20" y="-942.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="20" y="-931.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="20" y="-920.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="20" y="-909.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="20" y="-898.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
-<text text-anchor="start" x="20" y="-887.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="20" y="-876.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="20" y="-865.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
-<text text-anchor="start" x="20" y="-854.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
-<text text-anchor="start" x="20" y="-843.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
+<polygon fill="#ffffff" stroke="#000000" points="12,-1164.5 12,-1551.5 195,-1551.5 195,-1164.5 12,-1164.5"/>
+<text text-anchor="middle" x="103.5" y="-1539.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
+<polyline fill="none" stroke="#000000" points="12,-1532.5 195,-1532.5 "/>
+<text text-anchor="start" x="20" y="-1520.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<text text-anchor="start" x="20" y="-1509.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
+<text text-anchor="start" x="20" y="-1498.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
+<text text-anchor="start" x="20" y="-1487.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
+<text text-anchor="start" x="20" y="-1476.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
+<text text-anchor="start" x="20" y="-1465.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
+<text text-anchor="start" x="20" y="-1454.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
+<text text-anchor="start" x="20" y="-1443.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="20" y="-1432.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-1421.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
+<text text-anchor="start" x="20" y="-1410.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-1399.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
+<text text-anchor="start" x="20" y="-1388.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
+<text text-anchor="start" x="20" y="-1377.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
+<polyline fill="none" stroke="#000000" points="12,-1370.5 195,-1370.5 "/>
+<text text-anchor="start" x="20" y="-1358.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
+<text text-anchor="start" x="20" y="-1347.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
+<text text-anchor="start" x="20" y="-1336.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="20" y="-1325.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
+<text text-anchor="start" x="20" y="-1314.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="20" y="-1303.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1292.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1281.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-1270.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="20" y="-1259.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="20" y="-1248.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="20" y="-1237.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="20" y="-1226.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="20" y="-1215.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-1204.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-1193.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="20" y="-1182.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="20" y="-1171.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node7->Node6 -->
<g id="edge3" class="edge">
<title>Node7->Node6</title>
-<path fill="none" stroke="#191970" d="M103.5,-825.7972C103.5,-808.3163 103.5,-791.5917 103.5,-776.547"/>
-<polygon fill="none" stroke="#191970" points="100.0001,-826.1701 103.5,-836.1701 107.0001,-826.1701 100.0001,-826.1701"/>
+<path fill="none" stroke="#191970" d="M103.5,-1153.7972C103.5,-1136.3163 103.5,-1119.5917 103.5,-1104.547"/>
+<polygon fill="none" stroke="#191970" points="100.0001,-1154.1701 103.5,-1164.1701 107.0001,-1154.1701 100.0001,-1154.1701"/>
</g>
<!-- Node7->Node7 -->
<g id="edge4" class="edge">
<title>Node7->Node7</title>
-<path fill="none" stroke="#404040" d="M195.3625,-1063.9248C206.0482,-1057.6637 213,-1046.3555 213,-1030 213,-1019.0112 209.8618,-1010.3007 204.5615,-1003.8687"/>
-<polygon fill="none" stroke="#404040" points="204.5184,-1003.8322 197.3548,-1003.0056 195.3625,-996.0752 202.5261,-996.9017 204.5184,-1003.8322"/>
-<text text-anchor="middle" x="239" y="-1027.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
+<path fill="none" stroke="#404040" d="M195.3625,-1391.9248C206.0482,-1385.6637 213,-1374.3555 213,-1358 213,-1347.0112 209.8618,-1338.3007 204.5615,-1331.8687"/>
+<polygon fill="none" stroke="#404040" points="204.5184,-1331.8322 197.3548,-1331.0056 195.3625,-1324.0752 202.5261,-1324.9017 204.5184,-1331.8322"/>
+<text text-anchor="middle" x="239" y="-1355.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
</g>
<!-- Node8 -->
<g id="node5" class="node">
<title>Node8</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::tir::Stmt \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 25 more...\l}">
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-<text text-anchor="start" x="233" y="-786.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="281.5" y="-775.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
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-<text text-anchor="start" x="233" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-682.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-671.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="233" y="-638.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="233" y="-627.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
+<polygon fill="#ffffff" stroke="#000000" points="225,-948.5 225,-1126.5 338,-1126.5 338,-948.5 225,-948.5"/>
+<text text-anchor="start" x="233" y="-1114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="middle" x="281.5" y="-1103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
+<polyline fill="none" stroke="#000000" points="225,-1096.5 338,-1096.5 "/>
+<text text-anchor="middle" x="281.5" y="-1084.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="225,-1077.5 338,-1077.5 "/>
+<text text-anchor="start" x="233" y="-1065.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1054.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1043.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1032.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1021.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-1010.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-999.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-988.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-977.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="233" y="-966.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="233" y="-955.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
</a>
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<title>Node8->Node5</title>
-<path fill="none" stroke="#404040" d="M224.9775,-627.8327C221.9124,-625.0503 218.7508,-622.4227 215.5,-620 196.5625,-605.8863 179.5367,-620.9947 165.5,-602 133.2299,-558.3317 139.3986,-400.8187 145.1903,-317.8916"/>
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-<text text-anchor="middle" x="185.5" y="-504.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
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+<polygon fill="none" stroke="#404040" points="211.8384,-722.3024 206.6378,-717.307 209.2487,-710.5851 214.4493,-715.5805 211.8384,-722.3024"/>
+<text text-anchor="middle" x="278.5" y="-832.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
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<!-- Node9 -->
<g id="node6" class="node">
<title>Node9</title>
<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
-<polygon fill="#ffffff" stroke="#000000" points="370.5,-919 370.5,-1141 504.5,-1141 504.5,-919 370.5,-919"/>
-<text text-anchor="middle" x="437.5" y="-1129" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="370.5,-1122 504.5,-1122 "/>
-<text text-anchor="start" x="378.5" y="-1110" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
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-<text text-anchor="start" x="378.5" y="-1091" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="378.5" y="-1080" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="378.5" y="-1069" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="378.5" y="-1058" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="378.5" y="-1047" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
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-<text text-anchor="start" x="378.5" y="-1025" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="378.5" y="-1014" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
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-<text text-anchor="start" x="378.5" y="-970" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
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+<polygon fill="#ffffff" stroke="#000000" points="471.5,-1247 471.5,-1469 605.5,-1469 605.5,-1247 471.5,-1247"/>
+<text text-anchor="middle" x="538.5" y="-1457" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="471.5,-1450 605.5,-1450 "/>
+<text text-anchor="start" x="479.5" y="-1438" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="471.5,-1431 605.5,-1431 "/>
+<text text-anchor="start" x="479.5" y="-1419" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="479.5" y="-1408" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="479.5" y="-1397" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="479.5" y="-1386" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="479.5" y="-1375" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="479.5" y="-1364" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="479.5" y="-1353" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="479.5" y="-1342" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="479.5" y="-1331" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="479.5" y="-1320" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="479.5" y="-1309" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="479.5" y="-1298" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="479.5" y="-1287" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="479.5" y="-1276" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="479.5" y="-1265" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="479.5" y="-1254" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
</a>
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<title>Node9->Node8</title>
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-<polygon fill="none" stroke="#191970" points="375.853,-911.3445 383.3766,-918.8041 382.147,-908.2809 375.853,-911.3445"/>
+<path fill="none" stroke="#191970" d="M464.7035,-1271.0802C428.7688,-1228.17 385.2722,-1175.3537 347.5,-1127 344.4099,-1123.0443 341.2667,-1118.9689 338.1131,-1114.8389"/>
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-<g id="a_node8"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::PrimExpr \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 25 more...\l}">
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-<text text-anchor="start" x="223" y="-584" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="271.5" y="-573" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::PrimExpr ></text>
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-<text text-anchor="middle" x="271.5" y="-554" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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-<text text-anchor="start" x="223" y="-535" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="223" y="-524" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="223" y="-513" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="223" y="-502" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="223" y="-491" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="223" y="-480" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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-<text text-anchor="start" x="223" y="-458" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
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-<text text-anchor="start" x="223" y="-436" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="223" y="-425" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
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-<!-- Node9->Node11 -->
-<g id="edge9" class="edge">
-<title>Node9->Node11</title>
-<path fill="none" stroke="#191970" d="M425.8742,-908.538C414.2022,-824.1797 391.213,-711.03 346.5,-620 341.9089,-610.653 337.627,-610.4205 331.5,-602 330.1227,-600.1072 328.7428,-598.1831 327.3633,-596.2348"/>
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-<title>Node12</title>
-<g id="a_node9"><a xlink:href="classtvm_1_1tir_1_1Buffer.html" target="_top" xlink:title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">
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-<text text-anchor="start" x="341.5" y="-551.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Buffer()</text>
-<text text-anchor="start" x="341.5" y="-540.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ MakeStrideView()</text>
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-<text text-anchor="start" x="341.5" y="-419.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_COW_METHOD()</text>
+<!-- Node13 -->
+<g id="node10" class="node">
+<title>Node13</title>
+<g id="a_node10"><a xlink:href="classtvm_1_1BaseExpr.html" target="_top" xlink:title="Managed reference to BaseExprNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="356.5,-1003.5 356.5,-1071.5 510.5,-1071.5 510.5,-1003.5 356.5,-1003.5"/>
+<text text-anchor="middle" x="433.5" y="-1059.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::BaseExpr</text>
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+<text text-anchor="middle" x="433.5" y="-1040.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="356.5,-1033.5 510.5,-1033.5 "/>
+<text text-anchor="start" x="364.5" y="-1021.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="364.5" y="-1010.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
</a>
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-<!-- Node9->Node12 -->
+<!-- Node9->Node13 -->
<g id="edge11" class="edge">
-<title>Node9->Node12</title>
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+<path fill="none" stroke="#191970" d="M498.8576,-1236.9964C479.4677,-1177.811 457.5844,-1111.0148 444.698,-1071.6805"/>
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<!-- Node14 -->
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<title>Node14</title>
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+<text text-anchor="start" x="566.5" y="-529.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Map()</text>
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<g id="a_node13"><a xlink:href="classtvm_1_1runtime_1_1String.html" target="_top" xlink:title="Reference to string objects. ">
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<title>Node10</title>
<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode__inherit__graph.svg
@@ -21,7 +21,7 @@
<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ storage_scope</text>
<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ condition</text>
<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ annotations</text>
-<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
+<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer_var</text>
<text text-anchor="start" x="8" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
<polyline fill="none" stroke="#000000" points="0,-41.5 209,-41.5 "/>
<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode-members.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode-members.html
index 7128ab4e3a..7ebd9d110d 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode-members.html
@@ -79,38 +79,39 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af6aed95d70af7e44ce376a8d7be6c5f1">_type_index</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a05db88b0ab1b6f2d69f86a367c6403af">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a08e6e65098b35e2908e774de3ab970f3">AddCallback</a>(runtime::TypedPackedFunc< void()> callback)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">buffer</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#aa21f554fdcf6ab78c55cb54cdc69c0c2">callbacks</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af4407d2b59132e803ff791482dbe0145">deleter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#ab3aaced994da313ebabad6b955332f14">EnterWithScope</a>()</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a2c38ca68ceb49e11097dd80393e40da1">ExitWithScope</a>() final</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a9e84841ca982bff376a978ade0132631">FDeleter</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">GetOrAllocRuntimeTypeIndex</a>(const std::string &key, uint32_t static_tindex, uint32_t parent_tindex, uint32_t type_child_slots, bool type_child_slots_can_overflow)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">GetTypeKey</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a5693cbadcc1168b96db7b1cc5c200b86">GetTypeKeyHash</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ac9e5eed7719e322117bde996a171e33a">IncRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">Object</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ab7968feb6ad38ecaffc320e13819d826">Object</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#aa1612f69ea5b4225d4cda759cd517323">Object</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">operator=</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a55549a6c23987890246248682560a03d">RefCounterType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">stmts</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a4caa65dc8a549ed58fcb1d47964734d8">TVM_DECLARE_BASE_OBJECT_INFO</a>(TIRFrameNode, IRBuilderFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a290e180b9b57d3e133874fc0343a0453">tvm::script::ir_builder::IRBuilderFrameNode::TVM_DECLARE_BASE_OBJECT_INFO</a>(IRBuilderFrameNode, runtime::Object)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a6c8c6cc5c627cd29cc5eea3f4a6c8d80">TVM_DECLARE_FINAL_OBJECT_INFO</a>(DeclBufferFrameNode, TIRFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4bfc2586cb55f2af47728187b3256255">type_index_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</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_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a5284f329953ea259af3028144968fdcd">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a8e61799eac4e806aef5a87d07943883d">~IRBuilderFrameNode</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a71f494b77cd89321bcd2d9bb7c7835e0">allocated</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">buffer</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#aa21f554fdcf6ab78c55cb54cdc69c0c2">callbacks</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af4407d2b59132e803ff791482dbe0145">deleter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#ab3aaced994da313ebabad6b955332f14">EnterWithScope</a>()</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a2c38ca68ceb49e11097dd80393e40da1">ExitWithScope</a>() final</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a9e84841ca982bff376a978ade0132631">FDeleter</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">GetOrAllocRuntimeTypeIndex</a>(const std::string &key, uint32_t static_tindex, uint32_t parent_tindex, uint32_t type_child_slots, bool type_child_slots_can_overflow)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">static</span [...]
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">GetTypeKey</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a5693cbadcc1168b96db7b1cc5c200b86">GetTypeKeyHash</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ac9e5eed7719e322117bde996a171e33a">IncRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a90e90b3f4ba8a590baff78c75807bbc7">IsInstance</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">Object</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ab7968feb6ad38ecaffc320e13819d826">Object</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#aa1612f69ea5b4225d4cda759cd517323">Object</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a69c32fbd96181f5c21d2c878ab285e4f">operator=</a>(const Object &other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a55549a6c23987890246248682560a03d">RefCounterType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">stmts</a></td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a4caa65dc8a549ed58fcb1d47964734d8">TVM_DECLARE_BASE_OBJECT_INFO</a>(TIRFrameNode, IRBuilderFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#a290e180b9b57d3e133874fc0343a0453">tvm::script::ir_builder::IRBuilderFrameNode::TVM_DECLARE_BASE_OBJECT_INFO</a>(IRBuilderFrameNode, runtime::Object)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a6c8c6cc5c627cd29cc5eea3f4a6c8d80">TVM_DECLARE_FINAL_OBJECT_INFO</a>(DeclBufferFrameNode, TIRFrameNode)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a4bfc2586cb55f2af47728187b3256255">type_index_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">TypeIndex2Key</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6ee32a02dd44257da105fbbe5d9c8622">TypeIndex2KeyHash</a>(uint32_t tindex)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a6841f97e06e6614dd7e82c6dd41b818a">TypeKey2Index</a>(const std::string &key)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#afd548730a6139d19fe24473ad66026d7">unique</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a5284f329953ea259af3028144968fdcd">VisitAttrs</a>(tvm::AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html">tvm::script::ir_builder::tir::DeclBufferFrameNode</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_1ir__builder_1_1IRBuilderFrameNode.html#a8e61799eac4e806aef5a87d07943883d">~IRBuilderFrameNode</a>()=default</td><td class="entry"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html">tvm::script::ir_builder::IRBuilderFrameNode</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html
index 6db42229d7..ef6de9c43a 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html
@@ -76,13 +76,13 @@ $(function() {
<div class="dynheader">
Inheritance diagram for tvm::script::ir_builder::tir::DeclBufferFrameNode:</div>
<div class="dyncontent">
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+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__inherit__graph.svg" width="290" height="1139"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<div class="dynheader">
Collaboration diagram for tvm::script::ir_builder::tir::DeclBufferFrameNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__coll__graph.svg" width="603" height="1696"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__coll__graph.svg" width="603" height="1711"><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">
@@ -140,7 +140,11 @@ Public Member Functions</h2></td></tr>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a573cf6a08a4d6c55548308b77469b3fc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">tvm::tir::Buffer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">buffer</a></td></tr>
+<tr class="memdesc:a573cf6a08a4d6c55548308b77469b3fc"><td class="mdescLeft"> </td><td class="mdescRight">The declared buffer. <a href="#a573cf6a08a4d6c55548308b77469b3fc">More...</a><br /></td></tr>
<tr class="separator:a573cf6a08a4d6c55548308b77469b3fc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a71f494b77cd89321bcd2d9bb7c7835e0"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a71f494b77cd89321bcd2d9bb7c7835e0">allocated</a></td></tr>
+<tr class="memdesc:a71f494b77cd89321bcd2d9bb7c7835e0"><td class="mdescLeft"> </td><td class="mdescRight">The buffer allocated or not. <a href="#a71f494b77cd89321bcd2d9bb7c7835e0">More...</a><br /></td></tr>
+<tr class="separator:a71f494b77cd89321bcd2d9bb7c7835e0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td colspan="2" onclick="javascript:toggleInherit('pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode')"><img src="closed.png" alt="-"/> Public Attributes inherited from <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></td></tr>
<tr class="memitem:a13776bb5c2e5403138fbee06d4fdad40 inherit pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1tir_1_1Stmt.html">tvm::tir::Stmt</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40" [...]
