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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/04/16 00:19:40 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@351f31b51cd85648b66f2b344b96a7460052760b)
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 8497e0ac6 deploying docs (apache/tvm@351f31b51cd85648b66f2b344b96a7460052760b)
8497e0ac6 is described below
commit 8497e0ac6f77d5f852cd28c29455854cfde0eb1b
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sat Apr 16 00:19:36 2022 +0000
deploying docs (apache/tvm@351f31b51cd85648b66f2b344b96a7460052760b)
---
.../how_to/compile_models/from_mxnet.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 20 +-
.../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 | 18 +-
.../extend_tvm/bring_your_own_datatypes.rst.txt | 4 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 10 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 16 +-
.../tune_conv2d_layer_cuda.rst.txt | 405 +++++------
.../tune_network_cuda.rst.txt | 2 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_sparse_x86.rst.txt | 798 ++++++++++++++++++++-
.../tune_with_autotvm/sg_execution_times.rst.txt | 12 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 34 +-
.../work_with_microtvm/micro_autotune.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 12 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../work_with_schedules/sg_execution_times.rst.txt | 18 +-
.../how_to/work_with_schedules/tensorize.rst.txt | 2 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 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_relay_x86.rst.txt | 69 +-
.../tutorial/cross_compilation_and_rpc.rst.txt | 2 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 24 +-
.../tutorial/tensor_expr_get_started.rst.txt | 43 +-
docs/commit_hash | 2 +-
docs/how_to/compile_models/from_mxnet.html | 2 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 8 +-
docs/how_to/compile_models/sg_execution_times.html | 20 +-
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 18 +-
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 | 39 +-
docs/how_to/deploy_models/sg_execution_times.html | 18 +-
.../extend_tvm/bring_your_own_datatypes.html | 4 +-
docs/how_to/extend_tvm/sg_execution_times.html | 10 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 405 +++++------
.../tune_with_autoscheduler/tune_network_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autoscheduler/tune_sparse_x86.html | 798 ++++++++++++++++++++-
.../tune_with_autotvm/sg_execution_times.html | 12 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 34 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 16 +-
.../work_with_microtvm/sg_execution_times.html | 12 +-
.../how_to/work_with_relay/sg_execution_times.html | 8 +-
.../work_with_schedules/sg_execution_times.html | 18 +-
docs/how_to/work_with_schedules/tensorize.html | 2 +-
docs/reference/api/python/auto_scheduler.html | 4 +-
.../api/typedoc/classes/bytestreamreader.html | 12 +-
.../api/typedoc/classes/cachedcallstack.html | 34 +-
docs/reference/api/typedoc/classes/dldatatype.html | 12 +-
docs/reference/api/typedoc/classes/dldevice.html | 10 +-
.../reference/api/typedoc/classes/environment.html | 12 +-
docs/reference/api/typedoc/classes/ffilibrary.html | 20 +-
.../api/typedoc/classes/graphexecutor.html | 16 +-
docs/reference/api/typedoc/classes/instance.html | 40 +-
docs/reference/api/typedoc/classes/memory.html | 34 +-
docs/reference/api/typedoc/classes/module.html | 10 +-
docs/reference/api/typedoc/classes/ndarray.html | 22 +-
.../api/typedoc/classes/packedfunccell.html | 6 +-
docs/reference/api/typedoc/classes/rpcserver.html | 14 +-
docs/reference/api/typedoc/classes/scalar.html | 6 +-
.../api/typedoc/classes/webgpucontext.html | 12 +-
docs/reference/api/typedoc/enums/argtypecode.html | 30 +-
.../api/typedoc/enums/aynccallbackcode.html | 4 +-
.../api/typedoc/enums/dldatatypecode.html | 8 +-
.../api/typedoc/enums/rpcserverstate.html | 12 +-
docs/reference/api/typedoc/enums/sizeof.html | 18 +-
docs/reference/api/typedoc/index.html | 112 +--
.../api/typedoc/interfaces/disposable.html | 2 +-
.../api/typedoc/interfaces/functioninfo.html | 6 +-
.../api/typedoc/interfaces/libraryprovider.html | 4 +-
docs/searchindex.js | 2 +-
.../vta/tutorials/autotvm/sg_execution_times.html | 6 +-
.../tutorials/frontend/deploy_classification.html | 2 +-
.../vta/tutorials/frontend/deploy_detection.html | 2 +-
.../vta/tutorials/frontend/sg_execution_times.html | 6 +-
.../vta/tutorials/optimize/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/sg_execution_times.html | 6 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 2 +-
docs/tutorial/autotvm_relay_x86.html | 173 ++---
docs/tutorial/cross_compilation_and_rpc.html | 2 +-
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 24 +-
docs/tutorial/tensor_expr_get_started.html | 39 +-
111 files changed, 2687 insertions(+), 1172 deletions(-)
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 9ea0e5831..f2cc5fde3 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -98,7 +98,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip803b0a3a-6035-4765-a06c-8581bc5909be from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip67db9577-e96a-4c5c-8273-04062e07c5aa 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_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 93fe9b17a..a0c9134e3 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -201,7 +201,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.440 seconds)
+ **Total running time of the script:** ( 1 minutes 4.500 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
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 208c3f516..ab8075dae 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -79,7 +79,7 @@ Load a pretrained PyTorch model
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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+
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10%|# | 4.68M/44.7M [00:00<00:01, 27.9MB/s]
70%|####### | 31.4M/44.7M [00:00<00:00, 143MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 135MB/s]
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 ca3d703fb..109b28e41 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
@@ -5,14 +5,14 @@
Computation times
=================
-**04:41.387** total execution time for **how_to_compile_models** files:
+**04:39.864** total execution time for **how_to_compile_models** files:
-- **01:04.440**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **00:59.564**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:56.244**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:25.459**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:21.232**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:20.937**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:18.923**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:12.131**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.457**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:04.500**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **00:59.553**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:55.423**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:25.357**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:20.788**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:20.742**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:18.969**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:12.078**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.454**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
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 0c73e0d7c..40ab19085 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
@@ -393,7 +393,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7567 15.7586 15.8629 15.6674 0.0521
+ 15.6234 15.6349 15.7135 15.5296 0.0604
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 fe5ba44e2..51ba210d3 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
@@ -108,7 +108,7 @@ 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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/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').
