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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/09/09 00:43:29 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@64031d56d634a535c8e3832d9231855b688f0648)
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 f8daa769d deploying docs (apache/tvm@64031d56d634a535c8e3832d9231855b688f0648)
f8daa769d is described below
commit f8daa769d7168c90e88b842c0cdfbace7072cfc3
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Fri Sep 9 00:43:21 2022 +0000
deploying docs (apache/tvm@64031d56d634a535c8e3832d9231855b688f0648)
---
.../how_to/compile_models/from_darknet.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 | 8 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 14 +-
.../tune_conv2d_layer_cuda.rst.txt | 1064 +++-----------------
.../tune_network_cuda.rst.txt | 2 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 129 +--
.../tune_with_autotvm/sg_execution_times.rst.txt | 12 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 26 +-
.../work_with_microtvm/micro_autotune.rst.txt | 16 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 10 +-
.../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 | 36 +-
.../how_to/work_with_schedules/tensorize.rst.txt | 2 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 6 +-
.../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 | 2 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 54 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 22 +-
.../tutorial/tensor_expr_get_started.rst.txt | 46 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 14 +-
docs/how_to/compile_models/from_pytorch.html | 6 +-
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 | 17 +-
docs/how_to/deploy_models/deploy_prequantized.html | 6 +-
.../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 | 38 +-
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 | 8 +-
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 | 18 +-
.../tune_conv2d_layer_cuda.html | 1064 +++-----------------
.../tune_with_autoscheduler/tune_network_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autoscheduler/tune_sparse_x86.html | 129 +--
.../tune_with_autotvm/sg_execution_times.html | 12 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 26 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 16 +-
docs/how_to/work_with_microtvm/micro_train.html | 16 +-
.../work_with_microtvm/sg_execution_times.html | 10 +-
.../how_to/work_with_relay/sg_execution_times.html | 8 +-
docs/how_to/work_with_schedules/intrin_math.html | 2 +-
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docs/how_to/work_with_schedules/tensorize.html | 2 +-
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docs/tutorial/auto_scheduler_matmul_x86.html | 2 +-
docs/tutorial/autotvm_matmul_x86.html | 20 +-
docs/tutorial/autotvm_relay_x86.html | 258 ++---
docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 26 +-
docs/tutorial/tensor_expr_get_started.html | 46 +-
207 files changed, 1485 insertions(+), 3003 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 d2c79659f..ca8f9c75b 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -317,7 +317,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.760 seconds)
+ **Total running time of the script:** ( 1 minutes 3.814 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 08a12e57a..057b57521 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.zip54bb0021-5133-4f92-8ce3-4d90428cbeaf from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip17743ad1-f46e-4ab0-839d-28517721a0e3 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 a69ac4293..0ec003c4d 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -113,7 +113,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
0%| | 0.00/41.5M [00:00<?, ?B/s]
19%|#9 | 7.99M/41.5M [00:00<00:00, 74.8MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 70.8MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 57.7MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 57.2MB/s]
96%|#########6| 40.0M/41.5M [00:00<00:00, 57.5MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 57.0MB/s]
+
0%| | 0.00/41.5M [00:00<?, ?B/s]
15%|#5 | 6.33M/41.5M [00:00<00:00, 55.9MB/s]
35%|###4 | 14.3M/41.5M [00:00<00:00, 49.5MB/s]
46%|####6 | 19.1M/41.5M [00:00<00:00, 46.5MB/s]
57%|#####6 | 23.6M/41.5M [00:00<00:00, 43.2MB/s]
67%|######6 | 27.7M/41.5M [00:00<00:00, 41.9MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 40.8MB/s]
92%|#########2| 38.3M/41.5M [00:00<00:00, 47.3MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 45.9MB/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 9484dde21..4df5d1c56 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -94,7 +94,7 @@ Load a pretrained PyTorch model
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
0%| | 0.00/44.7M [00:00<?, ?B/s]
33%|###2 | 14.6M/44.7M [00:00<00:00, 153MB/s]
88%|########8 | 39.4M/44.7M [00:00<00:00, 216MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 213MB/s]
+
0%| | 0.00/44.7M [00:00<?, ?B/s]
46%|####5 | 20.5M/44.7M [00:00<00:00, 215MB/s]
92%|#########1| 41.0M/44.7M [00:00<00:00, 201MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 203MB/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 bb78a3640..a609d79da 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -423,7 +423,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.666 seconds)
+ **Total running time of the script:** ( 1 minutes 1.582 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 bc42c95a5..8c651d1c1 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
=================
-**05:08.598** total execution time for **how_to_compile_models** files:
+**05:03.669** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:05.760 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:03.814 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:02.666 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:01.582 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:42.052 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:39.641 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:28.186 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:27.847 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:25.550 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:26.077 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:24.510 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:25.103 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:22.321 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:21.335 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:19.734 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:19.831 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:15.300 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:15.976 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.519 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.462 | 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 d68fae0ae..63674937c 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -441,7 +441,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.3008 16.1240 17.5615 15.9027 0.4856
+ 15.7863 15.7302 16.1000 15.6958 0.1203
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 ec509f918..150d1db48 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -123,7 +123,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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+
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100%|##########| 170M/170M [00:00<00:00, 212MB/s]
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -292,7 +292,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 1.799 seconds)
+ **Total running time of the script:** ( 2 minutes 55.015 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 b6db0a340..e6b72fb0d 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -232,7 +232,7 @@ training. Other models require a full post training calibration.
.. code-block:: none
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 173MB/s]
+
0%| | 0.00/13.6M [00:00<?, ?B/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 169MB/s]
@@ -412,7 +412,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 90.3995 90.2252 95.9980 90.1096 0.6866
+ 90.3492 90.0470 97.2694 89.9033 1.0359
@@ -461,7 +461,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.847 seconds)
+ **Total running time of the script:** ( 1 minutes 8.675 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 67a905420..e401b9fe9 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -439,7 +439,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 120.8130 120.7515 122.9326 120.1069 0.4335
+ 120.4961 120.4841 126.6318 118.7601 0.8529
@@ -476,7 +476,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 53.843 seconds)
+ **Total running time of the script:** ( 1 minutes 58.396 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 dfeea9467..71b4811d4 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -255,7 +255,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.007 seconds)
+ **Total running time of the script:** ( 1 minutes 36.324 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 fd7838ad2..56b73d213 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -158,7 +158,7 @@ Convert and compile model for CPU.
data: None
input_sym_arg_type = in_param.infer_type()[0]
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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@@ -241,7 +241,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 45.158 seconds)
+ **Total running time of the script:** ( 2 minutes 35.106 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 6b53365c2..1ac90bde2 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**11:33.694** total execution time for **how_to_deploy_models** files:
+**11:28.273** 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``) | 03:01.799 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 02:55.015 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:45.158 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 02:35.106 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 01:53.843 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 01:58.396 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:25.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:36.324 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:12.847 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:08.675 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:30.279 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:29.453 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:22.651 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:22.730 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:22.103 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:22.569 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 413f52343..f94aa1034 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -476,7 +476,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip55fbf702-8d7e-4753-a895-bcbdad7dc2c4 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip208d4ed0-abb9-40e6-b534-4faa8be2f36e 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 4b6831c9e..0cb1abbd5 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
=================
-**00:42.954** total execution time for **how_to_extend_tvm** files:
+**00:40.720** 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``) | 00:39.653 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:37.632 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.296 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.170 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:00.997 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:00.909 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index adc9e1cf0..2d1d59332 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: 6882us [6882us] (45.61%; 45.61%)
- FoldScaleAxis: 8207us [6us] (54.39%; 54.39%)
- FoldConstant: 8202us [1735us] (54.35%; 99.93%)
- InferType: 6467us [6467us] (42.86%; 78.85%)
+ InferType: 6768us [6768us] (46.60%; 46.60%)
+ FoldScaleAxis: 7756us [6us] (53.40%; 53.40%)
+ FoldConstant: 7749us [1584us] (53.36%; 99.92%)
+ InferType: 6166us [6166us] (42.45%; 79.56%)
@@ -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: 6427us [6427us] (44.49%; 44.49%)
- FoldScaleAxis: 8018us [5us] (55.51%; 55.51%)
- FoldConstant: 8012us [1715us] (55.47%; 99.93%)
- InferType: 6298us [6298us] (43.60%; 78.60%)
+ InferType: 6231us [6231us] (44.59%; 44.59%)
+ FoldScaleAxis: 7742us [5us] (55.41%; 55.41%)
+ FoldConstant: 7738us [1568us] (55.37%; 99.94%)
+ InferType: 6170us [6170us] (44.15%; 79.73%)
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 0c36cdc66..3b5b9e192 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: 54.221659 ms
+ Convolution: 54.168631 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 281f839db..a2ae219c8 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -671,7 +671,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.995148 ms
+ conv2d with tensor core: 6.767923 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 9a300b593..a6986af31 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: 0.018730
- Baseline: 3.449067
+ Numpy running time: 0.018024
+ Baseline: 3.435788
@@ -239,7 +239,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.311143
+ Opt1: 0.296037
@@ -342,7 +342,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.345286
+ Opt2: 0.334691
@@ -438,7 +438,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118202
+ Opt3: 0.116833
@@ -563,7 +563,7 @@ flattening.
.. code-block:: none
- Opt4: 0.109385
+ Opt4: 0.111166
@@ -685,7 +685,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.110813
+ Opt5: 0.110955
@@ -810,7 +810,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.146608
+ Opt6: 0.146793
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 ed4edd3f1..cb9447441 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
=================
-**00:34.898** total execution time for **how_to_optimize_operators** files:
+**00:34.595** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:32.651 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:32.215 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.229 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.298 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.018 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.082 | 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 bf9af95e8..12c4ea2c2 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**06:31.626** total execution time for **how_to_tune_with_autoscheduler** files:
+**06:21.840** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 03:38.300 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 03:36.818 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:25.500 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:22.393 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 00:48.081 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 00:46.769 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:20.023 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:18.522 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:09.875 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:08.756 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:09.847 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:08.581 | 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 8bf8a29db..cbf265548 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
@@ -240,482 +240,104 @@ cooperative fetching, unrolling and operator fusion.
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute}
preflattened_buffer_map = {data_1: data_3: Buffer(data_2, float32, [1, 512, 7, 7], []), kernel_1: kernel_3: Buffer(kernel_2, float32, [512, 512, 3, 3], []), bias_1: bias_3: Buffer(bias_2, float32, [1, 512, 1, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [1, 512, 7, 7], [])} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 28;
- allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [3072]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[5] = 0f32
- conv2d_nchw_1[6] = 0f32
- conv2d_nchw_1[7] = 0f32
- conv2d_nchw_1[8] = 0f32
- conv2d_nchw_1[9] = 0f32
- conv2d_nchw_1[10] = 0f32
- conv2d_nchw_1[11] = 0f32
- conv2d_nchw_1[12] = 0f32
- conv2d_nchw_1[13] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 8;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [648]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [4608]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ for (ff.outer.inner.init: int32, 0, 2) {
+ let cse_var_1: int32 = (ff.outer.inner.init*7)
+ {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [196], [], scope="local", align=32)[cse_var_1] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 14)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 1)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 15)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 2)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 16)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 3)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 17)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 4)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 18)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 5)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 19)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 6)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 20)] = 0f32
+ }
+ }
for (rc.outer.outer: int32, 0, 64) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_2: int32 = (rc.outer.outer*72)
- let cse_var_1: int32 = (ry.outer.outer*3)
- {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope="shared")[(threadIdx.x_1*4)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*4), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1*4), 9)) - 8)], 0f3 [...]
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 1)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 1), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 2)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 2), 9))) && (floormod(((threadIdx.x_1*4) + 2), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 2), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 2), 9)) - 8)], 0f32, dtype=float32)
+ let cse_var_2: int32 = (rc.outer.outer*392)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [648], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((9 <= floormod(threadIdx.x_1, 81)) && (floormod(threadIdx.x_1, 81) < 72)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_2 + (floordiv(threadIdx.x_1, 81)*49)) + (floordiv(floormod(threadIdx.x_1, 81), 9)*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 31), 81)) && (floormod((threadIdx.x_1 + 31), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 112), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 31), 81), 9)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 62), 81)) && (floormod((threadIdx.x_1 + 62), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 224), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 62), 81), 9)*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 12), 81)) && (floormod((threadIdx.x_1 + 12), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 336), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 12), 81), 9)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 43), 81)) && (floormod((threadIdx.x_1 + 43), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 448), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 43), 81), 9)*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_1 < 88), dtype=bool) {
+ pad_temp.shared_1[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 74), 81)) && (floormod((threadIdx.x_1 + 74), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 560), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 74), 81), 9)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+ }
+ for (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer: int32, 0, 2) {
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*7) + floordiv(threadIdx.x_2, 16)) < 8), dtype=bool) {
+ for (ax0.ax1.fused.ax2.fused.ax3.fused.inner.s: int32, 0, 36) {
+ kernel.shared_1: Buffer(kernel.shared, float32, [4608], [], scope="shared")[(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*4032) + (threadIdx.x_2*36)) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)] = kernel[((((((blockIdx.x*294912) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*258048)) + (floordiv(threadIdx.x_2, 2)*4608)) + (rc.outer.outer*72)) + (floormod(threadIdx.x_2, 2)*36)) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)]
}
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 3)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 3), 9))) && (floormod(((threadIdx.x_1*4) + 3), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 3), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 3), 9)) - 8)], 0f32, dtype=float32)
+ }
+ }
+ for (rc.outer.inner: int32, 0, 4) {
+ for (ry.outer.inner: int32, 0, 3) {
+ for (rx.outer.inner: int32, 0, 3) {
+ for (ff.outer.inner: int32, 0, 2) {
+ for (rc.inner: int32, 0, 2) {
+ let cse_var_16: int32 = (ff.outer.inner*7)
+ let cse_var_15: int32 = (cse_var_16 + 6)
+ let cse_var_14: int32 = (cse_var_16 + 5)
+ let cse_var_13: int32 = (cse_var_16 + 4)
+ let cse_var_12: int32 = (cse_var_16 + 3)
+ let cse_var_11: int32 = (cse_var_16 + 20)
+ let cse_var_10: int32 = (cse_var_16 + 2)
+ let cse_var_9: int32 = (cse_var_16 + 19)
+ let cse_var_8: int32 = (cse_var_16 + 18)
+ let cse_var_7: int32 = (cse_var_16 + 17)
+ let cse_var_6: int32 = (cse_var_16 + 16)
+ let cse_var_5: int32 = (cse_var_16 + 15)
+ let cse_var_4: int32 = (cse_var_16 + 14)
+ let cse_var_3: int32 = (cse_var_16 + 1)
+ {
+ conv2d_nchw_1[cse_var_16] = (conv2d_nchw_1[cse_var_16] + (pad_temp.shared_1[(((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_4] = (conv2d_nchw_1[cse_var_4] + (pad_temp.shared_1[(((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_3] = (conv2d_nchw_1[cse_var_3] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_6] = (conv2d_nchw_1[cse_var_6] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_7] = (conv2d_nchw_1[cse_var_7] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_8] = (conv2d_nchw_1[cse_var_8] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_9] = (conv2d_nchw_1[cse_var_9] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ }
+ }
+ }
}
}
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1: Buffer(kernel.shared, float32, [3072], [], scope="shared")[threadIdx.x_2] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 64), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 128), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 36864)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 256), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 320), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 384)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 73728)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 448), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 512)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 512), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 576)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 110592)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 640), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 704)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 704), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 768)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 147456)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 832)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 832), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 896), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 960)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 184320)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1024)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1024), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1088)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1088), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1152)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 221184)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1216)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1216), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1280)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1280), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 258048)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1408)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1408), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1472)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1472), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1536)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 294912)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1600)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1600), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1664)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1664), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1728)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 331776)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1792)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1792), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1856)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1856), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1920)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 368640)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1984)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1984), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2048)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2048), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2112)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 405504)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2176)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2176), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2240)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2240), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2304)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 442368)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2368)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2368), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2432)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2432), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2496)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 479232)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2560)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2560), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2624)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2624), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2688)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 516096)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2752)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2752), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2816)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2816), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2880)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 552960)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2944)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2944), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 3008)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 3008), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[0]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[1]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[2]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[3]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[4]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[5]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[6]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[0]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*48) + 47)]))
}
}
}
for (i1.inner: int32, 0, 2) {
for (i3.inner: int32, 0, 7) {
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[((i1.inner*7) + i3.inner)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
+ let cse_var_17: int32 = ((i1.inner*7) + i3.inner)
+ {
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[cse_var_17] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+ compute[((((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner) + 1568)] = max((conv2d_nchw_1[(cse_var_17 + 14)] + bias[((((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner) + 32)]), 0f32)
+ }
}
}
}
@@ -771,7 +393,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.358 ms
+ Execution time of this operator: 0.420 ms
@@ -821,20 +443,20 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=2)
- conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=64)
- conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
+ conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
+ conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
- conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=1)
+ conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
- conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
- conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=7)
+ conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=7)
+ conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=2)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=4)
conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
- conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
+ conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=3)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2 [...]
@@ -842,10 +464,10 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
- compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
+ compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
- compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
+ compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=7)
compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
@@ -866,16 +488,16 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused = s[compute].fuse(compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i)
s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread_axis("threadIdx.x"))
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=36)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=4)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
- s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
+ s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 16)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -893,429 +515,73 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
#define int64_t long long
#define uint64_t unsigned long long
#endif
- extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[14];
- __shared__ float pad_temp_shared[72];
- __shared__ float kernel_shared[3072];
- conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
- conv2d_nchw[7] = 0.000000e+00f;
- conv2d_nchw[8] = 0.000000e+00f;
- conv2d_nchw[9] = 0.000000e+00f;
- conv2d_nchw[10] = 0.000000e+00f;
- conv2d_nchw[11] = 0.000000e+00f;
- conv2d_nchw[12] = 0.000000e+00f;
- conv2d_nchw[13] = 0.000000e+00f;
+ extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[648];
+ __shared__ float kernel_shared[4608];
+ for (int ff_outer_inner_init = 0; ff_outer_inner_init < 2; ++ff_outer_inner_init) {
+ conv2d_nchw[(ff_outer_inner_init * 7)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 14)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 1)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 15)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 2)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 16)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 3)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 17)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 4)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 18)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 5)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 19)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 6)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 20)] = 0.000000e+00f;
+ }
for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
- __syncthreads();
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[(((int)threadIdx.x) * 4)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 4) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 1)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 1) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 2)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 2) % 9))) && ((((((int)threadIdx.x) * 4) + 2) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 2) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 2) % 9)) - 8)] : 0.000000e+00f);
+ __syncthreads();
+ pad_temp_shared[((int)threadIdx.x)] = (((((9 <= (((int)threadIdx.x) % 81)) && ((((int)threadIdx.x) % 81) < 72)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 81) * 49)) + (((((int)threadIdx.x) % 81) / 9) * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((9 <= ((((int)threadIdx.x) + 31) % 81)) && (((((int)threadIdx.x) + 31) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 112) / 81) * 49)) + ((((((int)threadIdx.x) + 31) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = (((((9 <= ((((int)threadIdx.x) + 62) % 81)) && (((((int)threadIdx.x) + 62) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 224) / 81) * 49)) + ((((((int)threadIdx.x) + 62) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 336)] = (((((9 <= ((((int)threadIdx.x) + 12) % 81)) && (((((int)threadIdx.x) + 12) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 336) / 81) * 49)) + ((((((int)threadIdx.x) + 12) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 448)] = (((((9 <= ((((int)threadIdx.x) + 43) % 81)) && (((((int)threadIdx.x) + 43) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 448) / 81) * 49)) + ((((((int)threadIdx.x) + 43) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 88) {
+ pad_temp_shared[(((int)threadIdx.x) + 560)] = (((((9 <= ((((int)threadIdx.x) + 74) % 81)) && (((((int)threadIdx.x) + 74) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 560) / 81) * 49)) + ((((((int)threadIdx.x) + 74) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+ }
+ for (int ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer = 0; ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer < 2; ++ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer) {
+ if (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 7) + (((int)threadIdx.x) >> 4)) < 8) {
+ for (int ax0_ax1_fused_ax2_fused_ax3_fused_inner_s = 0; ax0_ax1_fused_ax2_fused_ax3_fused_inner_s < 36; ++ax0_ax1_fused_ax2_fused_ax3_fused_inner_s) {
+ kernel_shared[(((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 4032) + (((int)threadIdx.x) * 36)) + ax0_ax1_fused_ax2_fused_ax3_fused_inner_s)] = kernel[((((((((int)blockIdx.x) * 294912) + (ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 258048)) + ((((int)threadIdx.x) >> 1) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) & 1) * 36)) + ax0_ax1_fused_ax2_fused_ax3_fused_inner_s)];
+ }
}
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 3)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 3) % 9))) && ((((((int)threadIdx.x) * 4) + 3) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 3) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 3) % 9)) - 8)] : 0.000000e+00f);
+ }
+ __syncthreads();
+ for (int rc_outer_inner = 0; rc_outer_inner < 4; ++rc_outer_inner) {
+ for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
+ for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
+ for (int ff_outer_inner = 0; ff_outer_inner < 2; ++ff_outer_inner) {
+ for (int rc_inner = 0; rc_inner < 2; ++rc_inner) {
+ conv2d_nchw[(ff_outer_inner * 7)] = (conv2d_nchw[(ff_outer_inner * 7)] + (pad_temp_shared[(((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 14)] = (conv2d_nchw[((ff_outer_inner * 7) + 14)] + (pad_temp_shared[(((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 1)] = (conv2d_nchw[((ff_outer_inner * 7) + 1)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 15)] = (conv2d_nchw[((ff_outer_inner * 7) + 15)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 2)] = (conv2d_nchw[((ff_outer_inner * 7) + 2)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 16)] = (conv2d_nchw[((ff_outer_inner * 7) + 16)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 3)] = (conv2d_nchw[((ff_outer_inner * 7) + 3)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 17)] = (conv2d_nchw[((ff_outer_inner * 7) + 17)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 4)] = (conv2d_nchw[((ff_outer_inner * 7) + 4)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 18)] = (conv2d_nchw[((ff_outer_inner * 7) + 18)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 5)] = (conv2d_nchw[((ff_outer_inner * 7) + 5)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 19)] = (conv2d_nchw[((ff_outer_inner * 7) + 19)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 6)] = (conv2d_nchw[((ff_outer_inner * 7) + 6)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 20)] = (conv2d_nchw[((ff_outer_inner * 7) + 20)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ }
+ }
+ }
}
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 64) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 128) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 192)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 36864)];
- kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 256) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 320) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 384)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 73728)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 512)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 512) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 576)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 110592)];
- kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 640) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 704)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 704) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 768)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 147456)];
- kernel_shared[(((int)threadIdx.x) + 832)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 832) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 960)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 184320)];
- kernel_shared[(((int)threadIdx.x) + 1024)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1024) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1088)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1088) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1152)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 221184)];
- kernel_shared[(((int)threadIdx.x) + 1216)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1216) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1280)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1280) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 258048)];
- kernel_shared[(((int)threadIdx.x) + 1408)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1408) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1472)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1472) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1536)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 294912)];
- kernel_shared[(((int)threadIdx.x) + 1600)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1600) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1664)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1664) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1728)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 331776)];
- kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1792) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1856)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1856) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1920)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 368640)];
- kernel_shared[(((int)threadIdx.x) + 1984)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1984) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2048)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2048) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2112)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 405504)];
- kernel_shared[(((int)threadIdx.x) + 2176)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2176) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2240) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2304)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 442368)];
- kernel_shared[(((int)threadIdx.x) + 2368)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2368) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2432)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2432) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2496)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 479232)];
- kernel_shared[(((int)threadIdx.x) + 2560)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2560) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2624)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2624) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 516096)];
- kernel_shared[(((int)threadIdx.x) + 2752)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2752) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2816)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2816) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2880)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 552960)];
- kernel_shared[(((int)threadIdx.x) + 2944)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2944) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3008)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 3008) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[0] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[1] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[2] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[3] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[4] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[5] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[6] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[0] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
}
}
for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
for (int i3_inner = 0; i3_inner < 7; ++i3_inner) {
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
+ compute[((((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner) + 1568)] = max((conv2d_nchw[(((i1_inner * 7) + i3_inner) + 14)] + bias[((((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner) + 32)]), 0.000000e+00f);
}
}
}
@@ -1378,7 +644,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 38.300 seconds)
+ **Total running time of the script:** ( 3 minutes 36.818 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 23114a619..f713149a7 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -647,7 +647,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.7837 9.8206 9.8256 9.7047 0.0558
+ 9.8302 9.8440 9.8669 9.7798 0.0369
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 8e100f6ac..5c104b5a5 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -666,7 +666,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 763.6381 763.3983 764.7191 762.7969 0.8028
+ 756.2388 755.9175 757.5815 755.2175 0.9915
@@ -694,7 +694,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.500 seconds)
+ **Total running time of the script:** ( 1 minutes 22.393 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 82fcddb03..644ebc2c9 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -397,14 +397,14 @@ 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_5: placeholder_15: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (i.outer.inner: int32, 0, 8) {
- for (i.inner.init: int32, 0, 4) {
- let cse_var_1: int32 = ((i.outer.inner*64) + (i.inner.init*16))
+ preflattened_buffer_map = {compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_19: Buffer(placeholder_11, float32, [4916, 16, 1], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 64) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ for (i.inner.init: int32, 0, 32) {
+ let cse_var_1: int32 = ((i.outer.inner*512) + (i.inner.init*16))
{
- compute_5: Buffer(compute_4, float32, [512], [])[cse_var_1] = 0f32
+ compute_5: Buffer(compute_4, float32, [2048], [])[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
@@ -422,81 +422,54 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
compute_5[(cse_var_1 + 15)] = 0f32
}
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 4) {
- let cse_var_3: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ for (elem_idx: int32, 0, let cse_var_2: int32 = floordiv(i0.outer.i1.outer.fused, 2) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 32) {
+ let cse_var_21: int32 = floordiv(i0.outer.i1.outer.fused, 2)
+ let cse_var_20: int32 = (elem_idx*16)
+ let cse_var_19: int32 = ((i.outer.inner*8192) + (i.inner*256))
+ let cse_var_18: int32 = ((i.outer.inner*512) + (i.inner*16))
+ let cse_var_17: int32 = (cse_var_18 + 9)
+ let cse_var_16: int32 = (cse_var_18 + 8)
+ let cse_var_15: int32 = (cse_var_18 + 7)
+ let cse_var_14: int32 = (cse_var_18 + 6)
+ let cse_var_13: int32 = (cse_var_18 + 5)
+ let cse_var_12: int32 = (cse_var_18 + 4)
+ let cse_var_11: int32 = (cse_var_18 + 3)
+ let cse_var_10: int32 = (cse_var_18 + 2)
+ let cse_var_9: int32 = (cse_var_18 + 15)
+ let cse_var_8: int32 = (cse_var_18 + 14)
+ let cse_var_7: int32 = (cse_var_18 + 13)
+ let cse_var_6: int32 = (cse_var_18 + 12)
+ let cse_var_5: int32 = (cse_var_18 + 11)
+ let cse_var_4: int32 = (cse_var_18 + 10)
+ let cse_var_3: int32 = (cse_var_18 + 1)
{
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_4: int32 = ((i.outer.inner*64) + (i.inner*16))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[((placeholder_3[cse_var_3]*16) + (elem_idx*16))]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_5: int32 = (((i.outer.inner*64) + (i.inner*16)) + 1)
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 1)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_6: int32 = (((i.outer.inner*64) + (i.inner*16)) + 2)
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 2)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_7: int32 = (((i.outer.inner*64) + (i.inner*16)) + 3)
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 3)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_8: int32 = (((i.outer.inner*64) + (i.inner*16)) + 4)
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 4)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_9: int32 = (((i.outer.inner*64) + (i.inner*16)) + 5)
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 5)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_10: int32 = (((i.outer.inner*64) + (i.inner*16)) + 6)
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 6)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_11: int32 = (((i.outer.inner*64) + (i.inner*16)) + 7)
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 7)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_12: int32 = (((i.outer.inner*64) + (i.inner*16)) + 8)
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 8)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_13: int32 = (((i.outer.inner*64) + (i.inner*16)) + 9)
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 9)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_14: int32 = (((i.outer.inner*64) + (i.inner*16)) + 10)
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 10)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_15: int32 = (((i.outer.inner*64) + (i.inner*16)) + 11)
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 11)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_16: int32 = (((i.outer.inner*64) + (i.inner*16)) + 12)
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 12)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_17: int32 = (((i.outer.inner*64) + (i.inner*16)) + 13)
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 13)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_18: int32 = (((i.outer.inner*64) + (i.inner*16)) + 14)
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 14)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_19: int32 = (((i.outer.inner*64) + (i.inner*16)) + 15)
- compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 15)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_21]*16) + cse_var_20)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_20: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
- compute[ramp(cse_var_20, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_20, 1, 16)]), broadcast(0f32, 16))
+ for (i0.inner: int32, 0, 128) {
+ for (i1.inner: int32, 0, 8) {
+ let cse_var_23: int32 = (i0.outer.i1.outer.fused*8)
+ let cse_var_22: int32 = (((i0.inner*512) + cse_var_23) + i1.inner)
+ compute[cse_var_22] = max((compute_5[((((i0.inner*16) + cse_var_23) + i1.inner) - (floordiv(i0.outer.i1.outer.fused, 2)*16))] + placeholder_4[cse_var_22]), 0f32)
+ }
}
}
}
@@ -552,7 +525,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 2.112 ms
+ Execution time of this operator: 3.502 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 bc906caaf..59aa144ab 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
=================
-**00:46.570** total execution time for **how_to_tune_with_autotvm** files:
+**00:47.214** 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``) | 00:46.532 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:47.177 | 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``) | 00:00.023 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
-+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 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 34e59ab14..2e6558ce8 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -1156,8 +1156,8 @@ for this template
TimeoutError
[('tile_f', [-1, 2, 1, 64]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4909501
- No: 9 GFLOPS: 80.80/80.80 result: MeasureResult(costs=(0.002865089857142857,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.871819257736206, timestamp=1662659224.2793512) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
- No: 10 GFLOPS: 0.00/80.80 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 128.67/128.67 result: MeasureResult(costs=(0.0017991688214285715,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8581395149230957, timestamp=1662680461.6663718) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
+ No: 10 GFLOPS: 0.00/128.67 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1280,8 +1280,8 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5092711
- No: 11 GFLOPS: 260.52/260.52 result: MeasureResult(costs=(0.0008886077513812153,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5324485301971436, timestamp=1662659225.2490761) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
- No: 12 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 261.01/261.01 result: MeasureResult(costs=(0.0008869559944751382,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.78175687789917, timestamp=1662680462.586266) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
+ No: 12 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1404,7 +1404,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 128, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,183542
- No: 13 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1527,7 +1527,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 8, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2482196
- No: 14 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1650,9 +1650,9 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10306226
- No: 15 GFLOPS: 5.30/260.52 result: MeasureResult(costs=(0.0436854875,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.843956470489502, timestamp=1662659229.815484) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
- No: 16 GFLOPS: 3.34/260.52 result: MeasureResult(costs=(0.0693956865,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.612766742706299, timestamp=1662659231.0973122) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
- No: 17 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 5.27/261.01 result: MeasureResult(costs=(0.04395225775,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.882185459136963, timestamp=1662680467.2203062) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
+ No: 16 GFLOPS: 3.36/261.01 result: MeasureResult(costs=(0.06892849250000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.590003728866577, timestamp=1662680468.4531302) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
+ No: 17 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1670,8 +1670,8 @@ for this template
TimeoutError
[('tile_f', [-1, 2, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 16]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10195251
- No: 18 GFLOPS: 26.02/260.52 result: MeasureResult(costs=(0.008898506666666665,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1877410411834717, timestamp=1662659242.0215013) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
- No: 19 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 27.98/261.01 result: MeasureResult(costs=(0.008274430642857144,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.275554895401001, timestamp=1662680479.462969) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
+ No: 19 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1794,7 +1794,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 16, 4, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6956993
- No: 20 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+ No: 20 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1973,7 +1973,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
Finish loading 20 records
- Time cost of this operator: 0.001271
+ Time cost of this operator: 0.001219
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 a017066c5..32cd475ad 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -329,10 +329,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 311.2 98.735 (1, 2, 10, 10, 3) 2 1 [311.2]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.021 0.958 (1, 6, 10, 10) 1 1 [3.021]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.967 0.307 (1, 1, 10, 10, 3) 1 1 [0.967]
- Total_time - 315.188 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 310.3 98.729 (1, 2, 10, 10, 3) 2 1 [310.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.017 0.96 (1, 6, 10, 10) 1 1 [3.017]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.311 (1, 1, 10, 10, 3) 1 1 [0.978]
+ Total_time - 314.295 - - - - -
@@ -398,10 +398,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 190.7 98.425 (1, 1, 10, 10, 6) 2 1 [190.7]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.21 1.141 (1, 6, 10, 10) 1 1 [2.21]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.841 0.434 (1, 3, 10, 10, 1) 1 1 [0.841]
- Total_time - 193.751 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 195.3 98.499 (1, 6, 10, 10, 1) 2 1 [195.3]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.027 1.022 (1, 6, 10, 10) 1 1 [2.027]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.949 0.478 (1, 3, 10, 10, 1) 1 1 [0.949]
+ Total_time - 198.275 - - - - -
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 8f761d048..ae1bbbc5c 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/tmpye_kgrch/images/random'
+ '/tmp/tmpvv32kj0l/images/random'
@@ -325,8 +325,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpye_kgrch/images/target contains 8144 images
- /tmp/tmpye_kgrch/images/random contains 5000 images
+ /tmp/tmpvv32kj0l/images/target contains 8144 images
+ /tmp/tmpvv32kj0l/images/random contains 5000 images
@@ -501,13 +501,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 57s - loss: 0.2077 - accuracy: 0.9257 - val_loss: 0.1394 - val_accuracy: 0.9562
+ 328/328 - 56s - loss: 0.2202 - accuracy: 0.9226 - val_loss: 0.1345 - val_accuracy: 0.9630
Epoch 2/3
- 328/328 - 53s - loss: 0.1005 - accuracy: 0.9626 - val_loss: 0.1061 - val_accuracy: 0.9668
+ 328/328 - 52s - loss: 0.0926 - accuracy: 0.9648 - val_loss: 0.1574 - val_accuracy: 0.9513
Epoch 3/3
- 328/328 - 53s - loss: 0.0628 - accuracy: 0.9763 - val_loss: 0.2003 - val_accuracy: 0.9354
+ 328/328 - 52s - loss: 0.0646 - accuracy: 0.9745 - val_loss: 0.1738 - val_accuracy: 0.9471
- <keras.callbacks.History object at 0x7f9a06564ad0>
+ <keras.callbacks.History object at 0x7fe594f1ab90>
@@ -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 49.482 seconds)
+ **Total running time of the script:** ( 4 minutes 57.371 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 c282a27df..af347870c 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**05:45.653** total execution time for **how_to_work_with_microtvm** files:
+**05:51.117** total execution time for **how_to_work_with_microtvm** files:
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:49.482 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:57.371 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:44.014 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 00:42.232 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.748 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:08.208 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.407 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:03.303 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``) | 00:00.001 | 0.0 MB |
+---------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 4565c1d84..a8719f4d2 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
=================
-**00:44.399** total execution time for **how_to_work_with_relay** files:
+**00:42.813** 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``) | 00:32.265 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:31.554 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:10.389 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:09.858 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.738 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.394 | 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 8fc25e094..a417cedad 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 0x7f9985440c20>
+ <function my_cuda_math_rule at 0x7fe520748200>
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 82fc341a2..94a06f27d 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
=================
-**00:04.172** total execution time for **how_to_work_with_schedules** files:
+**00:04.302** total execution time for **how_to_work_with_schedules** files:
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:01.920 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:00.1000 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.539 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.528 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.102 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.042 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.027 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.015 | 0.0 MB |
-+------------------------------------------------------------------------------------------------+------------+--------+
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``) | 00:01.961 | 0.0 MB |
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.070 | 0.0 MB |
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.549 | 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_extern_op.py` (``extern_op.py``) | 00:00.098 | 0.0 MB |
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.041 | 0.0 MB |
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.028 | 0.0 MB |
++------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.015 | 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 5f56fe751..f8202f849 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/tmp7snqfpyv/input0.cc'\nsource_filename = \"/tmp/tmp7snqfpyv/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/tmp1smf4fmp/input0.cc'\nsource_filename = \"/tmp/tmp1smf4fmp/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 05d40e971..fe967dfd5 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
=================
-**00:23.008** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:21.166** total execution time for **topic_vta_tutorials_autotvm** files:
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:23.001 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``) | 00:21.160 | 0.0 MB |
+---------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.007 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``) | 00:00.006 | 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 a06efbc51..00b04ea55 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -291,7 +291,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
- resnet18_v1 inference graph built in 26.17s!
+ resnet18_v1 inference graph built in 22.72s!
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 43757019b..09d26c332 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -335,7 +335,7 @@ The compilation steps are:
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py: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 17.47s!
+ yolov3-tiny inference graph built in 15.95s!
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 c0d5760c7..17c4d24f2 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
=================
-**01:37.186** total execution time for **topic_vta_tutorials_frontend** files:
+**01:31.523** total execution time for **topic_vta_tutorials_frontend** files:
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:50.263 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:48.459 | 0.0 MB |
+------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:46.924 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``) | 00:43.063 | 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 8c609103c..e778c6ba0 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
=================
-**00:03.445** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.342** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:03.021 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.924 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.424 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.418 | 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 60cb6c26a..cb28b9d67 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
=================
-**00:00.775** total execution time for **topic_vta_tutorials** files:
+**00:00.769** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.423 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.412 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.352 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.357 | 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 1bba22da8..ed9106a34 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: 93.600 ms
+ Execution time of this operator: 94.485 ms
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index 1a8c31254..194316719 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -462,16 +462,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 10.37/10.37 result: MeasureResult(costs=(0.025874462999999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5470092296600342, timestamp=1662657993.078982) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
- No: 2 GFLOPS: 2.91/10.37 result: MeasureResult(costs=(0.0922017588,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6760056018829346, timestamp=1662657994.7612782) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
- No: 3 GFLOPS: 11.75/11.75 result: MeasureResult(costs=(0.0228441362,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5695548057556152, timestamp=1662657995.8522124) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
- No: 4 GFLOPS: 1.58/11.75 result: MeasureResult(costs=(0.16989027480000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.8370141983032227, timestamp=1662657999.3088498) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 5 GFLOPS: 3.53/11.75 result: MeasureResult(costs=(0.07609779539999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.361241340637207, timestamp=1662658000.7989016) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
- No: 6 GFLOPS: 1.84/11.75 result: MeasureResult(costs=(0.1460549784,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4554946422576904, timestamp=1662658003.859728) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
- No: 7 GFLOPS: 0.84/11.75 result: MeasureResult(costs=(0.3209910264,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.256304979324341, timestamp=1662658009.1572585) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 8 GFLOPS: 10.05/11.75 result: MeasureResult(costs=(0.0267209406,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6259303092956543, timestamp=1662658009.7900991) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
- No: 9 GFLOPS: 1.58/11.75 result: MeasureResult(costs=(0.1693808634,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.813093662261963, timestamp=1662658012.719757) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
- No: 10 GFLOPS: 2.50/11.75 result: MeasureResult(costs=(0.1075331796,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8286418914794922, timestamp=1662658014.604718) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
+ No: 1 GFLOPS: 10.49/10.49 result: MeasureResult(costs=(0.0255935978,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5469872951507568, timestamp=1662679248.870281) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
+ No: 2 GFLOPS: 2.67/10.49 result: MeasureResult(costs=(0.1006344324,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7545621395111084, timestamp=1662679250.6463335) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
+ No: 3 GFLOPS: 11.83/11.83 result: MeasureResult(costs=(0.022692057600000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5711381435394287, timestamp=1662679251.7169244) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+ No: 4 GFLOPS: 1.85/11.83 result: MeasureResult(costs=(0.1450325686,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4517223834991455, timestamp=1662679254.751538) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
+ No: 5 GFLOPS: 3.69/11.83 result: MeasureResult(costs=(0.0727773378,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.30503249168396, timestamp=1662679256.1901448) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
+ No: 6 GFLOPS: 1.72/11.83 result: MeasureResult(costs=(0.1556953856,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.614009380340576, timestamp=1662679259.3955157) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
+ No: 7 GFLOPS: 0.86/11.83 result: MeasureResult(costs=(0.3133077082,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.1336164474487305, timestamp=1662679264.5750794) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 8 GFLOPS: 10.55/11.83 result: MeasureResult(costs=(0.025448856599999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5543022155761719, timestamp=1662679265.1476328) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
+ No: 9 GFLOPS: 1.65/11.83 result: MeasureResult(costs=(0.16238124660000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.6933515071868896, timestamp=1662679267.960986) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+ No: 10 GFLOPS: 2.46/11.83 result: MeasureResult(costs=(0.10907074480000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8524575233459473, timestamp=1662679269.8713691) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 316b54369..6742bc11f 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -327,7 +327,7 @@ standard deviation.
.. code-block:: none
- {'mean': 498.022426169997, 'median': 498.1348002000004, 'std': 0.8748482383293634}
+ {'mean': 495.29929663003713, 'median': 495.0232297499497, 'std': 0.5934874365415119}
@@ -563,30 +563,30 @@ the tuning data to.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.47/ 17.47 GFLOPS | Progress: (4/20) | 6.49 s
[Task 1/25] Current/Best: 6.16/ 17.47 GFLOPS | Progress: (8/20) | 9.44 s
[Task 1/25] Current/Best: 11.50/ 22.77 GFLOPS | Progress: (12/20) | 11.95 s
[Task 1/25] Current/Best: 16.47/ 22.77 GFLOPS | Progress: (16/20) | 13.65 s
[Task 1/25] Current/Best: 11.60/ 23.78 GFLOPS | Progress: (20/20) | 15.42 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.27/ 12.87 GFLOPS | Progress: (4/20) | 3.88 s
[Task 2/25] Current/Best: 14.43/ 18.30 GFLOPS | Progress: (8/20) | 5.17 s
[Task 2/25] Current/Best: 21.01/ 21.01 GFLOPS | Progress: (12/20) | 6.50 s
[Task 2/25] Current/Best: 12.12/ 21.01 GFLOPS | Progress: (16/20) | 7.78 s
[Task 2/25] Current/Best: 19.31/ 21.01 GFLOPS | Progress: (20/20) | 9.38 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.76 GFLOPS | Progress: (4/20) | 5.91 s
[Task 3/25] Current/Best: 14.57/ 16.83 GFLOPS | Progress: (8/20) | 7.88 s
[Task 3/25] Current/Best: 14.94/ 16.83 GFLOPS | Progress: (12/20) | 9.60 s
[Task 3/25] Current/Best: 7.22/ 23.71 GFLOPS | Progress: (16/20) | 11.57 s
[Task 3/25] Current/Best: 12.62/ 23.71 GFLOPS | Progress: (20/20) | 16.10 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.50/ 19.85 GFLOPS | Progress: (4/20) | 2.44 s
[Task 4/25] Current/Best: 6.78/ 19.85 GFLOPS | Progress: (8/20) | 6.80 s
[Task 4/25] Current/Best: 22.12/ 22.12 GFLOPS | Progress: (12/20) | 11.37 s
[Task 4/25] Current/Best: 17.10/ 22.12 GFLOPS | Progress: (16/20) | 13.63 s
[Task 4/25] Current/Best: 13.18/ 22.12 GFLOPS | Progress: (20/20) | 15.65 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.34/ 10.08 GFLOPS | Progress: (4/20) | 2.67 s
[Task 5/25] Current/Best: 11.61/ 12.67 GFLOPS | Progress: (8/20) | 4.80 s
[Task 5/25] Current/Best: 11.34/ 17.97 GFLOPS | Progress: (12/20) | 7.99 s
[Task 5/25] Current/Best: 11.46/ 22.48 GFLOPS | Progress: (16/20) | 9.41 s
[Task 5/25] Current/Best: 12.01/ 22.48 GFLOPS | Progress: (20/20) | 11.30 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.09/ 20.07 GFLOPS | Progress: (4/20) | 4.06 s
[Task 6/25] Current/Best: 18.88/ 20.07 GFLOPS | Progress: (8/20) | 5.84 s
[Task 6/25] Current/Best: 13.13/ 20.07 GFLOPS | Progress: (12/20) | 7.78 s
[Task 6/25] Current/Best: 20.11/ 20.11 GFLOPS | Progress: (16/20) | 10.05 s
[Task 6/25] Current/Best: 3.73/ 20.11 GFLOPS | Progress: (20/20) | 12.57 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.35/ 12.84 GFLOPS | Progress: (4/20) | 3.65 s
[Task 7/25] Current/Best: 20.27/ 21.09 GFLOPS | Progress: (8/20) | 5.20 s
[Task 7/25] Current/Best: 16.08/ 21.09 GFLOPS | Progress: (12/20) | 7.16 s
[Task 7/25] Current/Best: 12.12/ 21.09 GFLOPS | Progress: (16/20) | 9.22 s
[Task 7/25] Current/Best: 6.24/ 21.61 GFLOPS | Progress: (20/20) | 11.71 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 9.77/ 13.89 GFLOPS | Progress: (4/20) | 2.96 s
[Task 8/25] Current/Best: 9.36/ 13.89 GFLOPS | Progress: (8/20) | 7.80 s
[Task 8/25] Current/Best: 12.97/ 13.89 GFLOPS | Progress: (12/20) | 13.93 s
[Task 8/25] Current/Best: 19.11/ 19.11 GFLOPS | Progress: (16/20) | 16.06 s
[Task 8/25] Current/Best: 19.57/ 19.57 GFLOPS | Progress: (20/20) | 22.78 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.33/ 15.76 GFLOPS | Progress: (4/20) | 12.04 s
[Task 9/25] Current/Best: 23.41/ 23.41 GFLOPS | Progress: (8/20) | 13.83 s
[Task 9/25] Current/Best: 8.29/ 23.41 GFLOPS | Progress: (12/20) | 16.22 s
[Task 9/25] Current/Best: 17.92/ 23.41 GFLOPS | Progress: (16/20) | 18.95 s
[Task 9/25] Current/Best: 8.98/ 23.41 GFLOPS | Progress: (20/20) | 26.77 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.26/ 18.26 GFLOPS | Progress: (4/20) | 2.65 s
[Task 10/25] Current/Best: 15.52/ 18.26 GFLOPS | Progress: (8/20) | 4.25 s
[Task 10/25] Current/Best: 12.58/ 18.77 GFLOPS | Progress: (12/20) | 5.79 s
[Task 10/25] Current/Best: 18.99/ 20.12 GFLOPS | Progress: (16/20) | 6.91 s
[Task 10/25] Current/Best: 8.77/ 20.12 GFLOPS | Progress: (20/20
) | 8.47 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.30/ 18.08 GFLOPS | Progress: (4/20) | 3.38 s
[Task 11/25] Current/Best: 16.86/ 18.08 GFLOPS | Progress: (8/20) | 6.13 s
[Task 11/25] Current/Best: 18.02/ 18.08 GFLOPS | Progress: (12/20) | 8.19 s
[Task 11/25] Current/Best: 13.45/ 20.97 GFLOPS | Progress: (16/20) | 10.97 s
[Task 11/25] Current/Best: 19.42/ 21.60 GFLOPS | Progress: (20/20) | 13.03 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.75/ 17.81 GFLOPS | Progress: (4/20) | 5.40 s
[Task 12/25] Current/Best: 5.16/ 17.81 GFLOPS | Progress: (8/20) | 9.12 s
[Task 12/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (12/20) | 11.14 s
[Task 12/25] Current/Best: 14.41/ 18.96 GFLOPS | Progress: (16/20) | 14.00 s
[Task 12/25] Current/Best: 14.91/ 18.96 GFLOPS | Progress: (20/20) | 15.93 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.67/ 17.23 GFLOPS | Progress: (4/20) | 3.78 s
[Task 13/25] Current/Best: 15.55/ 20.80 GFLOPS | Progress: (8/20) | 6.22 s
[Task 13/25] Current/Best: 19.63/ 21.77 GFLOPS | Progress: (12/20) | 9.13 s
[Task 13/25] Current/Best: 12.19/ 21.77 GFLOPS | Progress: (16/20) | 12.58 s
[Task 13/25] Current/Best: 18.22/ 21.77 GFLOPS | Progress: (20/20) | 14.87 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 12.09/ 13.17 GFLOPS | Progress: (4/20) | 3.48 s
[Task 14/25] Current/Best: 6.01/ 13.24 GFLOPS | Progress: (8/20) | 5.66 s
[Task 14/25] Current/Best: 19.41/ 19.41 GFLOPS | Progress: (12/20) | 8.24 s
[Task 14/25] Current/Best: 16.23/ 19.41 GFLOPS | Progress: (16/20) | 9.94 s Done.