<tr class="memdesc:a13776bb5c2e5403138fbee06d4fdad40 inherit pub_attribs_classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode"><td class="mdescLeft"> </td><td class="mdescRight">The Stmt within in this frame. <a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a13776bb5c2e5403138fbee06d4fdad40">More...</a><br /></td></tr>
@@ -328,6 +332,22 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
+</div>
+</div>
+<a id="a71f494b77cd89321bcd2d9bb7c7835e0"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a71f494b77cd89321bcd2d9bb7c7835e0">◆ </a></span>allocated</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">bool tvm::script::ir_builder::tir::DeclBufferFrameNode::allocated</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>The buffer allocated or not. </p>
+
</div>
</div>
<a id="a573cf6a08a4d6c55548308b77469b3fc"></a>
@@ -342,6 +362,8 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
+<p>The declared buffer. </p>
+
</div>
</div>
<hr/>The documentation for this class was generated from the following file:<ul>
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+<polyline fill="none" stroke="#000000" points="0,-571.5 207,-571.5 "/>
+<text text-anchor="start" x="8" y="-559.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ callbacks</text>
+<text text-anchor="start" x="8" y="-548.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="0,-541.5 207,-541.5 "/>
+<text text-anchor="start" x="8" y="-529.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="8" y="-518.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
+<text text-anchor="start" x="8" y="-507.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~IRBuilderFrameNode()</text>
+<text text-anchor="start" x="8" y="-496.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ EnterWithScope()</text>
+<text text-anchor="start" x="8" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
+<text text-anchor="start" x="8" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ AddCallback()</text>
</a>
</g>
</g>
<!-- Node6->Node5 -->
<g id="edge2" class="edge">
<title>Node6->Node5</title>
-<path fill="none" stroke="#191970" d="M130.1248,-446.5479C155.5087,-373.1821 192.7332,-265.5942 212.675,-207.9576"/>
-<polygon fill="none" stroke="#191970" points="126.7421,-445.6205 126.78,-456.2152 133.3574,-447.9093 126.7421,-445.6205"/>
+<path fill="none" stroke="#191970" d="M130.1248,-457.5479C155.5087,-384.1821 192.7332,-276.5942 212.675,-218.9576"/>
+<polygon fill="none" stroke="#191970" points="126.7421,-456.6205 126.78,-467.2152 133.3574,-458.9093 126.7421,-456.6205"/>
</g>
<!-- Node7 -->
<g id="node4" class="node">
<title>Node7</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1Object.html" target="_top" xlink:title="base class of all object containers. ">
-<polygon fill="#ffffff" stroke="#000000" points="12,-650.5 12,-1037.5 195,-1037.5 195,-650.5 12,-650.5"/>
-<text text-anchor="middle" x="103.5" y="-1025.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
-<polyline fill="none" stroke="#000000" points="12,-1018.5 195,-1018.5 "/>
-<text text-anchor="start" x="20" y="-1006.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<text text-anchor="start" x="20" y="-995.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
-<text text-anchor="start" x="20" y="-984.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
-<text text-anchor="start" x="20" y="-973.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
-<text text-anchor="start" x="20" y="-962.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
-<text text-anchor="start" x="20" y="-951.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
-<text text-anchor="start" x="20" y="-940.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
-<text text-anchor="start" x="20" y="-929.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
-<text text-anchor="start" x="20" y="-918.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="20" y="-907.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
-<text text-anchor="start" x="20" y="-896.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="20" y="-885.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
-<text text-anchor="start" x="20" y="-874.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
-<text text-anchor="start" x="20" y="-863.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
-<polyline fill="none" stroke="#000000" points="12,-856.5 195,-856.5 "/>
-<text text-anchor="start" x="20" y="-844.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
-<text text-anchor="start" x="20" y="-833.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
-<text text-anchor="start" x="20" y="-822.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
-<text text-anchor="start" x="20" y="-811.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
-<text text-anchor="start" x="20" y="-800.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<polygon fill="#ffffff" stroke="#000000" points="12,-661.5 12,-1048.5 195,-1048.5 195,-661.5 12,-661.5"/>
+<text text-anchor="middle" x="103.5" y="-1036.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
+<polyline fill="none" stroke="#000000" points="12,-1029.5 195,-1029.5 "/>
+<text text-anchor="start" x="20" y="-1017.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<text text-anchor="start" x="20" y="-1006.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
+<text text-anchor="start" x="20" y="-995.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
+<text text-anchor="start" x="20" y="-984.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
+<text text-anchor="start" x="20" y="-973.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
+<text text-anchor="start" x="20" y="-962.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
+<text text-anchor="start" x="20" y="-951.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
+<text text-anchor="start" x="20" y="-940.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="20" y="-929.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-918.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
+<text text-anchor="start" x="20" y="-907.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="20" y="-896.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
+<text text-anchor="start" x="20" y="-885.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
+<text text-anchor="start" x="20" y="-874.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
+<polyline fill="none" stroke="#000000" points="12,-867.5 195,-867.5 "/>
+<text text-anchor="start" x="20" y="-855.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
+<text text-anchor="start" x="20" y="-844.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
+<text text-anchor="start" x="20" y="-833.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="20" y="-822.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
+<text text-anchor="start" x="20" y="-811.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="20" y="-800.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
<text text-anchor="start" x="20" y="-789.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
<text text-anchor="start" x="20" y="-778.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
-<text text-anchor="start" x="20" y="-767.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="20" y="-767.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
<text text-anchor="start" x="20" y="-756.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="20" y="-745.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="20" y="-734.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="20" y="-723.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="20" y="-712.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
-<text text-anchor="start" x="20" y="-701.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="20" y="-690.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="20" y="-679.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
-<text text-anchor="start" x="20" y="-668.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
-<text text-anchor="start" x="20" y="-657.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-745.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="20" y="-734.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="20" y="-723.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="20" y="-712.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-701.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="20" y="-690.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="20" y="-679.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="20" y="-668.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node7->Node6 -->
<g id="edge3" class="edge">
<title>Node7->Node6</title>
-<path fill="none" stroke="#191970" d="M103.5,-639.7972C103.5,-622.3163 103.5,-605.5917 103.5,-590.547"/>
-<polygon fill="none" stroke="#191970" points="100.0001,-640.1701 103.5,-650.1701 107.0001,-640.1701 100.0001,-640.1701"/>
+<path fill="none" stroke="#191970" d="M103.5,-650.7972C103.5,-633.3163 103.5,-616.5917 103.5,-601.547"/>
+<polygon fill="none" stroke="#191970" points="100.0001,-651.1701 103.5,-661.1701 107.0001,-651.1701 100.0001,-651.1701"/>
</g>
<!-- Node7->Node7 -->
<g id="edge4" class="edge">
<title>Node7->Node7</title>
-<path fill="none" stroke="#404040" d="M195.3625,-877.9248C206.0482,-871.6637 213,-860.3555 213,-844 213,-833.0112 209.8618,-824.3007 204.5615,-817.8687"/>
-<polygon fill="none" stroke="#404040" points="204.5184,-817.8322 197.3548,-817.0056 195.3625,-810.0752 202.5261,-810.9017 204.5184,-817.8322"/>
-<text text-anchor="middle" x="239" y="-841.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
+<path fill="none" stroke="#404040" d="M195.3625,-888.9248C206.0482,-882.6637 213,-871.3555 213,-855 213,-844.0112 209.8618,-835.3007 204.5615,-828.8687"/>
+<polygon fill="none" stroke="#404040" points="204.5184,-828.8322 197.3548,-828.0056 195.3625,-821.0752 202.5261,-821.9017 204.5184,-828.8322"/>
+<text text-anchor="middle" x="239" y="-852.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #deleter_</text>
</g>
<!-- Node8 -->
<g id="node5" class="node">
<title>Node8</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::tir::Stmt \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 25 more...\l}">
-<polygon fill="#ffffff" stroke="#000000" points="225,-434.5 225,-612.5 338,-612.5 338,-434.5 225,-434.5"/>
-<text text-anchor="start" x="233" y="-600.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="281.5" y="-589.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
-<polyline fill="none" stroke="#000000" points="225,-582.5 338,-582.5 "/>
-<text text-anchor="middle" x="281.5" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="225,-563.5 338,-563.5 "/>
+<polygon fill="#ffffff" stroke="#000000" points="225,-445.5 225,-623.5 338,-623.5 338,-445.5 225,-445.5"/>
+<text text-anchor="start" x="233" y="-611.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
+<text text-anchor="middle" x="281.5" y="-600.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::tir::Stmt ></text>
+<polyline fill="none" stroke="#000000" points="225,-593.5 338,-593.5 "/>
+<text text-anchor="middle" x="281.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="225,-574.5 338,-574.5 "/>
+<text text-anchor="start" x="233" y="-562.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="233" y="-551.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="233" y="-540.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="233" y="-529.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
@@ -142,122 +144,121 @@
<text text-anchor="start" x="233" y="-507.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="233" y="-496.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
<text text-anchor="start" x="233" y="-485.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="233" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
+<text text-anchor="start" x="233" y="-474.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
<text text-anchor="start" x="233" y="-463.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="233" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="233" y="-441.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
+<text text-anchor="start" x="233" y="-452.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 25 more...</text>
</a>
</g>
</g>
<!-- Node8->Node5 -->
<g id="edge5" class="edge">
<title>Node8->Node5</title>
-<path fill="none" stroke="#404040" d="M245.1547,-434.3806C243.3742,-428.2209 241.7931,-422.0556 240.5,-416 226.262,-349.3234 224.2706,-270.4138 224.8297,-219.8123"/>
-<polygon fill="none" stroke="#404040" points="224.8332,-219.5806 220.9275,-213.5188 225.0209,-207.5821 228.9265,-213.6439 224.8332,-219.5806"/>
-<text text-anchor="middle" x="260.5" y="-318.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
+<path fill="none" stroke="#404040" d="M245.1547,-445.3806C243.3742,-439.2209 241.7931,-433.0556 240.5,-427 226.262,-360.3234 224.2706,-281.4138 224.8297,-230.8123"/>
+<polygon fill="none" stroke="#404040" points="224.8332,-230.5806 220.9275,-224.5188 225.0209,-218.5821 228.9265,-224.6439 224.8332,-230.5806"/>
+<text text-anchor="middle" x="260.5" y="-329.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +stmts</text>
</g>
<!-- Node9 -->
<g id="node6" class="node">
<title>Node9</title>
<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
-<polygon fill="#ffffff" stroke="#000000" points="291.5,-733 291.5,-955 425.5,-955 425.5,-733 291.5,-733"/>
-<text text-anchor="middle" x="358.5" y="-943" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="291.5,-936 425.5,-936 "/>
-<text text-anchor="start" x="299.5" y="-924" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="291.5,-917 425.5,-917 "/>
+<polygon fill="#ffffff" stroke="#000000" points="291.5,-744 291.5,-966 425.5,-966 425.5,-744 291.5,-744"/>
+<text text-anchor="middle" x="358.5" y="-954" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="291.5,-947 425.5,-947 "/>
+<text text-anchor="start" x="299.5" y="-935" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="291.5,-928 425.5,-928 "/>
+<text text-anchor="start" x="299.5" y="-916" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
<text text-anchor="start" x="299.5" y="-905" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="299.5" y="-894" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="299.5" y="-883" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="299.5" y="-872" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="299.5" y="-861" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="299.5" y="-850" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="299.5" y="-839" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="299.5" y="-828" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="299.5" y="-817" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="299.5" y="-806" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
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+<text text-anchor="start" x="299.5" y="-894" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="299.5" y="-883" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="299.5" y="-872" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="299.5" y="-861" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
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+<text text-anchor="start" x="299.5" y="-828" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="299.5" y="-817" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="299.5" y="-806" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="299.5" y="-795" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="299.5" y="-784" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="299.5" y="-773" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="299.5" y="-762" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="299.5" y="-751" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
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-<text text-anchor="start" x="297.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ vload()</text>
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-<text text-anchor="start" x="297.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
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+<text text-anchor="middle" x="366.5" y="-414.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::tir::Buffer</text>
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+<text text-anchor="middle" x="366.5" y="-395.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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+<text text-anchor="start" x="297.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Buffer()</text>
+<text text-anchor="start" x="297.5" y="-365.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ MakeStrideView()</text>
+<text text-anchor="start" x="297.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ MakeSlice()</text>
+<text text-anchor="start" x="297.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ access_ptr()</text>
+<text text-anchor="start" x="297.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ vload()</text>
+<text text-anchor="start" x="297.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ vstore()</text>
+<text text-anchor="start" x="297.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetFlattenedBuffer()</text>
+<text text-anchor="start" x="297.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ OffsetOf()</text>
+<text text-anchor="start" x="297.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ scope()</text>
+<text text-anchor="start" x="297.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="297.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_METHODS()</text>
+<text text-anchor="start" x="297.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_OBJECT_REF</text>
+<text text-anchor="start" x="297.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_COW_METHOD()</text>
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<title>Node9->Node11</title>
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<title>Node10</title>
<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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+<text text-anchor="start" x="296.5" y="-1213.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="296.5" y="-1202.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="296.5" y="-1191.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="296.5" y="-1180.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="296.5" y="-1169.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
<text text-anchor="start" x="296.5" y="-1158.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="296.5" y="-1147.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="296.5" y="-1136.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
-<text text-anchor="start" x="296.5" y="-1125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
-<text text-anchor="start" x="296.5" y="-1114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="296.5" y="-1103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="296.5" y="-1092.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