@@ -253,7 +253,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 2.481 seconds)
+ **Total running time of the script:** ( 2 minutes 58.290 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 eb875af30..da37e7c3f 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -187,7 +187,7 @@ 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
-
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+
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@@ -344,7 +344,7 @@ 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)
- 90.2895 90.1853 92.5166 89.9874 0.3500
+ 90.1806 90.1495 91.6179 89.9756 0.1878
@@ -384,7 +384,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.050 seconds)
+ **Total running time of the script:** ( 1 minutes 3.795 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 8b3570da8..2196766b3 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
@@ -351,7 +351,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 119.9716 119.9055 121.8111 118.9488 0.3938
+ 118.9514 118.9186 121.8738 118.0731 0.4844
@@ -385,7 +385,7 @@ 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 53.883 seconds)
+ **Total running time of the script:** ( 1 minutes 57.873 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 3e4177772..0f43d247e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -221,7 +221,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 11.117 seconds)
+ **Total running time of the script:** ( 1 minutes 9.391 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 81d52b21f..50fbb77b7 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
@@ -137,7 +137,7 @@ 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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+
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@@ -202,7 +202,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 25.474 seconds)
+ **Total running time of the script:** ( 2 minutes 21.103 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 d728575cf..4182e629a 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
@@ -5,13 +5,13 @@
Computation times
=================
-**10:27.080** total execution time for **how_to_deploy_models** files:
+**10:19.589** total execution time for **how_to_deploy_models** files:
-- **03:02.481**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:25.474**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:53.883**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:11.117**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:05.050**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:27.508**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.368**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.197**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **02:58.290**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:21.103**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:57.873**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:09.391**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:03.795**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:27.182**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:21.776**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.179**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
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 7ce78c8ef..10fad31f4 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
@@ -423,7 +423,7 @@ 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.zipcc3bc45d-40fd-49a6-9ec8-1b9e57b89287 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip87c00332-7f47-42da-8a75-970612d26974 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
@@ -525,7 +525,7 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
.. code-block:: none
- Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
+ Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index 35be40ad0..8a0caa413 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
@@ -5,9 +5,9 @@
Computation times
=================
-**00:38.214** total execution time for **how_to_extend_tvm** files:
+**00:37.555** total execution time for **how_to_extend_tvm** files:
-- **00:34.706**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.247**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.060**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.202**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:34.169**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.187**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.007**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.192**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
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 e93360d26..c79251d9b 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
@@ -199,10 +199,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 6287us [6287us] (45.51%; 45.51%)
- FoldScaleAxis: 7529us [2us] (54.49%; 54.49%)
- FoldConstant: 7527us [1591us] (54.48%; 99.97%)
- InferType: 5936us [5936us] (42.96%; 78.87%)
+ InferType: 5881us [5881us] (44.91%; 44.91%)
+ FoldScaleAxis: 7213us [2us] (55.09%; 55.09%)
+ FoldConstant: 7211us [1499us] (55.07%; 99.97%)
+ InferType: 5712us [5712us] (43.62%; 79.21%)
@@ -239,10 +239,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 6044us [6044us] (44.57%; 44.57%)
- FoldScaleAxis: 7517us [2us] (55.43%; 55.43%)
- FoldConstant: 7515us [1551us] (55.42%; 99.97%)
- InferType: 5964us [5964us] (43.98%; 79.36%)
+ InferType: 5735us [5735us] (44.33%; 44.33%)
+ FoldScaleAxis: 7201us [2us] (55.67%; 55.67%)
+ FoldConstant: 7199us [1533us] (55.65%; 99.97%)
+ InferType: 5666us [5666us] (43.80%; 78.70%)
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index 1857a0d09..95acc2dec 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
@@ -295,7 +295,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 54.264198 ms
+ Convolution: 33.608237 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 40bb2cee6..2839c7f3e 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
@@ -626,7 +626,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 7.099950 ms
+ conv2d with tensor core: 8.191855 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 c6bf72c56..88750a1f9 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018656
- Baseline: 3.459400
+ Numpy running time: 0.018082
+ Baseline: 3.226418
@@ -209,7 +209,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.295284
+ Opt1: 0.302598
@@ -307,7 +307,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.333298
+ Opt2: 0.333762
@@ -398,7 +398,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118302
+ Opt3: 0.115119
@@ -516,7 +516,7 @@ flattening.
.. code-block:: none
- Opt4: 0.110819
+ Opt4: 0.112020
@@ -633,7 +633,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.111621
+ Opt5: 0.111046
@@ -753,7 +753,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.144608
+ Opt6: 0.144175
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 5f9b27d75..7dfc2cb8d 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