-
[Task 14/25] Current/Best: 17.52/ 19.41 GFLOPS | Progress: (20/20) | 11.81 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.14/ 17.30 GFLOPS | Progress: (4/20) | 2.79 s
[Task 15/25] Current/Best: 14.57/ 18.09 GFLOPS | Progress: (8/20) | 4.16 s
[Task 15/25] Current/Best: 10.36/ 22.29 GFLOPS | Progress: (12/20) | 6.32 s
[Task 15/25] Current/Best: 20.42/ 22.29 GFLOPS | Progress: (16/20) | 9.38 s
[Task 15/25] Current/Best: 9.69/ 22.29 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: 20.28/ 20.28 GFLOPS | Progress: (4/20) | 3.04 s
[Task 16/25] Current/Best: 3.01/ 20.28 GFLOPS | Progress: (8/20) | 4.65 s
[Task 16/25] Current/Best: 19.72/ 20.28 GFLOPS | Progress: (12/20) | 5.87 s
[Task 16/25] Current/Best: 17.86/ 20.28 GFLOPS | Progress: (16/20) |
7.27 s
[Task 16/25] Current/Best: 10.04/ 22.10 GFLOPS | Progress: (20/20) | 9.32 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 13.47/ 18.39 GFLOPS | Progress: (4/20) | 4.82 s
[Task 17/25] Current/Best: 14.36/ 23.12 GFLOPS | Progress: (8/20) | 7.71 s
[Task 17/25] Current/Best: 18.29/ 23.12 GFLOPS | Progress: (12/20) | 9.76 s
[Task 17/25] Current/Best: 16.44/ 23.12 GFLOPS | Progress: (16/20) | 11.92 s
[Task 17/25] Current/Best: 10.03/ 23.12 GFLOPS | Progress: (20/20) | 14.08 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.13/ 17.97 GFLOPS | Progress: (4/20) | 3.80 s
[Task 18/25] Current/Best: 10.53/ 19.33 GFLOPS | Progress: (8/20) | 7.25 s
[Task 18/25] Current/Best: 18.97/ 19.33 GFLOPS | Progress: (12/20) | 9.18 s
[Task 18/25] Current/Best: 9.90/ 19.33 GFLOPS | Progress: (16/20) | 12.86 s
[Task 18/25] Current/Best: 20.46/ 20.46 GFLOPS | Progress: (20/20) | 14.37 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 6.93/ 20.27 GFLOPS | Progress: (4/20) | 6.25 s
[Task 19/25] Current/Best: 2.69/ 20.27 GFLOPS | Progress: (8/20) | 9.53 s
[Task 19/25] Current/Best: 19.36/ 21.17 GFLOPS | Progress: (12/20) | 12.34 s
[Task 19/25] Current/Best: 15.31/ 21.17 GFLOPS | Progress: (16/20) | 15.20 s
[Task 19/25] Current/Best: 2.69/ 22.85 GFLOPS | Progress: (20/20) | 17.97 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.18/ 15.47 GFLOPS | Progress: (4/20) | 3.41 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.60/ 17.60 GFLOPS | Progress: (4/20) | 6.40 s
[Task 1/25] Current/Best: 6.16/ 17.60 GFLOPS | Progress: (8/20) | 9.45 s
[Task 1/25] Current/Best: 11.52/ 22.72 GFLOPS | Progress: (12/20) | 11.94 s
[Task 1/25] Current/Best: 16.54/ 22.72 GFLOPS | Progress: (16/20) | 13.63 s
[Task 1/25] Current/Best: 11.63/ 23.85 GFLOPS | Progress: (20/20) | 15.38 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.13/ 12.68 GFLOPS | Progress: (4/20) | 3.76 s
[Task 2/25] Current/Best: 14.05/ 18.79 GFLOPS | Progress: (8/20) | 5.06 s
[Task 2/25] Current/Best: 21.15/ 21.15 GFLOPS | Progress: (12/20) | 6.39 s
[Task 2/25] Current/Best: 12.75/ 21.15 GFLOPS | Progress: (16/20) | 7.65 s
[Task 2/25] Current/Best: 19.33/ 21.15 GFLOPS | Progress: (20/20) | 9.27 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.84 GFLOPS | Progress: (4/20) | 5.88 s
[Task 3/25] Current/Best: 15.35/ 16.84 GFLOPS | Progress: (8/20) | 7.81 s
[Task 3/25] Current/Best: 15.03/ 16.84 GFLOPS | Progress: (12/20) | 9.55 s
[Task 3/25] Current/Best: 7.22/ 23.84 GFLOPS | Progress: (16/20) | 11.47 s
[Task 3/25] Current/Best: 12.38/ 23.84 GFLOPS | Progress: (20/20) | 16.04 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.52/ 20.34 GFLOPS | Progress: (4/20) | 2.43 s
[Task 4/25] Current/Best: 6.72/ 20.34 GFLOPS | Progress: (8/20) | 7.12 s
[Task 4/25] Current/Best: 22.57/ 22.57 GFLOPS | Progress: (12/20) | 12.08 s
[Task 4/25] Current/Best: 17.00/ 22.57 GFLOPS | Progress: (16/20) | 14.45 s
[Task 4/25] Current/Best: 13.48/ 22.57 GFLOPS | Progress: (20/20) | 16.41 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.52/ 10.22 GFLOPS | Progress: (4/20) | 2.62 s
[Task 5/25] Current/Best: 11.70/ 12.69 GFLOPS | Progress: (8/20) | 4.68 s
[Task 5/25] Current/Best: 11.68/ 17.99 GFLOPS | Progress: (12/20) | 7.90 s
[Task 5/25] Current/Best: 11.54/ 22.50 GFLOPS | Progress: (16/20) | 9.31 s
[Task 5/25] Current/Best: 12.04/ 22.50 GFLOPS | Progress: (20/20) | 11.25 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.12/ 20.07 GFLOPS | Progress: (4/20) | 4.13 s
[Task 6/25] Current/Best: 18.94/ 20.07 GFLOPS | Progress: (8/20) | 5.91 s
[Task 6/25] Current/Best: 13.15/ 20.07 GFLOPS | Progress: (12/20) | 7.88 s
[Task 6/25] Current/Best: 20.09/ 20.09 GFLOPS | Progress: (16/20) | 10.14 s
[Task 6/25] Current/Best: 3.72/ 20.09 GFLOPS | Progress: (20/20) | 12.68 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 11.08/ 12.98 GFLOPS | Progress: (4/20) | 3.58 s
[Task 7/25] Current/Best: 20.30/ 21.03 GFLOPS | Progress: (8/20) | 5.10 s
[Task 7/25] Current/Best: 16.13/ 21.03 GFLOPS | Progress: (12/20) | 7.01 s
[Task 7/25] Current/Best: 12.18/ 21.03 GFLOPS | Progress: (16/20) | 9.06 s
[Task 7/25] Current/Best: 6.31/ 21.83 GFLOPS | Progress: (20/20) | 11.53 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 9.69/ 13.76 GFLOPS | Progress: (4/20) | 2.99 s
[Task 8/25] Current/Best: 9.20/ 13.76 GFLOPS | Progress: (8/20) | 8.13 s
[Task 8/25] Current/Best: 12.72/ 13.76 GFLOPS | Progress: (12/20) | 14.62 s
[Task 8/25] Current/Best: 18.98/ 18.98 GFLOPS | Progress: (16/20) | 16.73 s
[Task 8/25] Current/Best: 19.65/ 19.65 GFLOPS | Progress: (20/20) | 23.79 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.38/ 15.66 GFLOPS | Progress: (4/20) | 11.99 s
[Task 9/25] Current/Best: 23.42/ 23.42 GFLOPS | Progress: (8/20) | 13.79 s
[Task 9/25] Current/Best: 8.23/ 23.42 GFLOPS | Progress: (12/20) | 16.29 s
[Task 9/25] Current/Best: 17.98/ 23.42 GFLOPS | Progress: (16/20) | 19.02 s
[Task 9/25] Current/Best: 9.18/ 23.42 GFLOPS | Progress: (20/20) | 27.38 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.06/ 18.06 GFLOPS | Progress: (4/20) | 2.61 s
[Task 10/25] Current/Best: 15.60/ 18.06 GFLOPS | Progress: (8/20) | 4.24 s
[Task 10/25] Current/Best: 12.51/ 19.03 GFLOPS | Progress: (12/20) | 5.80 s
[Task 10/25] Current/Best: 19.11/ 20.23 GFLOPS | Progress: (16/20) | 6.91 s
[Task 10/25] Current/Best: 8.84/ 20.23 GFLOPS | Progress: (20/20
) | 8.49 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 11.58/ 18.19 GFLOPS | Progress: (4/20) | 3.40 s
[Task 11/25] Current/Best: 16.94/ 18.19 GFLOPS | Progress: (8/20) | 6.22 s
[Task 11/25] Current/Best: 18.33/ 18.33 GFLOPS | Progress: (12/20) | 8.33 s
[Task 11/25] Current/Best: 13.46/ 20.89 GFLOPS | Progress: (16/20) | 11.25 s
[Task 11/25] Current/Best: 19.32/ 21.59 GFLOPS | Progress: (20/20) | 13.38 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.73/ 17.92 GFLOPS | Progress: (4/20) | 5.73 s
[Task 12/25] Current/Best: 5.16/ 17.92 GFLOPS | Progress: (8/20) | 9.62 s
[Task 12/25] Current/Best: 18.94/ 18.94 GFLOPS | Progress: (12/20) | 11.64 s
[Task 12/25] Current/Best: 15.23/ 18.94 GFLOPS | Progress: (16/20) | 14.59 s
[Task 12/25] Current/Best: 15.15/ 18.94 GFLOPS | Progress: (20/20) | 16.50 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.60/ 17.38 GFLOPS | Progress: (4/20) | 3.78 s
[Task 13/25] Current/Best: 15.81/ 20.92 GFLOPS | Progress: (8/20) | 6.40 s
[Task 13/25] Current/Best: 19.68/ 22.04 GFLOPS | Progress: (12/20) | 9.52 s
[Task 13/25] Current/Best: 12.28/ 22.04 GFLOPS | Progress: (16/20) | 12.99 s
[Task 13/25] Current/Best: 18.73/ 22.04 GFLOPS | Progress: (20/20) | 15.38 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.80/ 13.80 GFLOPS | Progress: (4/20) | 3.47 s
[Task 14/25] Current/Best: 6.12/ 13.80 GFLOPS | Progress: (8/20) | 5.69 s
[Task 14/25] Current/Best: 20.98/ 20.98 GFLOPS | Progress: (12/20) | 8.37 s
[Task 14/25] Current/Best: 17.66/ 20.98 GFLOPS | Progress: (16/20) | 10.02 s Done.
+
[Task 14/25] Current/Best: 16.97/ 20.98 GFLOPS | Progress: (20/20) | 11.86 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.20/ 17.62 GFLOPS | Progress: (4/20) | 2.77 s
[Task 15/25] Current/Best: 14.24/ 18.02 GFLOPS | Progress: (8/20) | 4.07 s
[Task 15/25] Current/Best: 10.39/ 22.42 GFLOPS | Progress: (12/20) | 6.30 s
[Task 15/25] Current/Best: 20.42/ 22.42 GFLOPS | Progress: (16/20) | 9.69 s
[Task 15/25] Current/Best: 9.70/ 22.42 GFLOPS | Progress: (20/20) | 10.71 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.23/ 20.23 GFLOPS | Progress: (4/20) | 3.03 s
[Task 16/25] Current/Best: 3.04/ 20.23 GFLOPS | Progress: (8/20) | 4.64 s
[Task 16/25] Current/Best: 19.52/ 20.23 GFLOPS | Progress: (12/20) | 5.87 s
[Task 16/25] Current/Best: 17.46/ 20.23 GFLOPS | Progress: (16/20) |
7.23 s
[Task 16/25] Current/Best: 9.98/ 21.99 GFLOPS | Progress: (20/20) | 9.40 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 13.10/ 18.22 GFLOPS | Progress: (4/20) | 4.84 s
[Task 17/25] Current/Best: 14.45/ 22.81 GFLOPS | Progress: (8/20) | 7.63 s
[Task 17/25] Current/Best: 17.69/ 22.81 GFLOPS | Progress: (12/20) | 9.69 s
[Task 17/25] Current/Best: 16.54/ 22.81 GFLOPS | Progress: (16/20) | 11.89 s
[Task 17/25] Current/Best: 10.06/ 22.81 GFLOPS | Progress: (20/20) | 14.05 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.31/ 17.96 GFLOPS | Progress: (4/20) | 3.84 s
[Task 18/25] Current/Best: 10.55/ 19.61 GFLOPS | Progress: (8/20) | 7.49 s
[Task 18/25] Current/Best: 19.53/ 19.61 GFLOPS | Progress: (12/20) | 9.43 s
[Task 18/25] Current/Best: 10.03/ 19.61 GFLOPS | Progress: (16/20) | 13.30 s
[Task 18/25] Current/Best: 20.72/ 20.72 GFLOPS | Progress: (20/20) | 14.81 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.16/ 20.41 GFLOPS | Progress: (4/20) | 6.11 s
[Task 19/25] Current/Best: 2.70/ 20.41 GFLOPS | Progress: (8/20) | 9.42 s
[Task 19/25] Current/Best: 19.93/ 21.61 GFLOPS | Progress: (12/20) | 12.42 s
[Task 19/25] Current/Best: 14.21/ 22.54 GFLOPS | Progress: (16/20) | 15.36 s
[Task 19/25] Current/Best: 2.70/ 23.20 GFLOPS | Progress: (20/20) | 18.22 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.66/ 14.87 GFLOPS | Progress: (4/20) | 3.40 s Done.
Done.
-
[Task 20/25] Current/Best: 10.40/ 15.47 GFLOPS | Progress: (8/20) | 6.88 s
[Task 20/25] Current/Best: 2.32/ 16.64 GFLOPS | Progress: (12/20) | 10.85 s
[Task 20/25] Current/Best: 12.18/ 16.64 GFLOPS | Progress: (16/20) | 14.49 s
[Task 20/25] Current/Best: 12.49/ 21.50 GFLOPS | Progress: (20/20) | 16.61 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.39/ 17.64 GFLOPS | Progress: (4/20) | 3.32 s
[Task 21/25] Current/Best: 14.55/ 17.64 GFLOPS | Progress: (8/20) | 4.91 s
[Task 21/25] Current/Best: 1.61/ 17.64 GFLOPS | Progress: (12/20) | 7.09 s
[Task 21/25] Current/Best: 18.09/ 18.09 GFLOPS | Progress: (16/20) | 10.61 s
[Task 21/25] Current/Best: 4.45/ 18.09 GFLOPS | Progress: (20/20) | 17.84 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 17.00 GFLOPS | Progress: (4/20
) | 2.76 s
[Task 22/25] Current/Best: 9.06/ 21.89 GFLOPS | Progress: (8/20) | 4.74 s
[Task 22/25] Current/Best: 19.76/ 21.89 GFLOPS | Progress: (12/20) | 7.07 s
[Task 22/25] Current/Best: 15.42/ 21.89 GFLOPS | Progress: (16/20) | 9.14 s
[Task 22/25] Current/Best: 13.64/ 21.89 GFLOPS | Progress: (20/20) | 10.82 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.46/ 20.49 GFLOPS | Progress: (4/20) | 3.33 s
[Task 23/25] Current/Best: 15.52/ 20.49 GFLOPS | Progress: (8/20) | 6.65 s
[Task 23/25] Current/Best: 20.89/ 21.45 GFLOPS | Progress: (12/20) | 8.46 s
[Task 23/25] Current/Best: 6.28/ 21.45 GFLOPS | Progress: (16/20) | 15.59 s
[Task 23/25] Current/Best: 7.51/ 21.45 GFLOPS | Progress: (20/20) | 19.85 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.52/ 8.52 GFLOPS | Progress: (4/20) | 11.84 s
[Task 24/25] Current/Best: 1.90/ 8.52 GFLOPS | Progress: (8/20) | 22.88 s
[Task 24/25] Current/Best: 4.33/ 8.52 GFLOPS | Progress: (12/20) | 34.50 s Done.
-
[Task 24/25] Current/Best: 6.81/ 8.56 GFLOPS | Progress: (16/20) | 39.89 s
[Task 24/25] Current/Best: 3.29/ 8.75 GFLOPS | Progress: (20/20) | 46.03 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.94 GFLOPS | Progress: (4/20) | 11.68 s
[Task 25/25] Current/Best: 5.77/ 7.90 GFLOPS | Progress: (8/20) | 23.02 s
[Task 25/25] Current/Best: 5.79/ 7.90 GFLOPS | Progress: (12/20) | 34.34 s
[Task 25/25] Current/Best: 5.73/ 8.29 GFLOPS | Progress: (16/20) | 36.17 s
[Task 25/25] Current/Best: 2.80/ 8.55 GFLOPS | Progress: (20/20) | 46.86 s
+
[Task 20/25] Current/Best: 10.05/ 14.87 GFLOPS | Progress: (8/20) | 6.79 s
[Task 20/25] Current/Best: 2.32/ 16.72 GFLOPS | Progress: (12/20) | 10.74 s
[Task 20/25] Current/Best: 11.94/ 16.72 GFLOPS | Progress: (16/20) | 14.49 s
[Task 20/25] Current/Best: 12.25/ 22.14 GFLOPS | Progress: (20/20) | 16.61 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.42/ 17.72 GFLOPS | Progress: (4/20) | 3.31 s
[Task 21/25] Current/Best: 14.65/ 17.72 GFLOPS | Progress: (8/20) | 4.94 s
[Task 21/25] Current/Best: 1.61/ 17.72 GFLOPS | Progress: (12/20) | 7.08 s
[Task 21/25] Current/Best: 16.79/ 17.72 GFLOPS | Progress: (16/20) | 10.61 s
[Task 21/25] Current/Best: 4.47/ 17.72 GFLOPS | Progress: (20/20) | 17.89 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 16.97 GFLOPS | Progress: (4/20
) | 2.71 s
[Task 22/25] Current/Best: 8.83/ 21.93 GFLOPS | Progress: (8/20) | 4.77 s
[Task 22/25] Current/Best: 20.00/ 21.93 GFLOPS | Progress: (12/20) | 7.18 s
[Task 22/25] Current/Best: 15.32/ 21.93 GFLOPS | Progress: (16/20) | 9.31 s
[Task 22/25] Current/Best: 13.90/ 21.93 GFLOPS | Progress: (20/20) | 11.06 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.63/ 20.75 GFLOPS | Progress: (4/20) | 3.30 s
[Task 23/25] Current/Best: 14.39/ 20.75 GFLOPS | Progress: (8/20) | 6.71 s
[Task 23/25] Current/Best: 21.04/ 21.60 GFLOPS | Progress: (12/20) | 8.54 s
[Task 23/25] Current/Best: 6.26/ 21.60 GFLOPS | Progress: (16/20) | 15.53 s
[Task 23/25] Current/Best: 7.88/ 21.60 GFLOPS | Progress: (20/20) | 19.73 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.47/ 8.47 GFLOPS | Progress: (4/20) | 11.85 s
[Task 24/25] Current/Best: 3.66/ 8.47 GFLOPS | Progress: (8/20) | 23.11 s
[Task 24/25] Current/Best: 4.19/ 8.47 GFLOPS | Progress: (12/20) | 33.83 s Done.
+
[Task 24/25] Current/Best: 6.78/ 8.89 GFLOPS | Progress: (16/20) | 39.49 s
[Task 24/25] Current/Best: 3.35/ 8.89 GFLOPS | Progress: (20/20) | 45.54 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.76 GFLOPS | Progress: (4/20) | 11.62 s
[Task 25/25] Current/Best: 5.96/ 7.92 GFLOPS | Progress: (8/20) | 22.93 s
[Task 25/25] Current/Best: 6.11/ 7.92 GFLOPS | Progress: (12/20) | 34.41 s
[Task 25/25] Current/Best: 5.89/ 8.63 GFLOPS | Progress: (16/20) | 36.16 s
[Task 25/25] Current/Best: 2.88/ 9.13 GFLOPS | Progress: (20/20) | 46.82 s
@@ -748,8 +748,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 414.516118869999, 'median': 414.3010358000083, 'std': 0.9494320501595434}
- unoptimized: {'mean': 498.022426169997, 'median': 498.1348002000004, 'std': 0.8748482383293634}
+ optimized: {'mean': 409.14941426999576, 'median': 409.1582320499583, 'std': 0.7372720689148065}
+ unoptimized: {'mean': 495.29929663003713, 'median': 495.0232297499497, 'std': 0.5934874365415119}
@@ -772,7 +772,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 10 minutes 29.389 seconds)
+ **Total running time of the script:** ( 10 minutes 25.921 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 50f39610d..2af943871 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -282,7 +282,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.301e-07 secs/op
+ 1.23e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 82180833c..cac4bb96e 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, 0xd204330)), stage(b, placeholder(b, 0x208abee0)), 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, 0xe96b680)), stage(b, placeholder(b, 0x21e756d0)), 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 bf132782f..bf161fa5d 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,28 +5,28 @@
Computation times
=================
-**13:23.838** total execution time for **tutorial** files:
+**13:13.379** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 10:29.389 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 10:25.921 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:02.401 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.079 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 00:53.538 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 00:49.733 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:31.328 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:30.719 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:25.135 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:24.539 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.131 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.703 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.746 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.514 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.160 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.164 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.004 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.002 | 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 |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 15e6b4db7..0bf2fca38 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -301,8 +301,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000009
- naive: 0.000008
+ Numpy running time: 0.000008
+ naive: 0.000006
@@ -403,7 +403,7 @@ compile and run this new schedule with the parallel operation applied:
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- parallel: 0.000006
+ parallel: 0.000008
@@ -512,10 +512,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.79443000030733e-06 1.0
- naive 7.8523e-06 0.8928719655197203
- parallel 6.0315e-06 0.6858318276214859
- vector 2.45448e-05 2.7909483615359107
+ numpy 7.823169999028324e-06 1.0
+ naive 5.811899999999999e-06 0.7429085652902679
+ parallel 7.5448e-06 0.9644172376334786
+ vector 2.4551e-05 3.1382419151123333
@@ -936,7 +936,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018521
+ Numpy running time: 0.017740
@@ -996,7 +996,7 @@ optimizations.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- none: 3.509267
+ none: 3.437629
@@ -1101,7 +1101,7 @@ schedule.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- blocking: 0.318238
+ blocking: 0.299352
@@ -1199,7 +1199,7 @@ already cache friendly from our previous optimizations.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- vectorization: 0.348210
+ vectorization: 0.335210
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1275,7 +1275,7 @@ more cache friendly.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- loop permutation: 0.116925
+ loop permutation: 0.113478
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1376,7 +1376,7 @@ optimized schedule.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- array packing: 0.109618
+ array packing: 0.107497
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1471,7 +1471,7 @@ to `C` when all the block results are ready.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- block caching: 0.110714
+ block caching: 0.110857
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1559,7 +1559,7 @@ of thread-level parallelization.
/workspace/python/tvm/driver/build_module.py:267: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- parallelization: 0.145803
+ parallelization: 0.146027
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1640,13 +1640,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.5092672529000004 1.0
- blocking 0.31823783079999995 0.09068498004448464
- vectorization 0.3482101466 0.09922588435299275
- loop permutation 0.11692472299999998 0.03331884253140747
- array packing 0.1096180755 0.03123674191796405
- block caching 0.11071396920000001 0.03154902753801604
- parallelization 0.1458032665 0.041548065733526165
+ none 3.4376288085 1.0
+ blocking 0.2993515676 0.08708082933788923
+ vectorization 0.3352102837 0.09751206496499781
+ loop permutation 0.1134779515 0.03301053075288713
+ array packing 0.1074966189 0.03127057192277425
+ block caching 0.1108566097 0.03224798716658765
+ parallelization 0.1460270042 0.04247899128577483
@@ -1688,7 +1688,7 @@ the computation for specific platforms.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 2.401 seconds)
+ **Total running time of the script:** ( 1 minutes 1.079 seconds)
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index c3e89d7c8..1f5532ef9 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-299ca267e7641b5fa6e78dd131d0574e310f9a13
+64031d56d634a535c8e3832d9231855b688f0648
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 5ba6fe49a..ac98a0353 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -574,7 +574,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 5.760 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.814 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index dfe1c2e73..0c247327b 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -427,7 +427,7 @@ to download the full example code</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_from_mxnet_001.png" srcset="../../_images/sphx_glr_from_mxnet_001.png" alt="from mxnet" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip54bb0021-5133-4f92-8ce3-4d90428cbeaf 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.zip17743ad1-f46e-4ab0-839d-28517721a0e3 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 f8b170931..4d5ec4154 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -432,12 +432,14 @@ python3 -m pip install -f https://release.oneflow.info <span class="nv">oneflow<
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 19%|#9 | 7.99M/41.5M [00:00<00:00, 74.8MB/s]
- 39%|###8 | 16.0M/41.5M [00:00<00:00, 70.8MB/s]
- 58%|#####7 | 24.0M/41.5M [00:00<00:00, 57.7MB/s]
- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 57.2MB/s]
- 96%|#########6| 40.0M/41.5M [00:00<00:00, 57.5MB/s]
-100%|##########| 41.5M/41.5M [00:00<00:00, 57.0MB/s]
+ 15%|#5 | 6.33M/41.5M [00:00<00:00, 55.9MB/s]
+ 35%|###4 | 14.3M/41.5M [00:00<00:00, 49.5MB/s]
+ 46%|####6 | 19.1M/41.5M [00:00<00:00, 46.5MB/s]
+ 57%|#####6 | 23.6M/41.5M [00:00<00:00, 43.2MB/s]
+ 67%|######6 | 27.7M/41.5M [00:00<00:00, 41.9MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 40.8MB/s]
+ 92%|#########2| 38.3M/41.5M [00:00<00:00, 47.3MB/s]
+100%|##########| 41.5M/41.5M [00:00<00:00, 45.9MB/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 8dd119419..88293f115 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -414,9 +414,9 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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+100%|##########| 44.7M/44.7M [00:00<00:00, 203MB/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 c7c34ce89..9a68b9494 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -636,7 +636,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.666 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.582 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 1937c3016..ac75aae24 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>05:08.598</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:03.669</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -336,43 +336,43 @@
</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>01:05.760</p></td>
+<td><p>01:03.814</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>01:02.666</p></td>
+<td><p>01:01.582</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>00:42.052</p></td>
+<td><p>00:39.641</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>00:28.186</p></td>
+<td><p>00:27.847</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>00:25.550</p></td>
+<td><p>00:26.077</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
-<td><p>00:24.510</p></td>
+<td><p>00:25.103</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:22.321</p></td>
+<td><p>00:21.335</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:19.734</p></td>
+<td><p>00:19.831</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>00:15.300</p></td>
+<td><p>00:15.976</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>00:02.519</p></td>
+<td><p>00:02.462</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 409a541b9..d7c87efb4 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -653,7 +653,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.3008 16.1240 17.5615 15.9027 0.4856
+ 15.7863 15.7302 16.1000 15.6958 0.1203
</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 f3a60b3a1..56cbe37c8 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -436,13 +436,14 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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- 91%|######### | 154M/170M [00:00<00:00, 196MB/s]
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+ 9%|9 | 15.4M/170M [00:00<00:01, 162MB/s]
+ 22%|##1 | 36.9M/170M [00:00<00:00, 199MB/s]
+ 34%|###3 | 57.5M/170M [00:00<00:00, 207MB/s]
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+100%|##########| 170M/170M [00:00<00:00, 212MB/s]
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -537,7 +538,7 @@ 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 1.799 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 55.015 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 4b307b6f3..1f3e1021f 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -480,7 +480,7 @@ training. Other models require a full post training calibration.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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+100%|##########| 13.6M/13.6M [00:00<00:00, 169MB/s]
</pre></div>
</div>
</div>
@@ -569,7 +569,7 @@ 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)
- 90.3995 90.2252 95.9980 90.1096 0.6866
+ 90.3492 90.0470 97.2694 89.9033 1.0359
</pre></div>
</div>
<div class="admonition note">
@@ -608,7 +608,7 @@ 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 12.847 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.675 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 c895eb438..e0f0eb680 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -573,7 +573,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 120.8130 120.7515 122.9326 120.1069 0.4335
+ 120.4961 120.4841 126.6318 118.7601 0.8529
</pre></div>
</div>
<div class="admonition note">
@@ -601,7 +601,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 53.843 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 58.396 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 7ee0db3c7..9ff390ee8 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -509,7 +509,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.007 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.324 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 494496868..c1bed921d 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -441,25 +441,23 @@ 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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</div>
<p>Create TVM runtime and do inference
@@ -502,7 +500,7 @@ 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 45.158 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 35.106 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 18e9890fd..c1251c5c5 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>11:33.694</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:28.273</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -336,39 +336,39 @@
</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>03:01.799</p></td>
+<td><p>02:55.015</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>02:45.158</p></td>
+<td><p>02:35.106</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>01:53.843</p></td>
+<td><p>01:58.396</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>01:25.007</p></td>
+<td><p>01:36.324</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>01:12.847</p></td>
+<td><p>01:08.675</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:30.279</p></td>
+<td><p>00:29.453</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>00:22.651</p></td>
+<td><p>00:22.730</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>00:22.103</p></td>
+<td><p>00:22.569</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>00:00.007</p></td>
+<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index 19ec79b2c..e451cd560 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -612,7 +612,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
<span class="n">module</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="n">get_mobilenet</span><span class="p">()</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip55fbf702-8d7e-4753-a895-bcbdad7dc2c4 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.zip208d4ed0-abb9-40e6-b534-4faa8be2f36e 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 fc1db3e72..234985887 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:42.954</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:40.720</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,15 +336,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></td>
-<td><p>00:39.653</p></td>
+<td><p>00:37.632</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>00:02.296</p></td>
+<td><p>00:02.170</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></td>
-<td><p>00:00.997</p></td>
+<td><p>00:00.909</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index f0488adfb..d051a59d5 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -512,10 +512,10 @@ profile the execution time of each passes.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6882us [6882us] (45.61%; 45.61%)
-FoldScaleAxis: 8207us [6us] (54.39%; 54.39%)
- FoldConstant: 8202us [1735us] (54.35%; 99.93%)
- InferType: 6467us [6467us] (42.86%; 78.85%)
+InferType: 6768us [6768us] (46.60%; 46.60%)
+FoldScaleAxis: 7756us [6us] (53.40%; 53.40%)
+ FoldConstant: 7749us [1584us] (53.36%; 99.92%)
+ InferType: 6166us [6166us] (42.45%; 79.56%)
</pre></div>
</div>
</div>
@@ -537,10 +537,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6427us [6427us] (44.49%; 44.49%)
-FoldScaleAxis: 8018us [5us] (55.51%; 55.51%)
- FoldConstant: 8012us [1715us] (55.47%; 99.93%)
- InferType: 6298us [6298us] (43.60%; 78.60%)
+InferType: 6231us [6231us] (44.59%; 44.59%)
+FoldScaleAxis: 7742us [5us] (55.41%; 55.41%)
+ FoldConstant: 7738us [1568us] (55.37%; 99.94%)
+ InferType: 6170us [6170us] (44.15%; 79.73%)
</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 7792fafd9..71cda81b5 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -564,7 +564,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.221659 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.168631 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 b2817c4e7..ff6bd1f1c 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -906,7 +906,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 6.995148 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 6.767923 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 8411aabc3..7961901d4 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -461,8 +461,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018730
-Baseline: 3.449067
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018024
+Baseline: 3.435788
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -522,7 +522,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.311143
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.296037
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -589,7 +589,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.345286
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.334691
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -650,7 +650,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118202
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.116833
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -733,7 +733,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.109385
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.111166
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -819,7 +819,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110813
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110955
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -909,7 +909,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.146608
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.146793
</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 ea608457b..79c8a5c04 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.898</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.595</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -336,15 +336,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:32.651</p></td>
+<td><p>00:32.215</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>00:01.229</p></td>
+<td><p>00:01.298</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>00:01.018</p></td>
+<td><p>00:01.082</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 1f5244319..132cc1218 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:31.626</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>06:21.840</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -336,27 +336,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>03:38.300</p></td>
+<td><p>03:36.818</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>01:25.500</p></td>
+<td><p>01:22.393</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>00:48.081</p></td>
+<td><p>00:46.769</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:20.023</p></td>
+<td><p>00:18.522</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>00:09.875</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
+<td><p>00:08.756</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>00:09.847</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
+<td><p>00:08.581</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 b27ff0e9f..5cf17a876 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
@@ -491,482 +491,104 @@ cooperative fetching, unrolling and operator fusion.</p>
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute}
preflattened_buffer_map = {data_1: data_3: Buffer(data_2, float32, [1, 512, 7, 7], []), kernel_1: kernel_3: Buffer(kernel_2, float32, [512, 512, 3, 3], []), bias_1: bias_3: Buffer(bias_2, float32, [1, 512, 1, 1], []), compute_1: compute_3: Buffer(compute_2, float32, [1, 512, 7, 7], [])} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 28;
- allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
- allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [3072]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [14], [], scope="local", align=32)[0] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[5] = 0f32
- conv2d_nchw_1[6] = 0f32
- conv2d_nchw_1[7] = 0f32
- conv2d_nchw_1[8] = 0f32
- conv2d_nchw_1[9] = 0f32
- conv2d_nchw_1[10] = 0f32
- conv2d_nchw_1[11] = 0f32
- conv2d_nchw_1[12] = 0f32
- conv2d_nchw_1[13] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 8;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [648]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [4608]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
+ for (ff.outer.inner.init: int32, 0, 2) {
+ let cse_var_1: int32 = (ff.outer.inner.init*7)
+ {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [196], [], scope="local", align=32)[cse_var_1] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 14)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 1)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 15)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 2)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 16)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 3)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 17)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 4)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 18)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 5)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 19)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 6)] = 0f32
+ conv2d_nchw_1[(cse_var_1 + 20)] = 0f32
+ }
+ }
for (rc.outer.outer: int32, 0, 64) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_2: int32 = (rc.outer.outer*72)
- let cse_var_1: int32 = (ry.outer.outer*3)
- {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope="shared")[(threadIdx.x_1*4)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*4), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + [...]
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 1)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 1), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0 [...]
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 2)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 2), 9))) && (floormod(((threadIdx.x_1*4) + 2), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 2), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 2), 9)) - 8)], 0 [...]
+ let cse_var_2: int32 = (rc.outer.outer*392)
+ {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [648], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else(((((9 <= floormod(threadIdx.x_1, 81)) && (floormod(threadIdx.x_1, 81) < 72)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_2 + (floordiv(threadIdx.x_1, 81)*49)) + (floordiv(floormod(threadIdx.x_1, 81), 9)*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 31), 81)) && (floormod((threadIdx.x_1 + 31), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 112), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 31), 81), 9)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 62), 81)) && (floormod((threadIdx.x_1 + 62), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 224), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 62), 81), 9)*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 12), 81)) && (floormod((threadIdx.x_1 + 12), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 336), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 12), 81), 9)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 43), 81)) && (floormod((threadIdx.x_1 + 43), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 448), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 43), 81), 9)*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((threadIdx.x_1 < 88), dtype=bool) {
+ pad_temp.shared_1[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 74), 81)) && (floormod((threadIdx.x_1 + 74), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_2 + (floordiv((threadIdx.x_1 + 560), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 74), 81), 9)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+ }
+ for (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer: int32, 0, 2) {
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+ if @tir.likely((((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*7) + floordiv(threadIdx.x_2, 16)) < 8), dtype=bool) {
+ for (ax0.ax1.fused.ax2.fused.ax3.fused.inner.s: int32, 0, 36) {
+ kernel.shared_1: Buffer(kernel.shared, float32, [4608], [], scope="shared")[(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*4032) + (threadIdx.x_2*36)) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)] = kernel[((((((blockIdx.x*294912) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*258048)) + (floordiv(threadIdx.x_2, 2)*4608)) + (rc.outer.outer*72)) + (floormod(threadIdx.x_2, 2)*36)) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)]
}
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared_1[((threadIdx.x_1*4) + 3)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 3), 9))) && (floormod(((threadIdx.x_1*4) + 3), 9) < 8)), data[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 3), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 3), 9)) - 8)], 0 [...]
+ }
+ }
+ for (rc.outer.inner: int32, 0, 4) {
+ for (ry.outer.inner: int32, 0, 3) {
+ for (rx.outer.inner: int32, 0, 3) {
+ for (ff.outer.inner: int32, 0, 2) {
+ for (rc.inner: int32, 0, 2) {
+ let cse_var_16: int32 = (ff.outer.inner*7)
+ let cse_var_15: int32 = (cse_var_16 + 6)
+ let cse_var_14: int32 = (cse_var_16 + 5)
+ let cse_var_13: int32 = (cse_var_16 + 4)
+ let cse_var_12: int32 = (cse_var_16 + 3)
+ let cse_var_11: int32 = (cse_var_16 + 20)
+ let cse_var_10: int32 = (cse_var_16 + 2)
+ let cse_var_9: int32 = (cse_var_16 + 19)
+ let cse_var_8: int32 = (cse_var_16 + 18)
+ let cse_var_7: int32 = (cse_var_16 + 17)
+ let cse_var_6: int32 = (cse_var_16 + 16)
+ let cse_var_5: int32 = (cse_var_16 + 15)
+ let cse_var_4: int32 = (cse_var_16 + 14)
+ let cse_var_3: int32 = (cse_var_16 + 1)
+ {
+ conv2d_nchw_1[cse_var_16] = (conv2d_nchw_1[cse_var_16] + (pad_temp.shared_1[(((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_4] = (conv2d_nchw_1[cse_var_4] + (pad_temp.shared_1[(((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_3] = (conv2d_nchw_1[cse_var_3] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_5] = (conv2d_nchw_1[cse_var_5] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 1)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_6] = (conv2d_nchw_1[cse_var_6] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 2)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_7] = (conv2d_nchw_1[cse_var_7] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 3)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_8] = (conv2d_nchw_1[cse_var_8] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 4)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_9] = (conv2d_nchw_1[cse_var_9] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 5)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner)]))
+ conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[((((((rc.outer.inner*162) + (rc.inner*81)) + (ry.outer.inner*9)) + (floormod(threadIdx.x, 7)*9)) + rx.outer.inner) + 6)]*kernel.shared_1[(((((((floordiv(threadIdx.x, 7)*144) + (ff.outer.inner*72)) + (rc.outer.inner*18)) + (rc.inner*9)) + (ry.outer.inner*3)) + rx.outer.inner) + 2304)]))
+ }
+ }
+ }
}
}
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1: Buffer(kernel.shared, float32, [3072], [], scope="shared")[threadIdx.x_2] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 64), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 128), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 36864)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 256), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 320), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 384)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 73728)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 448), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 512)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 512), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 576)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 110592)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 640), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 704)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 704), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 768)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 147456)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 832)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 832), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 896)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 896), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 960)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 184320)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1024)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1024), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1088)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1088), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1152)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 221184)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1216)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1216), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1280)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1280), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1344)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 258048)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1408)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1408), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1472)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1472), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1536)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 294912)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1600)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1600), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1664)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1664), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1728)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 331776)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1792)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1792), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1856)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1856), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1920)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 368640)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 1984)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1984), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2048)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2048), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2112)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 405504)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2176)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2176), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2240)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2240), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2304)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 442368)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2368)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2368), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2432)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2432), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2496)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 479232)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2560)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2560), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2624)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2624), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2688)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 516096)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2752)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2752), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2816)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2816), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2880)] = kernel[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 552960)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 2944)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2944), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1[(threadIdx.x_2 + 3008)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 3008), 24)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[0]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[1]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[2]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[3]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[4]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[5]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[6]*kernel.shared_1[(threadIdx.x*48)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 3)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[0]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 24)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 27)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 1)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 4)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 25)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 28)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*48) + 2)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*48) + 5)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*48) + 26)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*48) + 29)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 6)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 9)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 30)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 33)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 7)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 10)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 31)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 34)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*48) + 8)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*48) + 11)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*48) + 32)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*48) + 35)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 12)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 15)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 36)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 39)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 13)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 16)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 37)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 40)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*48) + 14)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*48) + 17)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*48) + 38)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*48) + 41)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 18)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 21)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 42)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 45)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 19)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 22)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 43)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 46)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*48) + 20)]))
- conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*48) + 23)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*48) + 47)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*48) + 44)]))
- conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*48) + 47)]))
}
}
}
for (i1.inner: int32, 0, 2) {
for (i3.inner: int32, 0, 7) {
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[((i1.inner*7) + i3.inner)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
+ let cse_var_17: int32 = ((i1.inner*7) + i3.inner)
+ {
+ compute[(((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner)] = max((conv2d_nchw_1[cse_var_17] + bias[(((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+ compute[((((((blockIdx.x*3136) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + i3.inner) + 1568)] = max((conv2d_nchw_1[(cse_var_17 + 14)] + bias[((((blockIdx.x*64) + (floordiv(threadIdx.x, 7)*2)) + i1.inner) + 32)]), 0f32)
+ }
}
}
}
@@ -1004,7 +626,7 @@ 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: 0.358 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.420 ms
</pre></div>
</div>
</div>
@@ -1035,20 +657,20 @@ conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_
conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=2)
-conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=64)
-conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
+conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=16)
+conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
-conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=1)
+conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=7)
conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
-conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
-conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=7)
+conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=7)
+conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=2)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=4)
conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
-conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
+conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=3)
conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
conv2d_nchw_rx_o_o, conv2d_nchw_rx_o_i = s[conv2d_nchw].split(conv2d_nchw_rx_o_i, factor=3)
s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nchw_yy_o_o_o_o, conv2d_nchw_xx_o_o_o_o, conv2d_nchw_nn_o_o_o_i, conv2d_nchw_ff_o_o_o_i, conv2d_nchw_yy_o_o_o_i, conv2d_nchw_xx_o_o_o_i, conv2d_nchw_nn_o_o_i, conv2d_nchw_ff_o_o_i, conv2d_nchw_yy_o_o_i, conv2d_nchw_xx_o_o_i, conv2d_nchw_rc_o_o, conv2d_nchw_ry_o_o, conv2d_nchw_rx_o_o, conv2d_nchw_rc_o_i, conv2d_nchw_ry_o_i, conv2d_nchw_rx_o_i, conv2d_nchw_nn_o_i, conv2d_nchw_ff_o_i, conv2d_nchw_yy_o_i, conv2d_nc [...]