+<text text-anchor="start" x="296.5" y="-1147.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
+<text text-anchor="start" x="296.5" y="-1136.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
+<text text-anchor="start" x="296.5" y="-1125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="296.5" y="-1114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="296.5" y="-1103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
</a>
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<!-- Node10->Node9 -->
<g id="edge7" class="edge">
<title>Node10->Node9</title>
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-<text text-anchor="middle" x="378" y="-1059" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
+<path fill="none" stroke="#404040" d="M358.5,-1096.4973C358.5,-1060.1115 358.5,-1017.409 358.5,-978.1129"/>
+<polygon fill="none" stroke="#404040" points="358.5001,-978.0376 354.5,-972.0377 358.5,-966.0376 362.5,-972.0376 358.5001,-978.0376"/>
+<text text-anchor="middle" x="378" y="-1070" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
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+<path fill="none" stroke="#404040" d="M359.2975,-237.4483C355.2972,-205.6935 349.0967,-170.3838 339.5,-139 336.8379,-130.2941 333.4085,-121.3806 329.6492,-112.7273"/>
+<polygon fill="none" stroke="#404040" points="329.5281,-112.4612 323.4009,-108.659 324.554,-101.5407 330.6813,-105.3429 329.5281,-112.4612"/>
+<text text-anchor="middle" x="376.5" y="-176.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +buffer</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__inherit__graph.svg
index 254b3424ad..d08af4c51c 100644
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+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode__inherit__graph.svg
@@ -4,19 +4,20 @@
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-->
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<title>tvm::script::ir_builder::tir::DeclBufferFrameNode</title>
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<!-- Node0 -->
<g id="node1" class="node">
<title>Node0</title>
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+<polygon fill="#bfbfbf" stroke="#000000" points="0,-.5 0,-112.5 209,-112.5 209,-.5 0,-.5"/>
+<text text-anchor="start" x="8" y="-100.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="104.5" y="-89.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::DeclBufferFrameNode</text>
+<polyline fill="none" stroke="#000000" points="0,-82.5 209,-82.5 "/>
+<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
+<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ allocated</text>
<text text-anchor="start" x="8" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
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<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
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-<text text-anchor="start" x="9" y="-145.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
+<polygon fill="#ffffff" stroke="#000000" points="1,-149.5 1,-239.5 208,-239.5 208,-149.5 1,-149.5"/>
+<text text-anchor="start" x="9" y="-227.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="104.5" y="-216.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::TIRFrameNode</text>
+<polyline fill="none" stroke="#000000" points="1,-209.5 208,-209.5 "/>
+<text text-anchor="start" x="9" y="-197.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ stmts</text>
+<text text-anchor="start" x="9" y="-186.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="1,-179.5 208,-179.5 "/>
+<text text-anchor="start" x="9" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="9" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
</a>
</g>
</g>
<!-- Node1->Node0 -->
<g id="edge1" class="edge">
<title>Node1->Node0</title>
-<path fill="none" stroke="#191970" d="M104.5,-128.2098C104.5,-119.5051 104.5,-110.5409 104.5,-101.9407"/>
-<polygon fill="none" stroke="#191970" points="101.0001,-128.2716 104.5,-138.2716 108.0001,-128.2716 101.0001,-128.2716"/>
+<path fill="none" stroke="#191970" d="M104.5,-139.2882C104.5,-130.5993 104.5,-121.6063 104.5,-112.8911"/>
+<polygon fill="none" stroke="#191970" points="101.0001,-139.3136 104.5,-149.3136 108.0001,-139.3137 101.0001,-139.3136"/>
</g>
<!-- Node2 -->
<g id="node3" class="node">
<title>Node2</title>
<g id="a_node3"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html" target="_top" xlink:title="{tvm::script::ir_builder\l::IRBuilderFrameNode\n|+ callbacks\l+ _type_key\l|+ VisitAttrs()\l+ TVM_DECLARE_BASE_OBJECT_INFO()\l+ ~IRBuilderFrameNode()\l+ EnterWithScope()\l+ ExitWithScope()\l+ AddCallback()\l}">
-<polygon fill="#ffffff" stroke="#000000" points="1,-265.5 1,-399.5 208,-399.5 208,-265.5 1,-265.5"/>
-<text text-anchor="start" x="9" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="104.5" y="-376.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::IRBuilderFrameNode</text>
-<polyline fill="none" stroke="#000000" points="1,-369.5 208,-369.5 "/>
-<text text-anchor="start" x="9" y="-357.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ callbacks</text>
-<text text-anchor="start" x="9" y="-346.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<polyline fill="none" stroke="#000000" points="1,-339.5 208,-339.5 "/>
-<text text-anchor="start" x="9" y="-327.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
-<text text-anchor="start" x="9" y="-316.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
-<text text-anchor="start" x="9" y="-305.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~IRBuilderFrameNode()</text>
-<text text-anchor="start" x="9" y="-294.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ EnterWithScope()</text>
-<text text-anchor="start" x="9" y="-283.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
-<text text-anchor="start" x="9" y="-272.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ AddCallback()</text>
+<polygon fill="#ffffff" stroke="#000000" points="1,-276.5 1,-410.5 208,-410.5 208,-276.5 1,-276.5"/>
+<text text-anchor="start" x="9" y="-398.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
+<text text-anchor="middle" x="104.5" y="-387.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::IRBuilderFrameNode</text>
+<polyline fill="none" stroke="#000000" points="1,-380.5 208,-380.5 "/>
+<text text-anchor="start" x="9" y="-368.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ callbacks</text>
+<text text-anchor="start" x="9" y="-357.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<polyline fill="none" stroke="#000000" points="1,-350.5 208,-350.5 "/>
+<text text-anchor="start" x="9" y="-338.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
+<text text-anchor="start" x="9" y="-327.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_BASE_OBJECT_INFO()</text>
+<text text-anchor="start" x="9" y="-316.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~IRBuilderFrameNode()</text>
+<text text-anchor="start" x="9" y="-305.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ EnterWithScope()</text>
+<text text-anchor="start" x="9" y="-294.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ExitWithScope()</text>
+<text text-anchor="start" x="9" y="-283.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ AddCallback()</text>
</a>
</g>
</g>
<!-- Node2->Node1 -->
<g id="edge2" class="edge">
<title>Node2->Node1</title>
-<path fill="none" stroke="#191970" d="M104.5,-255.2384C104.5,-246.2246 104.5,-237.2648 104.5,-228.8878"/>
-<polygon fill="none" stroke="#191970" points="101.0001,-255.41 104.5,-265.41 108.0001,-255.41 101.0001,-255.41"/>
+<path fill="none" stroke="#191970" d="M104.5,-266.2384C104.5,-257.2246 104.5,-248.2648 104.5,-239.8878"/>
+<polygon fill="none" stroke="#191970" points="101.0001,-266.41 104.5,-276.41 108.0001,-266.41 101.0001,-266.41"/>
</g>
<!-- Node3 -->
<g id="node4" class="node">
<title>Node3</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1Object.html" target="_top" xlink:title="base class of all object containers. ">
-<polygon fill="#ffffff" stroke="#000000" points="13,-436.5 13,-834.5 196,-834.5 196,-436.5 13,-436.5"/>
-<text text-anchor="middle" x="104.5" y="-822.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
-<polyline fill="none" stroke="#000000" points="13,-815.5 196,-815.5 "/>
-<text text-anchor="start" x="21" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
-<text text-anchor="start" x="21" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
-<text text-anchor="start" x="21" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
-<text text-anchor="start" x="21" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
-<text text-anchor="start" x="21" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
-<text text-anchor="start" x="21" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
-<text text-anchor="start" x="21" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
-<text text-anchor="start" x="21" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
-<text text-anchor="start" x="21" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="21" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
-<text text-anchor="start" x="21" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
-<text text-anchor="start" x="21" y="-682.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
-<text text-anchor="start" x="21" y="-671.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
-<text text-anchor="start" x="21" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
-<text text-anchor="start" x="21" y="-649.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># deleter_</text>
-<polyline fill="none" stroke="#000000" points="13,-642.5 196,-642.5 "/>
-<text text-anchor="start" x="21" y="-630.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
-<text text-anchor="start" x="21" y="-619.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
-<text text-anchor="start" x="21" y="-608.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
-<text text-anchor="start" x="21" y="-597.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
-<text text-anchor="start" x="21" y="-586.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<polygon fill="#ffffff" stroke="#000000" points="13,-447.5 13,-845.5 196,-845.5 196,-447.5 13,-447.5"/>
+<text text-anchor="middle" x="104.5" y="-833.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Object</text>
+<polyline fill="none" stroke="#000000" points="13,-826.5 196,-826.5 "/>
+<text text-anchor="start" x="21" y="-814.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
+<text text-anchor="start" x="21" y="-803.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_final</text>
+<text text-anchor="start" x="21" y="-792.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots</text>
+<text text-anchor="start" x="21" y="-781.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_child_slots_can</text>
+<text text-anchor="start" x="21" y="-770.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_overflow</text>
+<text text-anchor="start" x="21" y="-759.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_visit</text>
+<text text-anchor="start" x="21" y="-748.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_attrs</text>
+<text text-anchor="start" x="21" y="-737.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_sequal</text>
+<text text-anchor="start" x="21" y="-726.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="21" y="-715.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_has_method_shash</text>
+<text text-anchor="start" x="21" y="-704.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_reduce</text>
+<text text-anchor="start" x="21" y="-693.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_index</text>
+<text text-anchor="start" x="21" y="-682.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># type_index_</text>
+<text text-anchor="start" x="21" y="-671.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># ref_counter_</text>
+<text text-anchor="start" x="21" y="-660.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># deleter_</text>
+<polyline fill="none" stroke="#000000" points="13,-653.5 196,-653.5 "/>
+<text text-anchor="start" x="21" y="-641.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ type_index()</text>
+<text text-anchor="start" x="21" y="-630.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKey()</text>
+<text text-anchor="start" x="21" y="-619.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ GetTypeKeyHash()</text>
+<text text-anchor="start" x="21" y="-608.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ IsInstance()</text>
+<text text-anchor="start" x="21" y="-597.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="21" y="-586.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
<text text-anchor="start" x="21" y="-575.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
<text text-anchor="start" x="21" y="-564.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
-<text text-anchor="start" x="21" y="-553.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Object()</text>
+<text text-anchor="start" x="21" y="-553.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
<text text-anchor="start" x="21" y="-542.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="21" y="-531.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="21" y="-520.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
-<text text-anchor="start" x="21" y="-509.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
-<text text-anchor="start" x="21" y="-498.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
-<text text-anchor="start" x="21" y="-487.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
-<text text-anchor="start" x="21" y="-476.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
-<text text-anchor="start" x="21" y="-465.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
-<text text-anchor="start" x="21" y="-454.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
-<text text-anchor="start" x="21" y="-443.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="21" y="-531.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2Key()</text>
+<text text-anchor="start" x="21" y="-520.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeIndex2KeyHash()</text>
+<text text-anchor="start" x="21" y="-509.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TypeKey2Index()</text>
+<text text-anchor="start" x="21" y="-498.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _GetOrAllocRuntimeTypeIndex()</text>
+<text text-anchor="start" x="21" y="-487.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ RuntimeTypeIndex()</text>
+<text text-anchor="start" x="21" y="-476.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># IncRef()</text>
+<text text-anchor="start" x="21" y="-465.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DecRef()</text>
+<text text-anchor="start" x="21" y="-454.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetOrAllocRuntimeTypeIndex()</text>
</a>
</g>
</g>
<!-- Node3->Node2 -->
<g id="edge3" class="edge">
<title>Node3->Node2</title>
-<path fill="none" stroke="#191970" d="M104.5,-426.1914C104.5,-416.9431 104.5,-408.0329 104.5,-399.631"/>
-<polygon fill="none" stroke="#191970" points="101.0001,-426.3515 104.5,-436.3515 108.0001,-426.3515 101.0001,-426.3515"/>
+<path fill="none" stroke="#191970" d="M104.5,-437.1914C104.5,-427.9431 104.5,-419.0329 104.5,-410.631"/>
+<polygon fill="none" stroke="#191970" points="101.0001,-437.3515 104.5,-447.3515 108.0001,-437.3515 101.0001,-437.3515"/>
</g>
</g>
</svg>
diff --git a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
index f22bb5bac1..23743f0212 100644
--- a/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode__inherit__graph.svg
@@ -33,7 +33,7 @@
<text text-anchor="start" x="8" y="-136.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ dtype</text>
<text text-anchor="start" x="8" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ extents</text>
<text text-anchor="start" x="8" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ data</text>
-<text text-anchor="start" x="8" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
+<text text-anchor="start" x="8" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer_var</text>
<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ annotations</text>
<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
<polyline fill="none" stroke="#000000" points="0,-74.5 209,-74.5 "/>
@@ -62,7 +62,7 @@
<text text-anchor="start" x="235" y="-120" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ storage_scope</text>
<text text-anchor="start" x="235" y="-109" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ condition</text>
<text text-anchor="start" x="235" y="-98" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ annotations</text>
-<text text-anchor="start" x="235" y="-87" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
+<text text-anchor="start" x="235" y="-87" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer_var</text>
<text text-anchor="start" x="235" y="-76" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
<polyline fill="none" stroke="#000000" points="227,-69 436,-69 "/>
<text text-anchor="start" x="235" y="-57" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
@@ -185,24 +185,25 @@
<!-- Node9 -->
<g id="node10" class="node">
<title>Node9</title>
-<g id="a_node10"><a xlink:href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html" target="_top" xlink:title="{tvm::script::ir_builder\l::tir::DeclBufferFrameNode\n|+ buffer\l+ _type_key\l|+ VisitAttrs()\l+ TVM_DECLARE_FINAL_OBJECT_INFO()\l+ ExitWithScope()\l}">
-<polygon fill="#ffffff" stroke="#000000" points="1362,-55.5 1362,-156.5 1571,-156.5 1571,-55.5 1362,-55.5"/>
-<text text-anchor="start" x="1370" y="-144.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::script::ir_builder</text>
-<text text-anchor="middle" x="1466.5" y="-133.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::tir::DeclBufferFrameNode</text>
-<polyline fill="none" stroke="#000000" points="1362,-126.5 1571,-126.5 "/>
-<text text-anchor="start" x="1370" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ buffer</text>
-<text text-anchor="start" x="1370" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_key</text>
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-<text text-anchor="start" x="1370" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ VisitAttrs()</text>
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+<text text-anchor="start" x="1370" y="-68" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DECLARE_FINAL_OBJECT_INFO()</text>
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diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index ce96c66e8d..6f9567c677 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -195,6 +195,9 @@ $(function() {
<li>AllocateConst()
: <a class="el" href="classtvm_1_1tir_1_1AllocateConst.html#ac7cde2c6f98d30492596fd401425e8bc">tvm::tir::AllocateConst</a>
</li>
+<li>allocated
+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a71f494b77cd89321bcd2d9bb7c7835e0">tvm::script::ir_builder::tir::DeclBufferFrameNode</a>
+</li>
<li>allocated_size
: <a class="el" href="structtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfoNode.html#ae22f04d799f7cc35273d85fd3d9851fd">tvm::tir::usmp::AllocatedPoolInfoNode</a>