@@ -5,8 +5,8 @@
Computation times
=================
-**00:34.975** total execution time for **how_to_optimize_operators** files:
+**00:34.203** total execution time for **how_to_optimize_operators** files:
-- **00:32.346**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.418**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.211**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:31.709**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.346**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.148**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
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 23382d486..04df6fe2c 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
@@ -5,11 +5,11 @@
Computation times
=================
-**04:53.342** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:21.888**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:18.235**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:39.453**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:16.968**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.615**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.184**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**04:56.539** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:17.771**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:19.538**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:39.1000**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:22.188**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:08.630**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.413**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
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 8129d3394..2766600dd 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
@@ -221,136 +221,118 @@ cooperative fetching, unrolling and operator fusion.
bias: Buffer(bias_2: Pointer(float32), float32, [512], []),
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
- 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, [36]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=8)[0] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[6] = 0f32
- conv2d_nchw_1[8] = 0f32
- conv2d_nchw_1[10] = 0f32
- conv2d_nchw_1[12] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[5] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 32;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [252]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [192]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [49], [], scope="local", align=16)[0] = 0f32
conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[14] = 0f32
+ conv2d_nchw_1[21] = 0f32
+ conv2d_nchw_1[1] = 0f32
+ conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[15] = 0f32
+ conv2d_nchw_1[22] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[16] = 0f32
+ conv2d_nchw_1[23] = 0f32
+ conv2d_nchw_1[3] = 0f32
+ conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[17] = 0f32
+ conv2d_nchw_1[24] = 0f32
+ conv2d_nchw_1[4] = 0f32
conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[18] = 0f32
+ conv2d_nchw_1[25] = 0f32
+ conv2d_nchw_1[5] = 0f32
+ conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[19] = 0f32
+ conv2d_nchw_1[26] = 0f32
+ conv2d_nchw_1[6] = 0f32
conv2d_nchw_1[13] = 0f32
+ conv2d_nchw_1[20] = 0f32
+ conv2d_nchw_1[27] = 0f32
for (rc.outer.outer: int32, 0, 128) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_2: int32 = (rc.outer.outer*36)
- let cse_var_1: int32 = (ry.outer.outer*3)
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_2: int32 = (rc.outer.outer*196)
+ let cse_var_1: int32 = (rc.outer.outer*36)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [36], [], scope="shared")[(threadIdx.x_1*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, 3))), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 8)], 0f32, dtype=float32)
- pad_temp.shared_1[((threadIdx.x_1*3) + 1)] = @tir.if_then_else(((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 7)], 0f32, dtype=float32)
- pad_temp.shared_1[((threadIdx.x_1*3) + 2)] = @tir.if_then_else((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (floormod(threadIdx.x_1, 3) < 2)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 6)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [252], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((7 <= threadIdx.x_1) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((cse_var_2 + threadIdx.x_1) + rx.outer.outer) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 28)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + ((floordiv(threadIdx.x_1, 7) + 4)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 8), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtyp [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 84)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 12), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 16), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dt [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 140)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 20), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 24), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dt [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 28), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else((((floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 32), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1: Buffer(kernel.shared, float32, [192], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 28)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 7), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 4), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 84)] = kernel[((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 4), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 140)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 35), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ if @tir.likely((threadIdx.x_2 < 24), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 64512)]
}