@@ -1056,10 +678,10 @@ compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
-compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
+compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
-compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
+compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=7)
compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
@@ -1080,16 +702,16 @@ s[compute].bind(compute_i0_o_o_i_i1_o_o_i_fused_i2_o_o_i_fused_i3_o_o_i_fused, t
compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused = s[compute].fuse(compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i)
s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread_axis("threadIdx.x"))
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
-kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
+kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=36)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=4)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
-s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
+s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 16)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -1107,429 +729,73 @@ CUDA source code:
#define int64_t long long
#define uint64_t unsigned long long
#endif
-extern "C" __global__ void __launch_bounds__(64) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float conv2d_nchw[14];
- __shared__ float pad_temp_shared[72];
- __shared__ float kernel_shared[3072];
- conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
- conv2d_nchw[7] = 0.000000e+00f;
- conv2d_nchw[8] = 0.000000e+00f;
- conv2d_nchw[9] = 0.000000e+00f;
- conv2d_nchw[10] = 0.000000e+00f;
- conv2d_nchw[11] = 0.000000e+00f;
- conv2d_nchw[12] = 0.000000e+00f;
- conv2d_nchw[13] = 0.000000e+00f;
+extern "C" __global__ void __launch_bounds__(112) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[648];
+ __shared__ float kernel_shared[4608];
+ for (int ff_outer_inner_init = 0; ff_outer_inner_init < 2; ++ff_outer_inner_init) {
+ conv2d_nchw[(ff_outer_inner_init * 7)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 14)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 1)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 15)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 2)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 16)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 3)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 17)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 4)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 18)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 5)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 19)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 6)] = 0.000000e+00f;
+ conv2d_nchw[((ff_outer_inner_init * 7) + 20)] = 0.000000e+00f;
+ }
for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
- __syncthreads();
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[(((int)threadIdx.x) * 4)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 4) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 1)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 1) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8)] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 2)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 2) % 9))) && ((((((int)threadIdx.x) * 4) + 2) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 2) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 2) % 9)) - 8)] : 0.000000e+00f);
+ __syncthreads();
+ pad_temp_shared[((int)threadIdx.x)] = (((((9 <= (((int)threadIdx.x) % 81)) && ((((int)threadIdx.x) % 81) < 72)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 81) * 49)) + (((((int)threadIdx.x) % 81) / 9) * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((9 <= ((((int)threadIdx.x) + 31) % 81)) && (((((int)threadIdx.x) + 31) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 112) / 81) * 49)) + ((((((int)threadIdx.x) + 31) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = (((((9 <= ((((int)threadIdx.x) + 62) % 81)) && (((((int)threadIdx.x) + 62) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 224) / 81) * 49)) + ((((((int)threadIdx.x) + 62) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 336)] = (((((9 <= ((((int)threadIdx.x) + 12) % 81)) && (((((int)threadIdx.x) + 12) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 336) / 81) * 49)) + ((((((int)threadIdx.x) + 12) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 448)] = (((((9 <= ((((int)threadIdx.x) + 43) % 81)) && (((((int)threadIdx.x) + 43) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 448) / 81) * 49)) + ((((((int)threadIdx.x) + 43) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+ if (((int)threadIdx.x) < 88) {
+ pad_temp_shared[(((int)threadIdx.x) + 560)] = (((((9 <= ((((int)threadIdx.x) + 74) % 81)) && (((((int)threadIdx.x) + 74) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 560) / 81) * 49)) + ((((((int)threadIdx.x) + 74) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+ }
+ for (int ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer = 0; ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer < 2; ++ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer) {
+ if (((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 7) + (((int)threadIdx.x) >> 4)) < 8) {
+ for (int ax0_ax1_fused_ax2_fused_ax3_fused_inner_s = 0; ax0_ax1_fused_ax2_fused_ax3_fused_inner_s < 36; ++ax0_ax1_fused_ax2_fused_ax3_fused_inner_s) {
+ kernel_shared[(((ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 4032) + (((int)threadIdx.x) * 36)) + ax0_ax1_fused_ax2_fused_ax3_fused_inner_s)] = kernel[((((((((int)blockIdx.x) * 294912) + (ax0_ax1_fused_ax2_fused_ax3_fused_outer_outer * 258048)) + ((((int)threadIdx.x) >> 1) * 4608)) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) & 1) * 36)) + ax0_ax1_fused_ax2_fused_ax3_fused_inner_s)];
+ }
}
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4) + 3)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 3) % 9))) && ((((((int)threadIdx.x) * 4) + 3) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 3) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 3) % 9)) - 8)] : 0.000000e+00f);
+ }
+ __syncthreads();
+ for (int rc_outer_inner = 0; rc_outer_inner < 4; ++rc_outer_inner) {
+ for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
+ for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
+ for (int ff_outer_inner = 0; ff_outer_inner < 2; ++ff_outer_inner) {
+ for (int rc_inner = 0; rc_inner < 2; ++rc_inner) {
+ conv2d_nchw[(ff_outer_inner * 7)] = (conv2d_nchw[(ff_outer_inner * 7)] + (pad_temp_shared[(((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 14)] = (conv2d_nchw[((ff_outer_inner * 7) + 14)] + (pad_temp_shared[(((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 1)] = (conv2d_nchw[((ff_outer_inner * 7) + 1)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 15)] = (conv2d_nchw[((ff_outer_inner * 7) + 15)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 1)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 2)] = (conv2d_nchw[((ff_outer_inner * 7) + 2)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 16)] = (conv2d_nchw[((ff_outer_inner * 7) + 16)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 2)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 3)] = (conv2d_nchw[((ff_outer_inner * 7) + 3)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 17)] = (conv2d_nchw[((ff_outer_inner * 7) + 17)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 3)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 4)] = (conv2d_nchw[((ff_outer_inner * 7) + 4)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 18)] = (conv2d_nchw[((ff_outer_inner * 7) + 18)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 4)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 5)] = (conv2d_nchw[((ff_outer_inner * 7) + 5)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 19)] = (conv2d_nchw[((ff_outer_inner * 7) + 19)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 5)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 6)] = (conv2d_nchw[((ff_outer_inner * 7) + 6)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[(((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner)]));
+ conv2d_nchw[((ff_outer_inner * 7) + 20)] = (conv2d_nchw[((ff_outer_inner * 7) + 20)] + (pad_temp_shared[((((((rc_outer_inner * 162) + (rc_inner * 81)) + (ry_outer_inner * 9)) + ((((int)threadIdx.x) % 7) * 9)) + rx_outer_inner) + 6)] * kernel_shared[((((((((((int)threadIdx.x) / 7) * 144) + (ff_outer_inner * 72)) + (rc_outer_inner * 18)) + (rc_inner * 9)) + (ry_outer_inner * 3)) + rx_outer_inner) + 2304)]));
+ }
+ }
+ }
}
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
- kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 64) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 128) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 192)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 36864)];
- kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 256) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 320) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 384)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 73728)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 512)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 512) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 576)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 110592)];
- kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 640) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 704)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 704) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 768)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 147456)];
- kernel_shared[(((int)threadIdx.x) + 832)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 832) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 896)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 960)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 184320)];
- kernel_shared[(((int)threadIdx.x) + 1024)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1024) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1088)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1088) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1152)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 221184)];
- kernel_shared[(((int)threadIdx.x) + 1216)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1216) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1280)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1280) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1344)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 258048)];
- kernel_shared[(((int)threadIdx.x) + 1408)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1408) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1472)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1472) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1536)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 294912)];
- kernel_shared[(((int)threadIdx.x) + 1600)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1600) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1664)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1664) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1728)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 331776)];
- kernel_shared[(((int)threadIdx.x) + 1792)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1792) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1856)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1856) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 1920)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 368640)];
- kernel_shared[(((int)threadIdx.x) + 1984)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1984) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2048)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2048) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2112)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 405504)];
- kernel_shared[(((int)threadIdx.x) + 2176)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2176) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2240)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2240) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2304)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 442368)];
- kernel_shared[(((int)threadIdx.x) + 2368)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2368) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2432)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2432) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2496)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 479232)];
- kernel_shared[(((int)threadIdx.x) + 2560)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2560) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2624)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2624) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2688)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 516096)];
- kernel_shared[(((int)threadIdx.x) + 2752)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2752) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2816)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2816) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- kernel_shared[(((int)threadIdx.x) + 2880)] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 552960)];
- kernel_shared[(((int)threadIdx.x) + 2944)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2944) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
- kernel_shared[(((int)threadIdx.x) + 3008)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 3008) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
- __syncthreads();
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[0] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[1] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[2] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[3] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[4] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[5] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[6] * kernel_shared[(((int)threadIdx.x) * 48)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 3)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[0] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 24)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 27)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 1)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 4)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 25)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 28)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 48) + 2)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 48) + 5)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 48) + 26)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 48) + 29)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 6)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 9)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 30)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 33)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 7)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 10)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 31)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 34)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 48) + 8)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 48) + 11)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 48) + 32)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 48) + 35)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 12)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 15)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 36)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 39)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 13)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 16)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 37)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 40)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 48) + 14)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 48) + 17)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 48) + 38)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 48) + 41)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 18)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 21)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 42)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 45)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 19)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 22)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 43)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 46)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 48) + 20)]));
- conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 48) + 23)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 48) + 44)]));
- conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 48) + 47)]));
}
}
for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
for (int i3_inner = 0; i3_inner < 7; ++i3_inner) {
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner)] = max((conv2d_nchw[((i1_inner * 7) + i3_inner)] + bias[(((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner)]), 0.000000e+00f);
+ compute[((((((((int)blockIdx.x) * 3136) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)threadIdx.x) % 7) * 7)) + i3_inner) + 1568)] = max((conv2d_nchw[(((i1_inner * 7) + i3_inner) + 14)] + bias[((((((int)blockIdx.x) * 64) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner) + 32)]), 0.000000e+00f);
}
}
}
@@ -1567,7 +833,7 @@ In the example below we resume the status and do more 5 trials.</p>
Get devices for measurement successfully!
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 38.300 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 36.818 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 127f3f894..5ff398fdb 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -906,7 +906,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 9.7837 9.8206 9.8256 9.7047 0.0558
+ 9.8302 9.8440 9.8669 9.7798 0.0369
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 8e2e8a530..037fc5ec3 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -925,7 +925,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 763.6381 763.3983 764.7191 762.7969 0.8028
+ 756.2388 755.9175 757.5815 755.2175 0.9915
</pre></div>
</div>
</div>
@@ -947,7 +947,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 25.500 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.393 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 173c1cc5d..c56626153 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -625,14 +625,14 @@ 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_5: placeholder_15: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_16: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_9: placeholder_19: Buffer(placeholder_14, float32, [128, 512], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [512]), storage_scope = global {
- for (i.outer.inner: int32, 0, 8) {
- for (i.inner.init: int32, 0, 4) {
- let cse_var_1: int32 = ((i.outer.inner*64) + (i.inner.init*16))
+ preflattened_buffer_map = {compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_7: placeholder_15: Buffer(placeholder_12, int32, [4916], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_8: placeholder_17: Buffer(placeholder_13, int32, [33], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), placeholder_6: placeholder_19: Buffer(placeholder_11, float32, [4916, 16, 1], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 64) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 4) {
+ for (i.inner.init: int32, 0, 32) {
+ let cse_var_1: int32 = ((i.outer.inner*512) + (i.inner.init*16))
{
- compute_5: Buffer(compute_4, float32, [512], [])[cse_var_1] = 0f32
+ compute_5: Buffer(compute_4, float32, [2048], [])[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
@@ -650,81 +650,54 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
compute_5[(cse_var_1 + 15)] = 0f32
}
}
- for (elem_idx: int32, 0, let cse_var_2: int32 = floormod(i0.outer.i1.outer.fused, 32) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 4) {
- let cse_var_3: int32 = floormod(i0.outer.i1.outer.fused, 32)
+ for (elem_idx: int32, 0, let cse_var_2: int32 = floordiv(i0.outer.i1.outer.fused, 2) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 32) {
+ let cse_var_21: int32 = floordiv(i0.outer.i1.outer.fused, 2)
+ let cse_var_20: int32 = (elem_idx*16)
+ let cse_var_19: int32 = ((i.outer.inner*8192) + (i.inner*256))
+ let cse_var_18: int32 = ((i.outer.inner*512) + (i.inner*16))
+ let cse_var_17: int32 = (cse_var_18 + 9)
+ let cse_var_16: int32 = (cse_var_18 + 8)
+ let cse_var_15: int32 = (cse_var_18 + 7)
+ let cse_var_14: int32 = (cse_var_18 + 6)
+ let cse_var_13: int32 = (cse_var_18 + 5)
+ let cse_var_12: int32 = (cse_var_18 + 4)
+ let cse_var_11: int32 = (cse_var_18 + 3)
+ let cse_var_10: int32 = (cse_var_18 + 2)
+ let cse_var_9: int32 = (cse_var_18 + 15)
+ let cse_var_8: int32 = (cse_var_18 + 14)
+ let cse_var_7: int32 = (cse_var_18 + 13)
+ let cse_var_6: int32 = (cse_var_18 + 12)
+ let cse_var_5: int32 = (cse_var_18 + 11)
+ let cse_var_4: int32 = (cse_var_18 + 10)
+ let cse_var_3: int32 = (cse_var_18 + 1)
{
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_4: int32 = ((i.outer.inner*64) + (i.inner*16))
- compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[((placeholder_3[cse_var_3]*16) + (elem_idx*16))]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_5: int32 = (((i.outer.inner*64) + (i.inner*16)) + 1)
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 1)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_6: int32 = (((i.outer.inner*64) + (i.inner*16)) + 2)
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 2)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_7: int32 = (((i.outer.inner*64) + (i.inner*16)) + 3)
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 3)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_8: int32 = (((i.outer.inner*64) + (i.inner*16)) + 4)
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 4)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_9: int32 = (((i.outer.inner*64) + (i.inner*16)) + 5)
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 5)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_10: int32 = (((i.outer.inner*64) + (i.inner*16)) + 6)
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 6)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_11: int32 = (((i.outer.inner*64) + (i.inner*16)) + 7)
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 7)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_12: int32 = (((i.outer.inner*64) + (i.inner*16)) + 8)
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 8)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_13: int32 = (((i.outer.inner*64) + (i.inner*16)) + 9)
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 9)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_14: int32 = (((i.outer.inner*64) + (i.inner*16)) + 10)
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 10)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_15: int32 = (((i.outer.inner*64) + (i.inner*16)) + 11)
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 11)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_16: int32 = (((i.outer.inner*64) + (i.inner*16)) + 12)
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 12)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_17: int32 = (((i.outer.inner*64) + (i.inner*16)) + 13)
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 13)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_18: int32 = (((i.outer.inner*64) + (i.inner*16)) + 14)
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 14)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
- if @tir.likely((elem_idx < (placeholder_3[(cse_var_3 + 1)] - placeholder_3[cse_var_3])), dtype=bool) {
- let cse_var_19: int32 = (((i.outer.inner*64) + (i.inner*16)) + 15)
- compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_3]*16) + (elem_idx*16)) + 15)]*max(placeholder[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + placeholder_2[(placeholder_3[cse_var_3] + elem_idx)])], 0f32)))
- }
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[((placeholder_3[cse_var_21]*16) + cse_var_20)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 1)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 2)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 3)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 4)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 5)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 6)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 7)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 8)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 9)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_4] = (compute_5[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 10)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 11)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 12)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 13)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 14)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_21]*16) + cse_var_20) + 15)]*max(placeholder[(cse_var_19 + placeholder_2[(placeholder_3[cse_var_21] + elem_idx)])], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 32) {
- let cse_var_20: int32 = (((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16))
- compute[ramp(cse_var_20, 1, 16)] = max((compute_5[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_20, 1, 16)]), broadcast(0f32, 16))
+ for (i0.inner: int32, 0, 128) {
+ for (i1.inner: int32, 0, 8) {
+ let cse_var_23: int32 = (i0.outer.i1.outer.fused*8)
+ let cse_var_22: int32 = (((i0.inner*512) + cse_var_23) + i1.inner)
+ compute[cse_var_22] = max((compute_5[((((i0.inner*16) + cse_var_23) + i1.inner) - (floordiv(i0.outer.i1.outer.fused, 2)*16))] + placeholder_4[cse_var_22]), 0f32)
+ }
}
}
}
@@ -762,7 +735,7 @@ 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: 2.112 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 3.502 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 19839707e..7b710f285 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:46.570</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:47.214</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,22 +336,22 @@
</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>00:46.532</p></td>
+<td><p>00:47.177</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>00:00.023</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>00:00.006</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 286102da7..8930aa4bb 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -1436,8 +1436,8 @@ No: 8 GFLOPS: 0.00/0.00 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 2, 1, 64]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4909501
-No: 9 GFLOPS: 80.80/80.80 result: MeasureResult(costs=(0.002865089857142857,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.871819257736206, timestamp=1662659224.2793512) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
-No: 10 GFLOPS: 0.00/80.80 result: Traceback (most recent call last):
+No: 9 GFLOPS: 128.67/128.67 result: MeasureResult(costs=(0.0017991688214285715,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8581395149230957, timestamp=1662680461.6663718) [('tile_f', [-1, 1, 4, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5072689
+No: 10 GFLOPS: 0.00/128.67 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1560,8 +1560,8 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 8]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 64, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5092711
-No: 11 GFLOPS: 260.52/260.52 result: MeasureResult(costs=(0.0008886077513812153,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5324485301971436, timestamp=1662659225.2490761) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
-No: 12 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 11 GFLOPS: 261.01/261.01 result: MeasureResult(costs=(0.0008869559944751382,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.78175687789917, timestamp=1662680462.586266) [('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
+No: 12 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1684,7 +1684,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 128, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 256]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,183542
-No: 13 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1807,7 +1807,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 8, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 64]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2482196
-No: 14 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -1930,9 +1930,9 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10306226
-No: 15 GFLOPS: 5.30/260.52 result: MeasureResult(costs=(0.0436854875,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.843956470489502, timestamp=1662659229.815484) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
-No: 16 GFLOPS: 3.34/260.52 result: MeasureResult(costs=(0.0693956865,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.612766742706299, timestamp=1662659231.0973122) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
-No: 17 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 15 GFLOPS: 5.27/261.01 result: MeasureResult(costs=(0.04395225775,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.882185459136963, timestamp=1662680467.2203062) [('tile_f', [-1, 2, 2, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5330964
+No: 16 GFLOPS: 3.36/261.01 result: MeasureResult(costs=(0.06892849250000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.590003728866577, timestamp=1662680468.4531302) [('tile_f', [-1, 8, 4, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2140058
+No: 17 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1950,8 +1950,8 @@ No: 17 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 2, 2, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 16]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10195251
-No: 18 GFLOPS: 26.02/260.52 result: MeasureResult(costs=(0.008898506666666665,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1877410411834717, timestamp=1662659242.0215013) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
-No: 19 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 18 GFLOPS: 27.98/261.01 result: MeasureResult(costs=(0.008274430642857144,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.275554895401001, timestamp=1662680479.462969) [('tile_f', [-1, 4, 8, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6068603
+No: 19 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -2074,7 +2074,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 871, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 16, 4, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6956993
-No: 20 GFLOPS: 0.00/260.52 result: Traceback (most recent call last):
+No: 20 GFLOPS: 0.00/261.01 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 588, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 540, in _build_func_common
@@ -2237,7 +2237,7 @@ and measure running time.</p>
Best config:
[('tile_f', [-1, 8, 2, 1]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4264713
Finish loading 20 records
-Time cost of this operator: 0.001271
+Time cost of this operator: 0.001219
</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 fffc30a43..68c8aa953 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -584,10 +584,10 @@ the tuned operator.</p>
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 311.2 98.735 (1, 2, 10, 10, 3) 2 1 [311.2]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.021 0.958 (1, 6, 10, 10) 1 1 [3.021]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.967 0.307 (1, 1, 10, 10, 3) 1 1 [0.967]
-Total_time - 315.188 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 310.3 98.729 (1, 2, 10, 10, 3) 2 1 [310.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.017 0.96 (1, 6, 10, 10) 1 1 [3.017]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.978 0.311 (1, 1, 10, 10, 3) 1 1 [0.978]
+Total_time - 314.295 - - - - -
</pre></div>
</div>
</div>
@@ -640,10 +640,10 @@ Total_time -
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 190.7 98.425 (1, 1, 10, 10, 6) 2 1 [190.7]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.21 1.141 (1, 6, 10, 10) 1 1 [2.21]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.841 0.434 (1, 3, 10, 10, 1) 1 1 [0.841]
-Total_time - 193.751 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 195.3 98.499 (1, 6, 10, 10, 1) 2 1 [195.3]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.027 1.022 (1, 6, 10, 10) 1 1 [2.027]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.949 0.478 (1, 3, 10, 10, 1) 1 1 [0.949]
+Total_time - 198.275 - - - - -
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 1522b73a4..ff40e8ec0 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -516,7 +516,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpye_kgrch/images/random'
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmpvv32kj0l/images/random'
</pre></div>
</div>
</div>
@@ -576,8 +576,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpye_kgrch/images/target contains 8144 images
-/tmp/tmpye_kgrch/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpvv32kj0l/images/target contains 8144 images
+/tmp/tmpvv32kj0l/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -689,13 +689,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 57s - loss: 0.2077 - accuracy: 0.9257 - val_loss: 0.1394 - val_accuracy: 0.9562
+328/328 - 56s - loss: 0.2202 - accuracy: 0.9226 - val_loss: 0.1345 - val_accuracy: 0.9630
Epoch 2/3
-328/328 - 53s - loss: 0.1005 - accuracy: 0.9626 - val_loss: 0.1061 - val_accuracy: 0.9668
+328/328 - 52s - loss: 0.0926 - accuracy: 0.9648 - val_loss: 0.1574 - val_accuracy: 0.9513
Epoch 3/3
-328/328 - 53s - loss: 0.0628 - accuracy: 0.9763 - val_loss: 0.2003 - val_accuracy: 0.9354
+328/328 - 52s - loss: 0.0646 - accuracy: 0.9745 - val_loss: 0.1738 - val_accuracy: 0.9471
-<keras.callbacks.History object at 0x7f9a06564ad0>
+<keras.callbacks.History object at 0x7fe594f1ab90>
</pre></div>
</div>
</div>
@@ -957,7 +957,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 49.482 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 57.371 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 a157e3ae6..ac48525b4 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>05:45.653</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>05:51.117</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -336,19 +336,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></td>
-<td><p>04:49.482</p></td>
+<td><p>04:57.371</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></td>
-<td><p>00:44.014</p></td>
+<td><p>00:42.232</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_aot.html#sphx-glr-how-to-work-with-microtvm-micro-aot-py"><span class="std std-ref">microTVM Host-Driven AoT</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_aot.py</span></code>)</p></td>
-<td><p>00:08.748</p></td>
+<td><p>00:08.208</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></td>
-<td><p>00:03.407</p></td>
+<td><p>00:03.303</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></td>
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 6dbd37be7..ad5fb2651 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>00:44.399</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:42.813</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,15 +336,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="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>00:32.265</p></td>
+<td><p>00:31.554</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>00:10.389</p></td>
+<td><p>00:09.858</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>00:01.738</p></td>
+<td><p>00:01.394</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 26fd9b5f9..dfa447ec5 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -522,7 +522,7 @@ The following example customizes CUDA lowering rule for <code class="code docuti
<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 0x7f9985440c20>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><function my_cuda_math_rule at 0x7fe520748200>
</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 7537fb890..8a01beb5d 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -327,7 +327,7 @@
<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>00:04.172</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:04.302</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -336,31 +336,31 @@
</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>00:01.920</p></td>
+<td><p>00:01.961</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>00:00.1000</p></td>
+<td><p>00:01.070</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>00:00.539</p></td>
+<td><p>00:00.549</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>00:00.528</p></td>
+<td><p>00:00.541</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>00:00.102</p></td>
+<td><p>00:00.098</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>00:00.042</p></td>
+<td><p>00:00.041</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>00:00.027</p></td>
+<td><p>00:00.028</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></td>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 0f4961326..37a3d15ca 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -577,7 +577,7 @@ 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/tmp7snqfpyv/input0.cc'\nsource_filename = \"/tmp/tmp7snqfpyv/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/tmp1smf4fmp/input0.cc'\nsource_filename = \"/tmp/tmp1smf4fmp/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) {
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diff --git a/docs/reference/api/doxygen/analyzer_8h_source.html b/docs/reference/api/doxygen/analyzer_8h_source.html
index 64a429ad8..2753a08a0 100644
--- a/docs/reference/api/doxygen/analyzer_8h_source.html
+++ b/docs/reference/api/doxygen/analyzer_8h_source.html
@@ -108,7 +108,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1arith_1_1IntSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html">tvm::arith::IntSetAnalyzer</a></div><div class="ttdoc">Integer set analyzer. </div><div class="ttdef"><b>Definition:</b> analyzer.h:362</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a0761897bf16ab73b848bf360e9b195a3"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a0761897bf16ab73b848bf360e9b195a3">tvm::arith::ConstIntBoundNode::min_value</a></div><div class="ttdeci">int64_t min_value</div><div class="ttdef"><b>Definition:</b> analyzer.h:70</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1transform_html_a817801e8c9488f712804d2d0b821acf0"><div class="ttname"><a href="namespacetvm_1_1tir_1_1transform.html#a817801e8c9488f712804d2d0b821acf0">tvm::tir::transform::Simplify</a></div><div class="ttdeci">Pass Simplify()</div><div class="ttdoc">Run arithmetic simplifications on the statements and expressions. </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:1268</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_1arith_1_1Analyzer_html_a435eba3ac3a839d3c53b74acfdc10146"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a435eba3ac3a839d3c53b74acfdc10146">tvm::arith::Analyzer::const_int_bound</a></div><div class="ttdeci">ConstIntBoundAnalyzer const_int_bound</div><div class="ttdoc">sub-analyzer: const integer bound </div><div class="ttdef"><b>Definition:</b> analyzer.h:431</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a652c9c965a3942f1ca45f7929ddd554c"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a652c9c965a3942f1ca45f7929ddd554c">tvm::arith::ConstIntBoundNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> analyzer.h:90</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1RewriteSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html">tvm::arith::RewriteSimplifier</a></div><div class="ttdoc">Rewrite-rule based simplifier. </div><div class="ttdef"><b>Definition:</b> analyzer.h:252</div></div>
diff --git a/docs/reference/api/doxygen/builder_8h_source.html b/docs/reference/api/doxygen/builder_8h_source.html
index 7a386ef14..6ea8cf668 100644
--- a/docs/reference/api/doxygen/builder_8h_source.html
+++ b/docs/reference/api/doxygen/builder_8h_source.html
@@ -99,7 +99,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1codegen_html_a0d6322c2dda54a66a3b82022f5f3632c"><div class="ttname"><a href="namespacetvm_1_1codegen.html#a0d6322c2dda54a66a3b82022f5f3632c">tvm::codegen::Build</a></div><div class="ttdeci">runtime::Module Build(IRModule mod, Target target)</div><div class="ttdoc">Build a module from array of lowered function. </div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1BuilderInputNode_html_af640877ef243c29d4845977c62f1e12d"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html#af640877ef243c29d4845977c62f1e12d">tvm::meta_schedule::BuilderInputNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> builder.h:46</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1BuilderInputNode_html_a6530833b23371eaeee737cc891b160b9"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html#a6530833b23371eaeee737cc891b160b9">tvm::meta_schedule::BuilderInputNode::params</a></div><div class="ttdeci">Optional< Map< String, runtime::NDArray > > params</div><div class="ttdoc">Parameters for Relay build module. </div><div class="ttdef"><b>Definition:</b> builder.h:44</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>
diff --git a/docs/reference/api/doxygen/call_8h_source.html b/docs/reference/api/doxygen/call_8h_source.html
index e8c5aaa76..739ca0e68 100644
--- a/docs/reference/api/doxygen/call_8h_source.html
+++ b/docs/reference/api/doxygen/call_8h_source.html
@@ -71,7 +71,7 @@ $(function() {
<div class="ttc" id="ir_2attrs_8h_html_a578da113eb199bad72e26c03ad24832f"><div class="ttname"><a href="ir_2attrs_8h.html#a578da113eb199bad72e26c03ad24832f">TVM_ATTR_FIELD</a></div><div class="ttdeci">#define TVM_ATTR_FIELD(FieldName)</div><div class="ttdoc">Declare an attribute field. </div><div class="ttdef"><b>Definition:</b> attrs.h:76</div></div>
<div class="ttc" id="structtvm_1_1relay_1_1CallLoweredAttrs_html_a567c253569e4efde147e5fb7c2f581c7"><div class="ttname"><a href="structtvm_1_1relay_1_1CallLoweredAttrs.html#a567c253569e4efde147e5fb7c2f581c7">tvm::relay::CallLoweredAttrs::metadata</a></div><div class="ttdeci">Map< String, ObjectRef > metadata</div><div class="ttdoc">Additional metadata attached to the call node. Should be replaced by explict fields. </div><div class="ttdef"><b>Definition:</b> call.h:39</div></div>
<div class="ttc" id="classtvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use "curiously recurring template pattern". </div><div class="ttdef"><b>Definition:</b> attrs.h:834</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:1268</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="structtvm_1_1relay_1_1CallLoweredAttrs_html"><div class="ttname"><a href="structtvm_1_1relay_1_1CallLoweredAttrs.html">tvm::relay::CallLoweredAttrs</a></div><div class="ttdoc">Metadata for calls to TIR functions, useful for program analysis crossing Relay and TIR...</div><div class="ttdef"><b>Definition:</b> call.h:37</div></div>
<div class="ttc" id="structtvm_1_1relay_1_1CallLoweredAttrs_html_aa56040c192aeb0d0ac6952bc4fd0fd6f"><div class="ttname"><a href="structtvm_1_1relay_1_1CallLoweredAttrs.html#aa56040c192aeb0d0ac6952bc4fd0fd6f">tvm::relay::CallLoweredAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(CallLoweredAttrs, "relay.attrs.CallLoweredAttrs")</div><div class="ttdef"><b>Definition:</b> call.h:41</div></div>
</div><!-- fragment --></div><!-- contents -->
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode-members.html b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode-members.html
index 11b368a4e..9b20e7e35 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode-members.html
@@ -100,10 +100,10 @@ $(function() {
<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_1MapNode.html#a26ef1b067ec33d0bcd86b72afc6bf608">key_type</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab5bf2de594d1445caba3beff09317d0b">kNextProbeLocation</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html">tvm::runtime::DenseMapNode</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_1MapNode.html#a4b03d8f363b6bcac8ff59cd40b2a9cca">KVType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab0425277cedf5598f463c24385a0e5a5">MapNode</a> class</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html">tvm::runtime::DenseMapNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49fbdf8758a6e4376c0c3ffcf573bc77">mapped_type</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a4b03d8f363b6bcac8ff59cd40b2a9cca">KVType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab0425277cedf5598f463c24385a0e5a5">MapNode</a> class</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html">tvm::runtime::DenseMapNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a49fbdf8758a6e4376c0c3ffcf573bc77">mapped_type</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ae0d84465db325f1e36e702d2b6232ad0">NextProbeLocation</a>(size_t index)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html">tvm::runtime::DenseMapNode</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span><span class="mlabel">static</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>
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode.html b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode.html
index a3a6131c0..bc1e58974 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode.html
@@ -65,8 +65,8 @@ $(function() {
<div class="summary">
<a href="#nested-classes">Classes</a> |
<a href="#pub-methods">Public Member Functions</a> |
+<a href="#pro-static-methods">Static Protected Member Functions</a> |
<a href="#pro-attribs">Protected Attributes</a> |
-<a href="#pro-static-attribs">Static Protected Attributes</a> |
<a href="#friends">Friends</a> |
<a href="classtvm_1_1runtime_1_1DenseMapNode-members.html">List of all members</a> </div>
<div class="headertitle">
@@ -165,6 +165,26 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:ae341e561272ff43cdcbc927bc29ac50d inherit pub_methods_classtvm_1_1runtime_1_1Object"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a> (<a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> &&other)</td></tr>
<tr class="separator:ae341e561272ff43cdcbc927bc29ac50d inherit pub_methods_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-static-methods"></a>
+Static Protected Member Functions</h2></td></tr>
+<tr class="memitem:ae0d84465db325f1e36e702d2b6232ad0"><td class="memItemLeft" align="right" valign="top">static uint64_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ae0d84465db325f1e36e702d2b6232ad0">NextProbeLocation</a> (size_t index)</td></tr>
+<tr class="separator:ae0d84465db325f1e36e702d2b6232ad0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="inherit_header pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td colspan="2" onclick="javascript:toggleInherit('pro_static_methods_classtvm_1_1runtime_1_1MapNode')"><img src="closed.png" alt="-"/> Static Protected Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td></tr>
+<tr class="memitem:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memTemplParams" colspan="2">template<typename IterType > </td></tr>
+<tr class="memitem:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memTemplItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a>< <a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">CreateFromRange</a> ( [...]
+<tr class="memdesc:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">Create the map using contents from the given iterators. <a href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">More...</a><br /></td></tr>
+<tr class="separator:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">InsertMaybeReHash</a> (const <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a4b03d8f363b6bcac8ff59cd40b2a9cca">KVType</a> &kv, <a class="el" href="classtvm_1_1run [...]
+<tr class="memdesc:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">InsertMaybeReHash an entry into the given hash map. <a href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">More...</a><br /></td></tr>
+<tr class="separator:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a>< <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">MapNode</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">CopyFrom</a> (<a class="el" h [...]
+<tr class="memdesc:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">Create an empty container with elements copying from another <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html" title="A specialization of small-sized hash map. ">SmallMapNode</a>. <a href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">More...</a><br /></td></tr>
+<tr class="separator:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="inherit_header pro_static_methods_classtvm_1_1runtime_1_1Object"><td colspan="2" onclick="javascript:toggleInherit('pro_static_methods_classtvm_1_1runtime_1_1Object')"><img src="closed.png" alt="-"/> Static Protected Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td></tr>
+<tr class="memitem:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="memItemLeft" align="right" valign="top">static uint32_t </td><td class="memItemRight" valign="bottom"><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></tr>
+<tr class="memdesc:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="mdescLeft"> </td><td class="mdescRight">Get the type index using type key. <a href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">More...</a><br /></td></tr>
+<tr class="separator:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
+</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-attribs"></a>
Protected Attributes</h2></td></tr>
<tr class="memitem:af7555a75a5dbdf2f1c1af3fd240e54e7"><td class="memItemLeft" align="right" valign="top">uint32_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#af7555a75a5dbdf2f1c1af3fd240e54e7">fib_shift_</a></td></tr>
@@ -191,12 +211,6 @@ Protected Attributes</h2></td></tr>
<tr class="memdesc:af4407d2b59132e803ff791482dbe0145 inherit pro_attribs_classtvm_1_1runtime_1_1Object"><td class="mdescLeft"> </td><td class="mdescRight">deleter of this object to enable customized allocation. If the deleter is nullptr, no deletion will be performed. The creator of the object must always set the deleter field properly. <a href="classtvm_1_1runtime_1_1Object.html#af4407d2b59132e803ff791482dbe0145">More...</a><br /></td></tr>
<tr class="separator:af4407d2b59132e803ff791482dbe0145 inherit pro_attribs_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
-<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-static-attribs"></a>
-Static Protected Attributes</h2></td></tr>
-<tr class="memitem:ab5bf2de594d1445caba3beff09317d0b"><td class="memItemLeft" align="right" valign="top">static constexpr uint64_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab5bf2de594d1445caba3beff09317d0b">kNextProbeLocation</a> [kNumJumpDists]</td></tr>
-<tr class="memdesc:ab5bf2de594d1445caba3beff09317d0b"><td class="mdescLeft"> </td><td class="mdescRight">Candidates of probing distance. <a href="#ab5bf2de594d1445caba3beff09317d0b">More...</a><br /></td></tr>
-<tr class="separator:ab5bf2de594d1445caba3beff09317d0b"><td class="memSeparator" colspan="2"> </td></tr>
-</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="friends"></a>
Friends</h2></td></tr>
<tr class="memitem:ab0425277cedf5598f463c24385a0e5a5"><td class="memItemLeft" align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab0425277cedf5598f463c24385a0e5a5">MapNode</a></td></tr>
@@ -267,21 +281,6 @@ Additional Inherited Members</h2></td></tr>
<tr class="memitem:a70fb5361147634605d6595bb89381f03 inherit pro_methods_classtvm_1_1runtime_1_1Object"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a> ()</td></tr>
<tr class="memdesc:a70fb5361147634605d6595bb89381f03 inherit pro_methods_classtvm_1_1runtime_1_1Object"><td class="mdescLeft"> </td><td class="mdescRight">developer function, decrease reference counter. <a href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">More...</a><br /></td></tr>
<tr class="separator:a70fb5361147634605d6595bb89381f03 inherit pro_methods_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="inherit_header pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td colspan="2" onclick="javascript:toggleInherit('pro_static_methods_classtvm_1_1runtime_1_1MapNode')"><img src="closed.png" alt="-"/> Static Protected Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></td></tr>
-<tr class="memitem:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memTemplParams" colspan="2">template<typename IterType > </td></tr>
-<tr class="memitem:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memTemplItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a>< <a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">CreateFromRange</a> ( [...]
-<tr class="memdesc:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">Create the map using contents from the given iterators. <a href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">More...</a><br /></td></tr>
-<tr class="separator:a6b54c7503c17ee3bb7eadcd1ac0ed009 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">InsertMaybeReHash</a> (const <a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a4b03d8f363b6bcac8ff59cd40b2a9cca">KVType</a> &kv, <a class="el" href="classtvm_1_1run [...]
-<tr class="memdesc:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">InsertMaybeReHash an entry into the given hash map. <a href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">More...</a><br /></td></tr>
-<tr class="separator:a6c6d3b97ee1bb90279026329eb3a9756 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a>< <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">MapNode</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">CopyFrom</a> (<a class="el" h [...]
-<tr class="memdesc:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="mdescLeft"> </td><td class="mdescRight">Create an empty container with elements copying from another <a class="el" href="classtvm_1_1runtime_1_1SmallMapNode.html" title="A specialization of small-sized hash map. ">SmallMapNode</a>. <a href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">More...</a><br /></td></tr>
-<tr class="separator:a2d2eef30b22325a3535a25a1f9728f63 inherit pro_static_methods_classtvm_1_1runtime_1_1MapNode"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="inherit_header pro_static_methods_classtvm_1_1runtime_1_1Object"><td colspan="2" onclick="javascript:toggleInherit('pro_static_methods_classtvm_1_1runtime_1_1Object')"><img src="closed.png" alt="-"/> Static Protected Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td></tr>
-<tr class="memitem:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="memItemLeft" align="right" valign="top">static uint32_t </td><td class="memItemRight" valign="bottom"><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></tr>
-<tr class="memdesc:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="mdescLeft"> </td><td class="mdescRight">Get the type index using type key. <a href="classtvm_1_1runtime_1_1Object.html#a726972ff315c446192df94027ddea032">More...</a><br /></td></tr>
-<tr class="separator:a726972ff315c446192df94027ddea032 inherit pro_static_methods_classtvm_1_1runtime_1_1Object"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>A specialization of hash map that implements the idea of array-based hash map. Another reference implementation can be found [1]. </p>
@@ -543,9 +542,8 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<h2 class="groupheader">Friends And Related Function Documentation</h2>
-<a id="ab0425277cedf5598f463c24385a0e5a5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab0425277cedf5598f463c24385a0e5a5">◆ </a></span>MapNode</h2>
+<a id="ae0d84465db325f1e36e702d2b6232ad0"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae0d84465db325f1e36e702d2b6232ad0">◆ </a></span>NextProbeLocation()</h2>
<div class="memitem">
<div class="memproto">
@@ -554,21 +552,26 @@ Additional Inherited Members</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">friend class <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">MapNode</a></td>
+ <td class="memname">static uint64_t tvm::runtime::DenseMapNode::NextProbeLocation </td>
+ <td>(</td>
+ <td class="paramtype">size_t </td>
+ <td class="paramname"><em>index</em></td><td>)</td>
+ <td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">friend</span></span> </td>
+<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">static</span><span class="mlabel">protected</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
+<p>Candidates of probing distance </p>
</div>
</div>
-<h2 class="groupheader">Member Data Documentation</h2>
-<a id="a58d530f3be4fac7ff99a574c2f6c8ddc"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a58d530f3be4fac7ff99a574c2f6c8ddc">◆ </a></span>data_</h2>
+<h2 class="groupheader">Friends And Related Function Documentation</h2>
+<a id="ab0425277cedf5598f463c24385a0e5a5"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ab0425277cedf5598f463c24385a0e5a5">◆ </a></span>MapNode</h2>
<div class="memitem">
<div class="memproto">
@@ -577,22 +580,21 @@ Additional Inherited Members</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">Block* tvm::runtime::DenseMapNode::data_</td>
+ <td class="memname">friend class <a class="el" href="classtvm_1_1runtime_1_1MapNode.html">MapNode</a></td>
</tr>
</table>
</td>
<td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">protected</span></span> </td>
+<span class="mlabels"><span class="mlabel">friend</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
-<p>array of data blocks </p>
-
</div>
</div>
-<a id="af7555a75a5dbdf2f1c1af3fd240e54e7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af7555a75a5dbdf2f1c1af3fd240e54e7">◆ </a></span>fib_shift_</h2>
+<h2 class="groupheader">Member Data Documentation</h2>
+<a id="a58d530f3be4fac7ff99a574c2f6c8ddc"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a58d530f3be4fac7ff99a574c2f6c8ddc">◆ </a></span>data_</h2>
<div class="memitem">
<div class="memproto">
@@ -601,7 +603,7 @@ Additional Inherited Members</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">uint32_t tvm::runtime::DenseMapNode::fib_shift_</td>
+ <td class="memname">Block* tvm::runtime::DenseMapNode::data_</td>
</tr>
</table>
</td>
@@ -611,12 +613,12 @@ Additional Inherited Members</h2></td></tr>
</table>
</div><div class="memdoc">
-<p>fib shift in Fibonacci Hashing </p>
+<p>array of data blocks </p>
</div>
</div>
-<a id="ab5bf2de594d1445caba3beff09317d0b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab5bf2de594d1445caba3beff09317d0b">◆ </a></span>kNextProbeLocation</h2>
+<a id="af7555a75a5dbdf2f1c1af3fd240e54e7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#af7555a75a5dbdf2f1c1af3fd240e54e7">◆ </a></span>fib_shift_</h2>
<div class="memitem">
<div class="memproto">
@@ -625,17 +627,17 @@ Additional Inherited Members</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">constexpr uint64_t tvm::runtime::DenseMapNode::kNextProbeLocation[kNumJumpDists]</td>
+ <td class="memname">uint32_t tvm::runtime::DenseMapNode::fib_shift_</td>
</tr>
</table>
</td>
<td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">static</span><span class="mlabel">protected</span></span> </td>
+<span class="mlabels"><span class="mlabel">protected</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
-<b>Initial value:</b><div class="fragment"><div class="line">{</div><div class="line"> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,</div><div class="line"> </div><div class="line"> </div><div class="line"> </div><div class="line"> </div><div class="line"> 21, 28, 36, 45, 55, 66, 78, 91, 105, 120,</div><div class="line"> 136, 153, 171, 190, 210, 231, 253, 276, 300, 325,</div><div class="line"> 351, 378, 406, 435, 465, 496, 528, 561, 595, 630,</div><div cla [...]
-<p>Candidates of probing distance. </p>
+
+<p>fib shift in Fibonacci Hashing </p>
</div>
</div>
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__coll__graph.svg b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__coll__graph.svg
index 4c916c2fd..1c5075b1f 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__coll__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__coll__graph.svg
@@ -16,16 +16,16 @@
<text text-anchor="middle" x="257.5" y="-133.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::DenseMapNode</text>
<polyline fill="none" stroke="#000000" points="176,-126.5 339,-126.5 "/>
<text text-anchor="start" x="184" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># fib_shift_</text>
-<text text-anchor="start" x="184" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># kNextProbeLocation</text>
-<polyline fill="none" stroke="#000000" points="176,-96.5 339,-96.5 "/>
-<text text-anchor="start" x="184" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
-<text text-anchor="start" x="184" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<polyline fill="none" stroke="#000000" points="176,-107.5 339,-107.5 "/>
+<text text-anchor="start" x="184" y="-95.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
+<text text-anchor="start" x="184" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<text text-anchor="start" x="184" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
<text text-anchor="start" x="184" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="184" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="184" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
-<text text-anchor="start" x="184" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
-<text text-anchor="start" x="184" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
-<text text-anchor="start" x="184" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="184" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
+<text text-anchor="start" x="184" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
+<text text-anchor="start" x="184" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
+<text text-anchor="start" x="184" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="184" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># NextProbeLocation()</text>
</g>
<!-- Node4 -->
<g id="node2" class="node">
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__inherit__graph.svg
index a3ddd4295..7e7353149 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1DenseMapNode__inherit__graph.svg
@@ -17,16 +17,16 @@
<polyline fill="none" stroke="#000000" points="23,-137.5 186,-137.5 "/>
<text text-anchor="start" x="31" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># fib_shift_</text>
<text text-anchor="start" x="31" y="-114.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># data_</text>
-<text text-anchor="start" x="31" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># kNextProbeLocation</text>
-<polyline fill="none" stroke="#000000" points="23,-96.5 186,-96.5 "/>
-<text text-anchor="start" x="31" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
-<text text-anchor="start" x="31" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<polyline fill="none" stroke="#000000" points="23,-107.5 186,-107.5 "/>
+<text text-anchor="start" x="31" y="-95.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
+<text text-anchor="start" x="31" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<text text-anchor="start" x="31" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
<text text-anchor="start" x="31" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="31" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="31" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
-<text text-anchor="start" x="31" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
-<text text-anchor="start" x="31" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
-<text text-anchor="start" x="31" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="31" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
+<text text-anchor="start" x="31" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
+<text text-anchor="start" x="31" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
+<text text-anchor="start" x="31" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="31" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># NextProbeLocation()</text>
</g>
<!-- Node1 -->
<g id="node2" class="node">
diff --git a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1MapNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1MapNode__inherit__graph.svg
index f8e8223b4..09180487f 100644
--- a/docs/reference/api/doxygen/classtvm_1_1runtime_1_1MapNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1runtime_1_1MapNode__inherit__graph.svg
@@ -44,16 +44,16 @@
<polyline fill="none" stroke="#000000" points="0,-137.5 163,-137.5 "/>
<text text-anchor="start" x="8" y="-125.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># fib_shift_</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"># kNextProbeLocation</text>
-<polyline fill="none" stroke="#000000" points="0,-96.5 163,-96.5 "/>
-<text text-anchor="start" x="8" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
-<text text-anchor="start" x="8" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<polyline fill="none" stroke="#000000" points="0,-107.5 163,-107.5 "/>
+<text text-anchor="start" x="8" y="-95.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~DenseMapNode()</text>
+<text text-anchor="start" x="8" y="-84.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ count()</text>
+<text text-anchor="start" x="8" y="-73.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ at()</text>
-<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
-<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
-<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
-<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ find()</text>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ erase()</text>
+<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ begin()</text>
+<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ end()</text>
+<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># NextProbeLocation()</text>
</a>
</g>
</g>
diff --git a/docs/reference/api/doxygen/compute__dag_8h_source.html b/docs/reference/api/doxygen/compute__dag_8h_source.html
index fd7c4c681..7866a58d8 100644
--- a/docs/reference/api/doxygen/compute__dag_8h_source.html
+++ b/docs/reference/api/doxygen/compute__dag_8h_source.html
@@ -101,7 +101,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ComputeDAGNode_html_a15fd4fef58262fe33c2aaa4cda5cb178"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html#a15fd4fef58262fe33c2aaa4cda5cb178">tvm::auto_scheduler::ComputeDAGNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> compute_dag.h:185</div></div>
<div class="ttc" id="namespacetvm_1_1te_html_ac0effd02bbddf8ce2cce7073e175ca4c"><div class="ttname"><a href="namespacetvm_1_1te.html#ac0effd02bbddf8ce2cce7073e175ca4c">tvm::te::InferBound</a></div><div class="ttdeci">Map< IterVar, Range > InferBound(const Schedule &sch)</div><div class="ttdoc">Infer the bound of all iteration variables relates to the schedule. </div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ComputeDAGNode_html_a5a8b2184133c91f2b0324836bb4d3d0c"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html#a5a8b2184133c91f2b0324836bb4d3d0c">tvm::auto_scheduler::ComputeDAGNode::access_analyzer</a></div><div class="ttdeci">AccessAnalyzer access_analyzer</div><div class="ttdoc">The static read-write access analyzer. </div><div class="ttdef"><b>Definition:</b> compute_dag.h:183</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:1268</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_1auto__scheduler_1_1ComputeDAGNode_html_a2a3b40c0e1c5f387bb528223b26934bd"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html#a2a3b40c0e1c5f387bb528223b26934bd">tvm::auto_scheduler::ComputeDAGNode::ops</a></div><div class="ttdeci">Array< te::Operation > ops</div><div class="ttdoc">All used operations in topo order. </div><div class="ttdef"><b>Definition:</b> compute_dag.h:177</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode_html_ad70984d9ab1380470bcfba14834120bc"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html#ad70984d9ab1380470bcfba14834120bc">tvm::auto_scheduler::AccessAnalyzerNode::is_strictly_inlineable</a></div><div class="ttdeci">OperationMap< bool > is_strictly_inlineable</div><div class="ttdoc">Store whether the operation is strictly inlineable (e.g., injective, broadcast and elementwise [...]