</li>
@@ -380,7 +383,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a360b264ed892e620935b648e5a91a5ea">tvm::runtime::vm::Instruction</a>
</li>
<li>Array()
-: <a class="el" href="classtvm_1_1runtime_1_1Array.html#aacdeb14d66a7f85e94ee843c459f7959">tvm::runtime::Array< T, typename ></a>
+: <a class="el" href="classtvm_1_1runtime_1_1Array.html#af5035bdfd76577c89f9fcde46db7edc4">tvm::runtime::Array< T, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html#a23070656da6784a7e4c33c4b0ea9de35">tvm::runtime::ArrayNode</a>
</li>
<li>array
@@ -466,7 +469,7 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map< K, V, typename, typename ></a>
, <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html#a07d50937020663f46ce9b1f31f066a7a">tvm::runtime::ShapeTuple</a>
-, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#a0593c84ceb05afb1a3f87045a3dc3a59">tvm::runtime::SmallMapNode</a>
+, <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html#a866679f23f724edc2d165f530f058b09">tvm::runtime::SmallMapNode</a>
, <a class="el" href="classtvm_1_1runtime_1_1String.html#aaeda6a88310d41a22ce884fb1570b0d2">tvm::runtime::String</a>
, <a class="el" href="classtvm_1_1TracedMap.html#a886126f30f9b9329b9bcc1f63d62c4a4">tvm::TracedMap< K, V ></a>
</li>
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: <a class="el" href="classtvm_1_1AttributeAccessPathNode.html#a8f1bf5db4a1495e454b196856fd5cf21">tvm::AttributeAccessPathNode</a>
</li>
<li>AttrInitEntry()
-: <a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html#af07c4a3a8f4663ac03ae238ab7b9d791">tvm::detail::AttrInitEntry< T ></a>
+: <a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html#ad68ac350b0d49e97caab8443cc8fb08b">tvm::detail::AttrInitEntry< T ></a>
</li>
<li>AttrInitVisitor()
: <a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html#ac3c800c9249fee195db2a5fa473fe960">tvm::detail::AttrInitVisitor< FFind ></a>
diff --git a/docs/reference/api/doxygen/functions_b.html b/docs/reference/api/doxygen/functions_b.html
index 70d677ab24..d98840ab9d 100644
--- a/docs/reference/api/doxygen/functions_b.html
+++ b/docs/reference/api/doxygen/functions_b.html
@@ -235,8 +235,6 @@ $(function() {
</li>
<li>buffer
: <a class="el" href="classtvm_1_1runtime_1_1vm_1_1StorageObj.html#ae787a0a25d67ff1952ac5e9eeb0d1a61">tvm::runtime::vm::StorageObj</a>
-, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a9334403129924bc1ed84d8753dd9f672">tvm::script::ir_builder::tir::AllocateConstFrameNode</a>
-, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ad7365c7b511a9f9dffc1aac531d046c2">tvm::script::ir_builder::tir::AllocateFrameNode</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">tvm::script::ir_builder::tir::DeclBufferFrameNode</a>
</li>
<li>Buffer()
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: <a class="el" href="classtvm_1_1tir_1_1BufferNode.html#a2ce595dfcf7a726c7e0f86a6942d2568">tvm::tir::BufferNode</a>
</li>
<li>buffer_var
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+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4b062ec7c2f519922051de6cfffd9762">tvm::script::ir_builder::tir::AllocateConstFrameNode</a>
+, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a22c42fc2abf33a4675850ae3ec826aec">tvm::script::ir_builder::tir::AllocateFrameNode</a>
+, <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#a8f0ca4a2b427645a93605494680cc669">tvm::tir::AllocateConstNode</a>
, <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html#acc0828bc8173ba2d46f90ddd2a329ae0">tvm::tir::AllocateNode</a>
, <a class="el" href="classtvm_1_1tir_1_1LoadNode.html#a2c69902eee069e824c822492068e6913">tvm::tir::LoadNode</a>
, <a class="el" href="classtvm_1_1tir_1_1StoreNode.html#aed2fc3d3c119126a61182666930e8729">tvm::tir::StoreNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_s.html b/docs/reference/api/doxygen/functions_func_s.html
index c1f056e6b5..93a8fe21e6 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
@@ -759,7 +759,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1Store.html#a2c4278b8bcdae57ada2022ecc7c290c3">tvm::tir::Store</a>
</li>
<li>Str()
-: <a class="el" href="classtvm_1_1script_1_1printer_1_1LiteralDoc.html#a789d7d73bd4d94612fa2a84c16b26b89">tvm::script::printer::LiteralDoc</a>
+: <a class="el" href="classtvm_1_1script_1_1printer_1_1LiteralDoc.html#a3511ed66e343f6db16cbd72feda03d5c">tvm::script::printer::LiteralDoc</a>
</li>
<li>str()
: <a class="el" href="classtvm_1_1TargetNode.html#a30cd67db46a9c4b098a8ba38fff22e26">tvm::TargetNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_u.html b/docs/reference/api/doxygen/functions_func_u.html
index 4b4e0f203d..611cae9ff1 100644
--- a/docs/reference/api/doxygen/functions_func_u.html
+++ b/docs/reference/api/doxygen/functions_func_u.html
@@ -106,7 +106,7 @@ $(function() {
, <a class="el" href="classtvm_1_1auto__scheduler_1_1CostModelNode.html#ae35b2b678760b8da57a43d3ae9c24da5">tvm::auto_scheduler::CostModelNode</a>
, <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html#a2d7849df6c7dbe93bf363c1d9f860a26">tvm::auto_scheduler::PythonBasedModelNode</a>
, <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html#a7febac6c05d8e2d407f466467769ee32">tvm::auto_scheduler::RandomModelNode</a>
-, <a class="el" href="classtvm_1_1IRModuleNode.html#a94a93385e64ce844299729af6a573015">tvm::IRModuleNode</a>
+, <a class="el" href="classtvm_1_1IRModuleNode.html#abdd8936c6fca33ef9b7c086f8fd58f84">tvm::IRModuleNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html#a1bba32eba84db583fe90d1a5bce085f1">tvm::meta_schedule::CostModelNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1PyCostModelNode.html#a970b00b0eb1bf6b88eea2711b58c4d1d">tvm::meta_schedule::PyCostModelNode</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_m.html b/docs/reference/api/doxygen/functions_m.html
index d32882998b..77097244db 100644
--- a/docs/reference/api/doxygen/functions_m.html
+++ b/docs/reference/api/doxygen/functions_m.html
@@ -350,7 +350,7 @@ $(function() {
: <a class="el" href="classtvm_1_1DiagnosticContextNode.html#adea7e38a6e47cbab7fb5639f208aa536">tvm::DiagnosticContextNode</a>
</li>
<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
, <a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html#a21f639900c480510650969df9c74d17d">tvm::runtime::ModuleNode</a>
</li>
<li>module_handle
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index d6095c5237..e07076f382 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -1136,7 +1136,7 @@ $(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#a02fca36e3ff55cc1e83635b02a11fca3">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#ac5d930b522e9fef9c07e51819d96d2f3">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_t.html b/docs/reference/api/doxygen/functions_t.html
index 1753f01dc3..7de8647362 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -199,7 +199,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#ab5fe485e1d730c36b096c060b8d2ef9d">tvm::te::Stage</a>
</li>
<li>Tensorize()
-: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#aaca1621ab9c3db0ddd04ac57de79d37f">tvm::tir::ScheduleNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#ae3794a03b566e5b1721b44c564992975">tvm::tir::ScheduleNode</a>
</li>
<li>TensorNode()
: <a class="el" href="classtvm_1_1te_1_1TensorNode.html#a153569448cb1bf9d2924d35639c3b8b8">tvm::te::TensorNode</a>
@@ -1390,7 +1390,7 @@ $(function() {
</li>
<li>TVMArgValue
: <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#a7e8b2c6a4fde079ee813c425d2eb6b24">tvm::runtime::ObjectPtr< T ></a>
-, <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html#a5fbd71750e5bbba6edc9094178af9276">tvm::runtime::TVMArgValue</a>
+, <a class="el" href="classtvm_1_1runtime_1_1TVMArgValue.html#a987b2fb283cea5484d4655e3f711c046">tvm::runtime::TVMArgValue</a>
</li>
<li>TVMMovableArgValue_
: <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#acd985550cba6cf8509122cbd996c1557">tvm::runtime::ObjectPtr< T ></a>
@@ -1412,7 +1412,7 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html#ae0ea8b4adc6dab8c74086bceaef6b3e1">tvm::runtime::ObjectPtr< T ></a>
, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#ae0ea8b4adc6dab8c74086bceaef6b3e1">tvm::runtime::ObjectRef</a>
, <a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html#ae0ea8b4adc6dab8c74086bceaef6b3e1">tvm::runtime::TVMPODValue_</a>
-, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#ac4a3850c0989e7c2d5cd8e0f096d0997">tvm::runtime::TVMRetValue</a>
+, <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#ab86bf21f214fca72e73a7f6e20ffab8d">tvm::runtime::TVMRetValue</a>
, <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#ae0ea8b4adc6dab8c74086bceaef6b3e1">tvm::runtime::TypedPackedFunc< R(Args...)></a>
</li>
<li>type
@@ -1493,7 +1493,7 @@ $(function() {
: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a0d72a6fa7263821c14bcd37837998ed9">tvm::TypedEnvFunc< R(Args...)></a>
</li>
<li>TypedPackedFunc()
-: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#aa3663a440db7a6951abd767109b9bf90">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a6b346a6d0b601eff5a100c7a207e9c86">tvm::runtime::TypedPackedFunc< R(Args...)></a>
</li>
<li>TypeIndex2Key()
: <a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object</a>
diff --git a/docs/reference/api/doxygen/functions_vars_a.html b/docs/reference/api/doxygen/functions_vars_a.html
index 950eac2ef4..d2e2c64c29 100644
--- a/docs/reference/api/doxygen/functions_vars_a.html
+++ b/docs/reference/api/doxygen/functions_vars_a.html
@@ -116,6 +116,9 @@ $(function() {
<li>Allocate
: <a class="el" href="structMemoryManagerInterface.html#a1dd2857f3cf22e16c431b0d67356e93b">MemoryManagerInterface</a>
</li>
+<li>allocated
+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a71f494b77cd89321bcd2d9bb7c7835e0">tvm::script::ir_builder::tir::DeclBufferFrameNode</a>
+</li>
<li>allocated_size
: <a class="el" href="structtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfoNode.html#ae22f04d799f7cc35273d85fd3d9851fd">tvm::tir::usmp::AllocatedPoolInfoNode</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_vars_b.html b/docs/reference/api/doxygen/functions_vars_b.html
index 96033d9a89..089eddb1a1 100644
--- a/docs/reference/api/doxygen/functions_vars_b.html
+++ b/docs/reference/api/doxygen/functions_vars_b.html
@@ -164,8 +164,6 @@ $(function() {
</li>
<li>buffer
: <a class="el" href="classtvm_1_1runtime_1_1vm_1_1StorageObj.html#ae787a0a25d67ff1952ac5e9eeb0d1a61">tvm::runtime::vm::StorageObj</a>
-, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a9334403129924bc1ed84d8753dd9f672">tvm::script::ir_builder::tir::AllocateConstFrameNode</a>
-, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ad7365c7b511a9f9dffc1aac531d046c2">tvm::script::ir_builder::tir::AllocateFrameNode</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">tvm::script::ir_builder::tir::DeclBufferFrameNode</a>
, <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html#ac5c69453b84222037c5d3e7a2f1d4316">tvm::tir::BufferLoadNode</a>
, <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html#a49068e03d737011da9d55ebf52139eac">tvm::tir::BufferRealizeNode</a>
@@ -189,7 +187,9 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1BufferNode.html#a2ce595dfcf7a726c7e0f86a6942d2568">tvm::tir::BufferNode</a>
</li>
<li>buffer_var
-: <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#a8f0ca4a2b427645a93605494680cc669">tvm::tir::AllocateConstNode</a>
+: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4b062ec7c2f519922051de6cfffd9762">tvm::script::ir_builder::tir::AllocateConstFrameNode</a>
+, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a22c42fc2abf33a4675850ae3ec826aec">tvm::script::ir_builder::tir::AllocateFrameNode</a>
+, <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html#a8f0ca4a2b427645a93605494680cc669">tvm::tir::AllocateConstNode</a>
, <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html#acc0828bc8173ba2d46f90ddd2a329ae0">tvm::tir::AllocateNode</a>
, <a class="el" href="classtvm_1_1tir_1_1LoadNode.html#a2c69902eee069e824c822492068e6913">tvm::tir::LoadNode</a>
, <a class="el" href="classtvm_1_1tir_1_1StoreNode.html#aed2fc3d3c119126a61182666930e8729">tvm::tir::StoreNode</a>
diff --git a/docs/reference/api/doxygen/globals_defs.html b/docs/reference/api/doxygen/globals_defs.html
index 5d1484953b..b2beb33a6d 100644
--- a/docs/reference/api/doxygen/globals_defs.html
+++ b/docs/reference/api/doxygen/globals_defs.html
@@ -372,6 +372,15 @@ $(function() {
<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST
: <a class="el" href="tir_2ir_8h.html#a411d701e2f5c55c20f1471c339b2866d">ir.h</a>
</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_LANES
+: <a class="el" href="tir_2ir_8h.html#af4333f8d928fe3c9e799afd516d0f04c">ir.h</a>
+</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_SIZES
+: <a class="el" href="tir_2ir_8h.html#a492f0c9cc3b778f85c7827e7e719e9c9">ir.h</a>
+</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_SIZES_LANES
+: <a class="el" href="tir_2ir_8h.html#a9410989688d1a84ad2d088103f988b26">ir.h</a>
+</li>
<li>TVM_TYPE_FUNCTOR_DISPATCH
: <a class="el" href="type__functor_8h.html#afaa114a04d18cd3f8f11995628692d74">type_functor.h</a>
</li>
diff --git a/docs/reference/api/doxygen/globals_t.html b/docs/reference/api/doxygen/globals_t.html
index ca1f577366..e367388c1e 100644
--- a/docs/reference/api/doxygen/globals_t.html
+++ b/docs/reference/api/doxygen/globals_t.html
@@ -289,6 +289,15 @@ $(function() {
<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST
: <a class="el" href="tir_2ir_8h.html#a411d701e2f5c55c20f1471c339b2866d">ir.h</a>
</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_LANES
+: <a class="el" href="tir_2ir_8h.html#af4333f8d928fe3c9e799afd516d0f04c">ir.h</a>
+</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_SIZES
+: <a class="el" href="tir_2ir_8h.html#a492f0c9cc3b778f85c7827e7e719e9c9">ir.h</a>
+</li>
+<li>TVM_TIR_IR_BUILDER_DEF_DTYPE_CAST_SIZES_LANES
+: <a class="el" href="tir_2ir_8h.html#a9410989688d1a84ad2d088103f988b26">ir.h</a>
+</li>
<li>TVM_TYPE_FUNCTOR_DISPATCH
: <a class="el" href="type__functor_8h.html#afaa114a04d18cd3f8f11995628692d74">type_functor.h</a>
</li>
diff --git a/docs/reference/api/doxygen/ir__builder_2tir_2frame_8h_source.html b/docs/reference/api/doxygen/ir__builder_2tir_2frame_8h_source.html
index bcd82a5f64..da2c9c5488 100644
--- a/docs/reference/api/doxygen/ir__builder_2tir_2frame_8h_source.html
+++ b/docs/reference/api/doxygen/ir__builder_2tir_2frame_8h_source.html
@@ -66,7 +66,7 @@ $(function() {
<div class="title">frame.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="ir__builder_2tir_2frame_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment [...]
+<a href="ir__builder_2tir_2frame_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment [...]
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html">tvm::script::ir_builder::tir::AllocateConstFrameNode</a></div><div class="ttdoc">A frame represents the allocate constant. </div><div class="ttdef"><b>Definition:</b> frame.h:495</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html">tvm::script::ir_builder::tir::AllocateFrameNode</a></div><div class="ttdoc">A frame represents the allocate. </div><div class="ttdef"><b>Definition:</b> frame.h:444</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1IfFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1IfFrame.html">tvm::script::ir_builder::tir::IfFrame</a></div><div class="ttdoc">Managed reference to IfFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:653</div></div>
@@ -87,8 +87,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode_html_acf1c9ac1eec92cbf916f2177481146fb"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode.html#acf1c9ac1eec92cbf916f2177481146fb">tvm::script::ir_builder::tir::BlockFrameNode::reads</a></div><div class="ttdeci">Optional< Array< tvm::tir::BufferRegion > > reads</div><div class="ttdoc">The read buffer regions of the block. </div><div class="ttdef"><b>Definition: [...]
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrame.html">tvm::script::ir_builder::tir::LaunchThreadFrame</a></div><div class="ttdoc">Managed reference to LaunchThreadFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:391</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrame.html">tvm::script::ir_builder::tir::AllocateConstFrame</a></div><div class="ttdoc">Managed reference to AllocateConstFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:533</div></div>
-<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode_html_a5284f329953ea259af3028144968fdcd"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a5284f329953ea259af3028144968fdcd">tvm::script::ir_builder::tir::DeclBufferFrameNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> frame.h:728</div></div>
-<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode_html_ad7365c7b511a9f9dffc1aac531d046c2"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ad7365c7b511a9f9dffc1aac531d046c2">tvm::script::ir_builder::tir::AllocateFrameNode::buffer</a></div><div class="ttdeci">tvm::tir::Buffer buffer</div><div class="ttdoc">The buffer. </div><div class="ttdef"><b>Definition:</b> frame.h:457</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode_html_a5284f329953ea259af3028144968fdcd"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a5284f329953ea259af3028144968fdcd">tvm::script::ir_builder::tir::DeclBufferFrameNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> frame.h:731</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrame.html">tvm::script::ir_builder::tir::BlockFrame</a></div><div class="ttdoc">Managed reference to BlockFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:185</div></div>
<div class="ttc" id="namespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">runtime implementation for LibTorch/TorchScript. </div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1RealizeFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1RealizeFrame.html">tvm::script::ir_builder::tir::RealizeFrame</a></div><div class="ttdoc">Managed reference to RealizeFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:434</div></div>
@@ -138,7 +137,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1tir_1_1Stmt_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Stmt.html">tvm::tir::Stmt</a></div><div class="ttdoc">Container of all statements. </div><div class="ttdef"><b>Definition:</b> stmt.h:57</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode_html_ac2fae69fd4b4923c2997d0e48e6820bd"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#ac2fae69fd4b4923c2997d0e48e6820bd">tvm::script::ir_builder::tir::AllocateFrameNode::storage_scope</a></div><div class="ttdeci">String storage_scope</div><div class="ttdoc">The storage scope. </div><div class="ttdef"><b>Definition:</b> frame.h:451</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode_html_aa70780de95c66113723fd753412ad34b"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode.html#aa70780de95c66113723fd753412ad34b">tvm::script::ir_builder::tir::BlockFrameNode::annotations</a></div><div class="ttdeci">Optional< Map< String, ObjectRef > > annotations</div><div class="ttdoc">The annotation of the block. </div><div class="ttdef"><b>Definition:</b> [...]