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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((floordiv(threadIdx.x_2, 4) + 16), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 64), 12), 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((floordiv(threadIdx.x_2, 4) + 32), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 128), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 64), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 256), 12), 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((floordiv(threadIdx.x_2, 4) + 80), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 320), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 112), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 12), 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((floordiv(threadIdx.x_2, 4) + 128), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 512), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 160), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 640), 12), 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((floordiv(threadIdx.x_2, 4) + 176), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 704), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 832)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 208), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 832), 12), 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((floordiv(threadIdx.x_2, 4) + 224), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1024)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 256), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1024), 12), 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((floordiv(threadIdx.x_2, 4) + 272), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1088), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1216)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 304), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1216), 12), 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((floordiv(threadIdx.x_2, 4) + 320), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1280), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1408)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 352), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1408), 12), 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((floordiv(threadIdx.x_2, 4) + 368), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1472), 12), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- for (ff.outer.inner: int32, 0, 2) {
+ for (ry.outer.inner: int32, 0, 3) {
for (rc.inner: int32, 0, 4) {
- let cse_var_16: int32 = (rc.inner*9)
- let cse_var_15: int32 = (ff.outer.inner + 8)
- let cse_var_14: int32 = (ff.outer.inner + 6)
- let cse_var_13: int32 = (ff.outer.inner + 4)
- let cse_var_12: int32 = (ff.outer.inner + 2)
- let cse_var_11: int32 = (ff.outer.inner + 12)
- let cse_var_10: int32 = (ff.outer.inner + 10)
- let cse_var_9: int32 = (cse_var_16 + 1)
- let cse_var_8: int32 = (cse_var_16 + 2)
- let cse_var_7: int32 = (cse_var_16 + 3)
- let cse_var_6: int32 = (cse_var_16 + 4)
- let cse_var_5: int32 = (cse_var_16 + 5)
- let cse_var_4: int32 = (cse_var_16 + 6)
- let cse_var_3: int32 = (cse_var_16 + 7)
- {
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_16]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_9]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_9]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[cse_var_3]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_3]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[(cse_var_16 + 8)]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- }
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
}
}
}
}
}
- for (i1.inner: int32, 0, 2) {
- compute[((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[i1.inner] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[(i1.inner + 2)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[(i1.inner + 6)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[(i1.inner + 10)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
+ for (i2.inner: int32, 0, 7) {
+ compute[((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[i2.inner] + bias[((blockIdx.x*16) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 196)] = max((conv2d_nchw_1[(i2.inner + 7)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 4)]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 392)] = max((conv2d_nchw_1[(i2.inner + 14)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 8)]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 588)] = max((conv2d_nchw_1[(i2.inner + 21)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 12)]), 0f32)
}
}
}
@@ -403,7 +385,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.448 ms
+ Execution time of this operator: 0.351 ms
@@ -448,36 +430,36 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
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_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
+ conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_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=4)
+ 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=4)
+ conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=7)
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_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=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=7)
+ conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
+ 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=4)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
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_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=3)
+ 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=1)
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 [...]
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_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
+ compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=4)
+ compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=4)
+ compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=7)
compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
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=1)
- compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
- compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
+ compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
+ compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -496,12 +478,12 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
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)
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=28)
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=3)
+ 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=28)
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", 64)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
@@ -521,92 +503,99 @@ 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[36];
- __shared__ float kernel_shared[1536];
+ extern "C" __global__ void __launch_bounds__(28) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[252];
+ __shared__ float kernel_shared[192];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
- conv2d_nchw[8] = 0.000000e+00f;
- conv2d_nchw[10] = 0.000000e+00f;
- conv2d_nchw[12] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[14] = 0.000000e+00f;
+ conv2d_nchw[21] = 0.000000e+00f;
+ conv2d_nchw[1] = 0.000000e+00f;
+ conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[15] = 0.000000e+00f;
+ conv2d_nchw[22] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[16] = 0.000000e+00f;
+ conv2d_nchw[23] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
+ conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[17] = 0.000000e+00f;
+ conv2d_nchw[24] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[18] = 0.000000e+00f;