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode_html_a99f270b8b0d0beb3367ea53215cc7440"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html#a99f270b8b0d0beb3367ea53215cc7440">tvm::auto_scheduler::AccessAnalyzerNode::is_output</a></div><div class="ttdeci">OperationMap< bool > is_output</div><div class="ttdoc">Store whether the operation is an output operation. </div><div class="ttdef"><b>Definition:</b> compute_dag.h:77</div></div>
diff --git a/docs/reference/api/doxygen/dataflow__matcher_8h_source.html b/docs/reference/api/doxygen/dataflow__matcher_8h_source.html
index 33cfe7789..888cc6384 100644
--- a/docs/reference/api/doxygen/dataflow__matcher_8h_source.html
+++ b/docs/reference/api/doxygen/dataflow__matcher_8h_source.html
@@ -84,7 +84,7 @@ $(function() {
<div class="ttc" id="dataflow__pattern_8h_html"><div class="ttname"><a href="dataflow__pattern_8h.html">dataflow_pattern.h</a></div><div class="ttdoc">A pattern language for matching dataflow properties. </div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1DFPatternCallbackNode_html"><div class="ttname"><a href="classtvm_1_1relay_1_1DFPatternCallbackNode.html">tvm::relay::DFPatternCallbackNode</a></div><div class="ttdoc">Base type of all dataflow pattern callbacks. </div><div class="ttdef"><b>Definition:</b> dataflow_matcher.h:42</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:1268</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="dataflow__pattern__functor_8h_html"><div class="ttname"><a href="dataflow__pattern__functor_8h.html">dataflow_pattern_functor.h</a></div><div class="ttdoc">A set of passes for operating on pattern graphs. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
<div class="ttc" id="namespacetvm_1_1relay_html_a8e5c12794d464d6e4543b9e5c68d8707"><div class="ttname"><a href="namespacetvm_1_1relay.html#a8e5c12794d464d6e4543b9e5c68d8707">tvm::relay::MatchPattern</a></div><div class="ttdeci">bool MatchPattern(DFPattern pattern, Expr expr)</div><div class="ttdoc">Determine if a pattern matches an expression. </div></div>
diff --git a/docs/reference/api/doxygen/dataflow__pattern_8h_source.html b/docs/reference/api/doxygen/dataflow__pattern_8h_source.html
index d1b459dba..9b84e3f56 100644
--- a/docs/reference/api/doxygen/dataflow__pattern_8h_source.html
+++ b/docs/reference/api/doxygen/dataflow__pattern_8h_source.html
@@ -152,7 +152,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1relay_1_1ShapePatternNode_html"><div class="ttname"><a href="classtvm_1_1relay_1_1ShapePatternNode.html">tvm::relay::ShapePatternNode</a></div><div class="ttdoc">Pattern for Shapes. </div><div class="ttdef"><b>Definition:</b> dataflow_pattern.h:408</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1TypePatternNode_html_aab5faa2a58862707b8dc18b59cccac19"><div class="ttname"><a href="classtvm_1_1relay_1_1TypePatternNode.html#aab5faa2a58862707b8dc18b59cccac19">tvm::relay::TypePatternNode::type</a></div><div class="ttdeci">Type type</div><div class="ttdoc">The type to match. </div><div class="ttdef"><b>Definition:</b> dataflow_pattern.h:384</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1TuplePatternNode_html_a3dc7ac25d2780d4a064868aeec7cb54f"><div class="ttname"><a href="classtvm_1_1relay_1_1TuplePatternNode.html#a3dc7ac25d2780d4a064868aeec7cb54f">tvm::relay::TuplePatternNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> dataflow_pattern.h:271</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:1268</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_1relay_1_1AttrPatternNode_html_a4ccb04267d93985da28518886b56ed2f"><div class="ttname"><a href="classtvm_1_1relay_1_1AttrPatternNode.html#a4ccb04267d93985da28518886b56ed2f">tvm::relay::AttrPatternNode::pattern</a></div><div class="ttdeci">DFPattern pattern</div><div class="ttdoc">The pattern. </div><div class="ttdef"><b>Definition:</b> dataflow_pattern.h:469</div></div>
<div class="ttc" id="namespacetvm_html_a18256ba1213ce5ff3cf8037a314354b7"><div class="ttname"><a href="namespacetvm.html#a18256ba1213ce5ff3cf8037a314354b7">tvm::operator/</a></div><div class="ttdeci">PrimExpr operator/(PrimExpr a, PrimExpr b)</div><div class="ttdoc">division operator </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>
diff --git a/docs/reference/api/doxygen/detail_2extern_8h_source.html b/docs/reference/api/doxygen/detail_2extern_8h_source.html
index d60f51473..a45557ce8 100644
--- a/docs/reference/api/doxygen/detail_2extern_8h_source.html
+++ b/docs/reference/api/doxygen/detail_2extern_8h_source.html
@@ -78,7 +78,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_abd540cb73407771ecfb4f78722ce5a1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#abd540cb73407771ecfb4f78722ce5a1b">tvm::tir::builtin::tvm_stack_make_shape</a></div><div class="ttdeci">const Op & tvm_stack_make_shape()</div><div class="ttdoc">Allocate a shape tuple on stack, return the handle. </div></div>
<div class="ttc" id="namespacetvm_1_1te_html_ae0c71f84710b436cbe0b32289d0838f4"><div class="ttname"><a href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">tvm::te::var</a></div><div class="ttdeci">Var var(std::string name_hint, DataType t=DataType::Int(32))</div><div class="ttdoc">Construct a new Var expression. </div></div>
<div class="ttc" id="operation_8h_html"><div class="ttname"><a href="operation_8h.html">operation.h</a></div><div class="ttdoc">Operation node can generate one or multiple Tensors. </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:1268</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="builtin_8h_html"><div class="ttname"><a href="builtin_8h.html">builtin.h</a></div><div class="ttdoc">TIR builtin intrinsics. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1Evaluate_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Evaluate.html">tvm::tir::Evaluate</a></div><div class="ttdoc">Managed reference to EvaluateNode. </div><div class="ttdef"><b>Definition:</b> stmt.h:893</div></div>
<div class="ttc" id="namespacetvm_html_a41918af1a1dc386388639a9d3ad06c5d"><div class="ttname"><a href="namespacetvm.html#a41918af1a1dc386388639a9d3ad06c5d">tvm::DataType</a></div><div class="ttdeci">runtime::DataType DataType</div><div class="ttdef"><b>Definition:</b> data_type.h:389</div></div>
diff --git a/docs/reference/api/doxygen/executable_8h_source.html b/docs/reference/api/doxygen/executable_8h_source.html
index eda10cd44..d7f223e56 100644
--- a/docs/reference/api/doxygen/executable_8h_source.html
+++ b/docs/reference/api/doxygen/executable_8h_source.html
@@ -85,7 +85,7 @@ $(function() {
<div class="ttc" id="object_8h_html"><div class="ttname"><a href="object_8h.html">object.h</a></div><div class="ttdoc">A managed object in the TVM runtime. </div></div>
<div class="ttc" id="runtime_2module_8h_html"><div class="ttname"><a href="runtime_2module_8h.html">module.h</a></div><div class="ttdoc">Runtime container of the functions generated by TVM, This is used to support dynamically link...</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM. </div><div class="ttdef"><b>Definition:</b> module.h:50</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:1268</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_1runtime_1_1vm_1_1Executable_html_aae6b6508e423538a82c23a1724c87877"><div class="ttname"><a href="classtvm_1_1runtime_1_1vm_1_1Executable.html#aae6b6508e423538a82c23a1724c87877">tvm::runtime::vm::Executable::virtual_devices</a></div><div class="ttdeci">std::vector< Device > virtual_devices</div><div class="ttdoc">The (compile-time, virtual) devices corresponding to each device index. Currently we only support at ...</div><div class="ttdef"><b>Definit [...]
<div class="ttc" id="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/executor_8h_source.html b/docs/reference/api/doxygen/executor_8h_source.html
index 09da65088..74ad43d90 100644
--- a/docs/reference/api/doxygen/executor_8h_source.html
+++ b/docs/reference/api/doxygen/executor_8h_source.html
@@ -103,7 +103,7 @@ $(function() {
<div class="ttc" id="attr__registry__map_8h_html"><div class="ttname"><a href="attr__registry__map_8h.html">attr_registry_map.h</a></div><div class="ttdoc">Attribute map used in registry. </div></div>
<div class="ttc" id="classtvm_1_1relay_1_1ExecutorNode_html_a8e3cabcfef4e40924bd4182c613a71f9"><div class="ttname"><a href="classtvm_1_1relay_1_1ExecutorNode.html#a8e3cabcfef4e40924bd4182c613a71f9">tvm::relay::ExecutorNode::ShouldLinkParameters</a></div><div class="ttdeci">Bool ShouldLinkParameters() const</div><div class="ttdoc">Should Link Parameters into the module. </div><div class="ttdef"><b>Definition:</b> executor.h:66</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html_a481f01923b14e1851ebd38506e9c66ea"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">tvm::runtime::Object::type_index</a></div><div class="ttdeci">uint32_t type_index() const</div><div class="ttdef"><b>Definition:</b> object.h:175</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:1268</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_1runtime_1_1Object_html_a817ba6c23b7ee1821c48a75edf255a30"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object::TypeIndex2Key</a></div><div class="ttdeci">static std::string TypeIndex2Key(uint32_t tindex)</div><div class="ttdoc">Get the type key of the corresponding index from runtime. </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_1relay_1_1ExecutorNode_html_a2366308664268fe4fe6e74d208e859c1"><div class="ttname"><a href="classtvm_1_1relay_1_1ExecutorNode.html#a2366308664268fe4fe6e74d208e859c1">tvm::relay::ExecutorNode::name</a></div><div class="ttdeci">String name</div><div class="ttdoc">name of the Executor </div><div class="ttdef"><b>Definition:</b> executor.h:58</div></div>
diff --git a/docs/reference/api/doxygen/functions_func_m.html b/docs/reference/api/doxygen/functions_func_m.html
index 9fb918fa7..3e7870772 100644
--- a/docs/reference/api/doxygen/functions_func_m.html
+++ b/docs/reference/api/doxygen/functions_func_m.html
@@ -188,7 +188,7 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500">tvm::arith::ModularSet</a>
</li>
<li>Module()
-: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abd1380b3f813c2b6acefca3aaef425f4">tvm::runtime::Module</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Module.html#abfbc619b3b3166d63ec52e399c24bed9">tvm::runtime::Module</a>
</li>
<li>Move()
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a162dc8d73dc2306f066c3ee013ff096f">tvm::runtime::vm::Instruction</a>
diff --git a/docs/reference/api/doxygen/functions_func_n.html b/docs/reference/api/doxygen/functions_func_n.html
index 6a850c591..a6709a0f5 100644
--- a/docs/reference/api/doxygen/functions_func_n.html
+++ b/docs/reference/api/doxygen/functions_func_n.html
@@ -109,6 +109,9 @@ $(function() {
<li>NewFromDLTensor()
: <a class="el" href="classtvm_1_1runtime_1_1NDArray.html#a711df9392c6808f6e0ca7c35b11ee94b">tvm::runtime::NDArray</a>
</li>
+<li>NextProbeLocation()
+: <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ae0d84465db325f1e36e702d2b6232ad0">tvm::runtime::DenseMapNode</a>
+</li>
<li>NextTaskId()
: <a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html#a23752f62706ef3f0bfac98fb203e5062">tvm::meta_schedule::PyTaskSchedulerNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a079e2964ca86b5c32564140efa3e5626">tvm::meta_schedule::TaskSchedulerNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_s.html b/docs/reference/api/doxygen/functions_func_s.html
index 7307cd520..1e2a181f1 100644
--- a/docs/reference/api/doxygen/functions_func_s.html
+++ b/docs/reference/api/doxygen/functions_func_s.html
@@ -620,7 +620,7 @@ $(function() {
, <a class="el" href="classtvm_1_1TracedArray.html#a52b30a89b5c68811d0789f9f32b12627">tvm::TracedArray< T ></a>
</li>
<li>SizeVar()
-: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#ac470249315d9e395ad581d35dd5dcb05">tvm::tir::SizeVar</a>
+: <a class="el" href="classtvm_1_1tir_1_1SizeVar.html#a0f8cb8a92feb96343939d223db90f7cd">tvm::tir::SizeVar</a>
</li>
<li>Slice()
: <a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html#ab314819e8bcca6421e9a4f33e48578c3">tvm::te::Tensor::Slice</a>
@@ -746,7 +746,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html#ac29b9295c432a87658392872c644864f">tvm::runtime::DeviceAPI</a>
</li>
<li>String()
-: <a class="el" href="classtvm_1_1runtime_1_1String.html#a68df7bab89fca339e3918438dd80300d">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#acf549b3c43142639879e0fc31ea5cd77">tvm::runtime::String</a>
</li>
<li>StringImm()
: <a class="el" href="classtvm_1_1tir_1_1StringImm.html#a0f2830290e055f677c5d5dea98aab726">tvm::tir::StringImm</a>
diff --git a/docs/reference/api/doxygen/functions_func_t.html b/docs/reference/api/doxygen/functions_func_t.html
index 011dbed71..fd043d63b 100644
--- a/docs/reference/api/doxygen/functions_func_t.html
+++ b/docs/reference/api/doxygen/functions_func_t.html
@@ -1135,7 +1135,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html#a5882f7eda112e825eb5a87e45aeb85b0">tvm::runtime::TVMArgsSetter</a>
</li>
<li>TVMArgValue()
-: <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_1TVMMovableArgValue__.html#a8eca9048535541f374a5806f9648131b">tvm::runtime::TVMMovableArgValue_</a>
@@ -1147,7 +1147,7 @@ $(function() {
: <a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html#afe1837bdbafe8341c2031c5cebcf6e74">tvm::runtime::TVMPODValue_</a>
</li>
<li>TVMRetValue()
-: <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#a77455a8fe7d27b90a01a64f1cd28e9ec">tvm::runtime::TVMRetValue</a>
+: <a class="el" href="classtvm_1_1runtime_1_1TVMRetValue.html#ab86bf21f214fca72e73a7f6e20ffab8d">tvm::runtime::TVMRetValue</a>
</li>
<li>type()
: <a class="el" href="classtvm_1_1runtime_1_1vm_1_1Allocator.html#a7cfb6d4ea480436801276fe2e7660eb2">tvm::runtime::vm::Allocator</a>
@@ -1173,10 +1173,10 @@ $(function() {
: <a class="el" href="classtvm_1_1TypeData.html#a0a98fd1095812379d2bd1337db1511c1">tvm::TypeData</a>
</li>
<li>TypedEnvFunc()
-: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a41a6b9014d0feeb628ca7edfd0d26f0b">tvm::TypedEnvFunc< R(Args...)></a>
+: <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#af45a2ceff92e6f6c394ea766a45027a0">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a8941c80982a1b2a289440f3c79bb0ac8">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_func_u.html b/docs/reference/api/doxygen/functions_func_u.html
index 4b4e0f203..611cae9ff 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_k.html b/docs/reference/api/doxygen/functions_k.html
index c2365584b..92e05e31a 100644
--- a/docs/reference/api/doxygen/functions_k.html
+++ b/docs/reference/api/doxygen/functions_k.html
@@ -172,9 +172,6 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html#a6ac84681107f25f66b84209a346383d9">tvm::arith::ConstIntBound</a>
, <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a0d8f5f54f4f380f664016f466f100b3a">tvm::arith::ConstIntBoundNode</a>
</li>
-<li>kNextProbeLocation
-: <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab5bf2de594d1445caba3beff09317d0b">tvm::runtime::DenseMapNode</a>
-</li>
<li>kPayloadLength
: <a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1PacketFieldSizeBytes.html#a69c71abb0d8cd0b7ede781082ee0391b">tvm::runtime::micro_rpc::PacketFieldSizeBytes</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_n.html b/docs/reference/api/doxygen/functions_n.html
index 655839bf4..5df0d741c 100644
--- a/docs/reference/api/doxygen/functions_n.html
+++ b/docs/reference/api/doxygen/functions_n.html
@@ -180,6 +180,9 @@ $(function() {
<li>next_alloc
: <a class="el" href="structtvm__workspace__t.html#a5da9eaf15149d785a9b537f7c9e3945b">tvm_workspace_t</a>
</li>
+<li>NextProbeLocation()
+: <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ae0d84465db325f1e36e702d2b6232ad0">tvm::runtime::DenseMapNode</a>
+</li>
<li>NextTaskId()
: <a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html#a23752f62706ef3f0bfac98fb203e5062">tvm::meta_schedule::PyTaskSchedulerNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html#a079e2964ca86b5c32564140efa3e5626">tvm::meta_schedule::TaskSchedulerNode</a>
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index 0839c0a67..8128b905f 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -821,7 +821,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1DocNode.html#a29e21c8f39639d1d30697971267847a8">tvm::script::printer::DocNode</a>
</li>
<li>SourceMap()
-: <a class="el" href="classtvm_1_1parser_1_1SourceMap.html#a43518e78ad2060e9400d893078c48008">tvm::parser::SourceMap</a>
+: <a class="el" href="classtvm_1_1parser_1_1SourceMap.html#afc48463cc0967ab79876178613a5aff2">tvm::parser::SourceMap</a>
</li>
<li>space_generator
: <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a7bdfdd48530bfe380c5f6c143158a07f">tvm::meta_schedule::TuneContextNode</a>
@@ -885,7 +885,7 @@ $(function() {
: <a class="el" href="classtvm_1_1te_1_1Stage.html#a51432f38d9ec4792a2525023179ae604">tvm::te::Stage</a>
</li>
<li>SplitStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a64ed86582a56a2645b3e4eb44ecb31af">tvm::auto_scheduler::SplitStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html#a184575a8029d77f7a3bee23d81141df5">tvm::auto_scheduler::SplitStep</a>
</li>
<li>src
: <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html#a8ce159fc6db748e5092fa937de3fde53">tvm::arith::IntConstraintsTransformNode</a>
@@ -1000,7 +1000,7 @@ $(function() {
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtDoc.html#adec8d59e41d8a4093fb310089bf2c3ba">tvm::script::printer::StmtDoc</a>
</li>
<li>StmtNode()
-: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a67693c4e97ae49890ea74605fe1b1f74">tvm::tir::StmtNode</a>
+: <a class="el" href="classtvm_1_1tir_1_1StmtNode.html#a79e21b14d3ab57209577bf4a8f694a87">tvm::tir::StmtNode</a>
</li>
<li>stmts
: <a class="el" href="classtvm_1_1script_1_1printer_1_1StmtBlockDocNode.html#a17862bcb50fd1ef49cd9a47f065e612c">tvm::script::printer::StmtBlockDocNode</a>
@@ -1050,7 +1050,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html#a93d1d23f24d903db844f75f51fe09a36">tvm::tir::ScheduleNode</a>
</li>
<li>StorageAlignStep()
-: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#a99dbb8c55d9e7d78268b6d43fd348bc7">tvm::auto_scheduler::StorageAlignStep</a>
+: <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html#af50b7c2f020f8e0a80f5bcc8e559b394">tvm::auto_scheduler::StorageAlignStep</a>
</li>
<li>StorageType
: <a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html#a67e86db3290b1d3bd4aca7e7a2faf187">tvm::runtime::SimpleObjAllocator::ArrayHandler< ArrayType, ElemType ></a>
@@ -1108,7 +1108,7 @@ $(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#acf549b3c43142639879e0fc31ea5cd77">tvm::runtime::String</a>
+: <a class="el" href="classtvm_1_1runtime_1_1String.html#a02fca36e3ff55cc1e83635b02a11fca3">tvm::runtime::String</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj::FromStd</a>
, <a class="el" href="classtvm_1_1runtime_1_1StringObj.html#a7fb804f7dc96dd9f705c84095f37f1ca">tvm::runtime::StringObj</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index 26d939281..d9375f877 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -1431,10 +1431,10 @@ $(function() {
: <a class="el" href="classtvm_1_1TypeData.html#a0a98fd1095812379d2bd1337db1511c1">tvm::TypeData</a>
</li>
<li>TypedEnvFunc()
-: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a0d72a6fa7263821c14bcd37837998ed9">tvm::TypedEnvFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#a41a6b9014d0feeb628ca7edfd0d26f0b">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#a6b346a6d0b601eff5a100c7a207e9c86">tvm::runtime::TypedPackedFunc< R(Args...)></a>
+: <a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a36ca0d1876544463ee848766e70e5e96">tvm::runtime::TypedPackedFunc< R(Args...)></a>
</li>
<li>TypeIndex2Key()
: <a class="el" href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object</a>
@@ -1457,7 +1457,7 @@ $(function() {
: <a class="el" href="classtvm_1_1TypeRelation.html#ac26b1897eab8197ed26606ab81b7403b">tvm::TypeRelation</a>
</li>
<li>TypeReporter()
-: <a class="el" href="classtvm_1_1TypeReporter.html#aa3dc38a3c84d324d0b3a9f358460a091">tvm::TypeReporter</a>
+: <a class="el" href="classtvm_1_1TypeReporter.html#a8e7e05a07f9f7ad9bea91f27afac9051">tvm::TypeReporter</a>
</li>
<li>types
: <a class="el" href="classtvm_1_1TupleAffineTypeNode.html#a30c834b7e1cb64467e6587ac16ebb187">tvm::TupleAffineTypeNode</a>
diff --git a/docs/reference/api/doxygen/functions_u.html b/docs/reference/api/doxygen/functions_u.html
index aee008c4c..9051d7808 100644
--- a/docs/reference/api/doxygen/functions_u.html
+++ b/docs/reference/api/doxygen/functions_u.html
@@ -122,7 +122,7 @@ $(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#abdd8936c6fca33ef9b7c086f8fd58f84">tvm::IRModuleNode</a>
+, <a class="el" href="classtvm_1_1IRModuleNode.html#a94a93385e64ce844299729af6a573015">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_v.html b/docs/reference/api/doxygen/functions_v.html
index 12e5eda0d..f749f4ea7 100644
--- a/docs/reference/api/doxygen/functions_v.html
+++ b/docs/reference/api/doxygen/functions_v.html
@@ -628,7 +628,7 @@ $(function() {
<li>VisitType_()
: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a0e715c54558934e4504c366ff803d8e1">tvm::TypeFunctor< R(const Type &n, Args...)></a>
, <a class="el" href="classtvm_1_1TypeMutator.html#a18a04668d3fb464d957f3a26a4274104">tvm::TypeMutator</a>
-, <a class="el" href="classtvm_1_1TypeVisitor.html#af92188034706eec6c1ce5c8240f65cc0">tvm::TypeVisitor</a>
+, <a class="el" href="classtvm_1_1TypeVisitor.html#a292b19b578526ea74b1434dc50514a18">tvm::TypeVisitor</a>
</li>
<li>VisitTypeDefault_()
: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a91553f9e04c39b3821a70ae4f7b0c597">tvm::TypeFunctor< R(const Type &n, Args...)></a>
@@ -650,7 +650,7 @@ $(function() {
: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFrame.html#a8f8c990ee4fa7cb7472f5440f2ca3bde">tvm::runtime::vm::VMFrame</a>
</li>
<li>VMFunction()
-: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html#aea763069fe1dd6849ce0d1ec336931e0">tvm::runtime::vm::VMFunction</a>
+: <a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html#af9d2bdcf19642c21bc4909b9e9b6196d">tvm::runtime::vm::VMFunction</a>
</li>
<li>Void()
: <a class="el" href="classtvm_1_1runtime_1_1DataType.html#ab8dc0832aff8fd7421884c0fe20a3bfd">tvm::runtime::DataType</a>
diff --git a/docs/reference/api/doxygen/functions_vars_k.html b/docs/reference/api/doxygen/functions_vars_k.html
index c857e3da8..f4e3c3857 100644
--- a/docs/reference/api/doxygen/functions_vars_k.html
+++ b/docs/reference/api/doxygen/functions_vars_k.html
@@ -136,9 +136,6 @@ $(function() {
: <a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html#a6ac84681107f25f66b84209a346383d9">tvm::arith::ConstIntBound</a>
, <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a0d8f5f54f4f380f664016f466f100b3a">tvm::arith::ConstIntBoundNode</a>
</li>
-<li>kNextProbeLocation
-: <a class="el" href="classtvm_1_1runtime_1_1DenseMapNode.html#ab5bf2de594d1445caba3beff09317d0b">tvm::runtime::DenseMapNode</a>
-</li>
<li>kPayloadLength
: <a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1PacketFieldSizeBytes.html#a69c71abb0d8cd0b7ede781082ee0391b">tvm::runtime::micro_rpc::PacketFieldSizeBytes</a>
</li>
diff --git a/docs/reference/api/doxygen/greedy_8h_source.html b/docs/reference/api/doxygen/greedy_8h_source.html
index 157d93c8f..538d987f7 100644
--- a/docs/reference/api/doxygen/greedy_8h_source.html
+++ b/docs/reference/api/doxygen/greedy_8h_source.html
@@ -80,7 +80,7 @@ $(function() {
<div class="ttc" id="device__api_8h_html"><div class="ttname"><a href="device__api_8h.html">device_api.h</a></div><div class="ttdoc">Abstract device memory management API. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase_html_a48a1bdc94c70a008640b9a015e785729"><div class="ttname"><a href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a48a1bdc94c70a008640b9a015e785729">tvm::tir::usmp::algo::GreedyBase::IsValidPlacement</a></div><div class="ttdeci">bool IsValidPlacement(const PoolInfo &candidate_pool, const size_t &next_offset, const size_t &size_bytes)</div><div class="ttdoc">A helper function check whether a offset is val [...]
<div class="ttc" id="tir_2usmp_2utils_8h_html"><div class="ttname"><a href="tir_2usmp_2utils_8h.html">utils.h</a></div><div class="ttdoc">Utilities for Unified Static Memory Planner. </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:1268</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="builtin_8h_html"><div class="ttname"><a href="builtin_8h.html">builtin.h</a></div><div class="ttdoc">TIR builtin intrinsics. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase_html_a3581ab0723c1ab1e74cf479c7c81a803"><div class="ttname"><a href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a3581ab0723c1ab1e74cf479c7c81a803">tvm::tir::usmp::algo::GreedyBase::round_up_to_byte_alignment</a></div><div class="ttdeci">size_t round_up_to_byte_alignment(const size_t &non_aligned_byte_offset, const int &byte_alignment)</div><div class="ttdoc">Rounds up the offset to satisfy the alignement r [...]
<div class="ttc" id="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase_html_a95d49572c346fb536671fc1923f39c2a"><div class="ttname"><a href="classtvm_1_1tir_1_1usmp_1_1algo_1_1GreedyBase.html#a95d49572c346fb536671fc1923f39c2a">tvm::tir::usmp::algo::GreedyBase::GreedyBase</a></div><div class="ttdeci">GreedyBase()</div><div class="ttdef"><b>Definition:</b> greedy.h:47</div></div>
diff --git a/docs/reference/api/doxygen/int__set_8h_source.html b/docs/reference/api/doxygen/int__set_8h_source.html
index b3c1c3361..c19cd85d2 100644
--- a/docs/reference/api/doxygen/int__set_8h_source.html
+++ b/docs/reference/api/doxygen/int__set_8h_source.html
@@ -95,7 +95,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1arith_html_a31262f87a37f9f847ace3c5c8e81dcf5"><div class="ttname"><a href="namespacetvm_1_1arith.html#a31262f87a37f9f847ace3c5c8e81dcf5">tvm::arith::EstimateRegionLowerBound</a></div><div class="ttdeci">Optional< Array< IntSet > > EstimateRegionLowerBound(const Array< Range > &region, const Map< Var, Range > &var_dom, const PrimExpr &predicate, arith::Analyzer *analyzer)</div><div class="ttdoc">Analyze the region wi [...]
<div class="ttc" id="namespacetvm_1_1arith_html_a4c3dedfa4cba4ad39c953eb51eb83e4d"><div class="ttname"><a href="namespacetvm_1_1arith.html#a4c3dedfa4cba4ad39c953eb51eb83e4d">tvm::arith::UnionRegionLowerBound</a></div><div class="ttdeci">Array< IntSet > UnionRegionLowerBound(const Array< Array< IntSet >> &nd_int_sets)</div><div class="ttdoc">The union of N-dimensional integer sets. </div></div>
<div class="ttc" id="namespacetvm_1_1arith_html_ad27c4f216e41eb8e81296fb7ec4b9453"><div class="ttname"><a href="namespacetvm_1_1arith.html#ad27c4f216e41eb8e81296fb7ec4b9453">tvm::arith::UnionRegion</a></div><div class="ttdeci">Array< IntSet > UnionRegion(const Array< Array< IntSet >> &nd_int_sets)</div><div class="ttdoc">The union of N-dimensional integer sets. </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:1268</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="namespacetvm_1_1arith_html_a82bd85ab31c2ecf2108971c801bb528e"><div class="ttname"><a href="namespacetvm_1_1arith.html#a82bd85ab31c2ecf2108971c801bb528e">tvm::arith::EstimateRegionStrictBound</a></div><div class="ttdeci">Optional< Array< IntSet > > EstimateRegionStrictBound(const Array< Range > &region, const Map< Var, Range > &var_dom, const PrimExpr &predicate, arith::Analyzer *analyzer)</div><div class="ttdoc">Analyze the region [...]
<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_1arith_1_1IntSetNode_html_aedee1dbd20dfe7fd6aeddcc5be7b74d9"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetNode.html#aedee1dbd20dfe7fd6aeddcc5be7b74d9">tvm::arith::IntSetNode::_type_has_method_sequal_reduce</a></div><div class="ttdeci">static constexpr bool _type_has_method_sequal_reduce</div><div class="ttdef"><b>Definition:</b> int_set.h:60</div></div>
diff --git a/docs/reference/api/doxygen/int__solver_8h_source.html b/docs/reference/api/doxygen/int__solver_8h_source.html
index df70c2fbf..785303ba5 100644
--- a/docs/reference/api/doxygen/int__solver_8h_source.html
+++ b/docs/reference/api/doxygen/int__solver_8h_source.html
@@ -112,7 +112,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1arith_1_1IntGroupBoundsNode_html_a03cc7e9680291493657b6b585b3d6acc"><div class="ttname"><a href="classtvm_1_1arith_1_1IntGroupBoundsNode.html#a03cc7e9680291493657b6b585b3d6acc">tvm::arith::IntGroupBoundsNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> int_solver.h:85</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IntGroupBoundsNode_html_a55bc5cfb64d997ab5b1bb1b3f741b767"><div class="ttname"><a href="classtvm_1_1arith_1_1IntGroupBoundsNode.html#a55bc5cfb64d997ab5b1bb1b3f741b767">tvm::arith::IntGroupBoundsNode::equal</a></div><div class="ttdeci">Array< PrimExpr > equal</div><div class="ttdef"><b>Definition:</b> int_solver.h:62</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IntConstraintsNode_html_a078c29fba655311710227460312e78b5"><div class="ttname"><a href="classtvm_1_1arith_1_1IntConstraintsNode.html#a078c29fba655311710227460312e78b5">tvm::arith::IntConstraintsNode::relations</a></div><div class="ttdeci">Array< PrimExpr > relations</div><div class="ttdef"><b>Definition:</b> int_solver.h:153</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:1268</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_1arith_1_1IntGroupBoundsNode_html_a8b2ee820770ac9c3c8a8769e1c174b01"><div class="ttname"><a href="classtvm_1_1arith_1_1IntGroupBoundsNode.html#a8b2ee820770ac9c3c8a8769e1c174b01">tvm::arith::IntGroupBoundsNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(IntGroupBoundsNode, Object)</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IntConstraintsTransformNode_html_afcbf2cc97faab0052dd97cae3baa90f7"><div class="ttname"><a href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html#afcbf2cc97faab0052dd97cae3baa90f7">tvm::arith::IntConstraintsTransformNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> int_solver.h:216</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>
diff --git a/docs/reference/api/doxygen/interpreter_8h_source.html b/docs/reference/api/doxygen/interpreter_8h_source.html
index 28b31a0cd..5544a8200 100644
--- a/docs/reference/api/doxygen/interpreter_8h_source.html
+++ b/docs/reference/api/doxygen/interpreter_8h_source.html
@@ -107,7 +107,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="namespacetvm_1_1relay_html_ae87c7a3eb9be1113b92a7102806ab627"><div class="ttname"><a href="namespacetvm_1_1relay.html#ae87c7a3eb9be1113b92a7102806ab627">tvm::relay::Eval</a></div><div class="ttdeci">ObjectRef Eval(Expr expr, Map< GlobalTypeVar, TypeData > type_definitions, std::unordered_set< String > import_set, Device device, Target target, Map< String, ObjectRef > attrs={})</div><div class="ttdoc">Evaluates expr and returns its result. </div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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_1relay_1_1RecClosureObj_html_a7a56c67a71f2d6d6621cdb0747b9dce0"><div class="ttname"><a href="classtvm_1_1relay_1_1RecClosureObj.html#a7a56c67a71f2d6d6621cdb0747b9dce0">tvm::relay::RecClosureObj::clos</a></div><div class="ttdeci">InterpreterClosure clos</div><div class="ttdoc">The closure. </div><div class="ttdef"><b>Definition:</b> interpreter.h:84</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1RecClosureObj_html"><div class="ttname"><a href="classtvm_1_1relay_1_1RecClosureObj.html">tvm::relay::RecClosureObj</a></div><div class="ttdoc">The container type of RecClosure. </div><div class="ttdef"><b>Definition:</b> interpreter.h:81</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1Var_html"><div class="ttname"><a href="classtvm_1_1relay_1_1Var.html">tvm::relay::Var</a></div><div class="ttdef"><b>Definition:</b> expr.h:235</div></div>
diff --git a/docs/reference/api/doxygen/ir_2attrs_8h_source.html b/docs/reference/api/doxygen/ir_2attrs_8h_source.html
index 57023d7ec..25ef837f4 100644
--- a/docs/reference/api/doxygen/ir_2attrs_8h_source.html
+++ b/docs/reference/api/doxygen/ir_2attrs_8h_source.html
@@ -169,7 +169,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use "curiously recurring template pattern". </div><div class="ttdef"><b>Definition:</b> attrs.h:834</div></div>
<div class="ttc" id="classtvm_1_1AttrsNode_html_acd05137ba529ac7cd07053e3da885205"><div class="ttname"><a href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode::VisitNonDefaultAttrs</a></div><div class="ttdeci">void VisitNonDefaultAttrs(AttrVisitor *v)</div><div class="ttdoc">Visit attributes that do not equal the default value. </div><div class="ttdef"><b>Definition:</b> attrs.h:841</div></div>
<div class="ttc" id="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry_html_ae88a65b8d90a7c55fc6ea6bb1863b425"><div class="ttname"><a href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html#ae88a65b8d90a7c55fc6ea6bb1863b425">tvm::detail::AttrTriggerNonDefaultEntry::set_default</a></div><div class="ttdeci">TSelf & set_default(const T &value)</div><div class="ttdef"><b>Definition:</b> attrs.h:798</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:1268</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_1runtime_1_1TVMPODValue___html_aefca71073146f4be36d6a4a0de33d6e0"><div class="ttname"><a href="classtvm_1_1runtime_1_1TVMPODValue__.html#aefca71073146f4be36d6a4a0de33d6e0">tvm::runtime::TVMPODValue_::type_code</a></div><div class="ttdeci">int type_code() const</div><div class="ttdef"><b>Definition:</b> packed_func.h:610</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1TVMArgValue_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1TVMArgValue.html">tvm::runtime::TVMArgValue</a></div><div class="ttdoc">A single argument value to PackedFunc. Containing both type_code and TVMValue. </div><div class="ttdef"><b>Definition:</b> packed_func.h:646</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/ir_2module_8h_source.html b/docs/reference/api/doxygen/ir_2module_8h_source.html
index 67bab04d2..3b576f812 100644
--- a/docs/reference/api/doxygen/ir_2module_8h_source.html
+++ b/docs/reference/api/doxygen/ir_2module_8h_source.html
@@ -134,7 +134,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1IRModuleNode_html_a5e21563666240e1deb1f92807d910268"><div class="ttname"><a href="classtvm_1_1IRModuleNode.html#a5e21563666240e1deb1f92807d910268">tvm::IRModuleNode::Import</a></div><div class="ttdeci">void Import(const String &path)</div><div class="ttdoc">Import Relay code from the file at path. </div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="classtvm_1_1TypeData_html"><div class="ttname"><a href="classtvm_1_1TypeData.html">tvm::TypeData</a></div><div class="ttdoc">Stores all data for an Algebraic Data Type (ADT). </div><div class="ttdef"><b>Definition:</b> adt.h:149</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:1268</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="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1BaseFunc_html"><div class="ttname"><a href="classtvm_1_1BaseFunc.html">tvm::BaseFunc</a></div><div class="ttdoc">Managed reference to BaseFuncNode. </div><div class="ttdef"><b>Definition:</b> function.h:143</div></div>
<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>
diff --git a/docs/reference/api/doxygen/ir_2span_8h_source.html b/docs/reference/api/doxygen/ir_2span_8h_source.html
index f706e07fd..bfd918168 100644
--- a/docs/reference/api/doxygen/ir_2span_8h_source.html
+++ b/docs/reference/api/doxygen/ir_2span_8h_source.html
@@ -94,7 +94,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
<div class="ttc" id="classtvm_1_1SpanNode_html"><div class="ttname"><a href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></div><div class="ttdoc">Stores locations in frontend source that generated a node. </div><div class="ttdef"><b>Definition:</b> span.h:82</div></div>
<div class="ttc" id="object_8h_html"><div class="ttname"><a href="object_8h.html">object.h</a></div><div class="ttdoc">A managed object in the TVM runtime. </div></div>
-<div class="ttc" id="namespacetvm_1_1runtime_html_aff337677f23f7d665960f553fb52ab86"><div class="ttname"><a href="namespacetvm_1_1runtime.html#aff337677f23f7d665960f553fb52ab86">tvm::runtime::Merge</a></div><div class="ttdeci">Map< K, V > Merge(Map< K, V > lhs, const Map< K, V > &rhs)</div><div class="ttdoc">Merge two Maps. </div><div class="ttdef"><b>Definition:</b> map.h:1468</div></div>
+<div class="ttc" id="namespacetvm_1_1runtime_html_aff337677f23f7d665960f553fb52ab86"><div class="ttname"><a href="namespacetvm_1_1runtime.html#aff337677f23f7d665960f553fb52ab86">tvm::runtime::Merge</a></div><div class="ttdeci">Map< K, V > Merge(Map< K, V > lhs, const Map< K, V > &rhs)</div><div class="ttdoc">Merge two Maps. </div><div class="ttdef"><b>Definition:</b> map.h:1471</div></div>
<div class="ttc" id="classtvm_1_1SourceNameNode_html_acbea8729c55af6e2451338e2be5a84ce"><div class="ttname"><a href="classtvm_1_1SourceNameNode.html#acbea8729c55af6e2451338e2be5a84ce">tvm::SourceNameNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> span.h:46</div></div>
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diff --git a/docs/reference/api/doxygen/ir_2transform_8h_source.html b/docs/reference/api/doxygen/ir_2transform_8h_source.html
index 7454a1819..06c4e78ca 100644
--- a/docs/reference/api/doxygen/ir_2transform_8h_source.html
+++ b/docs/reference/api/doxygen/ir_2transform_8h_source.html
@@ -98,7 +98,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html_a17d8d5ad92691f9e18e3e0ae8ef69e4f"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html#a17d8d5ad92691f9e18e3e0ae8ef69e4f">tvm::runtime::ObjectRef::defined</a></div><div class="ttdeci">bool defined() const</div><div class="ttdef"><b>Definition:</b> object.h:544</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassContext_html_a2d1a6fffe70703812245b8d834da9a44"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContext.html#a2d1a6fffe70703812245b8d834da9a44">tvm::transform::PassContext::operator-></a></div><div class="ttdeci">const PassContextNode * operator->() const</div><div class="ttdoc">const accessor. </div><div class="ttdef"><b>Definition:</b> transform.h:162</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs. </div><div class="ttdef"><b>Definition:</b> array.h:270</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1380</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1383</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1Pass_html"><div class="ttname"><a href="classtvm_1_1transform_1_1Pass.html">tvm::transform::Pass</a></div><div class="ttdef"><b>Definition:</b> transform.h:363</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassContextNode_html_a613725ab055b022ae84d7cabb755533d"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContextNode.html#a613725ab055b022ae84d7cabb755533d">tvm::transform::PassContextNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(PassContextNode, Object)</div></div>
<div class="ttc" id="namespacetvm_1_1relay_1_1transform_html_aae88cd0ad69cf64c7e9caf0a0c8ebb45"><div class="ttname"><a href="namespacetvm_1_1relay_1_1transform.html#aae88cd0ad69cf64c7e9caf0a0c8ebb45">tvm::relay::transform::PassInfoNode</a></div><div class="ttdeci">tvm::transform::PassInfoNode PassInfoNode</div><div class="ttdef"><b>Definition:</b> transform.h:46</div></div>
@@ -114,12 +114,12 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassNode_html_af04aed6a576c3a4b9c2969d6f190cd37"><div class="ttname"><a href="classtvm_1_1transform_1_1PassNode.html#af04aed6a576c3a4b9c2969d6f190cd37">tvm::transform::PassNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> transform.h:357</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassInfoNode_html"><div class="ttname"><a href="classtvm_1_1transform_1_1PassInfoNode.html">tvm::transform::PassInfoNode</a></div><div class="ttdoc">Meta data that will be used to help optimization and analysis. </div><div class="ttdef"><b>Definition:</b> transform.h:283</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1378</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1381</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassContextNode_html_ad42fa984f8ff1dad24cc77d0a39e96a0"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContextNode.html#ad42fa984f8ff1dad24cc77d0a39e96a0">tvm::transform::PassContextNode::disabled_pass</a></div><div class="ttdeci">Array< String > disabled_pass</div><div class="ttdoc">The list of disabled passes. </div><div class="ttdef"><b>Definition:</b> transform.h:86</div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassNode_html"><div class="ttname"><a href="classtvm_1_1transform_1_1PassNode.html">tvm::transform::PassNode</a></div><div class="ttdoc">PassNode is the base type of differnt types of optimization passes. It is designed as a pure class an...</div><div class="ttdef"><b>Definition:</b> transform.h:329</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassContextNode_html"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContextNode.html">tvm::transform::PassContextNode</a></div><div class="ttdoc">PassContextNode contains the information that a pass can rely on, such as analysis results...</div><div class="ttdef"><b>Definition:</b> transform.h:78</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:1268</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_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_1transform_1_1PassContextNode_html_acc5e97af0ff79af8ab2d6745745e8c63"><div class="ttname"><a href="classtvm_1_1transform_1_1PassContextNode.html#acc5e97af0ff79af8ab2d6745745e8c63">tvm::transform::PassContextNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> transform.h:127</div></div>
<div class="ttc" id="classtvm_1_1transform_1_1PassInfoNode_html_a8e22e5767cd899bb9aef1ee1c529a2a7"><div class="ttname"><a href="classtvm_1_1transform_1_1PassInfoNode.html#a8e22e5767cd899bb9aef1ee1c529a2a7">tvm::transform::PassInfoNode::opt_level</a></div><div class="ttdeci">int opt_level</div><div class="ttdoc">The minimal optimization level that this pass will be enabled. </div><div class="ttdef"><b>Definition:</b> transform.h:286</div></div>
diff --git a/docs/reference/api/doxygen/ir__docsifier_8h_source.html b/docs/reference/api/doxygen/ir__docsifier_8h_source.html
index d4c21315f..f1afb44e4 100644
--- a/docs/reference/api/doxygen/ir__docsifier_8h_source.html
+++ b/docs/reference/api/doxygen/ir__docsifier_8h_source.html
@@ -100,7 +100,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1script_1_1printer_1_1RootNodeContainerNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1printer_1_1RootNodeContainerNode.html">tvm::script::printer::RootNodeContainerNode</a></div><div class="ttdoc">A wrapper object to provide injection point for printer of each IR. </div><div class="ttdef"><b>Definition:</b> ir_docsifier.h:205</div></div>
<div class="ttc" id="classtvm_1_1script_1_1printer_1_1IRDocsifierNode_html"><div class="ttname"><a href="classtvm_1_1script_1_1printer_1_1IRDocsifierNode.html">tvm::script::printer::IRDocsifierNode</a></div><div class="ttdoc">IRDocsifier is the top-level interface in the IR->Doc process. </div><div class="ttdef"><b>Definition:</b> ir_docsifier.h:54</div></div>
<div class="ttc" id="classtvm_1_1script_1_1printer_1_1IRDocsifierNode_html_aa10aa1656e0acdf55ee471adc7279630"><div class="ttname"><a href="classtvm_1_1script_1_1printer_1_1IRDocsifierNode.html#aa10aa1656e0acdf55ee471adc7279630">tvm::script::printer::IRDocsifierNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> ir_docsifier.h:85</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:1268</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="printer_2frame_8h_html"><div class="ttname"><a href="printer_2frame_8h.html">frame.h</a></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_1runtime_1_1Array_html_aa39300bd019f435ab23353b991019811"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html#aa39300bd019f435ab23353b991019811">tvm::runtime::Array::pop_back</a></div><div class="ttdeci">void pop_back()</div><div class="ttdoc">Remove the last item of the list. </div><div class="ttdef"><b>Definition:</b> array.h:479</div></div>
diff --git a/docs/reference/api/doxygen/iter__affine__map_8h_source.html b/docs/reference/api/doxygen/iter__affine__map_8h_source.html
index fa927bb5b..ec9cdc37c 100644
--- a/docs/reference/api/doxygen/iter__affine__map_8h_source.html
+++ b/docs/reference/api/doxygen/iter__affine__map_8h_source.html
@@ -123,7 +123,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1arith_1_1IterMapExprNode_html_ab8a4d68ae04e4269485c18f97cd3db21"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExprNode.html#ab8a4d68ae04e4269485c18f97cd3db21">tvm::arith::IterMapExprNode::_type_child_slots</a></div><div class="ttdeci">static constexpr const uint32_t _type_child_slots</div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:72</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IterMapExprNode_html_a3acb5f8da15c333d417702a0175c33f8"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExprNode.html#a3acb5f8da15c333d417702a0175c33f8">tvm::arith::IterMapExprNode::TVM_DECLARE_BASE_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_BASE_OBJECT_INFO(IterMapExprNode, PrimExprNode)</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IterSumExprNode_html_a035f06f81d4011caaf2e1cebe989032a"><div class="ttname"><a href="classtvm_1_1arith_1_1IterSumExprNode.html#a035f06f81d4011caaf2e1cebe989032a">tvm::arith::IterSumExprNode::SHashReduce</a></div><div class="ttdeci">void SHashReduce(SHashReducer hash_reduce) const</div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:236</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:1268</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_1arith_1_1IterMapExpr_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExpr.html">tvm::arith::IterMapExpr</a></div><div class="ttdoc">Managed reference to IterMapExprNode. </div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:80</div></div>
<div class="ttc" id="namespacetvm_1_1arith_html_a243b60bbe2d3852099eb65454b240c63a850e59cdf8cc0407bf13366b876a0def"><div class="ttname"><a href="namespacetvm_1_1arith.html#a243b60bbe2d3852099eb65454b240c63a850e59cdf8cc0407bf13366b876a0def">tvm::arith::NoCheck</a></div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:269</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1IterSplitExprNode_html_ae5eefd2720ad679baec51ded674eeae1"><div class="ttname"><a href="classtvm_1_1arith_1_1IterSplitExprNode.html#ae5eefd2720ad679baec51ded674eeae1">tvm::arith::IterSplitExprNode::SHashReduce</a></div><div class="ttdeci">void SHashReduce(SHashReducer hash_reduce) const</div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:172</div></div>
diff --git a/docs/reference/api/doxygen/map_8h_source.html b/docs/reference/api/doxygen/map_8h_source.html
index a1a45b698..466dbc0a4 100644
--- a/docs/reference/api/doxygen/map_8h_source.html
+++ b/docs/reference/api/doxygen/map_8h_source.html
@@ -66,124 +66,125 @@ $(function() {
<div class="title">map.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="map_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"> * or more contrib [...]