-<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode_html_a573cf6a08a4d6c55548308b77469b3fc"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">tvm::script::ir_builder::tir::DeclBufferFrameNode::buffer</a></div><div class="ttdeci">tvm::tir::Buffer buffer</div><div class="ttdef"><b>Definition:</b> frame.h:726</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode_html_a573cf6a08a4d6c55548308b77469b3fc"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a573cf6a08a4d6c55548308b77469b3fc">tvm::script::ir_builder::tir::DeclBufferFrameNode::buffer</a></div><div class="ttdeci">tvm::tir::Buffer buffer</div><div class="ttdoc">The declared buffer. </div><div class="ttdef"><b>Definition:</b> frame.h:727</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode_html_afacff22429bac19053107c1024c3b91c"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode.html#afacff22429bac19053107c1024c3b91c">tvm::script::ir_builder::tir::LaunchThreadFrameNode::extent</a></div><div class="ttdeci">PrimExpr extent</div><div class="ttdoc">The extent of environment thread. </div><div class="ttdef"><b>Definition:</b> frame.h:362</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1String_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1String.html">tvm::runtime::String</a></div><div class="ttdoc">Reference to string objects. </div><div class="ttdef"><b>Definition:</b> string.h:97</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1TypedPackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1TypedPackedFunc.html">tvm::runtime::TypedPackedFunc< tvm::tir::Stmt(Array< tvm::tir::Var > loop_vars, Array< Range > loop_extents, tvm::tir::Stmt loop_body)></a></div></div>
@@ -172,14 +171,15 @@ $(function() {
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ThenFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ThenFrame.html">tvm::script::ir_builder::tir::ThenFrame</a></div><div class="ttdoc">Managed reference to ThenFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:686</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrameNode_html_a646ab14a567ccc1dbc05803fb45453ee"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrameNode.html#a646ab14a567ccc1dbc05803fb45453ee">tvm::script::ir_builder::tir::ForFrameNode::f_make_for_loop</a></div><div class="ttdeci">FMakeForLoop f_make_for_loop</div><div class="ttdoc">The for loop generating function. </div><div class="ttdef"><b>Definition:</b> frame.h:246</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode_html_a5a6ff996869df4462d45ce04cd8efcbb"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a5a6ff996869df4462d45ce04cd8efcbb">tvm::script::ir_builder::tir::AllocateFrameNode::condition</a></div><div class="ttdeci">PrimExpr condition</div><div class="ttdoc">The condition. </div><div class="ttdef"><b>Definition:</b> frame.h:453</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode_html_a22c42fc2abf33a4675850ae3ec826aec"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrameNode.html#a22c42fc2abf33a4675850ae3ec826aec">tvm::script::ir_builder::tir::AllocateFrameNode::buffer_var</a></div><div class="ttdeci">tvm::tir::Var buffer_var</div><div class="ttdoc">The buffer var. </div><div class="ttdef"><b>Definition:</b> frame.h:457</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode.html">tvm::script::ir_builder::tir::LaunchThreadFrameNode</a></div><div class="ttdoc">The LaunchThreadFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:359</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ThenFrameNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ThenFrameNode.html">tvm::script::ir_builder::tir::ThenFrameNode</a></div><div class="ttdoc">A frame that represents then. </div><div class="ttdef"><b>Definition:</b> frame.h:663</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode_html_a71f494b77cd89321bcd2d9bb7c7835e0"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrameNode.html#a71f494b77cd89321bcd2d9bb7c7835e0">tvm::script::ir_builder::tir::DeclBufferFrameNode::allocated</a></div><div class="ttdeci">bool allocated</div><div class="ttdoc">The buffer allocated or not. </div><div class="ttdef"><b>Definition:</b> frame.h:729</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Map_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html">tvm::runtime::Map</a></div><div class="ttdoc">Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than two places. </div><div class="ttdef"><b>Definition:</b> map.h:1271</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode_html_aa00a7144c5dc23443f757acf69766b4c"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderFrameNode.html#aa00a7144c5dc23443f757acf69766b4c">tvm::script::ir_builder::IRBuilderFrameNode::ExitWithScope</a></div><div class="ttdeci">virtual void ExitWithScope()</div><div class="ttdoc">The method called when exiting RAII scope. </div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode_html_aab83629426451c65210bc3ba1fcda452"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockFrameNode.html#aab83629426451c65210bc3ba1fcda452">tvm::script::ir_builder::tir::BlockFrameNode::predicate</a></div><div class="ttdeci">Optional< PrimExpr > predicate</div><div class="ttdoc">The predicate of the block realization, the block will only be executed when the predicate is true [...]
-<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrame.html">tvm::script::ir_builder::tir::DeclBufferFrame</a></div><div class="ttdef"><b>Definition:</b> frame.h:740</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1DeclBufferFrame.html">tvm::script::ir_builder::tir::DeclBufferFrame</a></div><div class="ttdef"><b>Definition:</b> frame.h:744</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Optional_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Optional.html">tvm::runtime::Optional</a></div><div class="ttdoc">Optional container that to represent to a Nullable variant of T. </div><div class="ttdef"><b>Definition:</b> optional.h:51</div></div>
-<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode_html_a9334403129924bc1ed84d8753dd9f672"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a9334403129924bc1ed84d8753dd9f672">tvm::script::ir_builder::tir::AllocateConstFrameNode::buffer</a></div><div class="ttdeci">tvm::tir::Buffer buffer</div><div class="ttdoc">The buffer. </div><div class="ttdef"><b>Definition:</b> frame.h:504</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1RealizeFrameNode_html_ab76c32f89b215b3d9f936233a10ee424"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1RealizeFrameNode.html#ab76c32f89b215b3d9f936233a10ee424">tvm::script::ir_builder::tir::RealizeFrameNode::condition</a></div><div class="ttdeci">PrimExpr condition</div><div class="ttdoc">The condition expression. </div><div class="ttdef"><b>Definition:</b> frame.h:409</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrameNode_html_a50f32b0496665b929e7ef56a4856eff7"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrameNode.html#a50f32b0496665b929e7ef56a4856eff7">tvm::script::ir_builder::tir::ForFrameNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> frame.h:248</div></div>
<div class="ttc" id="classtvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode. </div><div class="ttdef"><b>Definition:</b> expr.h:112</div></div>
@@ -193,6 +193,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode_html_a84f72325f3fe12915183c5581d125d3f"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrameNode.html#a84f72325f3fe12915183c5581d125d3f">tvm::script::ir_builder::tir::LaunchThreadFrameNode::iter_var</a></div><div class="ttdeci">tvm::tir::IterVar iter_var</div><div class="ttdoc">The iteration variable. </div><div class="ttdef"><b>Definition:</b> frame.h:366</div></div>
<div class="ttc" id="script_2ir__builder_2base_8h_html"><div class="ttname"><a href="script_2ir__builder_2base_8h.html">base.h</a></div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockInitFrame_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockInitFrame.html">tvm::script::ir_builder::tir::BlockInitFrame</a></div><div class="ttdoc">Managed reference to BlockInitFrameNode. </div><div class="ttdef"><b>Definition:</b> frame.h:220</div></div>
+<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode_html_a4b062ec7c2f519922051de6cfffd9762"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrameNode.html#a4b062ec7c2f519922051de6cfffd9762">tvm::script::ir_builder::tir::AllocateConstFrameNode::buffer_var</a></div><div class="ttdeci">tvm::tir::Var buffer_var</div><div class="ttdoc">The buffer var. </div><div class="ttdef"><b>Definition:</b> frame.h:504</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html">tvm::script::ir_builder::tir::TIRFrameNode</a></div><div class="ttdoc">A base frame that represents the TIR fame with body of statements. </div><div class="ttdef"><b>Definition:</b> frame.h:36</div></div>
<div class="ttc" id="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode_html_a66267c80c4042b46ea86449d27dba61a"><div class="ttname"><a href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1TIRFrameNode.html#a66267c80c4042b46ea86449d27dba61a">tvm::script::ir_builder::tir::TIRFrameNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> frame.h:41</div></div>
</div><!-- fragment --></div><!-- contents -->
diff --git a/docs/reference/api/doxygen/namespacemembers.html b/docs/reference/api/doxygen/namespacemembers.html
index a4baa4fa10..bc2a9a25a2 100644
--- a/docs/reference/api/doxygen/namespacemembers.html
+++ b/docs/reference/api/doxygen/namespacemembers.html
@@ -107,7 +107,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9787864f45dc62b7c3b6833003f96ea">tvm::script::ir_builder::tir</a>
</li>
<li>AllocateConst()
-: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acc2998389db5bc4d1d62610e908e5581">tvm::script::ir_builder::tir</a>
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a83286f6aa614fd519daa19573df13885">tvm::script::ir_builder::tir</a>
</li>
<li>AllocatorType
: <a class="el" href="namespacetvm_1_1runtime_1_1vm.html#a71f7eacb312dc56f649ce8627a160c33">tvm::runtime::vm</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_b.html b/docs/reference/api/doxygen/namespacemembers_b.html
index 3283656442..077648e1b4 100644
--- a/docs/reference/api/doxygen/namespacemembers_b.html
+++ b/docs/reference/api/doxygen/namespacemembers_b.html
@@ -178,7 +178,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1tir.html#ac3d21b3e013c52e289180cd6d7d0a420">tvm::tir</a>
</li>
<li>build()
-: <a class="el" href="namespacetvm.html#ab5392acd55c76e34323a71a4052f7bb2">tvm</a>
+: <a class="el" href="namespacetvm.html#a018d7138c17ef78300ee256f6d348d00">tvm</a>
</li>
<li>Build()
: <a class="el" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">tvm::codegen</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_f.html b/docs/reference/api/doxygen/namespacemembers_f.html
index 144d5859f9..2c6fa23a95 100644
--- a/docs/reference/api/doxygen/namespacemembers_f.html
+++ b/docs/reference/api/doxygen/namespacemembers_f.html
@@ -187,15 +187,75 @@ $(function() {
<li>Float16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaf6b6dcbbd344633ac7549017151b1f4">tvm::script::ir_builder::tir</a>
</li>
+<li>Float16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af233b18ee0522a900568b14b6f9ef8d6">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad5311856f8b8b4df884170d8c4c513c8">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6843af0b415dd3c3555b176cd3233b6b">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac42c4020fca22350e8159b86cefb4031">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1f72082ac88788080f123bf8c93500e6">tvm::script::ir_builder::tir</a>
+</li>
<li>Float32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8878f384c4a2861add3f43da4aa7e080">tvm::script::ir_builder::tir</a>
</li>
+<li>Float32x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acb206b62b60473442d995a4f868f57f1">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad859865f93db0e8b124464203253fdab">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afc23e87468bd379cef2e436dec7380ec">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad363b06dcc7a557663219774aae3ae59">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae6706e8560ac719f2957e0186de014b3">tvm::script::ir_builder::tir</a>
+</li>
<li>Float64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6ff858a2cfb49c10fc00ae68a1728086">tvm::script::ir_builder::tir</a>
</li>
+<li>Float64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acccc0b5d430d1205b3d1a782cab67aae">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae07732bbb9fea30ff6b9c03a7150beeb">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4051e0d30c5fe712f65fffb8eee90d29">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab2aebc810c46e788e51c8e01a9eb7536">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4bb3d4dabf70259c2820a0f36c9e5661">tvm::script::ir_builder::tir</a>
+</li>
<li>Float8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8f02a339b55424163e97c46f1c9f7976">tvm::script::ir_builder::tir</a>
</li>
+<li>Float8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac5b707e9c9bd8ff20640e9aa5609f7ad">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0c1842297c526585f5e7ecd5cf9fa835">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9d6a011a6d9a84e818bbed17821fca43">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9493b11608cc2420b3e3780726556f0c">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8d984cfc2c4f432a5e2bb16ed9723c9c">tvm::script::ir_builder::tir</a>
+</li>
<li>FloatImmNode
: <a class="el" href="namespacetvm_1_1tir.html#adb123be81447d2871f4c716ce2d1dc00">tvm::tir</a>
</li>
@@ -210,7 +270,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1topi.html#a093080eceeaae43a605b8c4e638c9471">tvm::topi</a>
</li>
<li>floordiv()
-: <a class="el" href="namespacetvm.html#a87200564215339b900ca546678fc71a4">tvm</a>
+: <a class="el" href="namespacetvm.html#af347f10e3572adb2d74ba4a53777db2b">tvm</a>
</li>
<li>floormod()
: <a class="el" href="namespacetvm.html#a8683adb542beba8ecc69354e50d62ef6">tvm</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_func.html b/docs/reference/api/doxygen/namespacemembers_func.html
index cb2a35b1df..f78004ff60 100644
--- a/docs/reference/api/doxygen/namespacemembers_func.html
+++ b/docs/reference/api/doxygen/namespacemembers_func.html
@@ -107,7 +107,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9787864f45dc62b7c3b6833003f96ea">tvm::script::ir_builder::tir</a>
</li>
<li>AllocateConst()
-: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acc2998389db5bc4d1d62610e908e5581">tvm::script::ir_builder::tir</a>
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a83286f6aa614fd519daa19573df13885">tvm::script::ir_builder::tir</a>
</li>
<li>AllocBuffer()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a62a57ebae9d35dc7aeb63949eeb42080">tvm::script::ir_builder::tir</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_b.html b/docs/reference/api/doxygen/namespacemembers_func_b.html
index e9ba619099..6c02aca0b7 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_b.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_b.html
@@ -151,7 +151,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1tir.html#ac3d21b3e013c52e289180cd6d7d0a420">tvm::tir</a>
</li>
<li>build()
-: <a class="el" href="namespacetvm.html#a67dce8ba3396d28be88593081fc291fb">tvm</a>
+: <a class="el" href="namespacetvm.html#a018d7138c17ef78300ee256f6d348d00">tvm</a>
</li>
<li>Build()
: <a class="el" href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">tvm::codegen</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_f.html b/docs/reference/api/doxygen/namespacemembers_func_f.html
index 6191e6ddcd..24c1302652 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_f.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_f.html
@@ -121,30 +121,90 @@ $(function() {
<li>Float16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaf6b6dcbbd344633ac7549017151b1f4">tvm::script::ir_builder::tir</a>
</li>
+<li>Float16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af233b18ee0522a900568b14b6f9ef8d6">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad5311856f8b8b4df884170d8c4c513c8">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6843af0b415dd3c3555b176cd3233b6b">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac42c4020fca22350e8159b86cefb4031">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1f72082ac88788080f123bf8c93500e6">tvm::script::ir_builder::tir</a>
+</li>
<li>Float32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8878f384c4a2861add3f43da4aa7e080">tvm::script::ir_builder::tir</a>
</li>
+<li>Float32x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acb206b62b60473442d995a4f868f57f1">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad859865f93db0e8b124464203253fdab">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afc23e87468bd379cef2e436dec7380ec">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad363b06dcc7a557663219774aae3ae59">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float32x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae6706e8560ac719f2957e0186de014b3">tvm::script::ir_builder::tir</a>
+</li>
<li>Float64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6ff858a2cfb49c10fc00ae68a1728086">tvm::script::ir_builder::tir</a>
</li>
+<li>Float64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acccc0b5d430d1205b3d1a782cab67aae">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae07732bbb9fea30ff6b9c03a7150beeb">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4051e0d30c5fe712f65fffb8eee90d29">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab2aebc810c46e788e51c8e01a9eb7536">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4bb3d4dabf70259c2820a0f36c9e5661">tvm::script::ir_builder::tir</a>
+</li>
<li>Float8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8f02a339b55424163e97c46f1c9f7976">tvm::script::ir_builder::tir</a>
</li>
+<li>Float8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac5b707e9c9bd8ff20640e9aa5609f7ad">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0c1842297c526585f5e7ecd5cf9fa835">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9d6a011a6d9a84e818bbed17821fca43">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9493b11608cc2420b3e3780726556f0c">tvm::script::ir_builder::tir</a>
+</li>
+<li>Float8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8d984cfc2c4f432a5e2bb16ed9723c9c">tvm::script::ir_builder::tir</a>
+</li>
<li>floor()
: <a class="el" href="namespacetvm.html#aaff65dde3044433b2220677aedf4855f">tvm</a>
, <a class="el" href="namespacetvm_1_1topi.html#a25a91afb87b3b708c7de945afb9a6e69">tvm::topi</a>
</li>
<li>floor_divide()
-: <a class="el" href="namespacetvm_1_1topi.html#a6a04e135b576e7b4a17047168535c594">tvm::topi</a>
+: <a class="el" href="namespacetvm_1_1topi.html#ac972318d35093be225a3336843110626">tvm::topi</a>
</li>
<li>floor_mod()
-: <a class="el" href="namespacetvm_1_1topi.html#a570eec3c86e45d5722249d869db39c83">tvm::topi</a>
+: <a class="el" href="namespacetvm_1_1topi.html#a093080eceeaae43a605b8c4e638c9471">tvm::topi</a>
</li>