+ conv2d_nchw[25] = 0.000000e+00f;
+ conv2d_nchw[5] = 0.000000e+00f;
+ conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[19] = 0.000000e+00f;
+ conv2d_nchw[26] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[13] = 0.000000e+00f;
+ conv2d_nchw[20] = 0.000000e+00f;
+ conv2d_nchw[27] = 0.000000e+00f;
for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
__syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 3)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((int)threadIdx.x) % 3))) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 8)] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) * 3) + 1)] = (((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 7)] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) * 3) + 2)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && ((((int)threadIdx.x) % 3) < 2)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 6)] : 0.000000e+00f);
+ pad_temp_shared[((int)threadIdx.x)] = ((((7 <= ((int)threadIdx.x)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((rc_outer_outer * 196) + ((int)threadIdx.x)) + rx_outer_outer) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 28)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((rc_outer_outer * 196) + ((int)threadIdx.x)) + rx_outer_outer) + 20)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 56) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 84)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 84) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 3) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 112) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 140)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 140) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 2) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 168)] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 168) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 196)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 196) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 1) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = ((((((int)threadIdx.x) < 21) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 224) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 5) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 28)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 28) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 4) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 56)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 56) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 8) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 84)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 112) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 4) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 140)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 140) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 8) % 12) * 3)) + rx_outer_outer)];
+ if (((int)threadIdx.x) < 24) {
+ kernel_shared[(((int)threadIdx.x) + 168)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 64512)];
}
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 73728)];
- kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 256) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 147456)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 448) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 221184)];
- kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 640) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 294912)];
- kernel_shared[(((int)threadIdx.x) + 832)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 832) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 368640)];
- kernel_shared[(((int)threadIdx.x) + 1024)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1024) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 442368)];
- kernel_shared[(((int)threadIdx.x) + 1216)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1216) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 516096)];
- kernel_shared[(((int)threadIdx.x) + 1408)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1408) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
__syncthreads();
- for (int ff_outer_inner = 0; ff_outer_inner < 2; ++ff_outer_inner) {
+ for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
for (int rc_inner = 0; rc_inner < 4; ++rc_inner) {
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[(rc_inner * 9)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 1)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[((rc_inner * 9) + 1)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 7)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 7)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 8)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
}
}
}
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- compute[(((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[i1_inner] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[(i1_inner + 2)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[(i1_inner + 4)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[(i1_inner + 6)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[(i1_inner + 8)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[(i1_inner + 10)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[(i1_inner + 12)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
+ for (int i2_inner = 0; i2_inner < 7; ++i2_inner) {
+ compute[((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[i2_inner] + bias[((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 196)] = max((conv2d_nchw[(i2_inner + 7)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 4)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 392)] = max((conv2d_nchw[(i2_inner + 14)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 8)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 588)] = max((conv2d_nchw[(i2_inner + 21)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 12)]), 0.000000e+00f);
}
}
@@ -665,7 +654,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 21.888 seconds)
+ **Total running time of the script:** ( 2 minutes 17.771 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 004eac4ed..6bbd851e8 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
@@ -614,7 +614,7 @@ 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)
- 10.0732 10.0926 10.1119 10.0150 0.0419
+ 9.6715 9.6668 9.6970 9.6506 0.0193
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 7f22a08b5..3d287b295 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
@@ -633,7 +633,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 740.6048 739.1237 745.2095 737.4813 3.3243
+ 762.0476 761.2159 765.8830 759.0438 2.8534
@@ -658,7 +658,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.235 seconds)
+ **Total running time of the script:** ( 1 minutes 19.538 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 910b6af27..6b7dcda10 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