+<a href="map_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"> * or more contrib [...]
<div class="ttc" id="structtvm_1_1runtime_1_1ObjectEqual_html"><div class="ttname"><a href="structtvm_1_1runtime_1_1ObjectEqual.html">tvm::runtime::ObjectEqual</a></div><div class="ttdoc">String-aware ObjectRef hash functor. </div><div class="ttdef"><b>Definition:</b> base.h:50</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_aa84cf88d4cc292125cd21e9222f005ec"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#aa84cf88d4cc292125cd21e9222f005ec">tvm::runtime::MapNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> map.h:189</div></div>
<div class="ttc" id="structtvm_1_1runtime_1_1TypeIndex_html_aed93c7318efc8052201d4c404b21a40da4ac0fbbbd83cb6e789b821b8ae8556f3"><div class="ttname"><a href="structtvm_1_1runtime_1_1TypeIndex.html#aed93c7318efc8052201d4c404b21a40da4ac0fbbbd83cb6e789b821b8ae8556f3">tvm::runtime::TypeIndex::kRuntimeMap</a></div><div class="ttdoc">runtime::Map. </div><div class="ttdef"><b>Definition:</b> object.h:70</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a58d530f3be4fac7ff99a574c2f6c8ddc"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a58d530f3be4fac7ff99a574c2f6c8ddc">tvm::runtime::DenseMapNode::data_</a></div><div class="ttdeci">Block * data_</div><div class="ttdoc">array of data blocks </div><div class="ttdef"><b>Definition:</b> map.h:1088</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a0d4e97d796619afb8d02cab10451edf5"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a0d4e97d796619afb8d02cab10451edf5">tvm::runtime::Map::iterator::iterator_category</a></div><div class="ttdeci">std::bidirectional_iterator_tag iterator_category</div><div class="ttdef"><b>Definition:</b> map.h:1413</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a0d4e97d796619afb8d02cab10451edf5"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a0d4e97d796619afb8d02cab10451edf5">tvm::runtime::Map::iterator::iterator_category</a></div><div class="ttdeci">std::bidirectional_iterator_tag iterator_category</div><div class="ttdef"><b>Definition:</b> map.h:1416</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a6b105410198a644ddbd1b83695711715"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a6b105410198a644ddbd1b83695711715">tvm::runtime::MapNode::iterator::iterator_category</a></div><div class="ttdeci">std::forward_iterator_tag iterator_category</div><div class="ttdef"><b>Definition:</b> map.h:238</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html">tvm::runtime::MapNode::iterator</a></div><div class="ttdef"><b>Definition:</b> map.h:236</div></div>
<div class="ttc" id="namespacetvm_html_aac2abc149c1a47944c37b560181b15c0"><div class="ttname"><a href="namespacetvm.html#aac2abc149c1a47944c37b560181b15c0">tvm::min</a></div><div class="ttdeci">PrimExpr min(PrimExpr a, PrimExpr b, Span span=Span())</div><div class="ttdoc">take minimum of two values </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_adf62c96244160116493dd6a3f6ca3b6e"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#adf62c96244160116493dd6a3f6ca3b6e">tvm::runtime::MapNode::iterator::difference_type</a></div><div class="ttdeci">int64_t difference_type</div><div class="ttdef"><b>Definition:</b> map.h:239</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a6b398835e5160e792634c8ee0783f284"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a6b398835e5160e792634c8ee0783f284">tvm::runtime::Map::operator=</a></div><div class="ttdeci">Map< K, V > & operator=(const Map< K, V > &other)</div><div class="ttdoc">move assign operator </div><div class="ttdef"><b>Definition:</b> map.h:1301</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a6b398835e5160e792634c8ee0783f284"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a6b398835e5160e792634c8ee0783f284">tvm::runtime::Map::operator=</a></div><div class="ttdeci">Map< K, V > & operator=(const Map< K, V > &other)</div><div class="ttdoc">move assign operator </div><div class="ttdef"><b>Definition:</b> map.h:1304</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectPtr_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectPtr.html">tvm::runtime::ObjectPtr</a></div><div class="ttdoc">A custom smart pointer for Object. </div><div class="ttdef"><b>Definition:</b> object.h:358</div></div>
<div class="ttc" id="optional_8h_html"><div class="ttname"><a href="optional_8h.html">optional.h</a></div><div class="ttdoc">Runtime Optional container types. </div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a865a58097e473b532b1373bd15a1e91f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a865a58097e473b532b1373bd15a1e91f">tvm::runtime::Map::operator[]</a></div><div class="ttdeci">const V operator[](const K &key) const</div><div class="ttdoc">Read element from map. </div><div class="ttdef"><b>Definition:</b> map.h:1346</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a865a58097e473b532b1373bd15a1e91f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a865a58097e473b532b1373bd15a1e91f">tvm::runtime::Map::operator[]</a></div><div class="ttdeci">const V operator[](const K &key) const</div><div class="ttdoc">Read element from map. </div><div class="ttdef"><b>Definition:</b> map.h:1349</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a26ef1b067ec33d0bcd86b72afc6bf608"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a26ef1b067ec33d0bcd86b72afc6bf608">tvm::runtime::MapNode::key_type</a></div><div class="ttdeci">ObjectRef key_type</div><div class="ttdoc">Type of the keys in the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:177</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ad1ae0eaa6dfdc48d5f037ee51a867fe7"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ad1ae0eaa6dfdc48d5f037ee51a867fe7">tvm::runtime::MapNode::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:1180</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ad1ae0eaa6dfdc48d5f037ee51a867fe7"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ad1ae0eaa6dfdc48d5f037ee51a867fe7">tvm::runtime::MapNode::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:1183</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ab7ea406f099e235de4944fa94c43812e"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ab7ea406f099e235de4944fa94c43812e">tvm::runtime::MapNode::slots_</a></div><div class="ttdeci">uint64_t slots_</div><div class="ttdoc">number of slots minus 1 </div><div class="ttdef"><b>Definition:</b> map.h:332</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ab37f503bb71aa9f8399d3d92fed4a0d3"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ab37f503bb71aa9f8399d3d92fed4a0d3">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map()</div><div class="ttdoc">default constructor </div><div class="ttdef"><b>Definition:</b> map.h:1276</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a6e1fd44c0112f97adb7db4090c224707"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a6e1fd44c0112f97adb7db4090c224707">tvm::runtime::Map::iterator::value_type</a></div><div class="ttdeci">const std::pair< K, V > value_type</div><div class="ttdef"><b>Definition:</b> map.h:1415</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ab37f503bb71aa9f8399d3d92fed4a0d3"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ab37f503bb71aa9f8399d3d92fed4a0d3">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map()</div><div class="ttdoc">default constructor </div><div class="ttdef"><b>Definition:</b> map.h:1279</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a6e1fd44c0112f97adb7db4090c224707"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a6e1fd44c0112f97adb7db4090c224707">tvm::runtime::Map::iterator::value_type</a></div><div class="ttdeci">const std::pair< K, V > value_type</div><div class="ttdef"><b>Definition:</b> map.h:1418</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_a0593c84ceb05afb1a3f87045a3dc3a59"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#a0593c84ceb05afb1a3f87045a3dc3a59">tvm::runtime::SmallMapNode::at</a></div><div class="ttdeci">const mapped_type & at(const key_type &key) const</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:364</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a4adb64a40bda0e39d95f8d82b2df5df3"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a4adb64a40bda0e39d95f8d82b2df5df3">tvm::runtime::MapNode::iterator::operator!=</a></div><div class="ttdeci">bool operator!=(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:255</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a540d6f4cb2b8a4430049fad7d24db3d1"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a540d6f4cb2b8a4430049fad7d24db3d1">tvm::runtime::Map::iterator::operator!=</a></div><div class="ttdeci">bool operator!=(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1424</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a540d6f4cb2b8a4430049fad7d24db3d1"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a540d6f4cb2b8a4430049fad7d24db3d1">tvm::runtime::Map::iterator::operator!=</a></div><div class="ttdeci">bool operator!=(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1427</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_aa8d46402a1b371bb9c711602942f1eab"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#aa8d46402a1b371bb9c711602942f1eab">tvm::runtime::SmallMapNode::erase</a></div><div class="ttdeci">void erase(const iterator &position)</div><div class="ttdoc">Erase the entry associated with the iterator. </div><div class="ttdef"><b>Definition:</b> map.h:401</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_a9979171ea0db97d08bffb1bb328c7b96"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#a9979171ea0db97d08bffb1bb328c7b96">tvm::runtime::SmallMapNode::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:382</div></div>
-<div class="ttc" id="map_8h_html_a1d45968795b6054f63824cb9c5512d5a"><div class="ttname"><a href="map_8h.html#a1d45968795b6054f63824cb9c5512d5a">TVM_DISPATCH_MAP</a></div><div class="ttdeci">#define TVM_DISPATCH_MAP(base, var, body)</div><div class="ttdef"><b>Definition:</b> map.h:1119</div></div>
+<div class="ttc" id="map_8h_html_a1d45968795b6054f63824cb9c5512d5a"><div class="ttname"><a href="map_8h.html#a1d45968795b6054f63824cb9c5512d5a">TVM_DISPATCH_MAP</a></div><div class="ttdeci">#define TVM_DISPATCH_MAP(base, var, body)</div><div class="ttdef"><b>Definition:</b> map.h:1122</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a773d2e9099e34ab3bcf3b1870d0aee28"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a773d2e9099e34ab3bcf3b1870d0aee28">tvm::runtime::DenseMapNode::at</a></div><div class="ttdeci">mapped_type & at(const key_type &key)</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:612</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ac62909410a98a078ff01f688cdf70ffe"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ac62909410a98a078ff01f688cdf70ffe">tvm::runtime::Map::operator=</a></div><div class="ttdeci">Map< K, V > & operator=(Map< K, V > &&other)</div><div class="ttdoc">copy assign operator </div><div class="ttdef"><b>Definition:</b> map.h:1292</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ac62909410a98a078ff01f688cdf70ffe"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ac62909410a98a078ff01f688cdf70ffe">tvm::runtime::Map::operator=</a></div><div class="ttdeci">Map< K, V > & operator=(Map< K, V > &&other)</div><div class="ttdoc">copy assign operator </div><div class="ttdef"><b>Definition:</b> map.h:1295</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_1runtime_1_1MapNode_html_a6c6d3b97ee1bb90279026329eb3a9756"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">tvm::runtime::MapNode::InsertMaybeReHash</a></div><div class="ttdeci">static void InsertMaybeReHash(const KVType &kv, ObjectPtr< Object > *map)</div><div class="ttdoc">InsertMaybeReHash an entry into the given hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1230</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a6c6d3b97ee1bb90279026329eb3a9756"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a6c6d3b97ee1bb90279026329eb3a9756">tvm::runtime::MapNode::InsertMaybeReHash</a></div><div class="ttdeci">static void InsertMaybeReHash(const KVType &kv, ObjectPtr< Object > *map)</div><div class="ttdoc">InsertMaybeReHash an entry into the given hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1233</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_ad605c9f9aaed23e669c2a3c595d08ba4"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ad605c9f9aaed23e669c2a3c595d08ba4">tvm::runtime::MapNode::iterator::iterator</a></div><div class="ttdeci">iterator()</div><div class="ttdoc">Default constructor. </div><div class="ttdef"><b>Definition:</b> map.h:247</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_ae0d84465db325f1e36e702d2b6232ad0"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#ae0d84465db325f1e36e702d2b6232ad0">tvm::runtime::DenseMapNode::NextProbeLocation</a></div><div class="ttdeci">static uint64_t NextProbeLocation(size_t index)</div><div class="ttdef"><b>Definition:</b> map.h:1089</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html_a133436a9ec5c4a768b94102bf95a660b"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a133436a9ec5c4a768b94102bf95a660b">tvm::runtime::Object::Object</a></div><div class="ttdeci">Object()</div><div class="ttdef"><b>Definition:</b> object.h:241</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ade3c126684dcdc6ed432f3bb7eb62099"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ade3c126684dcdc6ed432f3bb7eb62099">tvm::runtime::Map::iterator::operator==</a></div><div class="ttdeci">bool operator==(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1422</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ade3c126684dcdc6ed432f3bb7eb62099"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ade3c126684dcdc6ed432f3bb7eb62099">tvm::runtime::Map::iterator::operator==</a></div><div class="ttdeci">bool operator==(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1425</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_a3e3295669feb01b40d022786c47c7981"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#a3e3295669feb01b40d022786c47c7981">tvm::runtime::SmallMapNode::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:380</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_af7555a75a5dbdf2f1c1af3fd240e54e7"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#af7555a75a5dbdf2f1c1af3fd240e54e7">tvm::runtime::DenseMapNode::fib_shift_</a></div><div class="ttdeci">uint32_t fib_shift_</div><div class="ttdoc">fib shift in Fibonacci Hashing </div><div class="ttdef"><b>Definition:</b> map.h:1086</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_aa080c358ffab71cff472538a435eb615"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#aa080c358ffab71cff472538a435eb615">tvm::runtime::MapNode::iterator::operator++</a></div><div class="ttdeci">iterator & operator++()</div><div class="ttdoc">Prefix self increment, e.g. ++iter. </div><div class="ttdef"><b>Definition:</b> map.h:1152</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a156c1e32c6e7a8a39e43091166563170"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a156c1e32c6e7a8a39e43091166563170">tvm::runtime::Map::CopyOnWrite</a></div><div class="ttdeci">MapNode * CopyOnWrite()</div><div class="ttdoc">copy on write semantics Do nothing if current handle is the unique copy of the array. Otherwise make a new copy of the array to ensure the current handle hold a unique copy. </div><div class="ttdef">< [...]
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_aa080c358ffab71cff472538a435eb615"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#aa080c358ffab71cff472538a435eb615">tvm::runtime::MapNode::iterator::operator++</a></div><div class="ttdeci">iterator & operator++()</div><div class="ttdoc">Prefix self increment, e.g. ++iter. </div><div class="ttdef"><b>Definition:</b> map.h:1155</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a156c1e32c6e7a8a39e43091166563170"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a156c1e32c6e7a8a39e43091166563170">tvm::runtime::Map::CopyOnWrite</a></div><div class="ttdeci">MapNode * CopyOnWrite()</div><div class="ttdoc">copy on write semantics Do nothing if current handle is the unique copy of the array. Otherwise make a new copy of the array to ensure the current handle hold a unique copy. </div><div class="ttdef">< [...]
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a49fbdf8758a6e4376c0c3ffcf573bc77"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a49fbdf8758a6e4376c0c3ffcf573bc77">tvm::runtime::MapNode::mapped_type</a></div><div class="ttdeci">ObjectRef mapped_type</div><div class="ttdoc">Type of the values in the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:179</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a5c0c770f7667f911aa8bec879e3ac214"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a5c0c770f7667f911aa8bec879e3ac214">tvm::runtime::MapNode::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdoc">Number of elements in the SmallMapNode. </div><div class="ttdef"><b>Definition:</b> map.h:196</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a6071908cdeb00617d3b28a70d05ac649"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a6071908cdeb00617d3b28a70d05ac649">tvm::runtime::DenseMapNode::at</a></div><div class="ttdeci">const mapped_type & at(const key_type &key) const</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:606</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_abc18d8e58770915331c3257ebc80eadc"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#abc18d8e58770915331c3257ebc80eadc">tvm::runtime::MapNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(MapNode, Object)</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1IterVar_html"><div class="ttname"><a href="classtvm_1_1tir_1_1IterVar.html">tvm::tir::IterVar</a></div><div class="ttdoc">Iteration Variable, represents an iteration over an integer interval. </div><div class="ttdef"><b>Definition:</b> var.h:301</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ac40191ef3c2de0c546b48102fa43cd88"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ac40191ef3c2de0c546b48102fa43cd88">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(const std::unordered_map< K, V, Hash, Equal > &init)</div><div class="ttdoc">constructor from unordered_map </div><div class="ttdef"><b>Definition:</b> map.h:1332</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a8052ae36e24a4973c1a123c99cf5152c"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a8052ae36e24a4973c1a123c99cf5152c">tvm::runtime::Map::iterator::operator++</a></div><div class="ttdeci">iterator & operator++()</div><div class="ttdoc">Prefix self increment, e.g. ++iter. </div><div class="ttdef"><b>Definition:</b> map.h:1433</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ad486baa9df3b8061218bbad6cea53df9"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ad486baa9df3b8061218bbad6cea53df9">tvm::runtime::MapNode::Empty</a></div><div class="ttdeci">static ObjectPtr< MapNode > Empty()</div><div class="ttdoc">Create an empty container. </div><div class="ttdef"><b>Definition:</b> map.h:1199</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ac40191ef3c2de0c546b48102fa43cd88"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ac40191ef3c2de0c546b48102fa43cd88">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(const std::unordered_map< K, V, Hash, Equal > &init)</div><div class="ttdoc">constructor from unordered_map </div><div class="ttdef"><b>Definition:</b> map.h:1335</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a8052ae36e24a4973c1a123c99cf5152c"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a8052ae36e24a4973c1a123c99cf5152c">tvm::runtime::Map::iterator::operator++</a></div><div class="ttdeci">iterator & operator++()</div><div class="ttdoc">Prefix self increment, e.g. ++iter. </div><div class="ttdef"><b>Definition:</b> map.h:1436</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ad486baa9df3b8061218bbad6cea53df9"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ad486baa9df3b8061218bbad6cea53df9">tvm::runtime::MapNode::Empty</a></div><div class="ttdeci">static ObjectPtr< MapNode > Empty()</div><div class="ttdoc">Create an empty container. </div><div class="ttdef"><b>Definition:</b> map.h:1202</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a5230a8db9f60b62bc74d14ab8c3580ad"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a5230a8db9f60b62bc74d14ab8c3580ad">tvm::runtime::MapNode::iterator::operator++</a></div><div class="ttdeci">iterator operator++(int)</div><div class="ttdoc">Suffix self increment. </div><div class="ttdef"><b>Definition:</b> map.h:268</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_afc8d39f3c9e33bca6083253f7288d900"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#afc8d39f3c9e33bca6083253f7288d900">tvm::runtime::Map::empty</a></div><div class="ttdeci">bool empty() const</div><div class="ttdef"><b>Definition:</b> map.h:1358</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_af6f7942cbc239ec3eac4598e8542b4cc"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#af6f7942cbc239ec3eac4598e8542b4cc">tvm::runtime::Map::Get</a></div><div class="ttdeci">Optional< V > Get(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1382</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_afc8d39f3c9e33bca6083253f7288d900"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#afc8d39f3c9e33bca6083253f7288d900">tvm::runtime::Map::empty</a></div><div class="ttdeci">bool empty() const</div><div class="ttdef"><b>Definition:</b> map.h:1361</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_af6f7942cbc239ec3eac4598e8542b4cc"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#af6f7942cbc239ec3eac4598e8542b4cc">tvm::runtime::Map::Get</a></div><div class="ttdeci">Optional< V > Get(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1385</div></div>
<div class="ttc" id="runtime_2container_2base_8h_html"><div class="ttname"><a href="runtime_2container_2base_8h.html">base.h</a></div><div class="ttdoc">Base utilities for common POD(plain old data) container types. </div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a892484a52bf9ba0c48512154ba63a2bf"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a892484a52bf9ba0c48512154ba63a2bf">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(IterType begin, IterType end)</div><div class="ttdoc">constructor from iterator </div><div class="ttdef"><b>Definition:</b> map.h:1317</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a5389bd1ee67baed336ae520a230002e9"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a5389bd1ee67baed336ae520a230002e9">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(ObjectPtr< Object > n)</div><div class="ttdoc">constructor from pointer </div><div class="ttdef"><b>Definition:</b> map.h:1309</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a8c31c029a28ca7f5ab0ceb3fcf7ded89"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a8c31c029a28ca7f5ab0ceb3fcf7ded89">tvm::runtime::MapNode::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1184</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a892484a52bf9ba0c48512154ba63a2bf"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a892484a52bf9ba0c48512154ba63a2bf">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(IterType begin, IterType end)</div><div class="ttdoc">constructor from iterator </div><div class="ttdef"><b>Definition:</b> map.h:1320</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a5389bd1ee67baed336ae520a230002e9"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a5389bd1ee67baed336ae520a230002e9">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(ObjectPtr< Object > n)</div><div class="ttdoc">constructor from pointer </div><div class="ttdef"><b>Definition:</b> map.h:1312</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a8c31c029a28ca7f5ab0ceb3fcf7ded89"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a8c31c029a28ca7f5ab0ceb3fcf7ded89">tvm::runtime::MapNode::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1187</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_aeb11bbd3d8a715fa95e4e4c213902061"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#aeb11bbd3d8a715fa95e4e4c213902061">tvm::runtime::SmallMapNode::find</a></div><div class="ttdeci">iterator find(const key_type &key) const</div><div class="ttdoc">Index value associated with a key. </div><div class="ttdef"><b>Definition:</b> map.h:388</div></div>
<div class="ttc" id="namespacetvm_1_1runtime_html_a0537c9d197068a02c26cd702ab42f6ff"><div class="ttname"><a href="namespacetvm_1_1runtime.html#a0537c9d197068a02c26cd702ab42f6ff">tvm::runtime::make_inplace_array_object</a></div><div class="ttdeci">ObjectPtr< ArrayType > make_inplace_array_object(size_t num_elems, Args &&... args)</div><div class="ttdef"><b>Definition:</b> memory.h:200</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></div><div class="ttdoc">base class of all object containers. </div><div class="ttdef"><b>Definition:</b> object.h:167</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_ac3b9b13f9e074e20afe3bbd68cce35f3"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#ac3b9b13f9e074e20afe3bbd68cce35f3">tvm::runtime::DenseMapNode::~DenseMapNode</a></div><div class="ttdeci">~DenseMapNode()</div><div class="ttdoc">Destroy the DenseMapNode. </div><div class="ttdef"><b>Definition:</b> map.h:598</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html">tvm::runtime::SmallMapNode</a></div><div class="ttdoc">A specialization of small-sized hash map. </div><div class="ttdef"><b>Definition:</b> map.h:341</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a75e3f2657cdb7cc613bf922429983165"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a75e3f2657cdb7cc613bf922429983165">tvm::runtime::MapNode::iterator::iterator</a></div><div class="ttdeci">iterator(uint64_t index, const MapNode *self)</div><div class="ttdef"><b>Definition:</b> map.h:290</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a67431af5ae08050343eaf70629e5e310"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a67431af5ae08050343eaf70629e5e310">tvm::runtime::Map::erase</a></div><div class="ttdeci">void erase(const K &key)</div><div class="ttdef"><b>Definition:</b> map.h:1389</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ae9ecf711c97150ca34732459b3b2f125"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ae9ecf711c97150ca34732459b3b2f125">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(const Map< K, V > &other)</div><div class="ttdoc">copy constructor </div><div class="ttdef"><b>Definition:</b> map.h:1286</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a67431af5ae08050343eaf70629e5e310"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a67431af5ae08050343eaf70629e5e310">tvm::runtime::Map::erase</a></div><div class="ttdeci">void erase(const K &key)</div><div class="ttdef"><b>Definition:</b> map.h:1392</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ae9ecf711c97150ca34732459b3b2f125"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ae9ecf711c97150ca34732459b3b2f125">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(const Map< K, V > &other)</div><div class="ttdoc">copy constructor </div><div class="ttdef"><b>Definition:</b> map.h:1289</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1InplaceArrayBase_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1InplaceArrayBase.html">tvm::runtime::InplaceArrayBase</a></div><div class="ttdoc">Base template for classes with array like memory layout. </div><div class="ttdef"><b>Definition:</b> base.h:100</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a2d2eef30b22325a3535a25a1f9728f63"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">tvm::runtime::MapNode::CopyFrom</a></div><div class="ttdeci">static ObjectPtr< MapNode > CopyFrom(MapNode *from)</div><div class="ttdoc">Create an empty container with elements copying from another SmallMapNode. </div><div class="ttdef"><b>Definition:</b> map.h:1201</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a2d2eef30b22325a3535a25a1f9728f63"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a2d2eef30b22325a3535a25a1f9728f63">tvm::runtime::MapNode::CopyFrom</a></div><div class="ttdeci">static ObjectPtr< MapNode > CopyFrom(MapNode *from)</div><div class="ttdoc">Create an empty container with elements copying from another SmallMapNode. </div><div class="ttdef"><b>Definition:</b> map.h:1204</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_a99bd2454e0afbfb9fdf2644f5f709783"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#a99bd2454e0afbfb9fdf2644f5f709783">tvm::runtime::SmallMapNode::count</a></div><div class="ttdeci">size_t count(const key_type &key) const</div><div class="ttdoc">Count the number of times a key exists in the SmallMapNode. </div><div class="ttdef"><b>Definition:</b> map.h:358</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a6b54c7503c17ee3bb7eadcd1ac0ed009"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">tvm::runtime::MapNode::CreateFromRange</a></div><div class="ttdeci">static ObjectPtr< Object > CreateFromRange(IterType first, IterType last)</div><div class="ttdoc">Create the map using contents from the given iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1210</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a6b54c7503c17ee3bb7eadcd1ac0ed009"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a6b54c7503c17ee3bb7eadcd1ac0ed009">tvm::runtime::MapNode::CreateFromRange</a></div><div class="ttdeci">static ObjectPtr< Object > CreateFromRange(IterType first, IterType last)</div><div class="ttdoc">Create the map using contents from the given iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1213</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a182df92fc8085f81b68933da80782098"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a182df92fc8085f81b68933da80782098">tvm::runtime::DenseMapNode::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:633</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a65deca60bf7d1b512b0f42b26dbdb882"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a65deca60bf7d1b512b0f42b26dbdb882">tvm::runtime::DenseMapNode::find</a></div><div class="ttdeci">iterator find(const key_type &key) const</div><div class="ttdoc">Index value associated with a key. </div><div class="ttdef"><b>Definition:</b> map.h:618</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a467a2dacd260ee1e0fc5d233ba4b46d4"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a467a2dacd260ee1e0fc5d233ba4b46d4">tvm::runtime::DenseMapNode::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:645</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a9306ec8e65e9a6fb9bce97b34edf2e86"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a9306ec8e65e9a6fb9bce97b34edf2e86">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(std::initializer_list< std::pair< K, V >> init)</div><div class="ttdoc">constructor from initializer list </div><div class="ttdef"><b>Definition:</b> map.h:1324</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_abc9b8a0b8afac7b49c204c3e33f6b3be"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#abc9b8a0b8afac7b49c204c3e33f6b3be">tvm::runtime::Map::iterator::difference_type</a></div><div class="ttdeci">int64_t difference_type</div><div class="ttdef"><b>Definition:</b> map.h:1414</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a9306ec8e65e9a6fb9bce97b34edf2e86"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a9306ec8e65e9a6fb9bce97b34edf2e86">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(std::initializer_list< std::pair< K, V >> init)</div><div class="ttdoc">constructor from initializer list </div><div class="ttdef"><b>Definition:</b> map.h:1327</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_abc9b8a0b8afac7b49c204c3e33f6b3be"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#abc9b8a0b8afac7b49c204c3e33f6b3be">tvm::runtime::Map::iterator::difference_type</a></div><div class="ttdeci">int64_t difference_type</div><div class="ttdef"><b>Definition:</b> map.h:1417</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_ace1ea25bb95eb97d15788e83649db912"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#ace1ea25bb95eb97d15788e83649db912">tvm::runtime::DenseMapNode::count</a></div><div class="ttdeci">size_t count(const key_type &key) const</div><div class="ttdef"><b>Definition:</b> map.h:600</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1380</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_aa713b1b421fda78159a0a66740943c6c"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#aa713b1b421fda78159a0a66740943c6c">tvm::runtime::Map::count</a></div><div class="ttdeci">size_t count(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1353</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1383</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_aa713b1b421fda78159a0a66740943c6c"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#aa713b1b421fda78159a0a66740943c6c">tvm::runtime::Map::count</a></div><div class="ttdeci">size_t count(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1356</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html_ac261cdb80487fb29ac42b28678f8cbef"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html#ac261cdb80487fb29ac42b28678f8cbef">tvm::runtime::ObjectRef::data_</a></div><div class="ttdeci">ObjectPtr< Object > data_</div><div class="ttdoc">Internal pointer that backs the reference. </div><div class="ttdef"><b>Definition:</b> object.h:574</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_adf85d43ef116b85c8aa2a25599bc5584"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#adf85d43ef116b85c8aa2a25599bc5584">tvm::runtime::MapNode::iterator::operator-></a></div><div class="ttdeci">pointer operator->() const</div><div class="ttdoc">De-reference iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1147</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_adf85d43ef116b85c8aa2a25599bc5584"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#adf85d43ef116b85c8aa2a25599bc5584">tvm::runtime::MapNode::iterator::operator-></a></div><div class="ttdeci">pointer operator->() const</div><div class="ttdoc">De-reference iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1150</div></div>
<div class="ttc" id="namespacetvm_html_a0df5ca82d2c566f628ebb2f1e84a3fcb"><div class="ttname"><a href="namespacetvm.html#a0df5ca82d2c566f628ebb2f1e84a3fcb">tvm::max</a></div><div class="ttdeci">PrimExpr max(PrimExpr a, PrimExpr b, Span span=Span())</div><div class="ttdoc">take maximum of two values </div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ab2e291c9d0a9ad6f3eeae63df135a090"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ab2e291c9d0a9ad6f3eeae63df135a090">tvm::runtime::Map::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:1376</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ab2e291c9d0a9ad6f3eeae63df135a090"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ab2e291c9d0a9ad6f3eeae63df135a090">tvm::runtime::Map::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdef"><b>Definition:</b> map.h:1379</div></div>
<div class="ttc" id="structtvm_1_1runtime_1_1ObjectHash_html"><div class="ttname"><a href="structtvm_1_1runtime_1_1ObjectHash.html">tvm::runtime::ObjectHash</a></div><div class="ttdoc">String-aware ObjectRef equal functor. </div><div class="ttdef"><b>Definition:</b> base.h:40</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1InplaceArrayBase_html_a2a0235e3e9b03abffc3839d14e1b7342"><div class="ttname"><a href="classtvm_1_1runtime_1_1InplaceArrayBase.html#a2a0235e3e9b03abffc3839d14e1b7342">tvm::runtime::InplaceArrayBase::AddressOf</a></div><div class="ttdeci">void * AddressOf(size_t idx) const</div><div class="ttdoc">Return the raw pointer to the element at idx. </div><div class="ttdef"><b>Definition:</b> base.h:169</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a8ada6761aea90e293b2ce9beed519183"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a8ada6761aea90e293b2ce9beed519183">tvm::runtime::MapNode::_type_index</a></div><div class="ttdeci">static constexpr const uint32_t _type_index</div><div class="ttdef"><b>Definition:</b> map.h:188</div></div>
-<div class="ttc" id="map_8h_html_a04a1af748cfbdfdf0a5707c02c55652e"><div class="ttname"><a href="map_8h.html#a04a1af748cfbdfdf0a5707c02c55652e">TVM_DISPATCH_MAP_CONST</a></div><div class="ttdeci">#define TVM_DISPATCH_MAP_CONST(base, var, body)</div><div class="ttdef"><b>Definition:</b> map.h:1133</div></div>
+<div class="ttc" id="map_8h_html_a04a1af748cfbdfdf0a5707c02c55652e"><div class="ttname"><a href="map_8h.html#a04a1af748cfbdfdf0a5707c02c55652e">TVM_DISPATCH_MAP_CONST</a></div><div class="ttdeci">#define TVM_DISPATCH_MAP_CONST(base, var, body)</div><div class="ttdef"><b>Definition:</b> map.h:1136</div></div>
<div class="ttc" id="map_8h_html_a06c210bfb319f0bf0e436f4542e40369"><div class="ttname"><a href="map_8h.html#a06c210bfb319f0bf0e436f4542e40369">TVM_MAP_FAIL_IF_CHANGED</a></div><div class="ttdeci">#define TVM_MAP_FAIL_IF_CHANGED()</div><div class="ttdef"><b>Definition:</b> map.h:45</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a5bac4439279428fb3c0d44aa6b1cc798"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a5bac4439279428fb3c0d44aa6b1cc798">tvm::runtime::MapNode::iterator::self</a></div><div class="ttdeci">const MapNode * self</div><div class="ttdoc">The container it points to. </div><div class="ttdef"><b>Definition:</b> map.h:295</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_af27376d48f56d42f28440536d1774405"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#af27376d48f56d42f28440536d1774405">tvm::runtime::MapNode::iterator::value_type</a></div><div class="ttdeci">KVType value_type</div><div class="ttdef"><b>Definition:</b> map.h:240</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_abc5b09553663c05b863c4a406a343c92"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#abc5b09553663c05b863c4a406a343c92">tvm::runtime::MapNode::iterator::reference</a></div><div class="ttdeci">KVType & reference</div><div class="ttdef"><b>Definition:</b> map.h:242</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html">tvm::runtime::DenseMapNode</a></div><div class="ttdoc">A specialization of hash map that implements the idea of array-based hash map. Another reference impl...</div><div class="ttdef"><b>Definition:</b> map.h:571</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a08393c19a1c8b1c4057a33832cd48662"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a08393c19a1c8b1c4057a33832cd48662">tvm::runtime::MapNode::iterator::operator--</a></div><div class="ttdeci">iterator & operator--()</div><div class="ttdoc">Prefix self decrement, e.g. –iter. </div><div class="ttdef"><b>Definition:</b> map.h:1160</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a08393c19a1c8b1c4057a33832cd48662"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a08393c19a1c8b1c4057a33832cd48662">tvm::runtime::MapNode::iterator::operator--</a></div><div class="ttdeci">iterator & operator--()</div><div class="ttdoc">Prefix self decrement, e.g. –iter. </div><div class="ttdef"><b>Definition:</b> map.h:1163</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html">tvm::runtime::MapNode</a></div><div class="ttdoc">Shared content of all specializations of hash map. </div><div class="ttdef"><b>Definition:</b> map.h:174</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectPtr_html_a06d1de2ed3cfdde9f698155b14948fc7"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectPtr.html#a06d1de2ed3cfdde9f698155b14948fc7">tvm::runtime::ObjectPtr::get</a></div><div class="ttdeci">T * get() const</div><div class="ttdef"><b>Definition:</b> object.h:411</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_abd9253b9f7f2bcc9535a6047b3d1b529"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#abd9253b9f7f2bcc9535a6047b3d1b529">tvm::runtime::MapNode::iterator::operator--</a></div><div class="ttdeci">iterator operator--(int)</div><div class="ttdoc">Suffix self decrement. </div><div class="ttdef"><b>Definition:</b> map.h:275</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1378</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a7fbfe0e01b0fa54e151bd481956dcfec"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map::at</a></div><div class="ttdeci">const V at(const K &key) const</div><div class="ttdoc">Read element from map. </div><div class="ttdef"><b>Definition:</b> map.h:1340</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a932f36903b04ecbe0568d76890549680"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a932f36903b04ecbe0568d76890549680">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(Map< K, V > &&other)</div><div class="ttdoc">move constructor </div><div class="ttdef"><b>Definition:</b> map.h:1281</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a9733900c9d9d1af5687b7ba32ef7f5e9"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a9733900c9d9d1af5687b7ba32ef7f5e9">tvm::runtime::MapNode::find</a></div><div class="ttdeci">iterator find(const key_type &key) const</div><div class="ttdoc">Index value associated with a key. </div><div class="ttdef"><b>Definition:</b> map.h:1188</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1381</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a7fbfe0e01b0fa54e151bd481956dcfec"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a7fbfe0e01b0fa54e151bd481956dcfec">tvm::runtime::Map::at</a></div><div class="ttdeci">const V at(const K &key) const</div><div class="ttdoc">Read element from map. </div><div class="ttdef"><b>Definition:</b> map.h:1343</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a932f36903b04ecbe0568d76890549680"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a932f36903b04ecbe0568d76890549680">tvm::runtime::Map::Map</a></div><div class="ttdeci">Map(Map< K, V > &&other)</div><div class="ttdoc">move constructor </div><div class="ttdef"><b>Definition:</b> map.h:1284</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a9733900c9d9d1af5687b7ba32ef7f5e9"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a9733900c9d9d1af5687b7ba32ef7f5e9">tvm::runtime::MapNode::find</a></div><div class="ttdeci">iterator find(const key_type &key) const</div><div class="ttdoc">Index value associated with a key. </div><div class="ttdef"><b>Definition:</b> map.h:1191</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_ac09b2cd5327e5102ab373b482530f1e2"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#ac09b2cd5327e5102ab373b482530f1e2">tvm::runtime::MapNode::iterator::operator*</a></div><div class="ttdeci">reference operator*() const</div><div class="ttdoc">De-reference iterators. </div><div class="ttdef"><b>Definition:</b> map.h:259</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a26079aec4fc32333eb492a8c4a2ca849"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a26079aec4fc32333eb492a8c4a2ca849">tvm::runtime::Map::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdef"><b>Definition:</b> map.h:1348</div></div>
-<div class="ttc" id="namespacetvm_1_1runtime_html_aff337677f23f7d665960f553fb52ab86"><div class="ttname"><a href="namespacetvm_1_1runtime.html#aff337677f23f7d665960f553fb52ab86">tvm::runtime::Merge</a></div><div class="ttdeci">Map< K, V > Merge(Map< K, V > lhs, const Map< K, V > &rhs)</div><div class="ttdoc">Merge two Maps. </div><div class="ttdef"><b>Definition:</b> map.h:1468</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a26079aec4fc32333eb492a8c4a2ca849"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a26079aec4fc32333eb492a8c4a2ca849">tvm::runtime::Map::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdef"><b>Definition:</b> map.h:1351</div></div>
+<div class="ttc" id="namespacetvm_1_1runtime_html_aff337677f23f7d665960f553fb52ab86"><div class="ttname"><a href="namespacetvm_1_1runtime.html#aff337677f23f7d665960f553fb52ab86">tvm::runtime::Merge</a></div><div class="ttdeci">Map< K, V > Merge(Map< K, V > lhs, const Map< K, V > &rhs)</div><div class="ttdoc">Merge two Maps. </div><div class="ttdef"><b>Definition:</b> map.h:1471</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1DenseMapNode_html_a2e0612bed81949dd88fd440a467aa8c0"><div class="ttname"><a href="classtvm_1_1runtime_1_1DenseMapNode.html#a2e0612bed81949dd88fd440a467aa8c0">tvm::runtime::DenseMapNode::erase</a></div><div class="ttdeci">void erase(const iterator &position)</div><div class="ttdoc">Erase the entry associated with the iterator. </div><div class="ttdef"><b>Definition:</b> map.h:626</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:1268</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_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_1runtime_1_1MapNode_html_a3fda35a31720bc5d9c70d0b4fe26ecf0"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a3fda35a31720bc5d9c70d0b4fe26ecf0">tvm::runtime::MapNode::erase</a></div><div class="ttdeci">void erase(const key_type &key)</div><div class="ttdoc">Erase the entry associated with the key, do nothing if not exists. </div><div class="ttdef"><b>Definition:</b> map.h:234</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1SmallMapNode_html_a866679f23f724edc2d165f530f058b09"><div class="ttname"><a href="classtvm_1_1runtime_1_1SmallMapNode.html#a866679f23f724edc2d165f530f058b09">tvm::runtime::SmallMapNode::at</a></div><div class="ttdeci">mapped_type & at(const key_type &key)</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:374</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a264c17028af85fe4619852f804e9464a"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a264c17028af85fe4619852f804e9464a">tvm::runtime::MapNode::iterator::operator==</a></div><div class="ttdeci">bool operator==(const iterator &other) const</div><div class="ttdoc">Compare iterators. </div><div class="ttdef"><b>Definition:</b> map.h:250</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_ad052dfd6b3b90d3e5e20ebf5544d550b"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#ad052dfd6b3b90d3e5e20ebf5544d550b">tvm::runtime::MapNode::Map</a></div><div class="ttdeci">friend class Map</div><div class="ttdef"><b>Definition:</b> map.h:337</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a0c38eac8fa87129d754972cd305a6a89"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a0c38eac8fa87129d754972cd305a6a89">tvm::runtime::MapNode::iterator::pointer</a></div><div class="ttdeci">KVType * pointer</div><div class="ttdef"><b>Definition:</b> map.h:241</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ae7e2ecfde14f41cfbe28a2c845a023b7"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ae7e2ecfde14f41cfbe28a2c845a023b7">tvm::runtime::Map::iterator::reference</a></div><div class="ttdeci">value_type reference</div><div class="ttdef"><b>Definition:</b> map.h:1417</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ad3a78d88e3a9292d11ce04ff2dfe0702"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ad3a78d88e3a9292d11ce04ff2dfe0702">tvm::runtime::Map::Set</a></div><div class="ttdeci">void Set(const K &key, const V &value)</div><div class="ttdoc">set the Map. </div><div class="ttdef"><b>Definition:</b> map.h:1371</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a788c24447dd50bef05bf8cdc7c7f2f66"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a788c24447dd50bef05bf8cdc7c7f2f66">tvm::runtime::Map::iterator::operator*</a></div><div class="ttdeci">reference operator*() const</div><div class="ttdoc">De-reference iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1428</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ae7e2ecfde14f41cfbe28a2c845a023b7"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ae7e2ecfde14f41cfbe28a2c845a023b7">tvm::runtime::Map::iterator::reference</a></div><div class="ttdeci">value_type reference</div><div class="ttdef"><b>Definition:</b> map.h:1420</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ad3a78d88e3a9292d11ce04ff2dfe0702"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ad3a78d88e3a9292d11ce04ff2dfe0702">tvm::runtime::Map::Set</a></div><div class="ttdeci">void Set(const K &key, const V &value)</div><div class="ttdoc">set the Map. </div><div class="ttdef"><b>Definition:</b> map.h:1374</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_a788c24447dd50bef05bf8cdc7c7f2f66"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#a788c24447dd50bef05bf8cdc7c7f2f66">tvm::runtime::Map::iterator::operator*</a></div><div class="ttdeci">reference operator*() const</div><div class="ttdoc">De-reference iterators. </div><div class="ttdef"><b>Definition:</b> map.h:1431</div></div>
<div class="ttc" id="structtvm_1_1runtime_1_1NullOptType_html"><div class="ttname"><a href="structtvm_1_1runtime_1_1NullOptType.html">tvm::runtime::NullOptType</a></div><div class="ttdoc">Helper to represent nullptr for optional. </div><div class="ttdef"><b>Definition:</b> optional.h:35</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a670b7adb420248489fd57a9458ced561"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a670b7adb420248489fd57a9458ced561">tvm::runtime::MapNode::count</a></div><div class="ttdeci">size_t count(const key_type &key) const</div><div class="ttdoc">Count the number of times a key exists in the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1168</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a093955a395c75f89c5a7f8a71b13250a"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a093955a395c75f89c5a7f8a71b13250a">tvm::runtime::Map::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Release reference to all the elements. </div><div class="ttdef"><b>Definition:</b> map.h:1360</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ad8b40ddeffccb6f221601eda70202f9a"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map::iterator::iterator</a></div><div class="ttdeci">iterator()</div><div class="ttdef"><b>Definition:</b> map.h:1419</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a49edd4ddc34a4e0b097c34560b9b3b4e"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode::at</a></div><div class="ttdeci">const mapped_type & at(const key_type &key) const</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:1172</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ac1e67f17ae0b5d4c72670908469fff50"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ac1e67f17ae0b5d4c72670908469fff50">tvm::runtime::Map::iterator::operator++</a></div><div class="ttdeci">iterator operator++(int)</div><div class="ttdoc">Suffix self increment. </div><div class="ttdef"><b>Definition:</b> map.h:1438</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a670b7adb420248489fd57a9458ced561"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a670b7adb420248489fd57a9458ced561">tvm::runtime::MapNode::count</a></div><div class="ttdeci">size_t count(const key_type &key) const</div><div class="ttdoc">Count the number of times a key exists in the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1171</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a093955a395c75f89c5a7f8a71b13250a"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a093955a395c75f89c5a7f8a71b13250a">tvm::runtime::Map::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Release reference to all the elements. </div><div class="ttdef"><b>Definition:</b> map.h:1363</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ad8b40ddeffccb6f221601eda70202f9a"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ad8b40ddeffccb6f221601eda70202f9a">tvm::runtime::Map::iterator::iterator</a></div><div class="ttdeci">iterator()</div><div class="ttdef"><b>Definition:</b> map.h:1422</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a49edd4ddc34a4e0b097c34560b9b3b4e"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a49edd4ddc34a4e0b097c34560b9b3b4e">tvm::runtime::MapNode::at</a></div><div class="ttdeci">const mapped_type & at(const key_type &key) const</div><div class="ttdoc">Index value associated with a key, throw exception if the key does not exist. </div><div class="ttdef"><b>Definition:</b> map.h:1175</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html_ac1e67f17ae0b5d4c72670908469fff50"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html#ac1e67f17ae0b5d4c72670908469fff50">tvm::runtime::Map::iterator::operator++</a></div><div class="ttdeci">iterator operator++(int)</div><div class="ttdoc">Suffix self increment. </div><div class="ttdef"><b>Definition:</b> map.h:1441</div></div>
<div class="ttc" id="classtvm_1_1te_1_1IterVarAttr_html"><div class="ttname"><a href="classtvm_1_1te_1_1IterVarAttr.html">tvm::te::IterVarAttr</a></div><div class="ttdoc">Additional scheduable attributes about IterVar. </div><div class="ttdef"><b>Definition:</b> schedule.h:466</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a4b03d8f363b6bcac8ff59cd40b2a9cca"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a4b03d8f363b6bcac8ff59cd40b2a9cca">tvm::runtime::MapNode::KVType</a></div><div class="ttdeci">std::pair< ObjectRef, ObjectRef > KVType</div><div class="ttdoc">Type of value stored in the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:181</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_a2285f106f6afa29f512a7818ad59e9e5"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#a2285f106f6afa29f512a7818ad59e9e5">tvm::runtime::MapNode::size_</a></div><div class="ttdeci">uint64_t size_</div><div class="ttdoc">number of entries in the container </div><div class="ttdef"><b>Definition:</b> map.h:334</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_afac316cac6b4fc06d81c66df46482ba6"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#afac316cac6b4fc06d81c66df46482ba6">tvm::runtime::MapNode::erase</a></div><div class="ttdeci">void erase(const iterator &position)</div><div class="ttdoc">Erase the entry associated with the iterator. </div><div class="ttdef"><b>Definition:</b> map.h:1192</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html">tvm::runtime::Map::iterator</a></div><div class="ttdoc">Iterator of the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1411</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_html_afac316cac6b4fc06d81c66df46482ba6"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode.html#afac316cac6b4fc06d81c66df46482ba6">tvm::runtime::MapNode::erase</a></div><div class="ttdeci">void erase(const iterator &position)</div><div class="ttdoc">Erase the entry associated with the iterator. </div><div class="ttdef"><b>Definition:</b> map.h:1195</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_1_1iterator_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map_1_1iterator.html">tvm::runtime::Map::iterator</a></div><div class="ttdoc">Iterator of the hash map. </div><div class="ttdef"><b>Definition:</b> map.h:1414</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1MapNode_1_1iterator_html_a4c7cd9342748ba6abbc671a4258dc814"><div class="ttname"><a href="classtvm_1_1runtime_1_1MapNode_1_1iterator.html#a4c7cd9342748ba6abbc671a4258dc814">tvm::runtime::MapNode::iterator::index</a></div><div class="ttdeci">uint64_t index</div><div class="ttdoc">The position on the array. </div><div class="ttdef"><b>Definition:</b> map.h:293</div></div>
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diff --git a/docs/reference/api/doxygen/memory__pools_8h_source.html b/docs/reference/api/doxygen/memory__pools_8h_source.html
index 56457831c..5739f3d48 100644
--- a/docs/reference/api/doxygen/memory__pools_8h_source.html
+++ b/docs/reference/api/doxygen/memory__pools_8h_source.html
@@ -129,7 +129,7 @@ $(function() {
<div class="ttc" id="structtvm_1_1PoolInfoPropertiesNode_html_af68c3b0893a38f5732849049abc9f5dd"><div class="ttname"><a href="structtvm_1_1PoolInfoPropertiesNode.html#af68c3b0893a38f5732849049abc9f5dd">tvm::PoolInfoPropertiesNode::read_bandwidth_bytes_per_cycle</a></div><div class="ttdeci">Integer read_bandwidth_bytes_per_cycle</div><div class="ttdoc">The read bandwidth in bytes/cycle. </div><div class="ttdef"><b>Definition:</b> memory_pools.h:158</div></div>
<div class="ttc" id="structtvm_1_1PoolInfoNode_html_afbac7d6a6c6a212828ddec63b273e9d9"><div class="ttname"><a href="structtvm_1_1PoolInfoNode.html#afbac7d6a6c6a212828ddec63b273e9d9">tvm::PoolInfoNode::SEqualReduce</a></div><div class="ttdeci">bool SEqualReduce(const PoolInfoNode *other, SEqualReducer equal) const</div><div class="ttdef"><b>Definition:</b> memory_pools.h:79</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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="structtvm_1_1WorkspacePoolInfoNode_html_ab0a0059dfa7eac65407148975848d9f3"><div class="ttname"><a href="structtvm_1_1WorkspacePoolInfoNode.html#ab0a0059dfa7eac65407148975848d9f3">tvm::WorkspacePoolInfoNode::SHashReduce</a></div><div class="ttdeci">void SHashReduce(SHashReducer hash_reduce) const</div><div class="ttdef"><b>Definition:</b> memory_pools.h:230</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html_ac97054694d03dc5eac58315fb569ef88"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#ac97054694d03dc5eac58315fb569ef88">tvm::runtime::Object::_type_has_method_shash_reduce</a></div><div class="ttdeci">static constexpr bool _type_has_method_shash_reduce</div><div class="ttdef"><b>Definition:</b> object.h:234</div></div>
<div class="ttc" id="structtvm_1_1ConstantInfoNode_html_a54c2016e8451a448720d5ea668dea6b4"><div class="ttname"><a href="structtvm_1_1ConstantInfoNode.html#a54c2016e8451a448720d5ea668dea6b4">tvm::ConstantInfoNode::SEqualReduce</a></div><div class="ttdeci">bool SEqualReduce(const ConstantInfoNode *other, SEqualReducer equal) const</div><div class="ttdef"><b>Definition:</b> memory_pools.h:259</div></div>
diff --git a/docs/reference/api/doxygen/nn_2softmax_8h_source.html b/docs/reference/api/doxygen/nn_2softmax_8h_source.html
index ed060fa5b..bca963329 100644
--- a/docs/reference/api/doxygen/nn_2softmax_8h_source.html
+++ b/docs/reference/api/doxygen/nn_2softmax_8h_source.html
@@ -84,12 +84,12 @@ $(function() {
<div class="ttc" id="classtvm_1_1te_1_1Tensor_html"><div class="ttname"><a href="classtvm_1_1te_1_1Tensor.html">tvm::te::Tensor</a></div><div class="ttdoc">Tensor structure representing a possible input, or intermediate computation result. </div><div class="ttdef"><b>Definition:</b> tensor.h:102</div></div>
<div class="ttc" id="namespacetvm_1_1topi_html_a466452c7337b11c7237b8756cf7da621"><div class="ttname"><a href="namespacetvm_1_1topi.html#a466452c7337b11c7237b8756cf7da621">tvm::topi::exp</a></div><div class="ttdeci">Tensor exp(const Tensor &x, std::string name="T_" "exp", std::string tag=kElementWise)</div><div class="ttdef"><b>Definition:</b> elemwise.h:49</div></div>
<div class="ttc" id="operation_8h_html"><div class="ttname"><a href="operation_8h.html">operation.h</a></div><div class="ttdoc">Operation node can generate one or multiple Tensors. </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:1268</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="tags_8h_html"><div class="ttname"><a href="tags_8h.html">tags.h</a></div><div class="ttdoc">External function interface to rocBLAS libraries. </div></div>
<div class="ttc" id="namespacetvm_1_1te_html_afe4f57aeb3dd5ae9c0b58135e14d67ca"><div class="ttname"><a href="namespacetvm_1_1te.html#afe4f57aeb3dd5ae9c0b58135e14d67ca">tvm::te::compute</a></div><div class="ttdeci">Tensor compute(Array< PrimExpr > shape, FCompute fcompute, std::string name="tensor", std::string tag="", Map< String, ObjectRef > attrs={})</div><div class="ttdoc">Construct a new tensor by computing over shape, using the computation rule: resul [...]