<li>floordiv()
: <a class="el" href="namespacetvm.html#af347f10e3572adb2d74ba4a53777db2b">tvm</a>
</li>
<li>floormod()
-: <a class="el" href="namespacetvm.html#a8683adb542beba8ecc69354e50d62ef6">tvm</a>
+: <a class="el" href="namespacetvm.html#a93ac0e2663a2169194399b57ae39b003">tvm</a>
</li>
<li>fma()
: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a1e15b04fe89f7899e09e528946aa5bb4">tvm::tir::builtin</a>
@@ -165,10 +225,10 @@ $(function() {
: <a class="el" href="namespacetvm_1_1relay_1_1transform.html#aab7f62610e05ed0211f17d452ee37f5f">tvm::relay::transform</a>
</li>
<li>ForwardRewrite()
-: <a class="el" href="namespacetvm_1_1relay.html#ae6b8ec762f81290872d611c40cc72458">tvm::relay</a>
+: <a class="el" href="namespacetvm_1_1relay.html#a01b761b2455ca384e5b81100069ef9a3">tvm::relay</a>
</li>
<li>FreeTypeVars()
-: <a class="el" href="namespacetvm_1_1relay.html#a446e2b12f3e907336313004c3e0a0c86">tvm::relay</a>
+: <a class="el" href="namespacetvm_1_1relay.html#acc529cca9623ac81c8aa6e106cdb5850">tvm::relay</a>
</li>
<li>FreeVars()
: <a class="el" href="namespacetvm_1_1relay.html#af2c5771c67f06c0af32c7b4b4269e73f">tvm::relay</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_i.html b/docs/reference/api/doxygen/namespacemembers_func_i.html
index bb8c3b2511..a7dc16b061 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_i.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_i.html
@@ -129,24 +129,75 @@ $(function() {
<li>Int16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a71a8950ee36a0cc96b08f6a38a67beb8">tvm::script::ir_builder::tir</a>
</li>
+<li>Int16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a384760861c5679c8f3228fb069c4525b">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a535df06d8e6eec52c711be076ca919da">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aee6b73b52838f0921c9dae3a6820b9a6">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af817e48b37ce691b1538708ee4a96ae4">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a42335d7e171d3a46711e43ffd241e68d">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af90ae386a2b910cc52c17ebb6390404e">tvm::script::ir_builder::tir</a>
</li>
<li>Int32x16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4945336e84e5ce9b60b583ac0adb28d6">tvm::script::ir_builder::tir</a>
</li>
+<li>Int32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9f621b090a72a31cf4b80978d88ccf76">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32x4()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1d7bdb8458778293ced5af6ad9378283">tvm::script::ir_builder::tir</a>
</li>
+<li>Int32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a57b983ed9d4d5000d0372e64e526f9e0">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32x8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae89c85eabf166d4efddff53988ff57b5">tvm::script::ir_builder::tir</a>
</li>
<li>Int64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e3533536d109d1a5dd037c356ac2662">tvm::script::ir_builder::tir</a>
</li>
+<li>Int64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3c2d6fdb8690f5593c1d43b0cdf43d4e">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e047daab47ebe417cdda5e3209607af">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa84d61b0037299683391cfb1792064f9">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a61b20548797f6cbb80de229afe1cce53">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a972550fc782bca80bfc88281e88d6022">tvm::script::ir_builder::tir</a>
+</li>
<li>Int8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa8e3f67af7d6676f0980fa5fa6406ee7">tvm::script::ir_builder::tir</a>
</li>
+<li>Int8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4738b9544893c5f7992efefd8959af1d">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8092c0c7d3e9c111c2c247cae4eda1e9">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8324f437b88e52d038fb35b35ee094a0">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6750fab4bf57aeb380dabe6fce99c5a4">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac575b8dc0c148ea90512e7443fde3073">tvm::script::ir_builder::tir</a>
+</li>
<li>Intersect()
: <a class="el" href="namespacetvm_1_1arith.html#ad66a987ddb3d8aee69f9175bf412eb8a">tvm::arith</a>
</li>
diff --git a/docs/reference/api/doxygen/namespacemembers_func_u.html b/docs/reference/api/doxygen/namespacemembers_func_u.html
index 15a8a2258a..e19ae131b2 100644
--- a/docs/reference/api/doxygen/namespacemembers_func_u.html
+++ b/docs/reference/api/doxygen/namespacemembers_func_u.html
@@ -64,15 +64,75 @@ $(function() {
<li>UInt16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac18cfb33b4b074bebb62a0022e3fddd9">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5505117dc914ce1bfe08fe140134dbc9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a22c54a2af8641b558a1ad2895acb3005">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a31de2e593c18ff9cff149cc1c5f7ebb8">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afcd528fd1230b26fce48701ce44619e3">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0110fa08ba2f1b0d29292e4dc6d6f41d">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa7dab1502059beec51115d2fc80d7467">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt32x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab1a1aa826e5d680ad549247c55437325">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5a86c4391013e0b0791fa8d74e925f8f">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79e5972bf85473593165a557c7ac1ded">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1895112a229f0e1129328368f8124361">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3e9853322f7173158dba8bea26cfeefc">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#addcd399b813b5989ff7c45a50b9a0925">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a998c1aa94e4462047190261120acdae9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6b7b72765526b8f96cf3291f5e1c083b">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaef8e2bd7738e488d4fac10c757a3acf">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4b6ead30ca701c211d3eac598d86b25a">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a2fcd410ad9a6e3132821db76bf7eb537">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79bce011a3c96be3481e02fa595026e0">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4c909574d4809116f228e6212b13ec56">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9ab96e8f14b6ee6b140bd3b7261fdd9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab796b555e2ec6855db23676cdde3bb2d">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a297d6deaea7f8520c75b70828bb017cb">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ada38005ba6aef4e9dbae3028c1e40dd4">tvm::script::ir_builder::tir</a>
+</li>
<li>UnCPS()
: <a class="el" href="namespacetvm_1_1relay.html#a1677d450d58078811f52fd70d67db515">tvm::relay</a>
</li>
@@ -80,7 +140,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877">tvm::tir::builtin</a>
</li>
<li>UndefinedVars()
-: <a class="el" href="namespacetvm_1_1tir.html#a99de316b20c7af2e8b4f06773ee69a5a">tvm::tir</a>
+: <a class="el" href="namespacetvm_1_1tir.html#ae110e02855d8bc12eb8b6f92ed391844">tvm::tir</a>
</li>
<li>UnifiedStaticMemoryPlanner()
: <a class="el" href="namespacetvm_1_1tir_1_1transform.html#aca44076eb1085d664877596a8b8587d4">tvm::tir::transform</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_i.html b/docs/reference/api/doxygen/namespacemembers_i.html
index b0853ba5b0..6a1c8678ca 100644
--- a/docs/reference/api/doxygen/namespacemembers_i.html
+++ b/docs/reference/api/doxygen/namespacemembers_i.html
@@ -141,24 +141,75 @@ $(function() {
<li>Int16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a71a8950ee36a0cc96b08f6a38a67beb8">tvm::script::ir_builder::tir</a>
</li>
+<li>Int16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a384760861c5679c8f3228fb069c4525b">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a535df06d8e6eec52c711be076ca919da">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aee6b73b52838f0921c9dae3a6820b9a6">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af817e48b37ce691b1538708ee4a96ae4">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a42335d7e171d3a46711e43ffd241e68d">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af90ae386a2b910cc52c17ebb6390404e">tvm::script::ir_builder::tir</a>
</li>
<li>Int32x16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4945336e84e5ce9b60b583ac0adb28d6">tvm::script::ir_builder::tir</a>
</li>
+<li>Int32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9f621b090a72a31cf4b80978d88ccf76">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32x4()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1d7bdb8458778293ced5af6ad9378283">tvm::script::ir_builder::tir</a>
</li>
+<li>Int32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a57b983ed9d4d5000d0372e64e526f9e0">tvm::script::ir_builder::tir</a>
+</li>
<li>Int32x8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae89c85eabf166d4efddff53988ff57b5">tvm::script::ir_builder::tir</a>
</li>
<li>Int64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e3533536d109d1a5dd037c356ac2662">tvm::script::ir_builder::tir</a>
</li>
+<li>Int64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3c2d6fdb8690f5593c1d43b0cdf43d4e">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e047daab47ebe417cdda5e3209607af">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa84d61b0037299683391cfb1792064f9">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a61b20548797f6cbb80de229afe1cce53">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a972550fc782bca80bfc88281e88d6022">tvm::script::ir_builder::tir</a>
+</li>
<li>Int8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa8e3f67af7d6676f0980fa5fa6406ee7">tvm::script::ir_builder::tir</a>
</li>
+<li>Int8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4738b9544893c5f7992efefd8959af1d">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8092c0c7d3e9c111c2c247cae4eda1e9">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8324f437b88e52d038fb35b35ee094a0">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6750fab4bf57aeb380dabe6fce99c5a4">tvm::script::ir_builder::tir</a>
+</li>
+<li>Int8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac575b8dc0c148ea90512e7443fde3073">tvm::script::ir_builder::tir</a>
+</li>
<li>Intersect()
: <a class="el" href="namespacetvm_1_1arith.html#ad66a987ddb3d8aee69f9175bf412eb8a">tvm::arith</a>
</li>
@@ -178,7 +229,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1topi.html#ad5dcb2721aae4c9b84552b85db6e6cae">tvm::topi</a>
</li>
<li>is_const_int()
-: <a class="el" href="namespacetvm_1_1tir.html#a5c414d5e54c099ad7287be302aac8f02">tvm::tir</a>
+: <a class="el" href="namespacetvm_1_1tir.html#a65923ae132fcc764357a8765878fb528">tvm::tir</a>
</li>
<li>is_const_number()
: <a class="el" href="namespacetvm_1_1tir.html#a5b96c80ce43c8276e39c15787d997651">tvm::tir</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_s.html b/docs/reference/api/doxygen/namespacemembers_s.html
index 4e182cd946..9e4f6b83df 100644
--- a/docs/reference/api/doxygen/namespacemembers_s.html
+++ b/docs/reference/api/doxygen/namespacemembers_s.html
@@ -383,7 +383,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1tir.html#ae06122cce8e8888b5ed3568e7a4368bc">tvm::tir</a>
</li>
<li>subtract()
-: <a class="el" href="namespacetvm_1_1topi.html#a1447c5af8653fa4fcbe69ee287a0a8fa">tvm::topi</a>
+: <a class="el" href="namespacetvm_1_1topi.html#a461a374321614b257311660b8bc45d0d">tvm::topi</a>
</li>
<li>sum()
: <a class="el" href="namespacetvm.html#afdad0c0329bd39949ba8d296cfb85d76">tvm</a>
diff --git a/docs/reference/api/doxygen/namespacemembers_u.html b/docs/reference/api/doxygen/namespacemembers_u.html
index 762ab0dffc..a6ae30b37e 100644
--- a/docs/reference/api/doxygen/namespacemembers_u.html
+++ b/docs/reference/api/doxygen/namespacemembers_u.html
@@ -64,15 +64,75 @@ $(function() {
<li>UInt16()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac18cfb33b4b074bebb62a0022e3fddd9">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt16x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5505117dc914ce1bfe08fe140134dbc9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a22c54a2af8641b558a1ad2895acb3005">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a31de2e593c18ff9cff149cc1c5f7ebb8">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afcd528fd1230b26fce48701ce44619e3">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt16x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0110fa08ba2f1b0d29292e4dc6d6f41d">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt32()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa7dab1502059beec51115d2fc80d7467">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt32x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab1a1aa826e5d680ad549247c55437325">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5a86c4391013e0b0791fa8d74e925f8f">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79e5972bf85473593165a557c7ac1ded">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1895112a229f0e1129328368f8124361">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt32x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3e9853322f7173158dba8bea26cfeefc">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt64()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#addcd399b813b5989ff7c45a50b9a0925">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt64x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a998c1aa94e4462047190261120acdae9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6b7b72765526b8f96cf3291f5e1c083b">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaef8e2bd7738e488d4fac10c757a3acf">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4b6ead30ca701c211d3eac598d86b25a">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt64x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a2fcd410ad9a6e3132821db76bf7eb537">tvm::script::ir_builder::tir</a>
+</li>
<li>UInt8()
: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79bce011a3c96be3481e02fa595026e0">tvm::script::ir_builder::tir</a>
</li>
+<li>UInt8x16()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4c909574d4809116f228e6212b13ec56">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x32()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9ab96e8f14b6ee6b140bd3b7261fdd9">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x4()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab796b555e2ec6855db23676cdde3bb2d">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x64()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a297d6deaea7f8520c75b70828bb017cb">tvm::script::ir_builder::tir</a>
+</li>
+<li>UInt8x8()
+: <a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ada38005ba6aef4e9dbae3028c1e40dd4">tvm::script::ir_builder::tir</a>
+</li>
<li>UnCPS()
: <a class="el" href="namespacetvm_1_1relay.html#a1677d450d58078811f52fd70d67db515">tvm::relay</a>
</li>
@@ -80,7 +140,7 @@ $(function() {
: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a77a72a2e21fe0ea2118479924b4fb877">tvm::tir::builtin</a>
</li>
<li>UndefinedVars()
-: <a class="el" href="namespacetvm_1_1tir.html#a99de316b20c7af2e8b4f06773ee69a5a">tvm::tir</a>
+: <a class="el" href="namespacetvm_1_1tir.html#ae110e02855d8bc12eb8b6f92ed391844">tvm::tir</a>
</li>
<li>UnifiedStaticMemoryPlanner()
: <a class="el" href="namespacetvm_1_1tir_1_1transform.html#aca44076eb1085d664877596a8b8587d4">tvm::tir::transform</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html b/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
index 4cc91eef03..df9f1e61c3 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1script_1_1ir__builder_1_1tir.html
@@ -259,9 +259,9 @@ Functions</h2></td></tr>
<tr class="memitem:aa9787864f45dc62b7c3b6833003f96ea"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateFrame.html">AllocateFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9787864f45dc62b7c3b6833003f96ea">Allocate</a> (<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html" [...]
<tr class="memdesc:aa9787864f45dc62b7c3b6833003f96ea"><td class="mdescLeft"> </td><td class="mdescRight">The allocate node. <a href="#aa9787864f45dc62b7c3b6833003f96ea">More...</a><br /></td></tr>
<tr class="separator:aa9787864f45dc62b7c3b6833003f96ea"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:acc2998389db5bc4d1d62610e908e5581"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrame.html">AllocateConstFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acc2998389db5bc4d1d62610e908e5581">AllocateConst</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> data, <a class="el" href="names [...]
-<tr class="memdesc:acc2998389db5bc4d1d62610e908e5581"><td class="mdescLeft"> </td><td class="mdescRight">The allocate constant node. <a href="#acc2998389db5bc4d1d62610e908e5581">More...</a><br /></td></tr>
-<tr class="separator:acc2998389db5bc4d1d62610e908e5581"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a83286f6aa614fd519daa19573df13885"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AllocateConstFrame.html">AllocateConstFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a83286f6aa614fd519daa19573df13885">AllocateConst</a> (<a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> data, <a class="el" href="names [...]
+<tr class="memdesc:a83286f6aa614fd519daa19573df13885"><td class="mdescLeft"> </td><td class="mdescRight">The allocate constant node. <a href="#a83286f6aa614fd519daa19573df13885">More...</a><br /></td></tr>
+<tr class="separator:a83286f6aa614fd519daa19573df13885"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7a8f20d29de99ce15b603c5e6ace3ca0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1AttrFrame.html">AttrFrame</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7a8f20d29de99ce15b603c5e6ace3ca0">Attr</a> (<a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> node, <a class="el" href="classtvm_1_1runtime_1_1Strin [...]
<tr class="memdesc:a7a8f20d29de99ce15b603c5e6ace3ca0"><td class="mdescLeft"> </td><td class="mdescRight">Create an attribute. <a href="#a7a8f20d29de99ce15b603c5e6ace3ca0">More...</a><br /></td></tr>
<tr class="separator:a7a8f20d29de99ce15b603c5e6ace3ca0"><td class="memSeparator" colspan="2"> </td></tr>
@@ -298,22 +298,6 @@ Functions</h2></td></tr>
<tr class="memitem:a8e12c34d610496c0053a9491b0f04b32"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8e12c34d610496c0053a9491b0f04b32">Ptr</a> (<a class="el" href="classtvm_1_1runtime_1_1DataType.html">runtime::DataType</a> dtype, <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> storage_ [...]
<tr class="memdesc:a8e12c34d610496c0053a9491b0f04b32"><td class="mdescLeft"> </td><td class="mdescRight">The pointer declaration function. <a href="#a8e12c34d610496c0053a9491b0f04b32">More...</a><br /></td></tr>
<tr class="separator:a8e12c34d610496c0053a9491b0f04b32"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa8e3f67af7d6676f0980fa5fa6406ee7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa8e3f67af7d6676f0980fa5fa6406ee7">Int8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" hr [...]