@@ -362,26 +362,790 @@ 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} {
- for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
- allocate(compute_3: Pointer(global float32), float32, [2048]), storage_scope = global {
- for (i.outer.inner: int32, 0, 32) {
- for (i.inner.init: int32, 0, 4) {
- for (j.init: int32, 0, 16) {
- compute_4: Buffer(compute_3, float32, [2048], [])[(((i.outer.inner*64) + (i.inner.init*16)) + j.init)] = 0f32
- }
- }
- for (elem_idx: int32, 0, (placeholder_3[(i0.outer.i1.outer.fused + 1)] - placeholder_3[i0.outer.i1.outer.fused])) {
- for (i.inner: int32, 0, 4) {
- for (j: int32, 0, 16) {
- let cse_var_1: int32 = (((i.outer.inner*64) + (i.inner*16)) + j)
- compute_4[cse_var_1] = (compute_4[cse_var_1] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + j)]*max(placeholder[(((i.outer.inner*1024) + (i.inner*256)) + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ for (i0.outer: int32, 0, 2) "parallel" {
+ allocate(compute_3: Pointer(global float32), float32, [1024]), storage_scope = global;
+ for (i1.outer: int32, 0, 32) {
+ for (i.outer.inner: int32, 0, 4) {
+ let cse_var_1: int32 = (i.outer.inner*256)
+ {
+ compute_4: Buffer(compute_3, float32, [1024], [])[cse_var_1] = 0f32
+ compute_4[(cse_var_1 + 1)] = 0f32
+ compute_4[(cse_var_1 + 2)] = 0f32
+ compute_4[(cse_var_1 + 3)] = 0f32
+ compute_4[(cse_var_1 + 4)] = 0f32
+ compute_4[(cse_var_1 + 5)] = 0f32
+ compute_4[(cse_var_1 + 6)] = 0f32
+ compute_4[(cse_var_1 + 7)] = 0f32
+ compute_4[(cse_var_1 + 8)] = 0f32
+ compute_4[(cse_var_1 + 9)] = 0f32
+ compute_4[(cse_var_1 + 10)] = 0f32
+ compute_4[(cse_var_1 + 11)] = 0f32
+ compute_4[(cse_var_1 + 12)] = 0f32
+ compute_4[(cse_var_1 + 13)] = 0f32
+ compute_4[(cse_var_1 + 14)] = 0f32
+ compute_4[(cse_var_1 + 15)] = 0f32
+ compute_4[(cse_var_1 + 16)] = 0f32
+ compute_4[(cse_var_1 + 17)] = 0f32
+ compute_4[(cse_var_1 + 18)] = 0f32
+ compute_4[(cse_var_1 + 19)] = 0f32
+ compute_4[(cse_var_1 + 20)] = 0f32
+ compute_4[(cse_var_1 + 21)] = 0f32
+ compute_4[(cse_var_1 + 22)] = 0f32
+ compute_4[(cse_var_1 + 23)] = 0f32
+ compute_4[(cse_var_1 + 24)] = 0f32
+ compute_4[(cse_var_1 + 25)] = 0f32
+ compute_4[(cse_var_1 + 26)] = 0f32
+ compute_4[(cse_var_1 + 27)] = 0f32
+ compute_4[(cse_var_1 + 28)] = 0f32
+ compute_4[(cse_var_1 + 29)] = 0f32
+ compute_4[(cse_var_1 + 30)] = 0f32
+ compute_4[(cse_var_1 + 31)] = 0f32
+ compute_4[(cse_var_1 + 32)] = 0f32
+ compute_4[(cse_var_1 + 33)] = 0f32
+ compute_4[(cse_var_1 + 34)] = 0f32
+ compute_4[(cse_var_1 + 35)] = 0f32
+ compute_4[(cse_var_1 + 36)] = 0f32
+ compute_4[(cse_var_1 + 37)] = 0f32
+ compute_4[(cse_var_1 + 38)] = 0f32
+ compute_4[(cse_var_1 + 39)] = 0f32
+ compute_4[(cse_var_1 + 40)] = 0f32
+ compute_4[(cse_var_1 + 41)] = 0f32
+ compute_4[(cse_var_1 + 42)] = 0f32
+ compute_4[(cse_var_1 + 43)] = 0f32
+ compute_4[(cse_var_1 + 44)] = 0f32
+ compute_4[(cse_var_1 + 45)] = 0f32
+ compute_4[(cse_var_1 + 46)] = 0f32
+ compute_4[(cse_var_1 + 47)] = 0f32
+ compute_4[(cse_var_1 + 48)] = 0f32
+ compute_4[(cse_var_1 + 49)] = 0f32
+ compute_4[(cse_var_1 + 50)] = 0f32
+ compute_4[(cse_var_1 + 51)] = 0f32
+ compute_4[(cse_var_1 + 52)] = 0f32
+ compute_4[(cse_var_1 + 53)] = 0f32
+ compute_4[(cse_var_1 + 54)] = 0f32
+ compute_4[(cse_var_1 + 55)] = 0f32
+ compute_4[(cse_var_1 + 56)] = 0f32
+ compute_4[(cse_var_1 + 57)] = 0f32
+ compute_4[(cse_var_1 + 58)] = 0f32
+ compute_4[(cse_var_1 + 59)] = 0f32
+ compute_4[(cse_var_1 + 60)] = 0f32
+ compute_4[(cse_var_1 + 61)] = 0f32
+ compute_4[(cse_var_1 + 62)] = 0f32
+ compute_4[(cse_var_1 + 63)] = 0f32
+ compute_4[(cse_var_1 + 64)] = 0f32
+ compute_4[(cse_var_1 + 65)] = 0f32
+ compute_4[(cse_var_1 + 66)] = 0f32
+ compute_4[(cse_var_1 + 67)] = 0f32
+ compute_4[(cse_var_1 + 68)] = 0f32
+ compute_4[(cse_var_1 + 69)] = 0f32
+ compute_4[(cse_var_1 + 70)] = 0f32
+ compute_4[(cse_var_1 + 71)] = 0f32
+ compute_4[(cse_var_1 + 72)] = 0f32
+ compute_4[(cse_var_1 + 73)] = 0f32
+ compute_4[(cse_var_1 + 74)] = 0f32
+ compute_4[(cse_var_1 + 75)] = 0f32
+ compute_4[(cse_var_1 + 76)] = 0f32
+ compute_4[(cse_var_1 + 77)] = 0f32
+ compute_4[(cse_var_1 + 78)] = 0f32
+ compute_4[(cse_var_1 + 79)] = 0f32
+ compute_4[(cse_var_1 + 80)] = 0f32
+ compute_4[(cse_var_1 + 81)] = 0f32
+ compute_4[(cse_var_1 + 82)] = 0f32
+ compute_4[(cse_var_1 + 83)] = 0f32
+ compute_4[(cse_var_1 + 84)] = 0f32
+ compute_4[(cse_var_1 + 85)] = 0f32
+ compute_4[(cse_var_1 + 86)] = 0f32
+ compute_4[(cse_var_1 + 87)] = 0f32
+ compute_4[(cse_var_1 + 88)] = 0f32
+ compute_4[(cse_var_1 + 89)] = 0f32
+ compute_4[(cse_var_1 + 90)] = 0f32
+ compute_4[(cse_var_1 + 91)] = 0f32
+ compute_4[(cse_var_1 + 92)] = 0f32
+ compute_4[(cse_var_1 + 93)] = 0f32
+ compute_4[(cse_var_1 + 94)] = 0f32
+ compute_4[(cse_var_1 + 95)] = 0f32
+ compute_4[(cse_var_1 + 96)] = 0f32
+ compute_4[(cse_var_1 + 97)] = 0f32
+ compute_4[(cse_var_1 + 98)] = 0f32
+ compute_4[(cse_var_1 + 99)] = 0f32
+ compute_4[(cse_var_1 + 100)] = 0f32
+ compute_4[(cse_var_1 + 101)] = 0f32
+ compute_4[(cse_var_1 + 102)] = 0f32
+ compute_4[(cse_var_1 + 103)] = 0f32
+ compute_4[(cse_var_1 + 104)] = 0f32
+ compute_4[(cse_var_1 + 105)] = 0f32
+ compute_4[(cse_var_1 + 106)] = 0f32
+ compute_4[(cse_var_1 + 107)] = 0f32
+ compute_4[(cse_var_1 + 108)] = 0f32
+ compute_4[(cse_var_1 + 109)] = 0f32
+ compute_4[(cse_var_1 + 110)] = 0f32
+ compute_4[(cse_var_1 + 111)] = 0f32
+ compute_4[(cse_var_1 + 112)] = 0f32
+ compute_4[(cse_var_1 + 113)] = 0f32
+ compute_4[(cse_var_1 + 114)] = 0f32
+ compute_4[(cse_var_1 + 115)] = 0f32
+ compute_4[(cse_var_1 + 116)] = 0f32
+ compute_4[(cse_var_1 + 117)] = 0f32
+ compute_4[(cse_var_1 + 118)] = 0f32
+ compute_4[(cse_var_1 + 119)] = 0f32
+ compute_4[(cse_var_1 + 120)] = 0f32
+ compute_4[(cse_var_1 + 121)] = 0f32
+ compute_4[(cse_var_1 + 122)] = 0f32
+ compute_4[(cse_var_1 + 123)] = 0f32
+ compute_4[(cse_var_1 + 124)] = 0f32
+ compute_4[(cse_var_1 + 125)] = 0f32
+ compute_4[(cse_var_1 + 126)] = 0f32
+ compute_4[(cse_var_1 + 127)] = 0f32
+ compute_4[(cse_var_1 + 128)] = 0f32
+ compute_4[(cse_var_1 + 129)] = 0f32
+ compute_4[(cse_var_1 + 130)] = 0f32
+ compute_4[(cse_var_1 + 131)] = 0f32
+ compute_4[(cse_var_1 + 132)] = 0f32
+ compute_4[(cse_var_1 + 133)] = 0f32
+ compute_4[(cse_var_1 + 134)] = 0f32
+ compute_4[(cse_var_1 + 135)] = 0f32
+ compute_4[(cse_var_1 + 136)] = 0f32
+ compute_4[(cse_var_1 + 137)] = 0f32
+ compute_4[(cse_var_1 + 138)] = 0f32
+ compute_4[(cse_var_1 + 139)] = 0f32
+ compute_4[(cse_var_1 + 140)] = 0f32
+ compute_4[(cse_var_1 + 141)] = 0f32
+ compute_4[(cse_var_1 + 142)] = 0f32
+ compute_4[(cse_var_1 + 143)] = 0f32
+ compute_4[(cse_var_1 + 144)] = 0f32
+ compute_4[(cse_var_1 + 145)] = 0f32
+ compute_4[(cse_var_1 + 146)] = 0f32
+ compute_4[(cse_var_1 + 147)] = 0f32
+ compute_4[(cse_var_1 + 148)] = 0f32
+ compute_4[(cse_var_1 + 149)] = 0f32
+ compute_4[(cse_var_1 + 150)] = 0f32