<div class="ttc" id="namespacetvm_html_a82be70bd7794abca32473604cbb09569"><div class="ttname"><a href="namespacetvm.html#a82be70bd7794abca32473604cbb09569">tvm::exp</a></div><div class="ttdeci">PrimExpr exp(PrimExpr x, Span span=Span())</div><div class="ttdef"><b>Definition:</b> op.h:693</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>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ad3a78d88e3a9292d11ce04ff2dfe0702"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ad3a78d88e3a9292d11ce04ff2dfe0702">tvm::runtime::Map::Set</a></div><div class="ttdeci">void Set(const K &key, const V &value)</div><div class="ttdoc">set the Map. </div><div class="ttdef"><b>Definition:</b> map.h:1371</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_ad3a78d88e3a9292d11ce04ff2dfe0702"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#ad3a78d88e3a9292d11ce04ff2dfe0702">tvm::runtime::Map::Set</a></div><div class="ttdeci">void Set(const K &key, const V &value)</div><div class="ttdoc">set the Map. </div><div class="ttdef"><b>Definition:</b> map.h:1374</div></div>
<div class="ttc" id="classtvm_1_1Integer_html"><div class="ttname"><a href="classtvm_1_1Integer.html">tvm::Integer</a></div><div class="ttdoc">Container of constant int that adds more constructors. </div><div class="ttdef"><b>Definition:</b> expr.h:618</div></div>
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diff --git a/docs/reference/api/doxygen/operation_8h_source.html b/docs/reference/api/doxygen/operation_8h_source.html
index 5c76d5f14..dd6d089e9 100644
--- a/docs/reference/api/doxygen/operation_8h_source.html
+++ b/docs/reference/api/doxygen/operation_8h_source.html
@@ -142,7 +142,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1te_1_1TensorComputeOpNode_html_a0afdf35c3bb7d6affd303c467099667f"><div class="ttname"><a href="classtvm_1_1te_1_1TensorComputeOpNode.html#a0afdf35c3bb7d6affd303c467099667f">tvm::te::TensorComputeOpNode::input_regions</a></div><div class="ttdeci">Array< Region > input_regions</div><div class="ttdoc">region of input tensors </div><div class="ttdef"><b>Definition:</b> operation.h:283</div></div>
<div class="ttc" id="classtvm_1_1te_1_1ExternOpNode_html_a12bd3ed18f9735abe6850766132eeb4c"><div class="ttname"><a href="classtvm_1_1te_1_1ExternOpNode.html#a12bd3ed18f9735abe6850766132eeb4c">tvm::te::ExternOpNode::inputs</a></div><div class="ttdeci">Array< Tensor > inputs</div><div class="ttdoc">The input tensors. </div><div class="ttdef"><b>Definition:</b> operation.h:414</div></div>
<div class="ttc" id="classtvm_1_1te_1_1ExternOpNode_html_ae5c3fa995ba59e0e001d6b8f92e39c7a"><div class="ttname"><a href="classtvm_1_1te_1_1ExternOpNode.html#ae5c3fa995ba59e0e001d6b8f92e39c7a">tvm::te::ExternOpNode::input_placeholders</a></div><div class="ttdeci">Array< Buffer > input_placeholders</div><div class="ttdoc">Symbolic placeholder representation of inputs. </div><div class="ttdef"><b>Definition:</b> operation.h:416</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:1268</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_1te_1_1ScanOpNode_html_ace2bf7e43cd4197324ec6363626fc60a"><div class="ttname"><a href="classtvm_1_1te_1_1ScanOpNode.html#ace2bf7e43cd4197324ec6363626fc60a">tvm::te::ScanOpNode::update</a></div><div class="ttdeci">Array< Tensor > update</div><div class="ttdoc">the update function represented by tensor </div><div class="ttdef"><b>Definition:</b> operation.h:341</div></div>
<div class="ttc" id="classtvm_1_1te_1_1ScanOpNode_html_abcc62af0d7da8d97d9065fd82230162b"><div class="ttname"><a href="classtvm_1_1te_1_1ScanOpNode.html#abcc62af0d7da8d97d9065fd82230162b">tvm::te::ScanOpNode::inputs</a></div><div class="ttdeci">Array< Tensor > inputs</div><div class="ttdoc">the inputs to the scan, these are optionally provided But they can be helpful to provide hints to spe...</div><div class="ttdef"><b>Definition:</b> operation.h:348</div></div>
<div class="ttc" id="classtvm_1_1te_1_1PlaceholderOpNode_html_a301fb989a618e248d69120f6c7b33c3f"><div class="ttname"><a href="classtvm_1_1te_1_1PlaceholderOpNode.html#a301fb989a618e248d69120f6c7b33c3f">tvm::te::PlaceholderOpNode::shape</a></div><div class="ttdeci">Array< PrimExpr > shape</div><div class="ttdoc">The shape of the input. </div><div class="ttdef"><b>Definition:</b> operation.h:155</div></div>
diff --git a/docs/reference/api/doxygen/packed__func_8h_source.html b/docs/reference/api/doxygen/packed__func_8h_source.html
index 81bbc937b..aae7c9062 100644
--- a/docs/reference/api/doxygen/packed__func_8h_source.html
+++ b/docs/reference/api/doxygen/packed__func_8h_source.html
@@ -207,7 +207,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4_html_a0e4a4d01d86eca79c5d9e1e90322c5cb"><div class="ttname"><a href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#a0e4a4d01d86eca79c5d9e1e90322c5cb">tvm::runtime::TypedPackedFunc< R(Args...)>::operator==</a></div><div class="ttdeci">bool operator==(std::nullptr_t null) const</div><div class="ttdef"><b>Definition:</b> packed_func.h:360</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM. </div><div class="ttdef"><b>Definition:</b> module.h:50</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4_html_ae71734f7a1541c3b8513a2cdcc1ab161"><div class="ttname"><a href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html#ae71734f7a1541c3b8513a2cdcc1ab161">tvm::runtime::TypedPackedFunc< R(Args...)>::operator!=</a></div><div class="ttdeci">bool operator!=(std::nullptr_t null) const</div><div class="ttdef"><b>Definition:</b> packed_func.h:362</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:1268</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_1runtime_1_1Object_html_a817ba6c23b7ee1821c48a75edf255a30"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object::TypeIndex2Key</a></div><div class="ttdeci">static std::string TypeIndex2Key(uint32_t tindex)</div><div class="ttdoc">Get the type key of the corresponding index from runtime. </div></div>
<div class="ttc" id="namespacetvm_html_a0da40d3e210aa3b38a17982a7b7866b8"><div class="ttname"><a href="namespacetvm.html#a0da40d3e210aa3b38a17982a7b7866b8">tvm::ret</a></div><div class="ttdeci">PrimExpr ret(PrimExpr value, Span span=Span())</div><div class="ttdoc">Return the value. </div></div>
<div class="ttc" id="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
diff --git a/docs/reference/api/doxygen/papi_8h_source.html b/docs/reference/api/doxygen/papi_8h_source.html
index 7f8fae6e7..bf856acca 100644
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+++ b/docs/reference/api/doxygen/papi_8h_source.html
@@ -72,7 +72,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1runtime_1_1profiling_html_af49d404b75e55adc53c4282c4b247573"><div class="ttname"><a href="namespacetvm_1_1runtime_1_1profiling.html#af49d404b75e55adc53c4282c4b247573">tvm::runtime::profiling::CreatePAPIMetricCollector</a></div><div class="ttdeci">MetricCollector CreatePAPIMetricCollector(Map< DeviceWrapper, Array< String >> metrics)</div><div class="ttdoc">Construct a metric collector that collects data from hardware performance counters u [...]
<div class="ttc" id="classtvm_1_1runtime_1_1profiling_1_1MetricCollector_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html">tvm::runtime::profiling::MetricCollector</a></div><div class="ttdoc">Wrapper for MetricCollectorNode. </div><div class="ttdef"><b>Definition:</b> profiling.h:324</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs. </div><div class="ttdef"><b>Definition:</b> array.h:270</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:1268</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="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html">tvm::runtime::profiling::DeviceWrapper</a></div><div class="ttdoc">Wrapper for Device. </div><div class="ttdef"><b>Definition:</b> profiling.h:170</div></div>
</div><!-- fragment --></div><!-- contents -->
diff --git a/docs/reference/api/doxygen/parser_8h_source.html b/docs/reference/api/doxygen/parser_8h_source.html
index fa2460b39..20364c02a 100644
--- a/docs/reference/api/doxygen/parser_8h_source.html
+++ b/docs/reference/api/doxygen/parser_8h_source.html
@@ -74,7 +74,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1transform_1_1Pass_html"><div class="ttname"><a href="classtvm_1_1transform_1_1Pass.html">tvm::transform::Pass</a></div><div class="ttdef"><b>Definition:</b> transform.h:363</div></div>
<div class="ttc" id="namespacetvm_1_1parser_html_a6d1ba1bd4ba87b4400f2ec545f264336"><div class="ttname"><a href="namespacetvm_1_1parser.html#a6d1ba1bd4ba87b4400f2ec545f264336">tvm::parser::ParseModule</a></div><div class="ttdeci">IRModule ParseModule(const std::string &file_name, const std::string &file_content, const Optional< IRModule > &init_module=Optional< IRModule >(), const MetaTable &init_meta_table=MetaTable())</div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</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:1268</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_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="packed__func_8h_html"><div class="ttname"><a href="packed__func_8h.html">packed_func.h</a></div><div class="ttdoc">Type-erased function used across TVM API. </div></div>
<div class="ttc" id="registry_8h_html"><div class="ttname"><a href="registry_8h.html">registry.h</a></div><div class="ttdoc">This file defines the TVM global function registry. </div></div>
diff --git a/docs/reference/api/doxygen/profiler_8h_source.html b/docs/reference/api/doxygen/profiler_8h_source.html
index fbcd508b2..de3ef653b 100644
--- a/docs/reference/api/doxygen/profiler_8h_source.html
+++ b/docs/reference/api/doxygen/profiler_8h_source.html
@@ -84,7 +84,7 @@ $(function() {
<div class="ttc" id="object_8h_html_a3aea9b3f65aeb9150c0fa7800e5573c6"><div class="ttname"><a href="object_8h.html#a3aea9b3f65aeb9150c0fa7800e5573c6">TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">#define TVM_DECLARE_FINAL_OBJECT_INFO(TypeName, ParentType)</div><div class="ttdoc">helper macro to declare type information in a final class. </div><div class="ttdef"><b>Definition:</b> object.h:671</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1ScopedTimer_html_a419d438328a81a96b4579141a3cf83ca"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1ScopedTimer.html#a419d438328a81a96b4579141a3cf83ca">tvm::meta_schedule::ScopedTimer::~ScopedTimer</a></div><div class="ttdeci">~ScopedTimer()</div><div class="ttdef"><b>Definition:</b> profiler.h:41</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1ProfilerNode_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1ProfilerNode.html">tvm::meta_schedule::ProfilerNode</a></div><div class="ttdoc">A generic profiler. </div><div class="ttdef"><b>Definition:</b> profiler.h:55</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>
diff --git a/docs/reference/api/doxygen/profiling_8h_source.html b/docs/reference/api/doxygen/profiling_8h_source.html
index afe992ddb..8f6a5c376 100644
--- a/docs/reference/api/doxygen/profiling_8h_source.html
+++ b/docs/reference/api/doxygen/profiling_8h_source.html
@@ -116,7 +116,7 @@ $(function() {
<div class="ttc" id="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode_html_a1c3c3c0fc8f177ddedc0ec02ca77b123"><div class="ttname"><a href="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode.html#a1c3c3c0fc8f177ddedc0ec02ca77b123">tvm::runtime::profiling::DeviceWrapperNode::device</a></div><div class="ttdeci">Device device</div><div class="ttdef"><b>Definition:</b> profiling.h:160</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Module_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Module.html">tvm::runtime::Module</a></div><div class="ttdoc">Module container of TVM. </div><div class="ttdef"><b>Definition:</b> module.h:50</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1profiling_1_1CountNode_html_ae235fc123c4f3040cb88427702e2fc04"><div class="ttname"><a href="classtvm_1_1runtime_1_1profiling_1_1CountNode.html#ae235fc123c4f3040cb88427702e2fc04">tvm::runtime::profiling::CountNode::value</a></div><div class="ttdeci">int64_t value</div><div class="ttdef"><b>Definition:</b> profiling.h:462</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:1268</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="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode_html"><div class="ttname"><a href="structtvm_1_1runtime_1_1profiling_1_1DeviceWrapperNode.html">tvm::runtime::profiling::DeviceWrapperNode</a></div><div class="ttdoc">Wrapper for Device because Device is not passable across the PackedFunc interface. </div><div class="ttdef"><b>Definition:</b> profiling.h:158</div></div>
<div class="ttc" id="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/reflection_8h_source.html b/docs/reference/api/doxygen/reflection_8h_source.html
index 04903c375..f45f32a4a 100644
--- a/docs/reference/api/doxygen/reflection_8h_source.html
+++ b/docs/reference/api/doxygen/reflection_8h_source.html
@@ -101,7 +101,7 @@ $(function() {
<div class="ttc" id="data__type_8h_html"><div class="ttname"><a href="data__type_8h.html">data_type.h</a></div></div>
<div class="ttc" id="structtvm_1_1detail_1_1SelectSEqualReduce_html"><div class="ttname"><a href="structtvm_1_1detail_1_1SelectSEqualReduce.html">tvm::detail::SelectSEqualReduce</a></div><div class="ttdef"><b>Definition:</b> reflection.h:340</div></div>
<div class="ttc" id="structtvm_1_1detail_1_1SelectSHashReduce_html"><div class="ttname"><a href="structtvm_1_1detail_1_1SelectSHashReduce.html">tvm::detail::SelectSHashReduce</a></div><div class="ttdef"><b>Definition:</b> reflection.h:354</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:1268</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="structtvm_1_1detail_1_1ImplVisitAttrs_html"><div class="ttname"><a href="structtvm_1_1detail_1_1ImplVisitAttrs.html">tvm::detail::ImplVisitAttrs</a></div><div class="ttdef"><b>Definition:</b> reflection.h:287</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="namespacetvm_html_a15edab1c870f1fcd08b798b09ba60aa2"><div class="ttname"><a href="namespacetvm.html#a15edab1c870f1fcd08b798b09ba60aa2">tvm::GetAttrKeyByAddress</a></div><div class="ttdeci">Optional< String > GetAttrKeyByAddress(const Object *object, const void *attr_address)</div><div class="ttdoc">Given an object and an address of its attribute, return the key of the attribute. ...</div></div>
diff --git a/docs/reference/api/doxygen/relay_2transform_8h_source.html b/docs/reference/api/doxygen/relay_2transform_8h_source.html
index fcf230e3c..8fabf20c4 100644
--- a/docs/reference/api/doxygen/relay_2transform_8h_source.html
+++ b/docs/reference/api/doxygen/relay_2transform_8h_source.html
@@ -137,7 +137,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1relay_1_1transform_html_ab533a050ab0d54b41e543fb1fd369fb6"><div class="ttname"><a href="namespacetvm_1_1relay_1_1transform.html#ab533a050ab0d54b41e543fb1fd369fb6">tvm::relay::transform::DynamicToStatic</a></div><div class="ttdeci">Pass DynamicToStatic()</div><div class="ttdoc">Find Dynamic ops and make them static. </div></div>
<div class="ttc" id="namespacetvm_1_1relay_1_1transform_html_a744a05f8bba3c2ac238ba4569d926184"><div class="ttname"><a href="namespacetvm_1_1relay_1_1transform.html#a744a05f8bba3c2ac238ba4569d926184">tvm::relay::transform::PassContext</a></div><div class="ttdeci">tvm::transform::PassContext PassContext</div><div class="ttdef"><b>Definition:</b> transform.h:47</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
<div class="ttc" id="classtvm_1_1Type_html"><div class="ttname"><a href="classtvm_1_1Type.html">tvm::Type</a></div><div class="ttdoc">Managed reference to TypeNode. </div><div class="ttdef"><b>Definition:</b> type.h:93</div></div>
<div class="ttc" id="namespacetvm_1_1relay_1_1transform_html_abf8753e6152a3ce13488eea22827cac9"><div class="ttname"><a href="namespacetvm_1_1relay_1_1transform.html#abf8753e6152a3ce13488eea22827cac9">tvm::relay::transform::RemoveStandaloneReshapes</a></div><div class="ttdeci">Pass RemoveStandaloneReshapes()</div><div class="ttdoc">Removes non-fused reshapes after lowering the graph. InferType() cannot be invoked after calling this...</div></div>
diff --git a/docs/reference/api/doxygen/runtime_8h_source.html b/docs/reference/api/doxygen/runtime_8h_source.html
index 50577255b..065cb03e8 100644
--- a/docs/reference/api/doxygen/runtime_8h_source.html
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@@ -98,7 +98,7 @@ $(function() {
<div class="ttc" id="attr__registry__map_8h_html"><div class="ttname"><a href="attr__registry__map_8h.html">attr_registry_map.h</a></div><div class="ttdoc">Attribute map used in registry. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html_a481f01923b14e1851ebd38506e9c66ea"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">tvm::runtime::Object::type_index</a></div><div class="ttdeci">uint32_t type_index() const</div><div class="ttdef"><b>Definition:</b> object.h:175</div></div>
<div class="ttc" id="classtvm_1_1relay_1_1RuntimeNode_html_a3706b64e25b8ff8729322631b20c3681"><div class="ttname"><a href="classtvm_1_1relay_1_1RuntimeNode.html#a3706b64e25b8ff8729322631b20c3681">tvm::relay::RuntimeNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(RuntimeNode, Object)</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:1268</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_1runtime_1_1Object_html_a817ba6c23b7ee1821c48a75edf255a30"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object::TypeIndex2Key</a></div><div class="ttdeci">static std::string TypeIndex2Key(uint32_t tindex)</div><div class="ttdoc">Get the type key of the corresponding index from runtime. </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_1relay_1_1RuntimeNode_html_a272b4a534f93f1aa7304bc20b8f124a1"><div class="ttname"><a href="classtvm_1_1relay_1_1RuntimeNode.html#a272b4a534f93f1aa7304bc20b8f124a1">tvm::relay::RuntimeNode::GetAttr</a></div><div class="ttdeci">Optional< TObjectRef > GetAttr(const std::string &attr_key, Optional< TObjectRef > default_value=Optional< TObjectRef >(nullptr)) const</div><div class="ttdoc">Get an attribute. </div><div class="ttdef"><b>Defini [...]
diff --git a/docs/reference/api/doxygen/schedule__rule_8h_source.html b/docs/reference/api/doxygen/schedule__rule_8h_source.html
index 9c72f6a1e..aab2c8870 100644
--- a/docs/reference/api/doxygen/schedule__rule_8h_source.html
+++ b/docs/reference/api/doxygen/schedule__rule_8h_source.html
@@ -94,7 +94,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1meta__schedule_1_1ScheduleRuleNode_html_a0d1f91064bd94eb3b3dd128a1f93b384"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1ScheduleRuleNode.html#a0d1f91064bd94eb3b3dd128a1f93b384">tvm::meta_schedule::ScheduleRuleNode::Apply</a></div><div class="ttdeci">virtual runtime::Array< tir::Schedule > Apply(const tir::Schedule &sch, const tir::BlockRV &block)=0</div><div class="ttdoc">Apply a schedule rule to the specific block in the given [...]
<div class="ttc" id="object_8h_html"><div class="ttname"><a href="object_8h.html">object.h</a></div><div class="ttdoc">A managed object in the TVM runtime. </div></div>
<div class="ttc" id="object_8h_html_a3aea9b3f65aeb9150c0fa7800e5573c6"><div class="ttname"><a href="object_8h.html#a3aea9b3f65aeb9150c0fa7800e5573c6">TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">#define TVM_DECLARE_FINAL_OBJECT_INFO(TypeName, ParentType)</div><div class="ttdoc">helper macro to declare type information in a final class. </div><div class="ttdef"><b>Definition:</b> object.h:671</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:1268</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="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </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="reflection_8h_html"><div class="ttname"><a href="reflection_8h.html">reflection.h</a></div><div class="ttdoc">Reflection and serialization of compiler IR/AST nodes. </div></div>
diff --git a/docs/reference/api/doxygen/search/all_13.js b/docs/reference/api/doxygen/search/all_13.js
index 6b71495eb..7d19cdb99 100644
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+<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>
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<div class="ttc" id="classtvm_1_1tir_1_1PrimFunc_html"><div class="ttname"><a href="classtvm_1_1tir_1_1PrimFunc.html">tvm::tir::PrimFunc</a></div><div class="ttdoc">Managed reference to PrimFuncNode. </div><div class="ttdef"><b>Definition:</b> function.h:156</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1WhileNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1WhileNode.html">tvm::tir::WhileNode</a></div><div class="ttdoc">A While loop. </div><div class="ttdef"><b>Definition:</b> stmt.h:1022</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4_html_afb8d8cd85b95414ced0f27cd1c7a44d4"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#afb8d8cd85b95414ced0f27cd1c7a44d4">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)>::VisitStmt</a></div><div class="ttdeci">virtual R VisitStmt(const Stmt &n, Args... args)</div><div class="ttdoc">The func [...]
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1380</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1383</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4_html_ae51b328e2b59a50bed7112a93dba1aae"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#ae51b328e2b59a50bed7112a93dba1aae">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)>::VisitStmtDefault_</a></div><div class="ttdeci">virtual R VisitStmtDefault_(const Object *op, Args...)</div><div class="ttdef [...]
<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_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4_html_a82025a966ad57d3a52901f4657a89b70"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#a82025a966ad57d3a52901f4657a89b70">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)>::result_type</a></div><div class="ttdeci">R result_type</div><div class="ttdoc">the result type of this functor </div><div cl [...]
@@ -135,11 +135,11 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1tir_html_a34ae87f765e4d8230e3572428cdbe3e1"><div class="ttname"><a href="namespacetvm_1_1tir.html#a34ae87f765e4d8230e3572428cdbe3e1">tvm::tir::Substitute</a></div><div class="ttdeci">Stmt Substitute(Stmt stmt, std::function< Optional< PrimExpr >(const Var &var)> vmap)</div><div class="ttdoc">Substitute the var specified by vmap. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1StmtExprMutator_html"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtExprMutator.html">tvm::tir::StmtExprMutator</a></div><div class="ttdoc">Mutator that recursively mutates stmts and exprs on them. </div><div class="ttdef"><b>Definition:</b> stmt_functor.h:315</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1BufferStoreNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1BufferStoreNode.html">tvm::tir::BufferStoreNode</a></div><div class="ttdoc">Store value to the high dimension buffer. </div><div class="ttdef"><b>Definition:</b> stmt.h:286</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1378</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1381</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4_html_afb4abf8cb69c4a9105eb38e262e96bc7"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#afb4abf8cb69c4a9105eb38e262e96bc7">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)>::VisitStmt_</a></div><div class="ttdeci">virtual R VisitStmt_(const BlockRealizeNode *op, Args... args)</div><div class="ttde [...]
<div class="ttc" id="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4_html_a3b34df9540fdb87af912a5759db4b2a1"><div class="ttname"><a href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#a3b34df9540fdb87af912a5759db4b2a1">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)>::VisitStmt_</a></div><div class="ttdeci">virtual R VisitStmt_(const ForNode *op, Args... args)</div><div class="ttdef"><b>Def [...]
<div class="ttc" id="classtvm_1_1tir_1_1AssertStmtNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1AssertStmtNode.html">tvm::tir::AssertStmtNode</a></div><div class="ttdoc">Assert condition, if an error occurs, return the error message. </div><div class="ttdef"><b>Definition:</b> stmt.h:166</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:1268</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="namespacetvm_html_a0da40d3e210aa3b38a17982a7b7866b8"><div class="ttname"><a href="namespacetvm.html#a0da40d3e210aa3b38a17982a7b7866b8">tvm::ret</a></div><div class="ttdeci">PrimExpr ret(PrimExpr value, Span span=Span())</div><div class="ttdoc">Return the value. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_html_a47050a2baf7e047f4994700ce8959d50"><div class="ttname"><a href="namespacetvm_1_1tir.html#a47050a2baf7e047f4994700ce8959d50">tvm::tir::IRTransform</a></div><div class="ttdeci">Stmt IRTransform(Stmt stmt, const runtime::PackedFunc &preorder, const runtime::PackedFunc &postorder, Optional< Array< String >> only_enable=NullOpt)</div><div class="ttdoc">recursively visit the ir nodes in post DFS order, and transform it </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/tag_8h_source.html b/docs/reference/api/doxygen/tag_8h_source.html
index cab62362e..074f6cdfd 100644
--- a/docs/reference/api/doxygen/tag_8h_source.html
+++ b/docs/reference/api/doxygen/tag_8h_source.html
@@ -84,7 +84,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
<div class="ttc" id="attr__registry__map_8h_html"><div class="ttname"><a href="attr__registry__map_8h.html">attr_registry_map.h</a></div><div class="ttdoc">Attribute map used in registry. </div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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_1TargetTagRegEntry_html_a3c1b66885a103360f56a17ef1e4dde2e"><div class="ttname"><a href="classtvm_1_1TargetTagRegEntry.html#a3c1b66885a103360f56a17ef1e4dde2e">tvm::TargetTagRegEntry::set_config</a></div><div class="ttdeci">TargetTagRegEntry & set_config(Map< String, ObjectRef > config)</div><div class="ttdoc">Set the config dict corresponding to the target tag. </div><div class="ttdef"><b>Definition:</b> tag.h:129</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_1TargetTagNode_html"><div class="ttname"><a href="classtvm_1_1TargetTagNode.html">tvm::TargetTagNode</a></div><div class="ttdoc">A target tag. </div><div class="ttdef"><b>Definition:</b> tag.h:36</div></div>
diff --git a/docs/reference/api/doxygen/target_8h_source.html b/docs/reference/api/doxygen/target_8h_source.html
index 74616d0a1..39195fb7b 100644
--- a/docs/reference/api/doxygen/target_8h_source.html
+++ b/docs/reference/api/doxygen/target_8h_source.html
@@ -81,7 +81,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1TargetNode_html_a94129658128c764ddd0e2255a490be05"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a94129658128c764ddd0e2255a490be05">tvm::TargetNode::GetHost</a></div><div class="ttdeci">Optional< Target > GetHost() const</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a1b64ab2ca286e1cd63c181f469707218"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a1b64ab2ca286e1cd63c181f469707218">tvm::TargetNode::SHashReduce</a></div><div class="ttdeci">void SHashReduce(SHashReducer hash_reduce) const</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a30cd67db46a9c4b098a8ba38fff22e26"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a30cd67db46a9c4b098a8ba38fff22e26">tvm::TargetNode::str</a></div><div class="ttdeci">const std::string & str() const</div><div class="ttdoc">The raw string representation of the target. </div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_af6f7942cbc239ec3eac4598e8542b4cc"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#af6f7942cbc239ec3eac4598e8542b4cc">tvm::runtime::Map::Get</a></div><div class="ttdeci">Optional< V > Get(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1382</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_af6f7942cbc239ec3eac4598e8542b4cc"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#af6f7942cbc239ec3eac4598e8542b4cc">tvm::runtime::Map::Get</a></div><div class="ttdeci">Optional< V > Get(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1385</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_acedf257c039c25a6a16bf36b664d35c6"><div class="ttname"><a href="classtvm_1_1TargetNode.html#acedf257c039c25a6a16bf36b664d35c6">tvm::TargetNode::SEqualReduce</a></div><div class="ttdeci">bool SEqualReduce(const TargetNode *other, SEqualReducer equal) const</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></div><div class="ttdoc">base class of all object containers. </div><div class="ttdef"><b>Definition:</b> object.h:167</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_ac19a4ee0f0ec7ea607ec746bc4551b71"><div class="ttname"><a href="classtvm_1_1TargetNode.html#ac19a4ee0f0ec7ea607ec746bc4551b71">tvm::TargetNode::kind</a></div><div class="ttdeci">TargetKind kind</div><div class="ttdoc">The kind of the target device. </div><div class="ttdef"><b>Definition:</b> target.h:49</div></div>
@@ -91,7 +91,7 @@ $(function() {
<div class="ttc" id="target__kind_8h_html"><div class="ttname"><a href="target__kind_8h.html">target_kind.h</a></div><div class="ttdoc">Target kind registry. </div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs. </div><div class="ttdef"><b>Definition:</b> array.h:270</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_aa4f6c3e10daa0e968360e258029a9860"><div class="ttname"><a href="classtvm_1_1TargetNode.html#aa4f6c3e10daa0e968360e258029a9860">tvm::TargetNode::GetFeature</a></div><div class="ttdeci">Optional< TObjectRef > GetFeature(const std::string &attr_key, TObjectRef default_value) const</div><div class="ttdef"><b>Definition:</b> target.h:153</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1380</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_abce8c6206f11edfd3c493b843d52685f"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#abce8c6206f11edfd3c493b843d52685f">tvm::runtime::Map::find</a></div><div class="ttdeci">iterator find(const K &key) const</div><div class="ttdef"><b>Definition:</b> map.h:1383</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a41181a3757227725abc614e976b264ad"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a41181a3757227725abc614e976b264ad">tvm::TargetNode::ToDebugString</a></div><div class="ttdeci">String ToDebugString() const</div><div class="ttdoc">Returns a human readable representation of Target which includes all fields, especially the host...</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a65394b35be247cafb4376da9d6c81440"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a65394b35be247cafb4376da9d6c81440">tvm::TargetNode::_type_has_method_sequal_reduce</a></div><div class="ttdeci">static constexpr const bool _type_has_method_sequal_reduce</div><div class="ttdef"><b>Definition:</b> target.h:166</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>
@@ -108,9 +108,9 @@ $(function() {
<div class="ttc" id="classtvm_1_1TargetNode_html_ad4a9f21d97d244c2055e9ba2eca71ee5"><div class="ttname"><a href="classtvm_1_1TargetNode.html#ad4a9f21d97d244c2055e9ba2eca71ee5">tvm::TargetNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> target.h:81</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a13d1def3992d37107a7fd7c75e4370d3"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a13d1def3992d37107a7fd7c75e4370d3">tvm::TargetNode::_type_has_method_shash_reduce</a></div><div class="ttdeci">static constexpr const bool _type_has_method_shash_reduce</div><div class="ttdef"><b>Definition:</b> target.h:167</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_abdeae1bf6e037771b1b931f26dba15c6"><div class="ttname"><a href="classtvm_1_1TargetNode.html#abdeae1bf6e037771b1b931f26dba15c6">tvm::TargetNode::host</a></div><div class="ttdeci">Optional< ObjectRef > host</div><div class="ttdoc">Target host information, must be Target type. </div><div class="ttdef"><b>Definition:</b> target.h:51</div></div>
-<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1378</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Map_html_a60c1dac32729c4bf8351972da11793e4"><div class="ttname"><a href="classtvm_1_1runtime_1_1Map.html#a60c1dac32729c4bf8351972da11793e4">tvm::runtime::Map::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdef"><b>Definition:</b> map.h:1381</div></div>
<div class="ttc" id="classtvm_1_1TargetNode_html_a496626468eac236e9e046cb77a5f697e"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a496626468eac236e9e046cb77a5f697e">tvm::TargetNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> target.h:165</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:1268</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_1TargetNode_html_a998369eed05aa80140564c2f29742d46"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a998369eed05aa80140564c2f29742d46">tvm::TargetNode::features</a></div><div class="ttdeci">Map< String, ObjectRef > features</div><div class="ttdoc">Target features. </div><div class="ttdef"><b>Definition:</b> target.h:59</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_1TargetNode_html_a008fae4839d63a3a7a9bc7e0f0e40480"><div class="ttname"><a href="classtvm_1_1TargetNode.html#a008fae4839d63a3a7a9bc7e0f0e40480">tvm::TargetNode::GetAttr</a></div><div class="ttdeci">Optional< TObjectRef > GetAttr(const std::string &attr_key, Optional< TObjectRef > default_value=Optional< TObjectRef >(nullptr)) const</div><div class="ttdoc">Get an entry from attrs of the target. </div><div class="ttdef"><b>Definition:</ [...]
diff --git a/docs/reference/api/doxygen/target__kind_8h_source.html b/docs/reference/api/doxygen/target__kind_8h_source.html
index 21ee93b1a..68875d2f4 100644
--- a/docs/reference/api/doxygen/target__kind_8h_source.html
+++ b/docs/reference/api/doxygen/target__kind_8h_source.html
@@ -104,7 +104,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1TargetKindNode_html_a18459286d8d501892992a4209ad08652"><div class="ttname"><a href="classtvm_1_1TargetKindNode.html#a18459286d8d501892992a4209ad08652">tvm::TargetKindNode::device_type</a></div><div class="ttdeci">int device_type</div><div class="ttdoc">Device type of target kind. </div><div class="ttdef"><b>Definition:</b> target_kind.h:95</div></div>
<div class="ttc" id="classtvm_1_1TargetKindRegEntry_html_a4fa4f8e5fa280ddf3dc71310afd467a5"><div class="ttname"><a href="classtvm_1_1TargetKindRegEntry.html#a4fa4f8e5fa280ddf3dc71310afd467a5">tvm::TargetKindRegEntry::set_attr</a></div><div class="ttdeci">TargetKindRegEntry & set_attr(const String &attr_name, const ValueType &value, int plevel=10)</div><div class="ttdoc">Register additional attributes to target_kind. </div><div class="ttdef"><b>Definition:</b> target_kind.h:35 [...]