-<tr class="separator:aa8e3f67af7d6676f0980fa5fa6406ee7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a71a8950ee36a0cc96b08f6a38a67beb8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a71a8950ee36a0cc96b08f6a38a67beb8">Int16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
-<tr class="separator:a71a8950ee36a0cc96b08f6a38a67beb8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:af90ae386a2b910cc52c17ebb6390404e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af90ae386a2b910cc52c17ebb6390404e">Int32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
-<tr class="separator:af90ae386a2b910cc52c17ebb6390404e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7e3533536d109d1a5dd037c356ac2662"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e3533536d109d1a5dd037c356ac2662">Int64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
-<tr class="separator:a7e3533536d109d1a5dd037c356ac2662"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a79bce011a3c96be3481e02fa595026e0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79bce011a3c96be3481e02fa595026e0">UInt8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
-<tr class="separator:a79bce011a3c96be3481e02fa595026e0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac18cfb33b4b074bebb62a0022e3fddd9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac18cfb33b4b074bebb62a0022e3fddd9">UInt16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
-<tr class="separator:ac18cfb33b4b074bebb62a0022e3fddd9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa7dab1502059beec51115d2fc80d7467"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa7dab1502059beec51115d2fc80d7467">UInt32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
-<tr class="separator:aa7dab1502059beec51115d2fc80d7467"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:addcd399b813b5989ff7c45a50b9a0925"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#addcd399b813b5989ff7c45a50b9a0925">UInt64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
-<tr class="separator:addcd399b813b5989ff7c45a50b9a0925"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8f02a339b55424163e97c46f1c9f7976"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8f02a339b55424163e97c46f1c9f7976">Float8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
<tr class="separator:a8f02a339b55424163e97c46f1c9f7976"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aaf6b6dcbbd344633ac7549017151b1f4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaf6b6dcbbd344633ac7549017151b1f4">Float16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
@@ -322,12 +306,142 @@ Functions</h2></td></tr>
<tr class="separator:a8878f384c4a2861add3f43da4aa7e080"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6ff858a2cfb49c10fc00ae68a1728086"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6ff858a2cfb49c10fc00ae68a1728086">Float64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
<tr class="separator:a6ff858a2cfb49c10fc00ae68a1728086"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a79bce011a3c96be3481e02fa595026e0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79bce011a3c96be3481e02fa595026e0">UInt8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
+<tr class="separator:a79bce011a3c96be3481e02fa595026e0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac18cfb33b4b074bebb62a0022e3fddd9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac18cfb33b4b074bebb62a0022e3fddd9">UInt16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:ac18cfb33b4b074bebb62a0022e3fddd9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa7dab1502059beec51115d2fc80d7467"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa7dab1502059beec51115d2fc80d7467">UInt32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:aa7dab1502059beec51115d2fc80d7467"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:addcd399b813b5989ff7c45a50b9a0925"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#addcd399b813b5989ff7c45a50b9a0925">UInt64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:addcd399b813b5989ff7c45a50b9a0925"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa8e3f67af7d6676f0980fa5fa6406ee7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa8e3f67af7d6676f0980fa5fa6406ee7">Int8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" hr [...]
+<tr class="separator:aa8e3f67af7d6676f0980fa5fa6406ee7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a71a8950ee36a0cc96b08f6a38a67beb8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a71a8950ee36a0cc96b08f6a38a67beb8">Int16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
+<tr class="separator:a71a8950ee36a0cc96b08f6a38a67beb8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af90ae386a2b910cc52c17ebb6390404e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af90ae386a2b910cc52c17ebb6390404e">Int32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
+<tr class="separator:af90ae386a2b910cc52c17ebb6390404e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7e3533536d109d1a5dd037c356ac2662"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e3533536d109d1a5dd037c356ac2662">Int64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" h [...]
+<tr class="separator:a7e3533536d109d1a5dd037c356ac2662"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9d6a011a6d9a84e818bbed17821fca43"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9d6a011a6d9a84e818bbed17821fca43">Float8x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a9d6a011a6d9a84e818bbed17821fca43"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8d984cfc2c4f432a5e2bb16ed9723c9c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8d984cfc2c4f432a5e2bb16ed9723c9c">Float8x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a8d984cfc2c4f432a5e2bb16ed9723c9c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac5b707e9c9bd8ff20640e9aa5609f7ad"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac5b707e9c9bd8ff20640e9aa5609f7ad">Float8x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:ac5b707e9c9bd8ff20640e9aa5609f7ad"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0c1842297c526585f5e7ecd5cf9fa835"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0c1842297c526585f5e7ecd5cf9fa835">Float8x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a0c1842297c526585f5e7ecd5cf9fa835"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9493b11608cc2420b3e3780726556f0c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9493b11608cc2420b3e3780726556f0c">Float8x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a9493b11608cc2420b3e3780726556f0c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6843af0b415dd3c3555b176cd3233b6b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6843af0b415dd3c3555b176cd3233b6b">Float16x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a6843af0b415dd3c3555b176cd3233b6b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1f72082ac88788080f123bf8c93500e6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1f72082ac88788080f123bf8c93500e6">Float16x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a1f72082ac88788080f123bf8c93500e6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af233b18ee0522a900568b14b6f9ef8d6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af233b18ee0522a900568b14b6f9ef8d6">Float16x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:af233b18ee0522a900568b14b6f9ef8d6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad5311856f8b8b4df884170d8c4c513c8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad5311856f8b8b4df884170d8c4c513c8">Float16x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ad5311856f8b8b4df884170d8c4c513c8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac42c4020fca22350e8159b86cefb4031"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac42c4020fca22350e8159b86cefb4031">Float16x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ac42c4020fca22350e8159b86cefb4031"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afc23e87468bd379cef2e436dec7380ec"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afc23e87468bd379cef2e436dec7380ec">Float32x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:afc23e87468bd379cef2e436dec7380ec"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae6706e8560ac719f2957e0186de014b3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae6706e8560ac719f2957e0186de014b3">Float32x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:ae6706e8560ac719f2957e0186de014b3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acb206b62b60473442d995a4f868f57f1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acb206b62b60473442d995a4f868f57f1">Float32x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:acb206b62b60473442d995a4f868f57f1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad859865f93db0e8b124464203253fdab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad859865f93db0e8b124464203253fdab">Float32x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ad859865f93db0e8b124464203253fdab"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad363b06dcc7a557663219774aae3ae59"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ad363b06dcc7a557663219774aae3ae59">Float32x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ad363b06dcc7a557663219774aae3ae59"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4051e0d30c5fe712f65fffb8eee90d29"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4051e0d30c5fe712f65fffb8eee90d29">Float64x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a4051e0d30c5fe712f65fffb8eee90d29"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4bb3d4dabf70259c2820a0f36c9e5661"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4bb3d4dabf70259c2820a0f36c9e5661">Float64x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a4bb3d4dabf70259c2820a0f36c9e5661"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acccc0b5d430d1205b3d1a782cab67aae"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#acccc0b5d430d1205b3d1a782cab67aae">Float64x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:acccc0b5d430d1205b3d1a782cab67aae"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae07732bbb9fea30ff6b9c03a7150beeb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae07732bbb9fea30ff6b9c03a7150beeb">Float64x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ae07732bbb9fea30ff6b9c03a7150beeb"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab2aebc810c46e788e51c8e01a9eb7536"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab2aebc810c46e788e51c8e01a9eb7536">Float64x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class=" [...]
+<tr class="separator:ab2aebc810c46e788e51c8e01a9eb7536"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab796b555e2ec6855db23676cdde3bb2d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab796b555e2ec6855db23676cdde3bb2d">UInt8x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:ab796b555e2ec6855db23676cdde3bb2d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ada38005ba6aef4e9dbae3028c1e40dd4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ada38005ba6aef4e9dbae3028c1e40dd4">UInt8x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:ada38005ba6aef4e9dbae3028c1e40dd4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4c909574d4809116f228e6212b13ec56"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4c909574d4809116f228e6212b13ec56">UInt8x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a4c909574d4809116f228e6212b13ec56"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa9ab96e8f14b6ee6b140bd3b7261fdd9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa9ab96e8f14b6ee6b140bd3b7261fdd9">UInt8x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:aa9ab96e8f14b6ee6b140bd3b7261fdd9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a297d6deaea7f8520c75b70828bb017cb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a297d6deaea7f8520c75b70828bb017cb">UInt8x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a297d6deaea7f8520c75b70828bb017cb"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a31de2e593c18ff9cff149cc1c5f7ebb8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a31de2e593c18ff9cff149cc1c5f7ebb8">UInt16x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a31de2e593c18ff9cff149cc1c5f7ebb8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0110fa08ba2f1b0d29292e4dc6d6f41d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a0110fa08ba2f1b0d29292e4dc6d6f41d">UInt16x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a0110fa08ba2f1b0d29292e4dc6d6f41d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5505117dc914ce1bfe08fe140134dbc9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5505117dc914ce1bfe08fe140134dbc9">UInt16x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a5505117dc914ce1bfe08fe140134dbc9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a22c54a2af8641b558a1ad2895acb3005"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a22c54a2af8641b558a1ad2895acb3005">UInt16x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a22c54a2af8641b558a1ad2895acb3005"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afcd528fd1230b26fce48701ce44619e3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#afcd528fd1230b26fce48701ce44619e3">UInt16x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:afcd528fd1230b26fce48701ce44619e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a79e5972bf85473593165a557c7ac1ded"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a79e5972bf85473593165a557c7ac1ded">UInt32x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a79e5972bf85473593165a557c7ac1ded"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3e9853322f7173158dba8bea26cfeefc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3e9853322f7173158dba8bea26cfeefc">UInt32x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a3e9853322f7173158dba8bea26cfeefc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab1a1aa826e5d680ad549247c55437325"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ab1a1aa826e5d680ad549247c55437325">UInt32x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:ab1a1aa826e5d680ad549247c55437325"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5a86c4391013e0b0791fa8d74e925f8f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a5a86c4391013e0b0791fa8d74e925f8f">UInt32x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a5a86c4391013e0b0791fa8d74e925f8f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1895112a229f0e1129328368f8124361"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1895112a229f0e1129328368f8124361">UInt32x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a1895112a229f0e1129328368f8124361"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aaef8e2bd7738e488d4fac10c757a3acf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aaef8e2bd7738e488d4fac10c757a3acf">UInt64x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:aaef8e2bd7738e488d4fac10c757a3acf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2fcd410ad9a6e3132821db76bf7eb537"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a2fcd410ad9a6e3132821db76bf7eb537">UInt64x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a2fcd410ad9a6e3132821db76bf7eb537"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a998c1aa94e4462047190261120acdae9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a998c1aa94e4462047190261120acdae9">UInt64x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a998c1aa94e4462047190261120acdae9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6b7b72765526b8f96cf3291f5e1c083b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6b7b72765526b8f96cf3291f5e1c083b">UInt64x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a6b7b72765526b8f96cf3291f5e1c083b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4b6ead30ca701c211d3eac598d86b25a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4b6ead30ca701c211d3eac598d86b25a">UInt64x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="e [...]
+<tr class="separator:a4b6ead30ca701c211d3eac598d86b25a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8324f437b88e52d038fb35b35ee094a0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8324f437b88e52d038fb35b35ee094a0">Int8x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a8324f437b88e52d038fb35b35ee094a0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac575b8dc0c148ea90512e7443fde3073"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ac575b8dc0c148ea90512e7443fde3073">Int8x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:ac575b8dc0c148ea90512e7443fde3073"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4738b9544893c5f7992efefd8959af1d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4738b9544893c5f7992efefd8959af1d">Int8x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a4738b9544893c5f7992efefd8959af1d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8092c0c7d3e9c111c2c247cae4eda1e9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a8092c0c7d3e9c111c2c247cae4eda1e9">Int8x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a8092c0c7d3e9c111c2c247cae4eda1e9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6750fab4bf57aeb380dabe6fce99c5a4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a6750fab4bf57aeb380dabe6fce99c5a4">Int8x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a6750fab4bf57aeb380dabe6fce99c5a4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aee6b73b52838f0921c9dae3a6820b9a6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aee6b73b52838f0921c9dae3a6820b9a6">Int16x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:aee6b73b52838f0921c9dae3a6820b9a6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a42335d7e171d3a46711e43ffd241e68d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a42335d7e171d3a46711e43ffd241e68d">Int16x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a42335d7e171d3a46711e43ffd241e68d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a384760861c5679c8f3228fb069c4525b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a384760861c5679c8f3228fb069c4525b">Int16x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a384760861c5679c8f3228fb069c4525b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a535df06d8e6eec52c711be076ca919da"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a535df06d8e6eec52c711be076ca919da">Int16x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a535df06d8e6eec52c711be076ca919da"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af817e48b37ce691b1538708ee4a96ae4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#af817e48b37ce691b1538708ee4a96ae4">Int16x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:af817e48b37ce691b1538708ee4a96ae4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1d7bdb8458778293ced5af6ad9378283"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a1d7bdb8458778293ced5af6ad9378283">Int32x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
<tr class="separator:a1d7bdb8458778293ced5af6ad9378283"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae89c85eabf166d4efddff53988ff57b5"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#ae89c85eabf166d4efddff53988ff57b5">Int32x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
<tr class="separator:ae89c85eabf166d4efddff53988ff57b5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4945336e84e5ce9b60b583ac0adb28d6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a4945336e84e5ce9b60b583ac0adb28d6">Int32x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
<tr class="separator:a4945336e84e5ce9b60b583ac0adb28d6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9f621b090a72a31cf4b80978d88ccf76"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a9f621b090a72a31cf4b80978d88ccf76">Int32x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a9f621b090a72a31cf4b80978d88ccf76"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a57b983ed9d4d5000d0372e64e526f9e0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a57b983ed9d4d5000d0372e64e526f9e0">Int32x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a57b983ed9d4d5000d0372e64e526f9e0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa84d61b0037299683391cfb1792064f9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#aa84d61b0037299683391cfb1792064f9">Int64x4</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:aa84d61b0037299683391cfb1792064f9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a972550fc782bca80bfc88281e88d6022"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a972550fc782bca80bfc88281e88d6022">Int64x8</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
+<tr class="separator:a972550fc782bca80bfc88281e88d6022"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3c2d6fdb8690f5593c1d43b0cdf43d4e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a3c2d6fdb8690f5593c1d43b0cdf43d4e">Int64x16</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a3c2d6fdb8690f5593c1d43b0cdf43d4e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7e047daab47ebe417cdda5e3209607af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a7e047daab47ebe417cdda5e3209607af">Int64x32</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a7e047daab47ebe417cdda5e3209607af"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a61b20548797f6cbb80de229afe1cce53"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a61b20548797f6cbb80de229afe1cce53">Int64x64</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el [...]
+<tr class="separator:a61b20548797f6cbb80de229afe1cce53"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a95b135dca5d84261342b70d3d8a34acc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a95b135dca5d84261342b70d3d8a34acc">Boolean</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
<tr class="separator:a95b135dca5d84261342b70d3d8a34acc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a07e14252128fe48c5e9bbd33c1e460fc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1script_1_1ir__builder_1_1tir.html#a07e14252128fe48c5e9bbd33c1e460fc">Handle</a> (<a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > expr=<a class="el" [...]