+ compute_4[(cse_var_1 + 151)] = 0f32
+ compute_4[(cse_var_1 + 152)] = 0f32
+ compute_4[(cse_var_1 + 153)] = 0f32
+ compute_4[(cse_var_1 + 154)] = 0f32
+ compute_4[(cse_var_1 + 155)] = 0f32
+ compute_4[(cse_var_1 + 156)] = 0f32
+ compute_4[(cse_var_1 + 157)] = 0f32
+ compute_4[(cse_var_1 + 158)] = 0f32
+ compute_4[(cse_var_1 + 159)] = 0f32
+ compute_4[(cse_var_1 + 160)] = 0f32
+ compute_4[(cse_var_1 + 161)] = 0f32
+ compute_4[(cse_var_1 + 162)] = 0f32
+ compute_4[(cse_var_1 + 163)] = 0f32
+ compute_4[(cse_var_1 + 164)] = 0f32
+ compute_4[(cse_var_1 + 165)] = 0f32
+ compute_4[(cse_var_1 + 166)] = 0f32
+ compute_4[(cse_var_1 + 167)] = 0f32
+ compute_4[(cse_var_1 + 168)] = 0f32
+ compute_4[(cse_var_1 + 169)] = 0f32
+ compute_4[(cse_var_1 + 170)] = 0f32
+ compute_4[(cse_var_1 + 171)] = 0f32
+ compute_4[(cse_var_1 + 172)] = 0f32
+ compute_4[(cse_var_1 + 173)] = 0f32
+ compute_4[(cse_var_1 + 174)] = 0f32
+ compute_4[(cse_var_1 + 175)] = 0f32
+ compute_4[(cse_var_1 + 176)] = 0f32
+ compute_4[(cse_var_1 + 177)] = 0f32
+ compute_4[(cse_var_1 + 178)] = 0f32
+ compute_4[(cse_var_1 + 179)] = 0f32
+ compute_4[(cse_var_1 + 180)] = 0f32
+ compute_4[(cse_var_1 + 181)] = 0f32
+ compute_4[(cse_var_1 + 182)] = 0f32
+ compute_4[(cse_var_1 + 183)] = 0f32
+ compute_4[(cse_var_1 + 184)] = 0f32
+ compute_4[(cse_var_1 + 185)] = 0f32
+ compute_4[(cse_var_1 + 186)] = 0f32
+ compute_4[(cse_var_1 + 187)] = 0f32
+ compute_4[(cse_var_1 + 188)] = 0f32
+ compute_4[(cse_var_1 + 189)] = 0f32
+ compute_4[(cse_var_1 + 190)] = 0f32
+ compute_4[(cse_var_1 + 191)] = 0f32
+ compute_4[(cse_var_1 + 192)] = 0f32
+ compute_4[(cse_var_1 + 193)] = 0f32
+ compute_4[(cse_var_1 + 194)] = 0f32
+ compute_4[(cse_var_1 + 195)] = 0f32
+ compute_4[(cse_var_1 + 196)] = 0f32
+ compute_4[(cse_var_1 + 197)] = 0f32
+ compute_4[(cse_var_1 + 198)] = 0f32
+ compute_4[(cse_var_1 + 199)] = 0f32
+ compute_4[(cse_var_1 + 200)] = 0f32
+ compute_4[(cse_var_1 + 201)] = 0f32
+ compute_4[(cse_var_1 + 202)] = 0f32
+ compute_4[(cse_var_1 + 203)] = 0f32
+ compute_4[(cse_var_1 + 204)] = 0f32
+ compute_4[(cse_var_1 + 205)] = 0f32
+ compute_4[(cse_var_1 + 206)] = 0f32
+ compute_4[(cse_var_1 + 207)] = 0f32
+ compute_4[(cse_var_1 + 208)] = 0f32
+ compute_4[(cse_var_1 + 209)] = 0f32
+ compute_4[(cse_var_1 + 210)] = 0f32
+ compute_4[(cse_var_1 + 211)] = 0f32
+ compute_4[(cse_var_1 + 212)] = 0f32
+ compute_4[(cse_var_1 + 213)] = 0f32
+ compute_4[(cse_var_1 + 214)] = 0f32
+ compute_4[(cse_var_1 + 215)] = 0f32
+ compute_4[(cse_var_1 + 216)] = 0f32
+ compute_4[(cse_var_1 + 217)] = 0f32
+ compute_4[(cse_var_1 + 218)] = 0f32
+ compute_4[(cse_var_1 + 219)] = 0f32
+ compute_4[(cse_var_1 + 220)] = 0f32
+ compute_4[(cse_var_1 + 221)] = 0f32
+ compute_4[(cse_var_1 + 222)] = 0f32
+ compute_4[(cse_var_1 + 223)] = 0f32
+ compute_4[(cse_var_1 + 224)] = 0f32
+ compute_4[(cse_var_1 + 225)] = 0f32
+ compute_4[(cse_var_1 + 226)] = 0f32
+ compute_4[(cse_var_1 + 227)] = 0f32
+ compute_4[(cse_var_1 + 228)] = 0f32
+ compute_4[(cse_var_1 + 229)] = 0f32
+ compute_4[(cse_var_1 + 230)] = 0f32
+ compute_4[(cse_var_1 + 231)] = 0f32
+ compute_4[(cse_var_1 + 232)] = 0f32
+ compute_4[(cse_var_1 + 233)] = 0f32
+ compute_4[(cse_var_1 + 234)] = 0f32
+ compute_4[(cse_var_1 + 235)] = 0f32
+ compute_4[(cse_var_1 + 236)] = 0f32
+ compute_4[(cse_var_1 + 237)] = 0f32
+ compute_4[(cse_var_1 + 238)] = 0f32
+ compute_4[(cse_var_1 + 239)] = 0f32
+ compute_4[(cse_var_1 + 240)] = 0f32
+ compute_4[(cse_var_1 + 241)] = 0f32
+ compute_4[(cse_var_1 + 242)] = 0f32
+ compute_4[(cse_var_1 + 243)] = 0f32
+ compute_4[(cse_var_1 + 244)] = 0f32
+ compute_4[(cse_var_1 + 245)] = 0f32
+ compute_4[(cse_var_1 + 246)] = 0f32
+ compute_4[(cse_var_1 + 247)] = 0f32
+ compute_4[(cse_var_1 + 248)] = 0f32
+ compute_4[(cse_var_1 + 249)] = 0f32
+ compute_4[(cse_var_1 + 250)] = 0f32
+ compute_4[(cse_var_1 + 251)] = 0f32
+ compute_4[(cse_var_1 + 252)] = 0f32
+ compute_4[(cse_var_1 + 253)] = 0f32
+ compute_4[(cse_var_1 + 254)] = 0f32
+ compute_4[(cse_var_1 + 255)] = 0f32
+ for (elem_idx: int32, 0, (placeholder_3[(i1.outer + 1)] - placeholder_3[i1.outer])) {
+ let cse_var_258: int32 = (cse_var_1 + 184)
+ let cse_var_257: int32 = (cse_var_1 + 183)
+ let cse_var_256: int32 = (cse_var_1 + 182)
+ let cse_var_255: int32 = (cse_var_1 + 181)
+ let cse_var_254: int32 = (cse_var_1 + 180)
+ let cse_var_253: int32 = (cse_var_1 + 18)
+ let cse_var_252: int32 = (cse_var_1 + 179)
+ let cse_var_251: int32 = (cse_var_1 + 178)
+ let cse_var_250: int32 = (cse_var_1 + 177)
+ let cse_var_249: int32 = (cse_var_1 + 176)
+ let cse_var_248: int32 = (cse_var_1 + 175)
+ let cse_var_247: int32 = (cse_var_1 + 174)
+ let cse_var_246: int32 = (cse_var_1 + 173)
+ let cse_var_245: int32 = (cse_var_1 + 172)
+ let cse_var_244: int32 = (cse_var_1 + 171)
+ let cse_var_243: int32 = (cse_var_1 + 214)
+ let cse_var_242: int32 = (cse_var_1 + 17)
+ let cse_var_241: int32 = (cse_var_1 + 169)
+ let cse_var_240: int32 = (cse_var_1 + 168)
+ let cse_var_239: int32 = (cse_var_1 + 167)
+ let cse_var_238: int32 = (cse_var_1 + 166)
+ let cse_var_237: int32 = (cse_var_1 + 165)
+ let cse_var_236: int32 = (cse_var_1 + 164)
+ let cse_var_235: int32 = (cse_var_1 + 163)
+ let cse_var_234: int32 = (cse_var_1 + 162)
+ let cse_var_233: int32 = (cse_var_1 + 161)
+ let cse_var_232: int32 = (cse_var_1 + 160)
+ let cse_var_231: int32 = (cse_var_1 + 16)
+ let cse_var_230: int32 = (cse_var_1 + 159)
+ let cse_var_229: int32 = (cse_var_1 + 158)
+ let cse_var_228: int32 = (cse_var_1 + 157)
+ let cse_var_227: int32 = (cse_var_1 + 170)
+ let cse_var_226: int32 = (cse_var_1 + 212)
+ let cse_var_225: int32 = (cse_var_1 + 211)
+ let cse_var_224: int32 = (cse_var_1 + 210)
+ let cse_var_223: int32 = (cse_var_1 + 21)
+ let cse_var_222: int32 = (cse_var_1 + 209)
+ let cse_var_221: int32 = (cse_var_1 + 208)
+ let cse_var_220: int32 = (cse_var_1 + 207)
+ let cse_var_219: int32 = (cse_var_1 + 206)
+ let cse_var_218: int32 = (cse_var_1 + 205)
+ let cse_var_217: int32 = (cse_var_1 + 204)
+ let cse_var_216: int32 = (cse_var_1 + 203)
+ let cse_var_215: int32 = (cse_var_1 + 202)
+ let cse_var_214: int32 = (cse_var_1 + 201)
+ let cse_var_213: int32 = (cse_var_1 + 200)
+ let cse_var_212: int32 = (cse_var_1 + 20)
+ let cse_var_211: int32 = (cse_var_1 + 185)
+ let cse_var_210: int32 = (cse_var_1 + 199)
+ let cse_var_209: int32 = (cse_var_1 + 198)
+ let cse_var_208: int32 = (cse_var_1 + 197)
+ let cse_var_207: int32 = (cse_var_1 + 196)
+ let cse_var_206: int32 = (cse_var_1 + 195)
+ let cse_var_205: int32 = (cse_var_1 + 194)
+ let cse_var_204: int32 = (cse_var_1 + 193)
+ let cse_var_203: int32 = (cse_var_1 + 192)
+ let cse_var_202: int32 = (cse_var_1 + 191)
+ let cse_var_201: int32 = (cse_var_1 + 190)
+ let cse_var_200: int32 = (cse_var_1 + 19)
+ let cse_var_199: int32 = (cse_var_1 + 189)
+ let cse_var_198: int32 = (cse_var_1 + 188)
+ let cse_var_197: int32 = (cse_var_1 + 187)
+ let cse_var_196: int32 = (cse_var_1 + 186)