<div class="ttc" id="structtvm_1_1detail_1_1is__specialized_html_a3ea7783c457d7ddc82100674292724f4"><div class="ttname"><a href="structtvm_1_1detail_1_1is__specialized.html#a3ea7783c457d7ddc82100674292724f4">tvm::detail::is_specialized::type</a></div><div class="ttdeci">std::false_type type</div><div class="ttdef"><b>Definition:</b> target_kind.h:290</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:1268</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_1runtime_1_1Object_html_a817ba6c23b7ee1821c48a75edf255a30"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a817ba6c23b7ee1821c48a75edf255a30">tvm::runtime::Object::TypeIndex2Key</a></div><div class="ttdeci">static std::string TypeIndex2Key(uint32_t tindex)</div><div class="ttdoc">Get the type key of the corresponding index from runtime. </div></div>
<div class="ttc" id="classtvm_1_1TargetKindNode_html_a47f02c66d0f972befdfb29ec592ecba0"><div class="ttname"><a href="classtvm_1_1TargetKindNode.html#a47f02c66d0f972befdfb29ec592ecba0">tvm::TargetKindNode::preprocessor</a></div><div class="ttdeci">PackedFunc preprocessor</div><div class="ttdoc">Function used to preprocess on target creation. </div><div class="ttdef"><b>Definition:</b> target_kind.h:99</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/te_2schedule_8h_source.html b/docs/reference/api/doxygen/te_2schedule_8h_source.html
index f49e1e794..dcdd830d9 100644
--- a/docs/reference/api/doxygen/te_2schedule_8h_source.html
+++ b/docs/reference/api/doxygen/te_2schedule_8h_source.html
@@ -173,7 +173,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1te_1_1SingletonNode_html_a224061c98cfd22f25435e5ac9b0f8228"><div class="ttname"><a href="classtvm_1_1te_1_1SingletonNode.html#a224061c98cfd22f25435e5ac9b0f8228">tvm::te::SingletonNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> schedule.h:821</div></div>
<div class="ttc" id="classtvm_1_1te_1_1Stage_html_aa9ace0034447b461610ebc1c2de69a26"><div class="ttname"><a href="classtvm_1_1te_1_1Stage.html#aa9ace0034447b461610ebc1c2de69a26">tvm::te::Stage::bind</a></div><div class="ttdeci">Stage & bind(IterVar ivar, IterVar thread_ivar)</div><div class="ttdoc">Bind the IterVar to thread index. </div></div>
<div class="ttc" id="classtvm_1_1te_1_1Stage_html_aa2da6dafa58e8e7a1e251867791839d4"><div class="ttname"><a href="classtvm_1_1te_1_1Stage.html#aa2da6dafa58e8e7a1e251867791839d4">tvm::te::Stage::rolling_buffer</a></div><div class="ttdeci">Stage & rolling_buffer()</div><div class="ttdoc">Compute current stage with rolling buffering. </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:1268</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_1te_1_1Stage_html_a7045099f180e5cdcf9b1959b280a2d35"><div class="ttname"><a href="classtvm_1_1te_1_1Stage.html#a7045099f180e5cdcf9b1959b280a2d35">tvm::te::Stage::pragma</a></div><div class="ttdeci">Stage & pragma(IterVar var, const std::string &pragma_type, const PrimExpr &pragma_value=PrimExpr())</div><div class="ttdoc">Annotate the iteration with pragma. </div></div>
<div class="ttc" id="classtvm_1_1te_1_1TransformNode_html_a034d22228133e50074502bfe1f495935"><div class="ttname"><a href="classtvm_1_1te_1_1TransformNode.html#a034d22228133e50074502bfe1f495935">tvm::te::TransformNode::transformed_variables</a></div><div class="ttdeci">Array< IterVar > transformed_variables</div><div class="ttdoc">The variables generated by the transformation. </div><div class="ttdef"><b>Definition:</b> schedule.h:858</div></div>
<div class="ttc" id="classtvm_1_1te_1_1StageNode_html_a1d1f5c5e99f0c0c5d09a497b5c05443f"><div class="ttname"><a href="classtvm_1_1te_1_1StageNode.html#a1d1f5c5e99f0c0c5d09a497b5c05443f">tvm::te::StageNode::iter_var_attrs</a></div><div class="ttdeci">Map< IterVar, IterVarAttr > iter_var_attrs</div><div class="ttdoc">additional attributes about iter var. </div><div class="ttdef"><b>Definition:</b> schedule.h:542</div></div>
diff --git a/docs/reference/api/doxygen/tir_2analysis_8h_source.html b/docs/reference/api/doxygen/tir_2analysis_8h_source.html
index 7d4480cce..da26e2c03 100644
--- a/docs/reference/api/doxygen/tir_2analysis_8h_source.html
+++ b/docs/reference/api/doxygen/tir_2analysis_8h_source.html
@@ -99,7 +99,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="namespacetvm_1_1tir_html_ad41992c8a069ebdfde7ff87d67dd66bd"><div class="ttname"><a href="namespacetvm_1_1tir.html#ad41992c8a069ebdfde7ff87d67dd66bd">tvm::tir::UsesVar</a></div><div class="ttdeci">bool UsesVar(const Stmt &stmt, std::function< bool(const VarNode *)> vset_contains)</div><div class="ttdoc">Whether the given Stmt uses any var in the given variable set. </div></div>
<div class="ttc" id="namespacetvm_1_1relay_1_1transform_html_a744a05f8bba3c2ac238ba4569d926184"><div class="ttname"><a href="namespacetvm_1_1relay_1_1transform.html#a744a05f8bba3c2ac238ba4569d926184">tvm::relay::transform::PassContext</a></div><div class="ttdeci">tvm::transform::PassContext PassContext</div><div class="ttdef"><b>Definition:</b> transform.h:47</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:1268</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="namespacetvm_1_1tir_html_aeb03afda344eb4d3a5d2d3fd4e1d266d"><div class="ttname"><a href="namespacetvm_1_1tir.html#aeb03afda344eb4d3a5d2d3fd4e1d266d">tvm::tir::SideEffect</a></div><div class="ttdeci">CallEffectKind SideEffect(const PrimExpr &expr)</div><div class="ttdoc">Analyze the side effect. </div></div>
<div class="ttc" id="classtvm_1_1BaseFunc_html"><div class="ttname"><a href="classtvm_1_1BaseFunc.html">tvm::BaseFunc</a></div><div class="ttdoc">Managed reference to BaseFuncNode. </div><div class="ttdef"><b>Definition:</b> function.h:143</div></div>
<div class="ttc" id="structtvm_1_1tir_1_1ExprDeepEqual_html_a8f5ab569f52dea6a12420b21ddba6486"><div class="ttname"><a href="structtvm_1_1tir_1_1ExprDeepEqual.html#a8f5ab569f52dea6a12420b21ddba6486">tvm::tir::ExprDeepEqual::operator()</a></div><div class="ttdeci">bool operator()(const PrimExpr &lhs, const PrimExpr &rhs) const</div></div>
diff --git a/docs/reference/api/doxygen/tir_2expr_8h_source.html b/docs/reference/api/doxygen/tir_2expr_8h_source.html
index 43285984d..fec6aa9be 100644
--- a/docs/reference/api/doxygen/tir_2expr_8h_source.html
+++ b/docs/reference/api/doxygen/tir_2expr_8h_source.html
@@ -242,7 +242,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1PrimExprNode_html_a95af9234514ec5f11355db41524be7f9"><div class="ttname"><a href="classtvm_1_1PrimExprNode.html#a95af9234514ec5f11355db41524be7f9">tvm::PrimExprNode::dtype</a></div><div class="ttdeci">DataType dtype</div><div class="ttdoc">The runtime data type of the primitive expression. </div><div class="ttdef"><b>Definition:</b> expr.h:101</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1ReduceNode_html_aa6ab4c1ca407e1d14cdd2546b08bf0ad"><div class="ttname"><a href="classtvm_1_1tir_1_1ReduceNode.html#aa6ab4c1ca407e1d14cdd2546b08bf0ad">tvm::tir::ReduceNode::SHashReduce</a></div><div class="ttdeci">void SHashReduce(SHashReducer hash_reduce) const</div><div class="ttdef"><b>Definition:</b> expr.h:1103</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1MinNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1MinNode.html">tvm::tir::MinNode</a></div><div class="ttdoc">min(a, b) </div><div class="ttdef"><b>Definition:</b> expr.h:273</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:1268</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="namespacetvm_html_a0da40d3e210aa3b38a17982a7b7866b8"><div class="ttname"><a href="namespacetvm.html#a0da40d3e210aa3b38a17982a7b7866b8">tvm::ret</a></div><div class="ttdeci">PrimExpr ret(PrimExpr value, Span span=Span())</div><div class="ttdoc">Return the value. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1FloorModNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1FloorModNode.html">tvm::tir::FloorModNode</a></div><div class="ttdoc">The remainder of the floordiv. </div><div class="ttdef"><b>Definition:</b> expr.h:257</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1And_html"><div class="ttname"><a href="classtvm_1_1tir_1_1And.html">tvm::tir::And</a></div><div class="ttdoc">Managed reference to AndNode. </div><div class="ttdef"><b>Definition:</b> expr.h:465</div></div>
diff --git a/docs/reference/api/doxygen/tir_2function_8h_source.html b/docs/reference/api/doxygen/tir_2function_8h_source.html
index c8f286a61..ef12fbf85 100644
--- a/docs/reference/api/doxygen/tir_2function_8h_source.html
+++ b/docs/reference/api/doxygen/tir_2function_8h_source.html
@@ -111,7 +111,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1tir_1_1PrimFuncNode_html_aeb1f162516e09122852496f2a945d100"><div class="ttname"><a href="classtvm_1_1tir_1_1PrimFuncNode.html#aeb1f162516e09122852496f2a945d100">tvm::tir::PrimFuncNode::buffer_map</a></div><div class="ttdeci">Map< tir::Var, Buffer > buffer_map</div><div class="ttdoc">Maps some parameters to specific Buffer data structures. </div><div class="ttdef"><b>Definition:</b> function.h:92</div></div>
<div class="ttc" id="buffer_8h_html"><div class="ttname"><a href="buffer_8h.html">buffer.h</a></div><div class="ttdoc">Symbolic n-dimensional array, to represent a memory buffer. </div></div>
<div class="ttc" id="classtvm_1_1BaseFuncNode_html"><div class="ttname"><a href="classtvm_1_1BaseFuncNode.html">tvm::BaseFuncNode</a></div><div class="ttdoc">Base node of all functions. </div><div class="ttdef"><b>Definition:</b> function.h:77</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:1268</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_1BaseFunc_html"><div class="ttname"><a href="classtvm_1_1BaseFunc.html">tvm::BaseFunc</a></div><div class="ttdoc">Managed reference to BaseFuncNode. </div><div class="ttdef"><b>Definition:</b> function.h:143</div></div>
<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_1Type_html"><div class="ttname"><a href="classtvm_1_1Type.html">tvm::Type</a></div><div class="ttdoc">Managed reference to TypeNode. </div><div class="ttdef"><b>Definition:</b> type.h:93</div></div>
diff --git a/docs/reference/api/doxygen/tir_2usmp_2transform_8h_source.html b/docs/reference/api/doxygen/tir_2usmp_2transform_8h_source.html
index 39d322020..2499e1dc1 100644
--- a/docs/reference/api/doxygen/tir_2usmp_2transform_8h_source.html
+++ b/docs/reference/api/doxygen/tir_2usmp_2transform_8h_source.html
@@ -74,7 +74,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1transform_1_1Pass_html"><div class="ttname"><a href="classtvm_1_1transform_1_1Pass.html">tvm::transform::Pass</a></div><div class="ttdef"><b>Definition:</b> transform.h:363</div></div>
<div class="ttc" id="tir_2usmp_2utils_8h_html"><div class="ttname"><a href="tir_2usmp_2utils_8h.html">utils.h</a></div><div class="ttdoc">Utilities for Unified Static Memory Planner. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1usmp_1_1transform_html_a901e9d4d9288aacc08b1bc7cde535f56"><div class="ttname"><a href="namespacetvm_1_1tir_1_1usmp_1_1transform.html#a901e9d4d9288aacc08b1bc7cde535f56">tvm::tir::usmp::transform::Pass</a></div><div class="ttdeci">tvm::transform::Pass Pass</div><div class="ttdef"><b>Definition:</b> transform.h:35</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:1268</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>
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diff --git a/docs/reference/api/doxygen/tir_2usmp_2utils_8h_source.html b/docs/reference/api/doxygen/tir_2usmp_2utils_8h_source.html
index eda109b04..f712aea84 100644
--- a/docs/reference/api/doxygen/tir_2usmp_2utils_8h_source.html
+++ b/docs/reference/api/doxygen/tir_2usmp_2utils_8h_source.html
@@ -120,7 +120,7 @@ $(function() {
<div class="ttc" id="namespacetvm_1_1tir_1_1usmp_html_ad2424e3662cdcad9a18b496ba42ca10d"><div class="ttname"><a href="namespacetvm_1_1tir_1_1usmp.html#ad2424e3662cdcad9a18b496ba42ca10d">tvm::tir::usmp::CalculateExtentsSize</a></div><div class="ttdeci">Integer CalculateExtentsSize(const AllocateNode *op)</div><div class="ttdoc">Calculate the size of the extents in bytes. </div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:352</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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="namespacetvm_html_adb1d2ec4c6dde078fb6849479be21759"><div class="ttname"><a href="namespacetvm.html#adb1d2ec4c6dde078fb6849479be21759">tvm::kUSMPEnableOption</a></div><div class="ttdeci">constexpr const char * kUSMPEnableOption</div><div class="ttdoc">PassContext option to enable the USMP. </div><div class="ttdef"><b>Definition:</b> utils.h:39</div></div>
<div class="ttc" id="structtvm_1_1tir_1_1usmp_1_1BufferInfoNode_html_a49f502f888fb6a2816e455f548c5f050"><div class="ttname"><a href="structtvm_1_1tir_1_1usmp_1_1BufferInfoNode.html#a49f502f888fb6a2816e455f548c5f050">tvm::tir::usmp::BufferInfoNode::kind</a></div><div class="ttdeci">BufferInfoKind kind</div><div class="ttdoc">Whether BufferInfo object retains info about IO tensors or intermediaries. </div><div class="ttdef"><b>Definition:</b> utils.h:83</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>
diff --git a/docs/reference/api/doxygen/trace_8h_source.html b/docs/reference/api/doxygen/trace_8h_source.html
index 66d5057d2..4a0c4df90 100644
--- a/docs/reference/api/doxygen/trace_8h_source.html
+++ b/docs/reference/api/doxygen/trace_8h_source.html
@@ -87,7 +87,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1TraceNode_html_aa2d4cc1a9e3fab96ba4bb88ffb0144bc"><div class="ttname"><a href="classtvm_1_1tir_1_1TraceNode.html#aa2d4cc1a9e3fab96ba4bb88ffb0144bc">tvm::tir::TraceNode::AsPython</a></div><div class="ttdeci">Array< String > AsPython(bool remove_postproc) const</div><div class="ttdoc">Serialize the trace as a sequence of python statements. </div></div>
<div class="ttc" id="classtvm_1_1tir_1_1TraceNode_html_a043864167d253b3a850091ce81cd98a9"><div class="ttname"><a href="classtvm_1_1tir_1_1TraceNode.html#a043864167d253b3a850091ce81cd98a9">tvm::tir::TraceNode::WithDecision</a></div><div class="ttdeci">Trace WithDecision(Instruction inst, ObjectRef decision, bool remove_postproc) const</div><div class="ttdoc">Create a new trace with an instruction whose decision is changed, assuming this instruction exists in...</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:1268</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_1tir_1_1TraceNode_html"><div class="ttname"><a href="classtvm_1_1tir_1_1TraceNode.html">tvm::tir::TraceNode</a></div><div class="ttdoc">An execution trace of a scheduling program. </div><div class="ttdef"><b>Definition:</b> trace.h:58</div></div>
<div class="ttc" id="classtvm_1_1tir_1_1Trace_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Trace.html">tvm::tir::Trace</a></div><div class="ttdoc">Managed reference to TraceNode. </div><div class="ttdef"><b>Definition:</b> trace.h:141</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>
diff --git a/docs/reference/api/doxygen/traced__object_8h_source.html b/docs/reference/api/doxygen/traced__object_8h_source.html
index a5a134ca7..a800a219a 100644
--- a/docs/reference/api/doxygen/traced__object_8h_source.html
+++ b/docs/reference/api/doxygen/traced__object_8h_source.html
@@ -130,7 +130,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1TracedArrayIterator_html"><div class="ttname"><a href="classtvm_1_1TracedArrayIterator.html">tvm::TracedArrayIterator</a></div><div class="ttdoc">Iterator class for TracedArray<T> </div><div class="ttdef"><b>Definition:</b> traced_object.h:267</div></div>
<div class="ttc" id="classtvm_1_1TracedBasicValue_html_a02e789fc05a15bb1b734d5dbe7dcd578"><div class="ttname"><a href="classtvm_1_1TracedBasicValue.html#a02e789fc05a15bb1b734d5dbe7dcd578">tvm::TracedBasicValue::TracedBasicValue</a></div><div class="ttdeci">TracedBasicValue(const T &value, ObjectPath path)</div><div class="ttdef"><b>Definition:</b> traced_object.h:436</div></div>
<div class="ttc" id="classtvm_1_1ObjectPath_html"><div class="ttname"><a href="classtvm_1_1ObjectPath.html">tvm::ObjectPath</a></div><div class="ttdef"><b>Definition:</b> object_path.h:122</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:1268</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_1TracedMapIterator_html_a1bf325af09a30d4d3786530dbd10893f"><div class="ttname"><a href="classtvm_1_1TracedMapIterator.html#a1bf325af09a30d4d3786530dbd10893f">tvm::TracedMapIterator::TracedMapIterator</a></div><div class="ttdeci">TracedMapIterator(MapIter iter, ObjectPath map_path)</div><div class="ttdef"><b>Definition:</b> traced_object.h:179</div></div>
<div class="ttc" id="classtvm_1_1TracedArray_html_a958711e44075dffc086f2d0a1cb41d68"><div class="ttname"><a href="classtvm_1_1TracedArray.html#a958711e44075dffc086f2d0a1cb41d68">tvm::TracedArray::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdoc">Get an iterator to the first array element. </div><div class="ttdef"><b>Definition:</b> traced_object.h:357</div></div>
<div class="ttc" id="classtvm_1_1TracedBasicValue_html_af8ff1ec9e99e1a850865047f6c90ea86"><div class="ttname"><a href="classtvm_1_1TracedBasicValue.html#af8ff1ec9e99e1a850865047f6c90ea86">tvm::TracedBasicValue::GetPath</a></div><div class="ttdeci">const ObjectPath & GetPath() const</div><div class="ttdoc">Get the path of the wrapped value. </div><div class="ttdef"><b>Definition:</b> traced_object.h:447</div></div>
diff --git a/docs/reference/api/doxygen/transform__step_8h_source.html b/docs/reference/api/doxygen/transform__step_8h_source.html
index 0a0d791f3..9470a577d 100644
--- a/docs/reference/api/doxygen/transform__step_8h_source.html
+++ b/docs/reference/api/doxygen/transform__step_8h_source.html
@@ -154,7 +154,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode_html_a5691967a42b989a54cf8c40c1627988e"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html#a5691967a42b989a54cf8c40c1627988e">tvm::auto_scheduler::ComputeAtStepNode::target_iter_id</a></div><div class="ttdeci">int target_iter_id</div><div class="ttdoc">The index of iterator in target stage that this step will compute at to. </div><div class="ttdef"><b>Definition:</b> transform_step.h:809 [...]
<div class="ttc" id="namespacetvm_1_1auto__scheduler_html_aab09151bf58d2cb261e1254f22261741"><div class="ttname"><a href="namespacetvm_1_1auto__scheduler.html#aab09151bf58d2cb261e1254f22261741">tvm::auto_scheduler::StepReadFromRecord</a></div><div class="ttdeci">Step StepReadFromRecord(dmlc::JSONReader *reader)</div><div class="ttdoc">Read a step record from JSONReader and create the corresponding step. </div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1AnnotationStepNode_html_ae78e0233c8047687743e7557b3c00457"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html#ae78e0233c8047687743e7557b3c00457">tvm::auto_scheduler::AnnotationStepNode::iter_id</a></div><div class="ttdeci">int iter_id</div><div class="ttdoc">The index of the iterator to add annotation. </div><div class="ttdef"><b>Definition:</b> transform_step.h:255</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:1268</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_1auto__scheduler_1_1Iterator_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1Iterator.html">tvm::auto_scheduler::Iterator</a></div><div class="ttdoc">Managed reference to IteratorNode. </div><div class="ttdef"><b>Definition:</b> transform_step.h:144</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1SplitStepNode_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html">tvm::auto_scheduler::SplitStepNode</a></div><div class="ttdoc">Split step that corresponds to te::Stage::split with additional support of multiple-level of factors...</div><div class="ttdef"><b>Definition:</b> transform_step.h:501</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>
diff --git a/docs/reference/api/doxygen/tune__context_8h_source.html b/docs/reference/api/doxygen/tune__context_8h_source.html
index 3cb5c558d..313c6fece 100644
--- a/docs/reference/api/doxygen/tune__context_8h_source.html
+++ b/docs/reference/api/doxygen/tune__context_8h_source.html
@@ -112,7 +112,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuneContextNode_html_a2e861f72c090f9b5223b71e40d0a511b"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a2e861f72c090f9b5223b71e40d0a511b">tvm::meta_schedule::TuneContextNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> tune_context.h:121</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuneContextNode_html_a4b1da69a97fb1c10ffc5bd4f8872bb23"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a4b1da69a97fb1c10ffc5bd4f8872bb23">tvm::meta_schedule::TuneContextNode::builder_results</a></div><div class="ttdeci">Optional< Array< BuilderResult > > builder_results</div><div class="ttdoc">The building results. </div><div class="ttdef"><b>Definition:</b> tune_context.h:78</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </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:1268</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="map_8h_html"><div class="ttname"><a href="map_8h.html">map.h</a></div><div class="ttdoc">Runtime Map container types. </div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuneContextNode_html_a8b7bfb296b89ad8645fcf89bf645092a"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#a8b7bfb296b89ad8645fcf89bf645092a">tvm::meta_schedule::TuneContextNode::runner_futures</a></div><div class="ttdeci">Optional< Array< RunnerFuture > > runner_futures</div><div class="ttdoc">Packed functions to fetch the runner results asynchronously. </div><div class="ttdef"><b>Definition:</b> [...]
<div class="ttc" id="classtvm_1_1runtime_1_1PackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1PackedFunc.html">tvm::runtime::PackedFunc</a></div><div class="ttdoc">Packed function is a type-erased function. The arguments are passed by packed format. </div><div class="ttdef"><b>Definition:</b> packed_func.h:138</div></div>
diff --git a/docs/reference/api/doxygen/type__functor_8h_source.html b/docs/reference/api/doxygen/type__functor_8h_source.html
index 2bf689afa..642650e15 100644
--- a/docs/reference/api/doxygen/type__functor_8h_source.html
+++ b/docs/reference/api/doxygen/type__functor_8h_source.html
@@ -105,7 +105,7 @@ $(function() {
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html_a4d951e51832081b85875669eac90e940"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html#a4d951e51832081b85875669eac90e940">tvm::runtime::Object::GetTypeKey</a></div><div class="ttdeci">std::string GetTypeKey() const</div><div class="ttdef"><b>Definition:</b> object.h:180</div></div>
<div class="ttc" id="type__functor_8h_html_ad222ca7b5f1a4a8c626d1f1e4b53cdb0"><div class="ttname"><a href="type__functor_8h.html#ad222ca7b5f1a4a8c626d1f1e4b53cdb0">TYPE_FUNCTOR_DEFAULT</a></div><div class="ttdeci">#define TYPE_FUNCTOR_DEFAULT</div><div class="ttdef"><b>Definition:</b> type_functor.h:41</div></div>
<div class="ttc" id="classtvm_1_1TypeVarNode_html"><div class="ttname"><a href="classtvm_1_1TypeVarNode.html">tvm::TypeVarNode</a></div><div class="ttdoc">Type parameter in functions. </div><div class="ttdef"><b>Definition:</b> type.h:228</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:1268</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_1Type_html"><div class="ttname"><a href="classtvm_1_1Type.html">tvm::Type</a></div><div class="ttdoc">Managed reference to TypeNode. </div><div class="ttdef"><b>Definition:</b> type.h:93</div></div>
<div class="ttc" id="type__functor_8h_html_afaa114a04d18cd3f8f11995628692d74"><div class="ttname"><a href="type__functor_8h.html#afaa114a04d18cd3f8f11995628692d74">TVM_TYPE_FUNCTOR_DISPATCH</a></div><div class="ttdeci">#define TVM_TYPE_FUNCTOR_DISPATCH(OP)</div><div class="ttdef"><b>Definition:</b> type_functor.h:44</div></div>
<div class="ttc" id="classtvm_1_1PrimTypeNode_html"><div class="ttname"><a href="classtvm_1_1PrimTypeNode.html">tvm::PrimTypeNode</a></div><div class="ttdoc">Primitive data types used in the low-level IR. </div><div class="ttdef"><b>Definition:</b> type.h:106</div></div>
diff --git a/docs/reference/api/python/auto_scheduler.html b/docs/reference/api/python/auto_scheduler.html
index a0bb91ff1..58c8c68fe 100644
--- a/docs/reference/api/python/auto_scheduler.html
+++ b/docs/reference/api/python/auto_scheduler.html
@@ -1602,7 +1602,7 @@ history states as starting point to perform Evolutionary Search).</p></li>
<dl class="py class">
<dt class="sig sig-object py" id="tvm.auto_scheduler.SketchPolicy">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
+<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
<dd><p>The search policy that searches in a hierarchical search space defined by sketches.
The policy randomly samples programs from the space defined by sketches and use evolutionary
search to fine-tune them.</p>
@@ -1886,7 +1886,7 @@ Candidates:
<dl class="py function">
<dt class="sig sig-object py" id="tvm.auto_scheduler.auto_schedule">
-<span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">auto_schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em clas [...]
+<span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">auto_schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em clas [...]
<dd><p>THIS API IS DEPRECATED.</p>
<p>Run auto scheduling search for a task.</p>
<dl class="field-list simple">
diff --git a/docs/reference/api/typedoc/classes/bytestreamreader.html b/docs/reference/api/typedoc/classes/bytestreamreader.html
index 16a236d7a..22d0ec416 100644
--- a/docs/reference/api/typedoc/classes/bytestreamreader.html
+++ b/docs/reference/api/typedoc/classes/bytestreamreader.html
@@ -119,7 +119,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -141,7 +141,7 @@
<div class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Uint8Array</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
</ul>
</aside>
</section>
@@ -151,7 +151,7 @@
<div class="tsd-signature tsd-kind-icon">offset<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 0</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
</ul>
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@@ -168,7 +168,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Uint8Array</span></h4>
@@ -185,7 +185,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -202,7 +202,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
diff --git a/docs/reference/api/typedoc/classes/cachedcallstack.html b/docs/reference/api/typedoc/classes/cachedcallstack.html
index fdcf875a1..e6345209e 100644
--- a/docs/reference/api/typedoc/classes/cachedcallstack.html
+++ b/docs/reference/api/typedoc/classes/cachedcallstack.html
@@ -144,7 +144,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L223">memory.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L223">memory.ts:223</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -172,7 +172,7 @@
<div class="tsd-signature tsd-kind-icon">temp<wbr>Args<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><a href="../interfaces/disposable.html" class="tsd-signature-type">Disposable</a><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = []</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L208">memory.ts:208</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L208">memory.ts:208</a></li>
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<div class="tsd-comment tsd-typography">
@@ -194,7 +194,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L312">memory.ts:312</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L312">memory.ts:312</a></li>
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<div class="tsd-comment tsd-typography">
@@ -226,7 +226,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L284">memory.ts:284</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L284">memory.ts:284</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -262,7 +262,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L388">memory.ts:388</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L388">memory.ts:388</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -300,7 +300,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L376">memory.ts:376</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L376">memory.ts:376</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -340,7 +340,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L267">memory.ts:267</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L267">memory.ts:267</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -373,7 +373,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L243">memory.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L243">memory.ts:243</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -390,7 +390,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L321">memory.ts:321</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L321">memory.ts:321</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -422,7 +422,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L252">memory.ts:252</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L252">memory.ts:252</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -444,7 +444,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L359">memory.ts:359</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L359">memory.ts:359</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -470,7 +470,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L342">memory.ts:342</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L342">memory.ts:342</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -496,7 +496,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L350">memory.ts:350</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L350">memory.ts:350</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -522,7 +522,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L326">memory.ts:326</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L326">memory.ts:326</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -548,7 +548,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L363">memory.ts:363</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L363">memory.ts:363</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -574,7 +574,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L346">memory.ts:346</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L346">memory.ts:346</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -600,7 +600,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L334">memory.ts:334</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L334">memory.ts:334</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
diff --git a/docs/reference/api/typedoc/classes/dldatatype.html b/docs/reference/api/typedoc/classes/dldatatype.html
index 216a3a5f9..6045007a6 100644
--- a/docs/reference/api/typedoc/classes/dldatatype.html
+++ b/docs/reference/api/typedoc/classes/dldatatype.html
@@ -119,7 +119,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L262">runtime.ts:262</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
<div class="tsd-signature tsd-kind-icon">bits<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L260">runtime.ts:260</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L260">runtime.ts:260</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">code<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L258">runtime.ts:258</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L258">runtime.ts:258</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -177,7 +177,7 @@
<div class="tsd-signature tsd-kind-icon">lanes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L262">runtime.ts:262</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -199,7 +199,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L279">runtime.ts:279</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L279">runtime.ts:279</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -216,7 +216,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L270">runtime.ts:270</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L270">runtime.ts:270</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/dldevice.html b/docs/reference/api/typedoc/classes/dldevice.html
index 2987e7ad6..0ec7eee5c 100644
--- a/docs/reference/api/typedoc/classes/dldevice.html
+++ b/docs/reference/api/typedoc/classes/dldevice.html
@@ -118,7 +118,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L202">runtime.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L202">runtime.ts:202</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -146,7 +146,7 @@
<div class="tsd-signature tsd-kind-icon">device<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L200">runtime.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L200">runtime.ts:200</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -161,7 +161,7 @@
<div class="tsd-signature tsd-kind-icon">device<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L198">runtime.ts:198</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L198">runtime.ts:198</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -183,7 +183,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L223">runtime.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L223">runtime.ts:223</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -205,7 +205,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L230">runtime.ts:230</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L230">runtime.ts:230</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/environment.html b/docs/reference/api/typedoc/classes/environment.html
index 6f3b3841d..a9761fb9f 100644
--- a/docs/reference/api/typedoc/classes/environment.html
+++ b/docs/reference/api/typedoc/classes/environment.html
@@ -125,7 +125,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L86">environment.ts:86</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L86">environment.ts:86</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -169,7 +169,7 @@
<aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/libraryprovider.html">LibraryProvider</a>.<a href="../interfaces/libraryprovider.html#imports">imports</a></p>
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L70">environment.ts:70</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L70">environment.ts:70</a></li>
</ul>
</aside>
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@@ -179,7 +179,7 @@
<div class="tsd-signature tsd-kind-icon">logger<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L69">environment.ts:69</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L69">environment.ts:69</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -210,7 +210,7 @@
<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">ctypes.FTVMWasmPackedCFunc</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = [undefined,]</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L78">environment.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L78">environment.ts:78</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -228,7 +228,7 @@
<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<wbr>Free<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = []</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L84">environment.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L84">environment.ts:84</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -250,7 +250,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L105">environment.ts:105</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L105">environment.ts:105</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/ffilibrary.html b/docs/reference/api/typedoc/classes/ffilibrary.html
index 49ff79d78..1ea3c1ed3 100644
--- a/docs/reference/api/typedoc/classes/ffilibrary.html
+++ b/docs/reference/api/typedoc/classes/ffilibrary.html
@@ -131,7 +131,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L49">runtime.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L49">runtime.ts:49</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -156,7 +156,7 @@
<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L46">runtime.ts:46</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L46">runtime.ts:46</a></li>
</ul>
</aside>
</section>
@@ -166,7 +166,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L45">runtime.ts:45</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L45">runtime.ts:45</a></li>
</ul>
</aside>
</section>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L44">runtime.ts:44</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L44">runtime.ts:44</a></li>
</ul>
</aside>
</section>
@@ -186,7 +186,7 @@
<div class="tsd-signature tsd-kind-icon">webGPUContext<span class="tsd-signature-symbol">:</span> <a href="webgpucontext.html" class="tsd-signature-type">WebGPUContext</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L47">runtime.ts:47</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L47">runtime.ts:47</a></li>
</ul>
</aside>
</section>
@@ -203,7 +203,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L76">runtime.ts:76</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L76">runtime.ts:76</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -226,7 +226,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L66">runtime.ts:66</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L66">runtime.ts:66</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -243,7 +243,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L84">runtime.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L84">runtime.ts:84</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <a href="cachedcallstack.html" class="tsd-signature-type">CachedCallStack</a></h4>
@@ -260,7 +260,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L95">runtime.ts:95</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L95">runtime.ts:95</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -283,7 +283,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L72">runtime.ts:72</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L72">runtime.ts:72</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
diff --git a/docs/reference/api/typedoc/classes/graphexecutor.html b/docs/reference/api/typedoc/classes/graphexecutor.html
index c1e15c45b..1a070f410 100644
--- a/docs/reference/api/typedoc/classes/graphexecutor.html
+++ b/docs/reference/api/typedoc/classes/graphexecutor.html
@@ -130,7 +130,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L583">runtime.ts:583</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L583">runtime.ts:583</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">module<span class="tsd-signature-symbol">:</span> <a href="module.html" class="tsd-signature-type">Module</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L579">runtime.ts:579</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L579">runtime.ts:579</a></li>
</ul>
</aside>
</section>
@@ -179,7 +179,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L654">runtime.ts:654</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L654">runtime.ts:654</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -224,7 +224,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L597">runtime.ts:597</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L597">runtime.ts:597</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -241,7 +241,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L631">runtime.ts:631</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L631">runtime.ts:631</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -279,7 +279,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L644">runtime.ts:644</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L644">runtime.ts:644</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -310,7 +310,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L621">runtime.ts:621</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L621">runtime.ts:621</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -332,7 +332,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L609">runtime.ts:609</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L609">runtime.ts:609</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/instance.html b/docs/reference/api/typedoc/classes/instance.html
index 6ff931657..fd3c54012 100644
--- a/docs/reference/api/typedoc/classes/instance.html
+++ b/docs/reference/api/typedoc/classes/instance.html
@@ -139,7 +139,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L692">runtime.ts:692</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L692">runtime.ts:692</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -202,7 +202,7 @@
<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L684">runtime.ts:684</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L684">runtime.ts:684</a></li>
</ul>
</aside>
</section>
@@ -212,7 +212,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L683">runtime.ts:683</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L683">runtime.ts:683</a></li>
</ul>
</aside>
</section>
@@ -229,7 +229,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L932">runtime.ts:932</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L932">runtime.ts:932</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -260,7 +260,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L994">runtime.ts:994</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L994">runtime.ts:994</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -303,7 +303,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L924">runtime.ts:924</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L924">runtime.ts:924</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -341,7 +341,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L732">runtime.ts:732</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L732">runtime.ts:732</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -358,7 +358,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L952">runtime.ts:952</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L952">runtime.ts:952</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -402,7 +402,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L816">runtime.ts:816</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L816">runtime.ts:816</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -434,7 +434,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -465,7 +465,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L846">runtime.ts:846</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L846">runtime.ts:846</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -497,7 +497,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L750">runtime.ts:750</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L750">runtime.ts:750</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -520,7 +520,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -568,7 +568,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L789">runtime.ts:789</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L789">runtime.ts:789</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -608,7 +608,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L914">runtime.ts:914</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L914">runtime.ts:914</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -646,7 +646,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L1145">runtime.ts:1145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L1145">runtime.ts:1145</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -698,7 +698,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L740">runtime.ts:740</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L740">runtime.ts:740</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -722,7 +722,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L868">runtime.ts:868</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L868">runtime.ts:868</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -754,7 +754,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L857">runtime.ts:857</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L857">runtime.ts:857</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -786,7 +786,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L940">runtime.ts:940</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L940">runtime.ts:940</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/memory.html b/docs/reference/api/typedoc/classes/memory.html
index dad3abe80..26b865fe5 100644
--- a/docs/reference/api/typedoc/classes/memory.html
+++ b/docs/reference/api/typedoc/classes/memory.html
@@ -130,7 +130,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L40">memory.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L40">memory.ts:40</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -152,7 +152,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Memory</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L32">memory.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L32">memory.ts:32</a></li>
</ul>
</aside>
</section>
@@ -162,7 +162,7 @@
<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L33">memory.ts:33</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L33">memory.ts:33</a></li>
</ul>
</aside>
</section>
@@ -179,7 +179,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L154">memory.ts:154</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L154">memory.ts:154</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -210,7 +210,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L90">memory.ts:90</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L90">memory.ts:90</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -233,7 +233,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L97">memory.ts:97</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L97">memory.ts:97</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -256,7 +256,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L74">memory.ts:74</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L74">memory.ts:74</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -279,7 +279,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L81">memory.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L81">memory.ts:81</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -302,7 +302,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L104">memory.ts:104</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L104">memory.ts:104</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -325,7 +325,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L132">memory.ts:132</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L132">memory.ts:132</a></li>
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<div class="tsd-comment tsd-typography">
@@ -362,7 +362,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L145">memory.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L145">memory.ts:145</a></li>
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<div class="tsd-comment tsd-typography">
@@ -393,7 +393,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L60">memory.ts:60</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L60">memory.ts:60</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -416,7 +416,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L67">memory.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L67">memory.ts:67</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -439,7 +439,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L53">memory.ts:53</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L53">memory.ts:53</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -462,7 +462,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L114">memory.ts:114</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L114">memory.ts:114</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -485,7 +485,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L124">memory.ts:124</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L124">memory.ts:124</a></li>
</ul>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -502,7 +502,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/memory.ts#L175">memory.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/memory.ts#L175">memory.ts:175</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/module.html b/docs/reference/api/typedoc/classes/module.html
index 59a7368bc..a3c3f293e 100644
--- a/docs/reference/api/typedoc/classes/module.html
+++ b/docs/reference/api/typedoc/classes/module.html
@@ -124,7 +124,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L504">runtime.ts:504</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L504">runtime.ts:504</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -170,7 +170,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L502">runtime.ts:502</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L502">runtime.ts:502</a></li>
</ul>
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@@ -187,7 +187,7 @@
<li class="tsd-description">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L516">runtime.ts:516</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L516">runtime.ts:516</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -204,7 +204,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L530">runtime.ts:530</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L530">runtime.ts:530</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -236,7 +236,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L561">runtime.ts:561</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L561">runtime.ts:561</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/ndarray.html b/docs/reference/api/typedoc/classes/ndarray.html
index 164f06929..b34efccc9 100644
--- a/docs/reference/api/typedoc/classes/ndarray.html
+++ b/docs/reference/api/typedoc/classes/ndarray.html
@@ -130,7 +130,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L304">runtime.ts:304</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L304">runtime.ts:304</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -158,7 +158,7 @@
<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <a href="dldevice.html" class="tsd-signature-type">DLDevice</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L297">runtime.ts:297</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L297">runtime.ts:297</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -173,7 +173,7 @@
<div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L293">runtime.ts:293</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L293">runtime.ts:293</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -188,7 +188,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L289">runtime.ts:289</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L289">runtime.ts:289</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -203,7 +203,7 @@
<div class="tsd-signature tsd-kind-icon">ndim<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L291">runtime.ts:291</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L291">runtime.ts:291</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -218,7 +218,7 @@
<div class="tsd-signature tsd-kind-icon">shape<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L295">runtime.ts:295</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L295">runtime.ts:295</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -240,7 +240,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L370">runtime.ts:370</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L370">runtime.ts:370</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -273,7 +273,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L414">runtime.ts:414</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L414">runtime.ts:414</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -305,7 +305,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L355">runtime.ts:355</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L355">runtime.ts:355</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -322,7 +322,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L474">runtime.ts:474</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L474">runtime.ts:474</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -346,7 +346,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L443">runtime.ts:443</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L443">runtime.ts:443</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/packedfunccell.html b/docs/reference/api/typedoc/classes/packedfunccell.html
index 75f9fd01d..b608cb84c 100644
--- a/docs/reference/api/typedoc/classes/packedfunccell.html
+++ b/docs/reference/api/typedoc/classes/packedfunccell.html
@@ -122,7 +122,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L158">runtime.ts:158</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L158">runtime.ts:158</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L157">runtime.ts:157</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L157">runtime.ts:157</a></li>
</ul>
</aside>
</section>
@@ -164,7 +164,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L165">runtime.ts:165</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L165">runtime.ts:165</a></li>
</ul>
</aside>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
diff --git a/docs/reference/api/typedoc/classes/rpcserver.html b/docs/reference/api/typedoc/classes/rpcserver.html
index dd1b4cf3e..e3e1c4d24 100644
--- a/docs/reference/api/typedoc/classes/rpcserver.html
+++ b/docs/reference/api/typedoc/classes/rpcserver.html
@@ -115,7 +115,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">get<wbr>Imports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signat [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -201,7 +201,7 @@
<div class="tsd-signature tsd-kind-icon">key<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
</ul>
</aside>
</section>
@@ -211,7 +211,7 @@
<div class="tsd-signature tsd-kind-icon">logger<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
</ul>
</aside>
<div class="tsd-type-declaration">
@@ -242,7 +242,7 @@
<div class="tsd-signature tsd-kind-icon">socket<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">WebSocket</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
</ul>
</aside>
</section>
@@ -252,7 +252,7 @@
<div class="tsd-signature tsd-kind-icon">state<span class="tsd-signature-symbol">:</span> <a href="../enums/rpcserverstate.html" class="tsd-signature-type">RPCServerState</a><span class="tsd-signature-symbol"> = RPCServerState.InitHeader</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
</ul>
</aside>
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@@ -262,7 +262,7 @@
<div class="tsd-signature tsd-kind-icon">url<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
</ul>
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diff --git a/docs/reference/api/typedoc/classes/scalar.html b/docs/reference/api/typedoc/classes/scalar.html
index 66da79237..ed0527513 100644
--- a/docs/reference/api/typedoc/classes/scalar.html
+++ b/docs/reference/api/typedoc/classes/scalar.html
@@ -112,7 +112,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -137,7 +137,7 @@
<div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L145">runtime.ts:145</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -152,7 +152,7 @@
<div class="tsd-signature tsd-kind-icon">value<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L143">runtime.ts:143</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L143">runtime.ts:143</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/webgpucontext.html b/docs/reference/api/typedoc/classes/webgpucontext.html
index 6d03cef20..ec4f4eec2 100644
--- a/docs/reference/api/typedoc/classes/webgpucontext.html
+++ b/docs/reference/api/typedoc/classes/webgpucontext.html
@@ -120,7 +120,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -145,7 +145,7 @@
<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">GPUDevice</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
</ul>
</aside>
</section>
@@ -155,7 +155,7 @@
<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
</ul>
</aside>
</section>
@@ -172,7 +172,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -209,7 +209,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/enums/argtypecode.html b/docs/reference/api/typedoc/enums/argtypecode.html
index feb7b3d30..12504a456 100644
--- a/docs/reference/api/typedoc/enums/argtypecode.html
+++ b/docs/reference/api/typedoc/enums/argtypecode.html
@@ -106,7 +106,7 @@
<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 6</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
</ul>
</aside>
</section>
@@ -116,7 +116,7 @@
<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
</ul>
</aside>
</section>
@@ -126,7 +126,7 @@
<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
</ul>
</aside>
</section>
@@ -136,7 +136,7 @@
<div class="tsd-signature tsd-kind-icon">Null<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
</ul>
</aside>
</section>
@@ -146,7 +146,7 @@
<div class="tsd-signature tsd-kind-icon">TVMBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 12</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
</ul>
</aside>
</section>
@@ -156,7 +156,7 @@
<div class="tsd-signature tsd-kind-icon">TVMDLTensor<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 7</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
</ul>
</aside>
</section>
@@ -166,7 +166,7 @@
<div class="tsd-signature tsd-kind-icon">TVMData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
</ul>
</aside>
</section>
@@ -176,7 +176,7 @@
<div class="tsd-signature tsd-kind-icon">TVMModule<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 9</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
</ul>
</aside>
</section>
@@ -186,7 +186,7 @@
<div class="tsd-signature tsd-kind-icon">TVMNDArray<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 13</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
</ul>
</aside>
</section>
@@ -196,7 +196,7 @@
<div class="tsd-signature tsd-kind-icon">TVMObject<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
</ul>
</aside>
</section>
@@ -206,7 +206,7 @@
<div class="tsd-signature tsd-kind-icon">TVMObjectRValue<wbr>Ref<wbr>Arg<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 14</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
</ul>
</aside>
</section>
@@ -216,7 +216,7 @@
<div class="tsd-signature tsd-kind-icon">TVMOpaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
</ul>
</aside>
</section>
@@ -226,7 +226,7 @@
<div class="tsd-signature tsd-kind-icon">TVMPacked<wbr>Func<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 10</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
</ul>
</aside>
</section>
@@ -236,7 +236,7 @@
<div class="tsd-signature tsd-kind-icon">TVMStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 11</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
</ul>
</aside>
</section>
@@ -246,7 +246,7 @@
<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/aynccallbackcode.html b/docs/reference/api/typedoc/enums/aynccallbackcode.html
index 90177b0f2..6bf1daea3 100644
--- a/docs/reference/api/typedoc/enums/aynccallbackcode.html
+++ b/docs/reference/api/typedoc/enums/aynccallbackcode.html
@@ -93,7 +93,7 @@
<div class="tsd-signature tsd-kind-icon">k<wbr>Exception<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L676">runtime.ts:676</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L676">runtime.ts:676</a></li>
</ul>
</aside>
</section>
@@ -103,7 +103,7 @@
<div class="tsd-signature tsd-kind-icon">k<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L675">runtime.ts:675</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L675">runtime.ts:675</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/dldatatypecode.html b/docs/reference/api/typedoc/enums/dldatatypecode.html
index e2cb9c6ad..9aa7a3a29 100644
--- a/docs/reference/api/typedoc/enums/dldatatypecode.html
+++ b/docs/reference/api/typedoc/enums/dldatatypecode.html
@@ -95,7 +95,7 @@
<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L242">runtime.ts:242</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L242">runtime.ts:242</a></li>
</ul>
</aside>
</section>
@@ -105,7 +105,7 @@
<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L240">runtime.ts:240</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L240">runtime.ts:240</a></li>
</ul>
</aside>
</section>
@@ -115,7 +115,7 @@
<div class="tsd-signature tsd-kind-icon">Opaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L243">runtime.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L243">runtime.ts:243</a></li>
</ul>
</aside>
</section>
@@ -125,7 +125,7 @@
<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L241">runtime.ts:241</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L241">runtime.ts:241</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/rpcserverstate.html b/docs/reference/api/typedoc/enums/rpcserverstate.html
index 3b04afc71..14875ad50 100644
--- a/docs/reference/api/typedoc/enums/rpcserverstate.html
+++ b/docs/reference/api/typedoc/enums/rpcserverstate.html
@@ -90,7 +90,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
</ul>
</aside>
</section>
@@ -100,7 +100,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<wbr>Key<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
</ul>
</aside>
</section>
@@ -110,7 +110,7 @@
<div class="tsd-signature tsd-kind-icon">Init<wbr>Server<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
</ul>
</aside>
</section>
@@ -120,7 +120,7 @@
<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Body<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
</ul>
</aside>
</section>
@@ -130,7 +130,7 @@
<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Header<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
</ul>
</aside>
</section>
@@ -140,7 +140,7 @@
<div class="tsd-signature tsd-kind-icon">Wait<wbr>For<wbr>Callback<span class="tsd-signature-symbol">:</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/enums/sizeof.html b/docs/reference/api/typedoc/enums/sizeof.html
index 5271efb49..0d5059cd3 100644
--- a/docs/reference/api/typedoc/enums/sizeof.html
+++ b/docs/reference/api/typedoc/enums/sizeof.html
@@ -100,7 +100,7 @@
<div class="tsd-signature tsd-kind-icon">DLData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
</ul>
</aside>
</section>
@@ -110,7 +110,7 @@
<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32 + I32</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
</ul>
</aside>
</section>
@@ -120,7 +120,7 @@
<div class="tsd-signature tsd-kind-icon">F32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
</ul>
</aside>
</section>
@@ -130,7 +130,7 @@
<div class="tsd-signature tsd-kind-icon">F64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
</ul>
</aside>
</section>
@@ -140,7 +140,7 @@
<div class="tsd-signature tsd-kind-icon">I32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
</ul>
</aside>
</section>
@@ -150,7 +150,7 @@
<div class="tsd-signature tsd-kind-icon">I64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
</ul>
</aside>
</section>
@@ -160,7 +160,7 @@
<div class="tsd-signature tsd-kind-icon">TVMValue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
</ul>
</aside>
</section>
@@ -170,7 +170,7 @@
<div class="tsd-signature tsd-kind-icon">U16<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
</ul>
</aside>
</section>
@@ -180,7 +180,7 @@
<div class="tsd-signature tsd-kind-icon">U8<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
</ul>
</aside>
</section>
diff --git a/docs/reference/api/typedoc/index.html b/docs/reference/api/typedoc/index.html
index 5659d4811..04d20d0bd 100644
--- a/docs/reference/api/typedoc/index.html
+++ b/docs/reference/api/typedoc/index.html
@@ -174,7 +174,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Alloc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, ndim<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeCode<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeBits<span class="tsd [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>Bytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">num [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -282,7 +282,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>To<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>from<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, to<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-sig [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -326,7 +326,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>ToBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</sp [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -370,7 +370,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -406,7 +406,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMBackend<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number< [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -458,7 +458,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMCFunc<wbr>Set<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ret<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signa [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -506,7 +506,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMCb<wbr>Arg<wbr>ToReturn<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, code<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span c [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -545,7 +545,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Call<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-t [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -601,7 +601,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -637,7 +637,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Get<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span cla [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -676,7 +676,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>List<wbr>Global<wbr>Names<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>outSize<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, outArray<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -715,7 +715,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Register<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, f<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, override<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</spa [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -758,7 +758,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMGet<wbr>Last<wbr>Error<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -788,7 +788,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -824,7 +824,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Get<wbr>Function<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, funcName<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, queryImports<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">numbe [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -872,7 +872,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Import<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, dep<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-si [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -912,7 +912,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMSynchronize<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>deviceType<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, deviceId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signatur [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -954,7 +954,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Alloc<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>size<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -990,7 +990,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Free<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ptr<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1026,7 +1026,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Func<wbr>Create<wbr>FromCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resource<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1066,7 +1066,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>args<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1118,7 +1118,7 @@
<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<wbr>Finalizer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resourceHandle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1154,7 +1154,7 @@
<div class="tsd-signature tsd-kind-icon">GPUPointer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1169,7 +1169,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L36">runtime.ts:36</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
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<div class="tsd-comment tsd-typography">
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/support.ts#L25">support.ts:25</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/support.ts#L52">support.ts:52</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1337,7 +1337,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/compact.ts#L38">compact.ts:38</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1368,7 +1368,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1390,7 +1390,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/environment.ts#L32">environment.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/environment.ts#L32">environment.ts:32</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1421,7 +1421,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/compact.ts#L24">compact.ts:24</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/compact.ts#L24">compact.ts:24</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1443,7 +1443,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L1367">runtime.ts:1367</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L1367">runtime.ts:1367</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1508,7 +1508,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/support.ts#L62">support.ts:62</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/support.ts#L62">support.ts:62</a></li>
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<div class="tsd-comment tsd-typography">
@@ -1530,7 +1530,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L246">runtime.ts:246</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L246">runtime.ts:246</a></li>
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@@ -1539,7 +1539,7 @@
<div class="tsd-signature tsd-kind-icon">0<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "int"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L247">runtime.ts:247</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L247">runtime.ts:247</a></li>
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@@ -1549,7 +1549,7 @@
<div class="tsd-signature tsd-kind-icon">1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "uint"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L248">runtime.ts:248</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L248">runtime.ts:248</a></li>
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@@ -1559,7 +1559,7 @@
<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "float"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L249">runtime.ts:249</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L249">runtime.ts:249</a></li>
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@@ -1569,7 +1569,7 @@
<div class="tsd-signature tsd-kind-icon">3<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "handle"</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L250">runtime.ts:250</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L250">runtime.ts:250</a></li>
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@@ -1580,7 +1580,7 @@
<div class="tsd-signature tsd-kind-icon">Device<wbr>Enum<wbr>ToStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L175">runtime.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L175">runtime.ts:175</a></li>
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<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1589,7 +1589,7 @@
<div class="tsd-signature tsd-kind-icon">1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "cpu"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L176">runtime.ts:176</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L176">runtime.ts:176</a></li>
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@@ -1599,7 +1599,7 @@
<div class="tsd-signature tsd-kind-icon">15<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "webgpu"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L180">runtime.ts:180</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L180">runtime.ts:180</a></li>
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<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "cuda"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L177">runtime.ts:177</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L177">runtime.ts:177</a></li>
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<div class="tsd-signature tsd-kind-icon">4<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "opencl"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L178">runtime.ts:178</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L178">runtime.ts:178</a></li>
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@@ -1629,7 +1629,7 @@
<div class="tsd-signature tsd-kind-icon">8<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = "metal"</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L179">runtime.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L179">runtime.ts:179</a></li>
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@@ -1640,7 +1640,7 @@
<div class="tsd-signature tsd-kind-icon">Device<wbr>Str<wbr>ToEnum<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L183">runtime.ts:183</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L183">runtime.ts:183</a></li>
</ul>
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<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1649,7 +1649,7 @@
<div class="tsd-signature tsd-kind-icon">cl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L186">runtime.ts:186</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L186">runtime.ts:186</a></li>
</ul>
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</section>
@@ -1659,7 +1659,7 @@
<div class="tsd-signature tsd-kind-icon">cpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 1</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L184">runtime.ts:184</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L184">runtime.ts:184</a></li>
</ul>
</aside>
</section>
@@ -1669,7 +1669,7 @@
<div class="tsd-signature tsd-kind-icon">cuda<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 2</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L185">runtime.ts:185</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L185">runtime.ts:185</a></li>
</ul>
</aside>
</section>
@@ -1679,7 +1679,7 @@
<div class="tsd-signature tsd-kind-icon">metal<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 8</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L189">runtime.ts:189</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L189">runtime.ts:189</a></li>
</ul>
</aside>
</section>
@@ -1689,7 +1689,7 @@
<div class="tsd-signature tsd-kind-icon">opencl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 4</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L187">runtime.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L187">runtime.ts:187</a></li>
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@@ -1699,7 +1699,7 @@
<div class="tsd-signature tsd-kind-icon">vulkan<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 7</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L188">runtime.ts:188</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L188">runtime.ts:188</a></li>
</ul>
</aside>
</section>
@@ -1709,7 +1709,7 @@
<div class="tsd-signature tsd-kind-icon">webgpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 15</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/runtime.ts#L190">runtime.ts:190</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/runtime.ts#L190">runtime.ts:190</a></li>
</ul>
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diff --git a/docs/reference/api/typedoc/interfaces/disposable.html b/docs/reference/api/typedoc/interfaces/disposable.html
index e67328c36..1cc895695 100644
--- a/docs/reference/api/typedoc/interfaces/disposable.html
+++ b/docs/reference/api/typedoc/interfaces/disposable.html
@@ -113,7 +113,7 @@
<div class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/types.ts#L52">types.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/types.ts#L52">types.ts:52</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/interfaces/functioninfo.html b/docs/reference/api/typedoc/interfaces/functioninfo.html
index a7b317397..25d300505 100644
--- a/docs/reference/api/typedoc/interfaces/functioninfo.html
+++ b/docs/reference/api/typedoc/interfaces/functioninfo.html
@@ -95,7 +95,7 @@
<div class="tsd-signature tsd-kind-icon">arg_<wbr>types<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
</ul>
</aside>
</section>
@@ -105,7 +105,7 @@
<div class="tsd-signature tsd-kind-icon">launch_<wbr>param_<wbr>tags<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">></span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
</ul>
</aside>
</section>
@@ -115,7 +115,7 @@
<div class="tsd-signature tsd-kind-icon">name<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
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diff --git a/docs/reference/api/typedoc/interfaces/libraryprovider.html b/docs/reference/api/typedoc/interfaces/libraryprovider.html
index 5725564de..87562d5cb 100644
--- a/docs/reference/api/typedoc/interfaces/libraryprovider.html
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@@ -112,7 +112,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/types.ts#L34">types.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/types.ts#L34">types.ts:34</a></li>
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<div class="tsd-comment tsd-typography">
@@ -127,7 +127,7 @@
<div class="tsd-signature tsd-kind-icon">start<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>inst<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Instance</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/299ca267e/web/src/types.ts#L39">types.ts:39</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/64031d56d/web/src/types.ts#L39">types.ts:39</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
diff --git a/docs/searchindex.js b/docs/searchindex.js
index caad7cf47..49048d51f 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["arch/benchmark","arch/convert_layout","arch/debugger","arch/device_target_interactions","arch/frontend/tensorflow","arch/hybrid_script","arch/index","arch/inferbound","arch/introduction_to_module_serialization","arch/microtvm_design","arch/microtvm_project_api","arch/model_library_format","arch/pass_infra","arch/relay_intro","arch/relay_op_strategy","arch/runtime","arch/runtimes/vulkan","arch/security","arch/virtual_machine","contribute/ci","contribute/code_gu [...]