@@ -395,8 +509,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="acc2998389db5bc4d1d62610e908e5581"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#acc2998389db5bc4d1d62610e908e5581">◆ </a></span>AllocateConst()</h2>
+<a id="a83286f6aa614fd519daa19573df13885"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a83286f6aa614fd519daa19573df13885">◆ </a></span>AllocateConst()</h2>
<div class="memitem">
<div class="memproto">
@@ -422,8 +536,8 @@ Functions</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> > </td>
- <td class="paramname"><em>annotations</em> = <code><a class="el" href="namespacetvm.html#ab6c242e8ac09beb463fba306948b7f15">NullValue</a>< <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> >>()</code> </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> >> </td>
+ <td class="paramname"><em>annotations</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code> </td>
</tr>
<tr>
<td></td>
@@ -1135,8 +1249,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a8878f384c4a2861add3f43da4aa7e080"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8878f384c4a2861add3f43da4aa7e080">◆ </a></span>Float32()</h2>
+<a id="af233b18ee0522a900568b14b6f9ef8d6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#af233b18ee0522a900568b14b6f9ef8d6">◆ </a></span>Float16x16()</h2>
<div class="memitem">
<div class="memproto">
@@ -1145,7 +1259,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float16x16 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1161,8 +1275,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a6ff858a2cfb49c10fc00ae68a1728086"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6ff858a2cfb49c10fc00ae68a1728086">◆ </a></span>Float64()</h2>
+<a id="ad5311856f8b8b4df884170d8c4c513c8"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad5311856f8b8b4df884170d8c4c513c8">◆ </a></span>Float16x32()</h2>
<div class="memitem">
<div class="memproto">
@@ -1171,7 +1285,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float16x32 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1187,8 +1301,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a8f02a339b55424163e97c46f1c9f7976"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8f02a339b55424163e97c46f1c9f7976">◆ </a></span>Float8()</h2>
+<a id="a6843af0b415dd3c3555b176cd3233b6b"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a6843af0b415dd3c3555b176cd3233b6b">◆ </a></span>Float16x4()</h2>
<div class="memitem">
<div class="memproto">
@@ -1197,7 +1311,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float16x4 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1213,114 +1327,112 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a8dee79c7df5e244c728406bad1db7002"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8dee79c7df5e244c728406bad1db7002">◆ </a></span>FuncAttrs()</h2>
+<a id="ac42c4020fca22350e8159b86cefb4031"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ac42c4020fca22350e8159b86cefb4031">◆ </a></span>Float16x64()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">void tvm::script::ir_builder::tir::FuncAttrs </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float16x64 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> > </td>
- <td class="paramname"><em>attrs</em></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>The PrimFunc annotation statement. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">attrs</td><td>The annotations of the PrimFunc. </td></tr>
- </table>
- </dd>
-</dl>
-
</div>
</div>
-<a id="a6abb6566a443b59385ec26213336f1c0"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6abb6566a443b59385ec26213336f1c0">◆ </a></span>FuncName()</h2>
+<a id="a1f72082ac88788080f123bf8c93500e6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1f72082ac88788080f123bf8c93500e6">◆ </a></span>Float16x8()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">void tvm::script::ir_builder::tir::FuncName </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float16x8 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
- <td class="paramname"><em>name</em></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>The PrimFunc naming statement. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">name</td><td>The name of the PrimFunc. </td></tr>
- </table>
- </dd>
-</dl>
-
</div>
</div>
-<a id="a4d377c370c4d836034937d558f6fba11"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4d377c370c4d836034937d558f6fba11">◆ </a></span>FuncRet()</h2>
+<a id="a8878f384c4a2861add3f43da4aa7e080"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a8878f384c4a2861add3f43da4aa7e080">◆ </a></span>Float32()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1Type.html">Type</a> tvm::script::ir_builder::tir::FuncRet </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1Type.html">Type</a> </td>
- <td class="paramname"><em>ret_type</em></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>The PrimFunc return type statement. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">ret_type</td><td>The return type of the PrimFunc. </td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>The return type. </dd></dl>
-
</div>
</div>
-<a id="a0e2f9aeceb779e5186af2e9b55fbabca"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0e2f9aeceb779e5186af2e9b55fbabca">◆ </a></span>Grid()</h2>
+<a id="acb206b62b60473442d995a4f868f57f1"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#acb206b62b60473442d995a4f868f57f1">◆ </a></span>Float32x16()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrame.html">ForFrame</a> tvm::script::ir_builder::tir::Grid </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32x16 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
- <td class="paramname"><em>extents</em></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>The grid For statement. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">extents</td><td>The extents of the iteration. </td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>The <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrame.html" title="Managed reference to ForFrameNode. ">ForFrame</a>. </dd></dl>
-
</div>
</div>
-<a id="a07e14252128fe48c5e9bbd33c1e460fc"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a07e14252128fe48c5e9bbd33c1e460fc">◆ </a></span>Handle()</h2>
+<a id="ad859865f93db0e8b124464203253fdab"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad859865f93db0e8b124464203253fdab">◆ </a></span>Float32x32()</h2>
<div class="memitem">
<div class="memproto">
@@ -1329,7 +1441,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Handle </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32x32 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1345,55 +1457,60 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a31e99fdb505e94750f9c45bf35399371"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a31e99fdb505e94750f9c45bf35399371">◆ </a></span>If()</h2>
+<a id="afc23e87468bd379cef2e436dec7380ec"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#afc23e87468bd379cef2e436dec7380ec">◆ </a></span>Float32x4()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1IfFrame.html">IfFrame</a> tvm::script::ir_builder::tir::If </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32x4 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>condition</em></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>Create an if statement. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">condition</td><td>The condition of if statement. </td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>The result <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1IfFrame.html" title="Managed reference to IfFrameNode. ">IfFrame</a>. </dd></dl>
-
</div>
</div>
-<a id="abd98d9f9b1651dfb649d590adf9335cd"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abd98d9f9b1651dfb649d590adf9335cd">◆ </a></span>Init()</h2>
+<a id="ad363b06dcc7a557663219774aae3ae59"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad363b06dcc7a557663219774aae3ae59">◆ </a></span>Float32x64()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockInitFrame.html">BlockInitFrame</a> tvm::script::ir_builder::tir::Init </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32x64 </td>
<td>(</td>
- <td class="paramname"></td><td>)</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></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>The block initialization statement. </p>
-<dl class="section return"><dt>Returns</dt><dd>The <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1BlockInitFrame.html" title="Managed reference to BlockInitFrameNode. ">BlockInitFrame</a>. </dd></dl>
-
</div>
</div>
-<a id="a71a8950ee36a0cc96b08f6a38a67beb8"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a71a8950ee36a0cc96b08f6a38a67beb8">◆ </a></span>Int16()</h2>
+<a id="ae6706e8560ac719f2957e0186de014b3"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae6706e8560ac719f2957e0186de014b3">◆ </a></span>Float32x8()</h2>
<div class="memitem">
<div class="memproto">
@@ -1402,7 +1519,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int16 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float32x8 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1418,8 +1535,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="af90ae386a2b910cc52c17ebb6390404e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af90ae386a2b910cc52c17ebb6390404e">◆ </a></span>Int32()</h2>
+<a id="a6ff858a2cfb49c10fc00ae68a1728086"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a6ff858a2cfb49c10fc00ae68a1728086">◆ </a></span>Float64()</h2>
<div class="memitem">
<div class="memproto">
@@ -1428,7 +1545,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int32 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1444,8 +1561,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a4945336e84e5ce9b60b583ac0adb28d6"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4945336e84e5ce9b60b583ac0adb28d6">◆ </a></span>Int32x16()</h2>
+<a id="acccc0b5d430d1205b3d1a782cab67aae"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#acccc0b5d430d1205b3d1a782cab67aae">◆ </a></span>Float64x16()</h2>
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@@ -1454,7 +1571,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int32x16 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64x16 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1470,8 +1587,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a1d7bdb8458778293ced5af6ad9378283"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a1d7bdb8458778293ced5af6ad9378283">◆ </a></span>Int32x4()</h2>
+<a id="ae07732bbb9fea30ff6b9c03a7150beeb"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae07732bbb9fea30ff6b9c03a7150beeb">◆ </a></span>Float64x32()</h2>
<div class="memitem">
<div class="memproto">
@@ -1480,7 +1597,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int32x4 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64x32 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1496,8 +1613,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="ae89c85eabf166d4efddff53988ff57b5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae89c85eabf166d4efddff53988ff57b5">◆ </a></span>Int32x8()</h2>
+<a id="a4051e0d30c5fe712f65fffb8eee90d29"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a4051e0d30c5fe712f65fffb8eee90d29">◆ </a></span>Float64x4()</h2>
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@@ -1506,7 +1623,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int32x8 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64x4 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1522,8 +1639,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a7e3533536d109d1a5dd037c356ac2662"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a7e3533536d109d1a5dd037c356ac2662">◆ </a></span>Int64()</h2>
+<a id="ab2aebc810c46e788e51c8e01a9eb7536"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ab2aebc810c46e788e51c8e01a9eb7536">◆ </a></span>Float64x64()</h2>
<div class="memitem">
<div class="memproto">
@@ -1532,7 +1649,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int64 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64x64 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1548,8 +1665,8 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="aa8e3f67af7d6676f0980fa5fa6406ee7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa8e3f67af7d6676f0980fa5fa6406ee7">◆ </a></span>Int8()</h2>
+<a id="a4bb3d4dabf70259c2820a0f36c9e5661"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a4bb3d4dabf70259c2820a0f36c9e5661">◆ </a></span>Float64x8()</h2>
<div class="memitem">
<div class="memproto">
@@ -1558,7 +1675,7 @@ Functions</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Int8 </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float64x8 </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
<td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
@@ -1574,614 +1691,2093 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a1b6a75a0308366b04fee2fe37a22a297"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a1b6a75a0308366b04fee2fe37a22a297">◆ </a></span>LaunchThread()</h2>
+<a id="a8f02a339b55424163e97c46f1c9f7976"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a8f02a339b55424163e97c46f1c9f7976">◆ </a></span>Float8()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrame.html">LaunchThreadFrame</a> tvm::script::ir_builder::tir::LaunchThread </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> </td>
- <td class="paramname"><em>var</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>extent</em> </td>
- </tr>
- <tr>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<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>Launch a thread. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">var</td><td>The iteration variable. </td></tr>
- <tr><td class="paramname">extent</td><td>The extent of environment thread. </td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>The result <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LaunchThreadFrame.html" title="Managed reference to LaunchThreadFrameNode. ">LaunchThreadFrame</a>. </dd></dl>
-
</div>
</div>
-<a id="a6e3d4fbea2aa5cf1f7c18dfa9fac094f"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6e3d4fbea2aa5cf1f7c18dfa9fac094f">◆ </a></span>Let()</h2>
+<a id="ac5b707e9c9bd8ff20640e9aa5609f7ad"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ac5b707e9c9bd8ff20640e9aa5609f7ad">◆ </a></span>Float8x16()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LetFrame.html">LetFrame</a> tvm::script::ir_builder::tir::Let </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8x16 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> </td>
- <td class="paramname"><em>var</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>value</em> </td>
- </tr>
- <tr>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<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>The let binding. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramname">var</td><td>The variable to bind. </td></tr>
- <tr><td class="paramname">value</td><td>The value to be bound. </td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>The created <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1LetFrame.html" title="Managed reference to LetFrameNode. ">LetFrame</a>. </dd></dl>
-
</div>
</div>
-<a id="aeed7efeb604129dd83dc6862fd857992"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aeed7efeb604129dd83dc6862fd857992">◆ </a></span>MatchBuffer()</h2>
+<a id="a0c1842297c526585f5e7ecd5cf9fa835"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a0c1842297c526585f5e7ecd5cf9fa835">◆ </a></span>Float8x32()</h2>
<div class="memitem">
<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> tvm::script::ir_builder::tir::MatchBuffer </td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8x32 </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> </td>
- <td class="paramname"><em>param</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
- <td class="paramname"><em>shape</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="namespacetvm.html#a41918af1a1dc386388639a9d3ad06c5d">DataType</a> </td>
- <td class="paramname"><em>dtype</em> = <code><a class="el" href="classtvm_1_1runtime_1_1DataType.html#a237a714a6a16e14aa01fa4ac52426551">DataType::Float</a>(32)</code>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1tir_1_1Var.html">Var</a> > </td>
- <td class="paramname"><em>data</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
- <td class="paramname"><em>strides</em> = <code>{}</code>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>elem_offset</em> = <code><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a>()</code>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
- <td class="paramname"><em>storage_scope</em> = <code>"global"</code>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>align</em> = <code>-1</code>, </td>
</tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a9d6a011a6d9a84e818bbed17821fca43"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a9d6a011a6d9a84e818bbed17821fca43">◆ </a></span>Float8x4()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
<tr>
- <td class="paramkey"></td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8x4 </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>offset_factor</em> = <code>0</code>, </td>
</tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a9493b11608cc2420b3e3780726556f0c"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a9493b11608cc2420b3e3780726556f0c">◆ </a></span>Float8x64()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
<tr>
- <td class="paramkey"></td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8x64 </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
- <td class="paramname"><em>buffer_type</em> = <code>"default"</code>, </td>
</tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a8d984cfc2c4f432a5e2bb16ed9723c9c"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a8d984cfc2c4f432a5e2bb16ed9723c9c">◆ </a></span>Float8x8()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
<tr>
- <td class="paramkey"></td>
+ <td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::script::ir_builder::tir::Float8x8 </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>expr</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code></td><td>)</td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1IntImm.html">IntImm</a> > </td>
- <td class="paramname"><em>axis_separators</em> = <code>{}</code> </td>
</tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a8dee79c7df5e244c728406bad1db7002"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a8dee79c7df5e244c728406bad1db7002">◆ </a></span>FuncAttrs()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
<tr>
+ <td class="memname">void tvm::script::ir_builder::tir::FuncAttrs </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> > </td>
+ <td class="paramname"><em>attrs</em></td><td>)</td>
<td></td>
- <td>)</td>
- <td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
-<p>The buffer match statement. </p>
+<p>The PrimFunc annotation statement. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramname">param</td><td>The parameter of the PrimFunc to match. </td></tr>
- <tr><td class="paramname">shape</td><td>The type of the buffer prior to flattening. </td></tr>
- <tr><td class="paramname">dtype</td><td>The data type in the content of the buffer. </td></tr>
- <tr><td class="paramname">data</td><td>The pointer to the head of the data. </td></tr>
- <tr><td class="paramname">strides</td><td>The strides of each dimension. </td></tr>
- <tr><td class="paramname">elem_offset</td><td>The offset in terms of number of dtype elements (including lanes). </td></tr>
- <tr><td class="paramname">storage_scope</td><td>The optional storage scope of buffer data pointer. </td></tr>
- <tr><td class="paramname">align</td><td>The alignment requirement of data pointer in bytes. </td></tr>
- <tr><td class="paramname">offset_factor</td><td>The factor of elem_offset field. </td></tr>
- <tr><td class="paramname">buffer_type</td><td>The buffer type. </td></tr>
- <tr><td class="paramname">axis_separators</td><td>The separators between input axes when generating flattened output axes. </td></tr>
+ <tr><td class="paramname">attrs</td><td>The annotations of the PrimFunc. </td></tr>
</table>
</dd>
</dl>
-<dl class="section return"><dt>Returns</dt><dd>The matched buffer. </dd></dl>
-<dl><dt><b>Examples: </b></dt><dd><a class="el" href="_2workspace_2include_2tvm_2script_2ir_builder_2base_8h-example.html#a0">/workspace/include/tvm/script/ir_builder/base.h</a>.</dd>
-</dl>
+
</div>
</div>
-<a id="af443e6c0816e4e4ff97881df7a11a6db"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af443e6c0816e4e4ff97881df7a11a6db">◆ </a></span>Parallel()</h2>
+<a id="a6abb6566a443b59385ec26213336f1c0"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a6abb6566a443b59385ec26213336f1c0">◆ </a></span>FuncName()</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrame.html">ForFrame</a> tvm::script::ir_builder::tir::Parallel </td>
+ <td class="memname">void tvm::script::ir_builder::tir::FuncName </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>start</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
- <td class="paramname"><em>stop</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> >> </td>
- <td class="paramname"><em>annotations</em> = <code><a class="el" href="namespacetvm.html#aae7034e3e41c18e7fb78ff32bfc6a318">NullOpt</a></code> </td>
- </tr>
- <tr>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> </td>
+ <td class="paramname"><em>name</em></td><td>)</td>
<td></td>
- <td>)</td>
- <td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
-<p>The parallel For statement. </p>
+<p>The PrimFunc naming statement. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramname">start</td><td>The minimum value of iteration. </td></tr>
- <tr><td class="paramname">stop</td><td>The maximum value of iteration. </td></tr>
- <tr><td class="paramname">annotations</td><td>The optional annotations of the For statement. </td></tr>
+ <tr><td class="paramname">name</td><td>The name of the PrimFunc. </td></tr>
</table>
</dd>
</dl>
-<dl class="section return"><dt>Returns</dt><dd>The <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrame.html" title="Managed reference to ForFrameNode. ">ForFrame</a>. </dd></dl>
</div>
</div>
-<a id="aeb707d56c770edb33ebf73da27ebc1b9"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aeb707d56c770edb33ebf73da27ebc1b9">◆ </a></span>Prefetch()</h2>
+<a id="a4d377c370c4d836034937d558f6fba11"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a4d377c370c4d836034937d558f6fba11">◆ </a></span>FuncRet()</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">void tvm::script::ir_builder::tir::Prefetch </td>
+ <td class="memname"><a class="el" href="classtvm_1_1Type.html">Type</a> tvm::script::ir_builder::tir::FuncRet </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> </td>
- <td class="paramname"><em>buffer</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1Range.html">Range</a> > </td>
- <td class="paramname"><em>bounds</em> </td>
- </tr>
- <tr>
+ <td class="paramtype"><a class="el" href="classtvm_1_1Type.html">Type</a> </td>
+ <td class="paramname"><em>ret_type</em></td><td>)</td>
<td></td>
- <td>)</td>
- <td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
-<p>The prefetch hint for a buffer. </p>
+<p>The PrimFunc return type statement. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramname">buffer</td><td>The buffer to be prefetched. </td></tr>
- <tr><td class="paramname">bounds</td><td>The bounds to be prefetched. </td></tr>
+ <tr><td class="paramname">ret_type</td><td>The return type of the PrimFunc. </td></tr>
</table>
</dd>
</dl>
+<dl class="section return"><dt>Returns</dt><dd>The return type. </dd></dl>
</div>
</div>
-<a id="acdcdf58de7c6aec49464cfe166351a64"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#acdcdf58de7c6aec49464cfe166351a64">◆ </a></span>PreflattenedBuffer()</h2>
+<a id="a0e2f9aeceb779e5186af2e9b55fbabca"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a0e2f9aeceb779e5186af2e9b55fbabca">◆ </a></span>Grid()</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">void tvm::script::ir_builder::tir::PreflattenedBuffer </td>
+ <td class="memname"><a class="el" href="classtvm_1_1script_1_1ir__builder_1_1tir_1_1ForFrame.html">ForFrame</a> tvm::script::ir_builder::tir::Grid </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> </td>
- <td class="paramname"><em>postflattened_buffer</em>, </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>extents</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>The grid For statement. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">extents</td><td>The extents of the iteration. </td></tr>
+ </table>
+ </dd>
+</dl>
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