\ No newline at end of file
+Search.setIndex({docnames:["arch/benchmark","arch/convert_layout","arch/debugger","arch/device_target_interactions","arch/frontend/tensorflow","arch/hybrid_script","arch/index","arch/inferbound","arch/introduction_to_module_serialization","arch/microtvm_design","arch/microtvm_project_api","arch/model_library_format","arch/pass_infra","arch/relay_intro","arch/relay_op_strategy","arch/runtime","arch/runtimes/vulkan","arch/security","arch/virtual_machine","contribute/ci","contribute/code_gu [...]
\ No newline at end of file
diff --git a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
index 3b949be1e..bb85452db 100644
--- a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:23.008</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
+<p><strong>00:21.166</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 82%" />
@@ -336,11 +336,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-relay-vta-py"><span class="std std-ref">Auto-tuning a convolutional network on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_vta.py</span></code>)</p></td>
-<td><p>00:23.001</p></td>
+<td><p>00:21.160</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_alu_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-alu-vta-py"><span class="std std-ref">Auto-tuning a ALU fused op on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_alu_vta.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.006</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_classification.html b/docs/topic/vta/tutorials/frontend/deploy_classification.html
index f9adf6640..d4acca4fc 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_classification.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_classification.html
@@ -571,7 +571,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
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 26.17s!
+resnet18_v1 inference graph built in 22.72s!
</pre></div>
</div>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_detection.html b/docs/topic/vta/tutorials/frontend/deploy_detection.html
index 40009162d..7710a8352 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -589,7 +589,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
"target_host parameter is going to be deprecated. "
/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 17.47s!
+yolov3-tiny inference graph built in 15.95s!
</pre></div>
</div>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/sg_execution_times.html b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
index 1855b1787..4f248e477 100644
--- a/docs/topic/vta/tutorials/frontend/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-frontend-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>01:37.186</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:31.523</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,11 +336,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></td>
-<td><p>00:50.263</p></td>
+<td><p>00:48.459</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_classification.html#sphx-glr-topic-vta-tutorials-frontend-deploy-classification-py"><span class="std std-ref">Deploy Pretrained Vision Model from MxNet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_classification.py</span></code>)</p></td>
-<td><p>00:46.924</p></td>
+<td><p>00:43.063</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/topic/vta/tutorials/optimize/sg_execution_times.html b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
index eda66a9d4..5c312556d 100644
--- a/docs/topic/vta/tutorials/optimize/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-optimize-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.445</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.342</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -336,11 +336,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></td>
-<td><p>00:03.021</p></td>
+<td><p>00:02.924</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="matrix_multiply_opt.html#sphx-glr-topic-vta-tutorials-optimize-matrix-multiply-opt-py"><span class="std std-ref">Matrix Multiply Blocking</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply_opt.py</span></code>)</p></td>
-<td><p>00:00.424</p></td>
+<td><p>00:00.418</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/topic/vta/tutorials/sg_execution_times.html b/docs/topic/vta/tutorials/sg_execution_times.html
index 92a15cfd5..eb3cc111d 100644
--- a/docs/topic/vta/tutorials/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -327,7 +327,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.775</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
+<p><strong>00:00.769</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -336,11 +336,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></td>
-<td><p>00:00.423</p></td>
+<td><p>00:00.412</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="vta_get_started.html#sphx-glr-topic-vta-tutorials-vta-get-started-py"><span class="std std-ref">Get Started with VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">vta_get_started.py</span></code>)</p></td>
-<td><p>00:00.352</p></td>
+<td><p>00:00.357</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/tutorial/auto_scheduler_matmul_x86.html b/docs/tutorial/auto_scheduler_matmul_x86.html
index e60f394ed..171eb1d2f 100644
--- a/docs/tutorial/auto_scheduler_matmul_x86.html
+++ b/docs/tutorial/auto_scheduler_matmul_x86.html
@@ -565,7 +565,7 @@ 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: 93.600 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 94.485 ms
</pre></div>
</div>
</div>
diff --git a/docs/tutorial/autotvm_matmul_x86.html b/docs/tutorial/autotvm_matmul_x86.html
index 0461b6ffe..9564ea7d3 100644
--- a/docs/tutorial/autotvm_matmul_x86.html
+++ b/docs/tutorial/autotvm_matmul_x86.html
@@ -669,16 +669,16 @@ reduce variance, we take 5 measurements and average them.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>waiting for device...
device available
Get devices for measurement successfully!
-No: 1 GFLOPS: 10.37/10.37 result: MeasureResult(costs=(0.025874462999999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5470092296600342, timestamp=1662657993.078982) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
-No: 2 GFLOPS: 2.91/10.37 result: MeasureResult(costs=(0.0922017588,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6760056018829346, timestamp=1662657994.7612782) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
-No: 3 GFLOPS: 11.75/11.75 result: MeasureResult(costs=(0.0228441362,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5695548057556152, timestamp=1662657995.8522124) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
-No: 4 GFLOPS: 1.58/11.75 result: MeasureResult(costs=(0.16989027480000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.8370141983032227, timestamp=1662657999.3088498) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
-No: 5 GFLOPS: 3.53/11.75 result: MeasureResult(costs=(0.07609779539999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.361241340637207, timestamp=1662658000.7989016) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
-No: 6 GFLOPS: 1.84/11.75 result: MeasureResult(costs=(0.1460549784,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4554946422576904, timestamp=1662658003.859728) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
-No: 7 GFLOPS: 0.84/11.75 result: MeasureResult(costs=(0.3209910264,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.256304979324341, timestamp=1662658009.1572585) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
-No: 8 GFLOPS: 10.05/11.75 result: MeasureResult(costs=(0.0267209406,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6259303092956543, timestamp=1662658009.7900991) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
-No: 9 GFLOPS: 1.58/11.75 result: MeasureResult(costs=(0.1693808634,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.813093662261963, timestamp=1662658012.719757) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
-No: 10 GFLOPS: 2.50/11.75 result: MeasureResult(costs=(0.1075331796,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8286418914794922, timestamp=1662658014.604718) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
+No: 1 GFLOPS: 10.49/10.49 result: MeasureResult(costs=(0.0255935978,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5469872951507568, timestamp=1662679248.870281) [('tile_y', [-1, 1]), ('tile_x', [-1, 256])],None,80
+No: 2 GFLOPS: 2.67/10.49 result: MeasureResult(costs=(0.1006344324,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7545621395111084, timestamp=1662679250.6463335) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
+No: 3 GFLOPS: 11.83/11.83 result: MeasureResult(costs=(0.022692057600000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5711381435394287, timestamp=1662679251.7169244) [('tile_y', [-1, 64]), ('tile_x', [-1, 32])],None,56
+No: 4 GFLOPS: 1.85/11.83 result: MeasureResult(costs=(0.1450325686,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4517223834991455, timestamp=1662679254.751538) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
+No: 5 GFLOPS: 3.69/11.83 result: MeasureResult(costs=(0.0727773378,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.30503249168396, timestamp=1662679256.1901448) [('tile_y', [-1, 256]), ('tile_x', [-1, 16])],None,48
+No: 6 GFLOPS: 1.72/11.83 result: MeasureResult(costs=(0.1556953856,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.614009380340576, timestamp=1662679259.3955157) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
+No: 7 GFLOPS: 0.86/11.83 result: MeasureResult(costs=(0.3133077082,), error_no=MeasureErrorNo.NO_ERROR, all_cost=5.1336164474487305, timestamp=1662679264.5750794) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+No: 8 GFLOPS: 10.55/11.83 result: MeasureResult(costs=(0.025448856599999996,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5543022155761719, timestamp=1662679265.1476328) [('tile_y', [-1, 4]), ('tile_x', [-1, 64])],None,62
+No: 9 GFLOPS: 1.65/11.83 result: MeasureResult(costs=(0.16238124660000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.6933515071868896, timestamp=1662679267.960986) [('tile_y', [-1, 2]), ('tile_x', [-1, 2])],None,11
+No: 10 GFLOPS: 2.46/11.83 result: MeasureResult(costs=(0.10907074480000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.8524575233459473, timestamp=1662679269.8713691) [('tile_y', [-1, 4]), ('tile_x', [-1, 4])],None,22
</pre></div>
</div>
<p>With tuning completed, we can choose the configuration from the log file that
diff --git a/docs/tutorial/autotvm_relay_x86.html b/docs/tutorial/autotvm_relay_x86.html
index db160cdcb..f7290833b 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -551,7 +551,7 @@ standard deviation.</p>
<span class="nb">print</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">unoptimized</span></a><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 498.022426169997, 'median': 498.1348002000004, 'std': 0.8748482383293634}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 495.29929663003713, 'median': 495.0232297499497, 'std': 0.5934874365415119}
</pre></div>
</div>
</div>
@@ -706,178 +706,178 @@ depending on the specifics of the model and the target platform.</p>
"target_host parameter is going to be deprecated. "
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 1/25] Current/Best: 17.47/ 17.47 GFLOPS | Progress: (4/20) | 6.49 s
-[Task 1/25] Current/Best: 6.16/ 17.47 GFLOPS | Progress: (8/20) | 9.44 s
-[Task 1/25] Current/Best: 11.50/ 22.77 GFLOPS | Progress: (12/20) | 11.95 s
-[Task 1/25] Current/Best: 16.47/ 22.77 GFLOPS | Progress: (16/20) | 13.65 s
-[Task 1/25] Current/Best: 11.60/ 23.78 GFLOPS | Progress: (20/20) | 15.42 s Done.
+[Task 1/25] Current/Best: 17.60/ 17.60 GFLOPS | Progress: (4/20) | 6.40 s
+[Task 1/25] Current/Best: 6.16/ 17.60 GFLOPS | Progress: (8/20) | 9.45 s
+[Task 1/25] Current/Best: 11.52/ 22.72 GFLOPS | Progress: (12/20) | 11.94 s
+[Task 1/25] Current/Best: 16.54/ 22.72 GFLOPS | Progress: (16/20) | 13.63 s
+[Task 1/25] Current/Best: 11.63/ 23.85 GFLOPS | Progress: (20/20) | 15.38 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 2/25] Current/Best: 12.27/ 12.87 GFLOPS | Progress: (4/20) | 3.88 s
-[Task 2/25] Current/Best: 14.43/ 18.30 GFLOPS | Progress: (8/20) | 5.17 s
-[Task 2/25] Current/Best: 21.01/ 21.01 GFLOPS | Progress: (12/20) | 6.50 s
-[Task 2/25] Current/Best: 12.12/ 21.01 GFLOPS | Progress: (16/20) | 7.78 s
-[Task 2/25] Current/Best: 19.31/ 21.01 GFLOPS | Progress: (20/20) | 9.38 s Done.
+[Task 2/25] Current/Best: 12.13/ 12.68 GFLOPS | Progress: (4/20) | 3.76 s
+[Task 2/25] Current/Best: 14.05/ 18.79 GFLOPS | Progress: (8/20) | 5.06 s
+[Task 2/25] Current/Best: 21.15/ 21.15 GFLOPS | Progress: (12/20) | 6.39 s
+[Task 2/25] Current/Best: 12.75/ 21.15 GFLOPS | Progress: (16/20) | 7.65 s
+[Task 2/25] Current/Best: 19.33/ 21.15 GFLOPS | Progress: (20/20) | 9.27 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 3/25] Current/Best: 1.63/ 10.76 GFLOPS | Progress: (4/20) | 5.91 s
-[Task 3/25] Current/Best: 14.57/ 16.83 GFLOPS | Progress: (8/20) | 7.88 s
-[Task 3/25] Current/Best: 14.94/ 16.83 GFLOPS | Progress: (12/20) | 9.60 s
-[Task 3/25] Current/Best: 7.22/ 23.71 GFLOPS | Progress: (16/20) | 11.57 s
-[Task 3/25] Current/Best: 12.62/ 23.71 GFLOPS | Progress: (20/20) | 16.10 s Done.
+[Task 3/25] Current/Best: 1.63/ 10.84 GFLOPS | Progress: (4/20) | 5.88 s
+[Task 3/25] Current/Best: 15.35/ 16.84 GFLOPS | Progress: (8/20) | 7.81 s
+[Task 3/25] Current/Best: 15.03/ 16.84 GFLOPS | Progress: (12/20) | 9.55 s
+[Task 3/25] Current/Best: 7.22/ 23.84 GFLOPS | Progress: (16/20) | 11.47 s
+[Task 3/25] Current/Best: 12.38/ 23.84 GFLOPS | Progress: (20/20) | 16.04 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 4/25] Current/Best: 9.50/ 19.85 GFLOPS | Progress: (4/20) | 2.44 s
-[Task 4/25] Current/Best: 6.78/ 19.85 GFLOPS | Progress: (8/20) | 6.80 s
-[Task 4/25] Current/Best: 22.12/ 22.12 GFLOPS | Progress: (12/20) | 11.37 s
-[Task 4/25] Current/Best: 17.10/ 22.12 GFLOPS | Progress: (16/20) | 13.63 s
-[Task 4/25] Current/Best: 13.18/ 22.12 GFLOPS | Progress: (20/20) | 15.65 s Done.
+[Task 4/25] Current/Best: 9.52/ 20.34 GFLOPS | Progress: (4/20) | 2.43 s
+[Task 4/25] Current/Best: 6.72/ 20.34 GFLOPS | Progress: (8/20) | 7.12 s
+[Task 4/25] Current/Best: 22.57/ 22.57 GFLOPS | Progress: (12/20) | 12.08 s
+[Task 4/25] Current/Best: 17.00/ 22.57 GFLOPS | Progress: (16/20) | 14.45 s
+[Task 4/25] Current/Best: 13.48/ 22.57 GFLOPS | Progress: (20/20) | 16.41 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 5/25] Current/Best: 9.34/ 10.08 GFLOPS | Progress: (4/20) | 2.67 s
-[Task 5/25] Current/Best: 11.61/ 12.67 GFLOPS | Progress: (8/20) | 4.80 s
-[Task 5/25] Current/Best: 11.34/ 17.97 GFLOPS | Progress: (12/20) | 7.99 s
-[Task 5/25] Current/Best: 11.46/ 22.48 GFLOPS | Progress: (16/20) | 9.41 s
-[Task 5/25] Current/Best: 12.01/ 22.48 GFLOPS | Progress: (20/20) | 11.30 s Done.
+[Task 5/25] Current/Best: 9.52/ 10.22 GFLOPS | Progress: (4/20) | 2.62 s
+[Task 5/25] Current/Best: 11.70/ 12.69 GFLOPS | Progress: (8/20) | 4.68 s
+[Task 5/25] Current/Best: 11.68/ 17.99 GFLOPS | Progress: (12/20) | 7.90 s
+[Task 5/25] Current/Best: 11.54/ 22.50 GFLOPS | Progress: (16/20) | 9.31 s
+[Task 5/25] Current/Best: 12.04/ 22.50 GFLOPS | Progress: (20/20) | 11.25 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 6/25] Current/Best: 12.09/ 20.07 GFLOPS | Progress: (4/20) | 4.06 s
-[Task 6/25] Current/Best: 18.88/ 20.07 GFLOPS | Progress: (8/20) | 5.84 s
-[Task 6/25] Current/Best: 13.13/ 20.07 GFLOPS | Progress: (12/20) | 7.78 s
-[Task 6/25] Current/Best: 20.11/ 20.11 GFLOPS | Progress: (16/20) | 10.05 s
-[Task 6/25] Current/Best: 3.73/ 20.11 GFLOPS | Progress: (20/20) | 12.57 s Done.
+[Task 6/25] Current/Best: 12.12/ 20.07 GFLOPS | Progress: (4/20) | 4.13 s
+[Task 6/25] Current/Best: 18.94/ 20.07 GFLOPS | Progress: (8/20) | 5.91 s
+[Task 6/25] Current/Best: 13.15/ 20.07 GFLOPS | Progress: (12/20) | 7.88 s
+[Task 6/25] Current/Best: 20.09/ 20.09 GFLOPS | Progress: (16/20) | 10.14 s
+[Task 6/25] Current/Best: 3.72/ 20.09 GFLOPS | Progress: (20/20) | 12.68 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 7/25] Current/Best: 10.35/ 12.84 GFLOPS | Progress: (4/20) | 3.65 s
-[Task 7/25] Current/Best: 20.27/ 21.09 GFLOPS | Progress: (8/20) | 5.20 s
-[Task 7/25] Current/Best: 16.08/ 21.09 GFLOPS | Progress: (12/20) | 7.16 s
-[Task 7/25] Current/Best: 12.12/ 21.09 GFLOPS | Progress: (16/20) | 9.22 s
-[Task 7/25] Current/Best: 6.24/ 21.61 GFLOPS | Progress: (20/20) | 11.71 s Done.
+[Task 7/25] Current/Best: 11.08/ 12.98 GFLOPS | Progress: (4/20) | 3.58 s
+[Task 7/25] Current/Best: 20.30/ 21.03 GFLOPS | Progress: (8/20) | 5.10 s
+[Task 7/25] Current/Best: 16.13/ 21.03 GFLOPS | Progress: (12/20) | 7.01 s
+[Task 7/25] Current/Best: 12.18/ 21.03 GFLOPS | Progress: (16/20) | 9.06 s
+[Task 7/25] Current/Best: 6.31/ 21.83 GFLOPS | Progress: (20/20) | 11.53 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 8/25] Current/Best: 9.77/ 13.89 GFLOPS | Progress: (4/20) | 2.96 s
-[Task 8/25] Current/Best: 9.36/ 13.89 GFLOPS | Progress: (8/20) | 7.80 s
-[Task 8/25] Current/Best: 12.97/ 13.89 GFLOPS | Progress: (12/20) | 13.93 s
-[Task 8/25] Current/Best: 19.11/ 19.11 GFLOPS | Progress: (16/20) | 16.06 s
-[Task 8/25] Current/Best: 19.57/ 19.57 GFLOPS | Progress: (20/20) | 22.78 s Done.
+[Task 8/25] Current/Best: 9.69/ 13.76 GFLOPS | Progress: (4/20) | 2.99 s
+[Task 8/25] Current/Best: 9.20/ 13.76 GFLOPS | Progress: (8/20) | 8.13 s
+[Task 8/25] Current/Best: 12.72/ 13.76 GFLOPS | Progress: (12/20) | 14.62 s
+[Task 8/25] Current/Best: 18.98/ 18.98 GFLOPS | Progress: (16/20) | 16.73 s
+[Task 8/25] Current/Best: 19.65/ 19.65 GFLOPS | Progress: (20/20) | 23.79 s Done.
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 9/25] Current/Best: 14.33/ 15.76 GFLOPS | Progress: (4/20) | 12.04 s
-[Task 9/25] Current/Best: 23.41/ 23.41 GFLOPS | Progress: (8/20) | 13.83 s
-[Task 9/25] Current/Best: 8.29/ 23.41 GFLOPS | Progress: (12/20) | 16.22 s
-[Task 9/25] Current/Best: 17.92/ 23.41 GFLOPS | Progress: (16/20) | 18.95 s
-[Task 9/25] Current/Best: 8.98/ 23.41 GFLOPS | Progress: (20/20) | 26.77 s
+[Task 9/25] Current/Best: 14.38/ 15.66 GFLOPS | Progress: (4/20) | 11.99 s
+[Task 9/25] Current/Best: 23.42/ 23.42 GFLOPS | Progress: (8/20) | 13.79 s
+[Task 9/25] Current/Best: 8.23/ 23.42 GFLOPS | Progress: (12/20) | 16.29 s
+[Task 9/25] Current/Best: 17.98/ 23.42 GFLOPS | Progress: (16/20) | 19.02 s
+[Task 9/25] Current/Best: 9.18/ 23.42 GFLOPS | Progress: (20/20) | 27.38 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 10/25] Current/Best: 18.26/ 18.26 GFLOPS | Progress: (4/20) | 2.65 s
-[Task 10/25] Current/Best: 15.52/ 18.26 GFLOPS | Progress: (8/20) | 4.25 s
-[Task 10/25] Current/Best: 12.58/ 18.77 GFLOPS | Progress: (12/20) | 5.79 s
-[Task 10/25] Current/Best: 18.99/ 20.12 GFLOPS | Progress: (16/20) | 6.91 s
-[Task 10/25] Current/Best: 8.77/ 20.12 GFLOPS | Progress: (20/20) | 8.47 s Done.
+[Task 10/25] Current/Best: 18.06/ 18.06 GFLOPS | Progress: (4/20) | 2.61 s
+[Task 10/25] Current/Best: 15.60/ 18.06 GFLOPS | Progress: (8/20) | 4.24 s
+[Task 10/25] Current/Best: 12.51/ 19.03 GFLOPS | Progress: (12/20) | 5.80 s
+[Task 10/25] Current/Best: 19.11/ 20.23 GFLOPS | Progress: (16/20) | 6.91 s
+[Task 10/25] Current/Best: 8.84/ 20.23 GFLOPS | Progress: (20/20) | 8.49 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 11/25] Current/Best: 12.30/ 18.08 GFLOPS | Progress: (4/20) | 3.38 s
-[Task 11/25] Current/Best: 16.86/ 18.08 GFLOPS | Progress: (8/20) | 6.13 s
-[Task 11/25] Current/Best: 18.02/ 18.08 GFLOPS | Progress: (12/20) | 8.19 s
-[Task 11/25] Current/Best: 13.45/ 20.97 GFLOPS | Progress: (16/20) | 10.97 s
-[Task 11/25] Current/Best: 19.42/ 21.60 GFLOPS | Progress: (20/20) | 13.03 s Done.
+[Task 11/25] Current/Best: 11.58/ 18.19 GFLOPS | Progress: (4/20) | 3.40 s
+[Task 11/25] Current/Best: 16.94/ 18.19 GFLOPS | Progress: (8/20) | 6.22 s
+[Task 11/25] Current/Best: 18.33/ 18.33 GFLOPS | Progress: (12/20) | 8.33 s
+[Task 11/25] Current/Best: 13.46/ 20.89 GFLOPS | Progress: (16/20) | 11.25 s
+[Task 11/25] Current/Best: 19.32/ 21.59 GFLOPS | Progress: (20/20) | 13.38 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 12/25] Current/Best: 7.75/ 17.81 GFLOPS | Progress: (4/20) | 5.40 s
-[Task 12/25] Current/Best: 5.16/ 17.81 GFLOPS | Progress: (8/20) | 9.12 s
-[Task 12/25] Current/Best: 18.96/ 18.96 GFLOPS | Progress: (12/20) | 11.14 s
-[Task 12/25] Current/Best: 14.41/ 18.96 GFLOPS | Progress: (16/20) | 14.00 s
-[Task 12/25] Current/Best: 14.91/ 18.96 GFLOPS | Progress: (20/20) | 15.93 s Done.
+[Task 12/25] Current/Best: 7.73/ 17.92 GFLOPS | Progress: (4/20) | 5.73 s
+[Task 12/25] Current/Best: 5.16/ 17.92 GFLOPS | Progress: (8/20) | 9.62 s
+[Task 12/25] Current/Best: 18.94/ 18.94 GFLOPS | Progress: (12/20) | 11.64 s
+[Task 12/25] Current/Best: 15.23/ 18.94 GFLOPS | Progress: (16/20) | 14.59 s
+[Task 12/25] Current/Best: 15.15/ 18.94 GFLOPS | Progress: (20/20) | 16.50 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 13/25] Current/Best: 8.67/ 17.23 GFLOPS | Progress: (4/20) | 3.78 s
-[Task 13/25] Current/Best: 15.55/ 20.80 GFLOPS | Progress: (8/20) | 6.22 s
-[Task 13/25] Current/Best: 19.63/ 21.77 GFLOPS | Progress: (12/20) | 9.13 s
-[Task 13/25] Current/Best: 12.19/ 21.77 GFLOPS | Progress: (16/20) | 12.58 s
-[Task 13/25] Current/Best: 18.22/ 21.77 GFLOPS | Progress: (20/20) | 14.87 s Done.
+[Task 13/25] Current/Best: 8.60/ 17.38 GFLOPS | Progress: (4/20) | 3.78 s
+[Task 13/25] Current/Best: 15.81/ 20.92 GFLOPS | Progress: (8/20) | 6.40 s
+[Task 13/25] Current/Best: 19.68/ 22.04 GFLOPS | Progress: (12/20) | 9.52 s
+[Task 13/25] Current/Best: 12.28/ 22.04 GFLOPS | Progress: (16/20) | 12.99 s
+[Task 13/25] Current/Best: 18.73/ 22.04 GFLOPS | Progress: (20/20) | 15.38 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 14/25] Current/Best: 12.09/ 13.17 GFLOPS | Progress: (4/20) | 3.48 s
-[Task 14/25] Current/Best: 6.01/ 13.24 GFLOPS | Progress: (8/20) | 5.66 s
-[Task 14/25] Current/Best: 19.41/ 19.41 GFLOPS | Progress: (12/20) | 8.24 s
-[Task 14/25] Current/Best: 16.23/ 19.41 GFLOPS | Progress: (16/20) | 9.94 s Done.
+[Task 14/25] Current/Best: 13.80/ 13.80 GFLOPS | Progress: (4/20) | 3.47 s
+[Task 14/25] Current/Best: 6.12/ 13.80 GFLOPS | Progress: (8/20) | 5.69 s
+[Task 14/25] Current/Best: 20.98/ 20.98 GFLOPS | Progress: (12/20) | 8.37 s
+[Task 14/25] Current/Best: 17.66/ 20.98 GFLOPS | Progress: (16/20) | 10.02 s Done.
-[Task 14/25] Current/Best: 17.52/ 19.41 GFLOPS | Progress: (20/20) | 11.81 s
+[Task 14/25] Current/Best: 16.97/ 20.98 GFLOPS | Progress: (20/20) | 11.86 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 15/25] Current/Best: 16.14/ 17.30 GFLOPS | Progress: (4/20) | 2.79 s
-[Task 15/25] Current/Best: 14.57/ 18.09 GFLOPS | Progress: (8/20) | 4.16 s
-[Task 15/25] Current/Best: 10.36/ 22.29 GFLOPS | Progress: (12/20) | 6.32 s
-[Task 15/25] Current/Best: 20.42/ 22.29 GFLOPS | Progress: (16/20) | 9.38 s
-[Task 15/25] Current/Best: 9.69/ 22.29 GFLOPS | Progress: (20/20) | 10.41 s
+[Task 15/25] Current/Best: 16.20/ 17.62 GFLOPS | Progress: (4/20) | 2.77 s
+[Task 15/25] Current/Best: 14.24/ 18.02 GFLOPS | Progress: (8/20) | 4.07 s
+[Task 15/25] Current/Best: 10.39/ 22.42 GFLOPS | Progress: (12/20) | 6.30 s
+[Task 15/25] Current/Best: 20.42/ 22.42 GFLOPS | Progress: (16/20) | 9.69 s
+[Task 15/25] Current/Best: 9.70/ 22.42 GFLOPS | Progress: (20/20) | 10.71 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 16/25] Current/Best: 20.28/ 20.28 GFLOPS | Progress: (4/20) | 3.04 s
-[Task 16/25] Current/Best: 3.01/ 20.28 GFLOPS | Progress: (8/20) | 4.65 s
-[Task 16/25] Current/Best: 19.72/ 20.28 GFLOPS | Progress: (12/20) | 5.87 s
-[Task 16/25] Current/Best: 17.86/ 20.28 GFLOPS | Progress: (16/20) | 7.27 s
-[Task 16/25] Current/Best: 10.04/ 22.10 GFLOPS | Progress: (20/20) | 9.32 s Done.
+[Task 16/25] Current/Best: 20.23/ 20.23 GFLOPS | Progress: (4/20) | 3.03 s
+[Task 16/25] Current/Best: 3.04/ 20.23 GFLOPS | Progress: (8/20) | 4.64 s
+[Task 16/25] Current/Best: 19.52/ 20.23 GFLOPS | Progress: (12/20) | 5.87 s
+[Task 16/25] Current/Best: 17.46/ 20.23 GFLOPS | Progress: (16/20) | 7.23 s
+[Task 16/25] Current/Best: 9.98/ 21.99 GFLOPS | Progress: (20/20) | 9.40 s Done.
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 17/25] Current/Best: 13.47/ 18.39 GFLOPS | Progress: (4/20) | 4.82 s
-[Task 17/25] Current/Best: 14.36/ 23.12 GFLOPS | Progress: (8/20) | 7.71 s
-[Task 17/25] Current/Best: 18.29/ 23.12 GFLOPS | Progress: (12/20) | 9.76 s
-[Task 17/25] Current/Best: 16.44/ 23.12 GFLOPS | Progress: (16/20) | 11.92 s
-[Task 17/25] Current/Best: 10.03/ 23.12 GFLOPS | Progress: (20/20) | 14.08 s Done.
+[Task 17/25] Current/Best: 13.10/ 18.22 GFLOPS | Progress: (4/20) | 4.84 s
+[Task 17/25] Current/Best: 14.45/ 22.81 GFLOPS | Progress: (8/20) | 7.63 s
+[Task 17/25] Current/Best: 17.69/ 22.81 GFLOPS | Progress: (12/20) | 9.69 s
+[Task 17/25] Current/Best: 16.54/ 22.81 GFLOPS | Progress: (16/20) | 11.89 s
+[Task 17/25] Current/Best: 10.06/ 22.81 GFLOPS | Progress: (20/20) | 14.05 s Done.
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 18/25] Current/Best: 11.13/ 17.97 GFLOPS | Progress: (4/20) | 3.80 s
-[Task 18/25] Current/Best: 10.53/ 19.33 GFLOPS | Progress: (8/20) | 7.25 s
-[Task 18/25] Current/Best: 18.97/ 19.33 GFLOPS | Progress: (12/20) | 9.18 s
-[Task 18/25] Current/Best: 9.90/ 19.33 GFLOPS | Progress: (16/20) | 12.86 s
-[Task 18/25] Current/Best: 20.46/ 20.46 GFLOPS | Progress: (20/20) | 14.37 s Done.
+[Task 18/25] Current/Best: 11.31/ 17.96 GFLOPS | Progress: (4/20) | 3.84 s
+[Task 18/25] Current/Best: 10.55/ 19.61 GFLOPS | Progress: (8/20) | 7.49 s
+[Task 18/25] Current/Best: 19.53/ 19.61 GFLOPS | Progress: (12/20) | 9.43 s
+[Task 18/25] Current/Best: 10.03/ 19.61 GFLOPS | Progress: (16/20) | 13.30 s
+[Task 18/25] Current/Best: 20.72/ 20.72 GFLOPS | Progress: (20/20) | 14.81 s Done.
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 19/25] Current/Best: 6.93/ 20.27 GFLOPS | Progress: (4/20) | 6.25 s
-[Task 19/25] Current/Best: 2.69/ 20.27 GFLOPS | Progress: (8/20) | 9.53 s
-[Task 19/25] Current/Best: 19.36/ 21.17 GFLOPS | Progress: (12/20) | 12.34 s
-[Task 19/25] Current/Best: 15.31/ 21.17 GFLOPS | Progress: (16/20) | 15.20 s
-[Task 19/25] Current/Best: 2.69/ 22.85 GFLOPS | Progress: (20/20) | 17.97 s Done.
+[Task 19/25] Current/Best: 7.16/ 20.41 GFLOPS | Progress: (4/20) | 6.11 s
+[Task 19/25] Current/Best: 2.70/ 20.41 GFLOPS | Progress: (8/20) | 9.42 s
+[Task 19/25] Current/Best: 19.93/ 21.61 GFLOPS | Progress: (12/20) | 12.42 s
+[Task 19/25] Current/Best: 14.21/ 22.54 GFLOPS | Progress: (16/20) | 15.36 s
+[Task 19/25] Current/Best: 2.70/ 23.20 GFLOPS | Progress: (20/20) | 18.22 s Done.
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 20/25] Current/Best: 9.18/ 15.47 GFLOPS | Progress: (4/20) | 3.41 s Done.
+[Task 20/25] Current/Best: 8.66/ 14.87 GFLOPS | Progress: (4/20) | 3.40 s Done.
Done.
-[Task 20/25] Current/Best: 10.40/ 15.47 GFLOPS | Progress: (8/20) | 6.88 s
-[Task 20/25] Current/Best: 2.32/ 16.64 GFLOPS | Progress: (12/20) | 10.85 s
-[Task 20/25] Current/Best: 12.18/ 16.64 GFLOPS | Progress: (16/20) | 14.49 s
-[Task 20/25] Current/Best: 12.49/ 21.50 GFLOPS | Progress: (20/20) | 16.61 s
+[Task 20/25] Current/Best: 10.05/ 14.87 GFLOPS | Progress: (8/20) | 6.79 s
+[Task 20/25] Current/Best: 2.32/ 16.72 GFLOPS | Progress: (12/20) | 10.74 s
+[Task 20/25] Current/Best: 11.94/ 16.72 GFLOPS | Progress: (16/20) | 14.49 s
+[Task 20/25] Current/Best: 12.25/ 22.14 GFLOPS | Progress: (20/20) | 16.61 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 21/25] Current/Best: 6.39/ 17.64 GFLOPS | Progress: (4/20) | 3.32 s
-[Task 21/25] Current/Best: 14.55/ 17.64 GFLOPS | Progress: (8/20) | 4.91 s
-[Task 21/25] Current/Best: 1.61/ 17.64 GFLOPS | Progress: (12/20) | 7.09 s
-[Task 21/25] Current/Best: 18.09/ 18.09 GFLOPS | Progress: (16/20) | 10.61 s
-[Task 21/25] Current/Best: 4.45/ 18.09 GFLOPS | Progress: (20/20) | 17.84 s
+[Task 21/25] Current/Best: 6.42/ 17.72 GFLOPS | Progress: (4/20) | 3.31 s
+[Task 21/25] Current/Best: 14.65/ 17.72 GFLOPS | Progress: (8/20) | 4.94 s
+[Task 21/25] Current/Best: 1.61/ 17.72 GFLOPS | Progress: (12/20) | 7.08 s
+[Task 21/25] Current/Best: 16.79/ 17.72 GFLOPS | Progress: (16/20) | 10.61 s
+[Task 21/25] Current/Best: 4.47/ 17.72 GFLOPS | Progress: (20/20) | 17.89 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 22/25] Current/Best: 2.70/ 17.00 GFLOPS | Progress: (4/20) | 2.76 s
-[Task 22/25] Current/Best: 9.06/ 21.89 GFLOPS | Progress: (8/20) | 4.74 s
-[Task 22/25] Current/Best: 19.76/ 21.89 GFLOPS | Progress: (12/20) | 7.07 s
-[Task 22/25] Current/Best: 15.42/ 21.89 GFLOPS | Progress: (16/20) | 9.14 s
-[Task 22/25] Current/Best: 13.64/ 21.89 GFLOPS | Progress: (20/20) | 10.82 s Done.
+[Task 22/25] Current/Best: 2.70/ 16.97 GFLOPS | Progress: (4/20) | 2.71 s
+[Task 22/25] Current/Best: 8.83/ 21.93 GFLOPS | Progress: (8/20) | 4.77 s
+[Task 22/25] Current/Best: 20.00/ 21.93 GFLOPS | Progress: (12/20) | 7.18 s
+[Task 22/25] Current/Best: 15.32/ 21.93 GFLOPS | Progress: (16/20) | 9.31 s
+[Task 22/25] Current/Best: 13.90/ 21.93 GFLOPS | Progress: (20/20) | 11.06 s Done.
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 23/25] Current/Best: 17.46/ 20.49 GFLOPS | Progress: (4/20) | 3.33 s
-[Task 23/25] Current/Best: 15.52/ 20.49 GFLOPS | Progress: (8/20) | 6.65 s
-[Task 23/25] Current/Best: 20.89/ 21.45 GFLOPS | Progress: (12/20) | 8.46 s
-[Task 23/25] Current/Best: 6.28/ 21.45 GFLOPS | Progress: (16/20) | 15.59 s
-[Task 23/25] Current/Best: 7.51/ 21.45 GFLOPS | Progress: (20/20) | 19.85 s Done.
+[Task 23/25] Current/Best: 17.63/ 20.75 GFLOPS | Progress: (4/20) | 3.30 s
+[Task 23/25] Current/Best: 14.39/ 20.75 GFLOPS | Progress: (8/20) | 6.71 s
+[Task 23/25] Current/Best: 21.04/ 21.60 GFLOPS | Progress: (12/20) | 8.54 s
+[Task 23/25] Current/Best: 6.26/ 21.60 GFLOPS | Progress: (16/20) | 15.53 s
+[Task 23/25] Current/Best: 7.88/ 21.60 GFLOPS | Progress: (20/20) | 19.73 s Done.
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 24/25] Current/Best: 8.52/ 8.52 GFLOPS | Progress: (4/20) | 11.84 s
-[Task 24/25] Current/Best: 1.90/ 8.52 GFLOPS | Progress: (8/20) | 22.88 s
-[Task 24/25] Current/Best: 4.33/ 8.52 GFLOPS | Progress: (12/20) | 34.50 s Done.
+[Task 24/25] Current/Best: 8.47/ 8.47 GFLOPS | Progress: (4/20) | 11.85 s
+[Task 24/25] Current/Best: 3.66/ 8.47 GFLOPS | Progress: (8/20) | 23.11 s
+[Task 24/25] Current/Best: 4.19/ 8.47 GFLOPS | Progress: (12/20) | 33.83 s Done.
-[Task 24/25] Current/Best: 6.81/ 8.56 GFLOPS | Progress: (16/20) | 39.89 s
-[Task 24/25] Current/Best: 3.29/ 8.75 GFLOPS | Progress: (20/20) | 46.03 s Done.
+[Task 24/25] Current/Best: 6.78/ 8.89 GFLOPS | Progress: (16/20) | 39.49 s
+[Task 24/25] Current/Best: 3.35/ 8.89 GFLOPS | Progress: (20/20) | 45.54 s Done.
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 25/25] Current/Best: 1.55/ 2.94 GFLOPS | Progress: (4/20) | 11.68 s
-[Task 25/25] Current/Best: 5.77/ 7.90 GFLOPS | Progress: (8/20) | 23.02 s
-[Task 25/25] Current/Best: 5.79/ 7.90 GFLOPS | Progress: (12/20) | 34.34 s
-[Task 25/25] Current/Best: 5.73/ 8.29 GFLOPS | Progress: (16/20) | 36.17 s
-[Task 25/25] Current/Best: 2.80/ 8.55 GFLOPS | Progress: (20/20) | 46.86 s
+[Task 25/25] Current/Best: 1.55/ 2.76 GFLOPS | Progress: (4/20) | 11.62 s
+[Task 25/25] Current/Best: 5.96/ 7.92 GFLOPS | Progress: (8/20) | 22.93 s
+[Task 25/25] Current/Best: 6.11/ 7.92 GFLOPS | Progress: (12/20) | 34.41 s
+[Task 25/25] Current/Best: 5.89/ 8.63 GFLOPS | Progress: (16/20) | 36.16 s
+[Task 25/25] Current/Best: 2.88/ 9.13 GFLOPS | Progress: (20/20) | 46.82 s
</pre></div>
</div>
<p>The output from this tuning process will look something like this:</p>
@@ -981,8 +981,8 @@ improvement in comparing the optimized model to the unoptimized model.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"unoptimized: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</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">unoptimized</span></a><span class="p">))</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 414.516118869999, 'median': 414.3010358000083, 'std': 0.9494320501595434}
-unoptimized: {'mean': 498.022426169997, 'median': 498.1348002000004, 'std': 0.8748482383293634}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 409.14941426999576, 'median': 409.1582320499583, 'std': 0.7372720689148065}
+unoptimized: {'mean': 495.29929663003713, 'median': 495.0232297499497, 'std': 0.5934874365415119}
</pre></div>
</div>
</div>
@@ -996,7 +996,7 @@ models.</p>
<p>Here we presented a simple example using ResNet-50 v2 locally. However, TVM
supports many more features including cross-compilation, remote execution and
profiling/benchmarking.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 10 minutes 29.389 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 10 minutes 25.921 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-autotvm-relay-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/57a45d9bef1af358191e7d50043e652c/autotvm_relay_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">autotvm_relay_x86.py</span></code></a></p>
diff --git a/docs/tutorial/cross_compilation_and_rpc.html b/docs/tutorial/cross_compilation_and_rpc.html
index 6b7c7b9c5..b0794782d 100644
--- a/docs/tutorial/cross_compilation_and_rpc.html
+++ b/docs/tutorial/cross_compilation_and_rpc.html
... 232 lines suppressed ...