+ let cse_var_195: int32 = (cse_var_1 + 2)
+ let cse_var_194: int32 = (cse_var_1 + 126)
+ let cse_var_193: int32 = (cse_var_1 + 125)
+ let cse_var_192: int32 = (cse_var_1 + 124)
+ let cse_var_191: int32 = (cse_var_1 + 123)
+ let cse_var_190: int32 = (cse_var_1 + 122)
+ let cse_var_189: int32 = (cse_var_1 + 121)
+ let cse_var_188: int32 = (cse_var_1 + 120)
+ let cse_var_187: int32 = (cse_var_1 + 12)
+ let cse_var_186: int32 = (cse_var_1 + 119)
+ let cse_var_185: int32 = (cse_var_1 + 118)
+ let cse_var_184: int32 = (cse_var_1 + 117)
+ let cse_var_183: int32 = (cse_var_1 + 116)
+ let cse_var_182: int32 = (cse_var_1 + 115)
+ let cse_var_181: int32 = (cse_var_1 + 114)
+ let cse_var_180: int32 = (cse_var_1 + 113)
+ let cse_var_179: int32 = (cse_var_1 + 156)
+ let cse_var_178: int32 = (cse_var_1 + 111)
+ let cse_var_177: int32 = (cse_var_1 + 110)
+ let cse_var_176: int32 = (cse_var_1 + 11)
+ let cse_var_175: int32 = (cse_var_1 + 109)
+ let cse_var_174: int32 = (cse_var_1 + 108)
+ let cse_var_173: int32 = (cse_var_1 + 107)
+ let cse_var_172: int32 = (cse_var_1 + 106)
+ let cse_var_171: int32 = (cse_var_1 + 105)
+ let cse_var_170: int32 = (cse_var_1 + 104)
+ let cse_var_169: int32 = (cse_var_1 + 103)
+ let cse_var_168: int32 = (cse_var_1 + 102)
+ let cse_var_167: int32 = (cse_var_1 + 101)
+ let cse_var_166: int32 = (cse_var_1 + 100)
+ let cse_var_165: int32 = (cse_var_1 + 10)
+ let cse_var_164: int32 = (cse_var_1 + 1)
+ let cse_var_163: int32 = (cse_var_1 + 112)
+ let cse_var_162: int32 = (cse_var_1 + 155)
+ let cse_var_161: int32 = (cse_var_1 + 154)
+ let cse_var_160: int32 = (cse_var_1 + 153)
+ let cse_var_159: int32 = (cse_var_1 + 152)
+ let cse_var_158: int32 = (cse_var_1 + 151)
+ let cse_var_157: int32 = (cse_var_1 + 150)
+ let cse_var_156: int32 = (cse_var_1 + 15)
+ let cse_var_155: int32 = (cse_var_1 + 149)
+ let cse_var_154: int32 = (cse_var_1 + 148)
+ let cse_var_153: int32 = (cse_var_1 + 147)
+ let cse_var_152: int32 = (cse_var_1 + 146)
+ let cse_var_151: int32 = (cse_var_1 + 145)
+ let cse_var_150: int32 = (cse_var_1 + 144)
+ let cse_var_149: int32 = (cse_var_1 + 143)
+ let cse_var_148: int32 = (cse_var_1 + 142)
+ let cse_var_147: int32 = (cse_var_1 + 127)
+ let cse_var_146: int32 = (cse_var_1 + 140)
+ let cse_var_145: int32 = (cse_var_1 + 14)
+ let cse_var_144: int32 = (cse_var_1 + 139)
+ let cse_var_143: int32 = (cse_var_1 + 138)
+ let cse_var_142: int32 = (cse_var_1 + 137)
+ let cse_var_141: int32 = (cse_var_1 + 136)
+ let cse_var_140: int32 = (cse_var_1 + 135)
+ let cse_var_139: int32 = (cse_var_1 + 134)
+ let cse_var_138: int32 = (cse_var_1 + 133)
+ let cse_var_137: int32 = (cse_var_1 + 132)
+ let cse_var_136: int32 = (cse_var_1 + 131)
+ let cse_var_135: int32 = (cse_var_1 + 130)
+ let cse_var_134: int32 = (cse_var_1 + 13)
+ let cse_var_133: int32 = (cse_var_1 + 129)
+ let cse_var_132: int32 = (cse_var_1 + 128)
+ let cse_var_131: int32 = (cse_var_1 + 141)
+ let cse_var_130: int32 = (cse_var_1 + 70)
+ let cse_var_129: int32 = (cse_var_1 + 7)
+ let cse_var_128: int32 = (cse_var_1 + 69)
+ let cse_var_127: int32 = (cse_var_1 + 68)
+ let cse_var_126: int32 = (cse_var_1 + 67)
+ let cse_var_125: int32 = (cse_var_1 + 66)
+ let cse_var_124: int32 = (cse_var_1 + 65)
+ let cse_var_123: int32 = (cse_var_1 + 64)
+ let cse_var_122: int32 = (cse_var_1 + 63)
+ let cse_var_121: int32 = (cse_var_1 + 62)
+ let cse_var_120: int32 = (cse_var_1 + 61)
+ let cse_var_119: int32 = (cse_var_1 + 60)
+ let cse_var_118: int32 = (cse_var_1 + 6)
+ let cse_var_117: int32 = (cse_var_1 + 59)
+ let cse_var_116: int32 = (cse_var_1 + 58)
+ let cse_var_115: int32 = (cse_var_1 + 213)
+ let cse_var_114: int32 = (cse_var_1 + 56)
+ let cse_var_113: int32 = (cse_var_1 + 55)
+ let cse_var_112: int32 = (cse_var_1 + 54)
+ let cse_var_111: int32 = (cse_var_1 + 53)
+ let cse_var_110: int32 = (cse_var_1 + 52)
+ let cse_var_109: int32 = (cse_var_1 + 51)
+ let cse_var_108: int32 = (cse_var_1 + 50)
+ let cse_var_107: int32 = (cse_var_1 + 5)
+ let cse_var_106: int32 = (cse_var_1 + 49)
+ let cse_var_105: int32 = (cse_var_1 + 48)
+ let cse_var_104: int32 = (cse_var_1 + 47)
+ let cse_var_103: int32 = (cse_var_1 + 46)
+ let cse_var_102: int32 = (cse_var_1 + 45)
+ let cse_var_101: int32 = (cse_var_1 + 44)
+ let cse_var_100: int32 = (cse_var_1 + 43)
+ let cse_var_99: int32 = (cse_var_1 + 57)
+ let cse_var_98: int32 = (elem_idx*16)
+ let cse_var_97: int32 = (cse_var_1 + 99)
+ let cse_var_96: int32 = (cse_var_1 + 98)
+ let cse_var_95: int32 = (cse_var_1 + 97)
+ let cse_var_94: int32 = (cse_var_1 + 96)
+ let cse_var_93: int32 = (cse_var_1 + 95)
+ let cse_var_92: int32 = (cse_var_1 + 94)
+ let cse_var_91: int32 = (cse_var_1 + 93)
+ let cse_var_90: int32 = (cse_var_1 + 92)
+ let cse_var_89: int32 = (cse_var_1 + 91)
+ let cse_var_88: int32 = (cse_var_1 + 90)
+ let cse_var_87: int32 = (cse_var_1 + 9)
+ let cse_var_86: int32 = (cse_var_1 + 89)
+ let cse_var_85: int32 = (cse_var_1 + 88)
+ let cse_var_84: int32 = (cse_var_1 + 87)
+ let cse_var_83: int32 = (cse_var_1 + 71)
+ let cse_var_82: int32 = (cse_var_1 + 85)
+ let cse_var_81: int32 = (cse_var_1 + 84)
+ let cse_var_80: int32 = (cse_var_1 + 83)
+ let cse_var_79: int32 = (cse_var_1 + 82)
+ let cse_var_78: int32 = (cse_var_1 + 81)
+ let cse_var_77: int32 = (cse_var_1 + 80)
+ let cse_var_76: int32 = (cse_var_1 + 8)
+ let cse_var_75: int32 = (cse_var_1 + 79)
+ let cse_var_74: int32 = (cse_var_1 + 78)
+ let cse_var_73: int32 = (cse_var_1 + 77)
+ let cse_var_72: int32 = (cse_var_1 + 76)
+ let cse_var_71: int32 = (cse_var_1 + 75)
+ let cse_var_70: int32 = (cse_var_1 + 74)
+ let cse_var_69: int32 = (cse_var_1 + 73)
+ let cse_var_68: int32 = (cse_var_1 + 72)
+ let cse_var_67: int32 = (cse_var_1 + 86)
+ let cse_var_66: int32 = (cse_var_1 + 242)
+ let cse_var_65: int32 = (cse_var_1 + 241)
+ let cse_var_64: int32 = (cse_var_1 + 240)
+ let cse_var_63: int32 = (cse_var_1 + 24)
+ let cse_var_62: int32 = (cse_var_1 + 239)
+ let cse_var_61: int32 = (cse_var_1 + 238)
+ let cse_var_60: int32 = (cse_var_1 + 237)
+ let cse_var_59: int32 = (cse_var_1 + 236)
+ let cse_var_58: int32 = (cse_var_1 + 235)
+ let cse_var_57: int32 = (cse_var_1 + 234)
+ let cse_var_56: int32 = (cse_var_1 + 233)
+ let cse_var_55: int32 = (cse_var_1 + 232)
+ let cse_var_54: int32 = (cse_var_1 + 231)
+ let cse_var_53: int32 = (cse_var_1 + 230)
+ let cse_var_52: int32 = (cse_var_1 + 23)
+ let cse_var_51: int32 = (cse_var_1 + 243)
+ let cse_var_50: int32 = (cse_var_1 + 228)
+ let cse_var_49: int32 = (cse_var_1 + 227)
+ let cse_var_48: int32 = (cse_var_1 + 226)
+ let cse_var_47: int32 = (cse_var_1 + 225)
+ let cse_var_46: int32 = (cse_var_1 + 224)
+ let cse_var_45: int32 = (cse_var_1 + 223)
+ let cse_var_44: int32 = (cse_var_1 + 222)
+ let cse_var_43: int32 = (cse_var_1 + 221)
+ let cse_var_42: int32 = (cse_var_1 + 220)
+ let cse_var_41: int32 = (cse_var_1 + 22)
+ let cse_var_40: int32 = (cse_var_1 + 219)
+ let cse_var_39: int32 = (cse_var_1 + 218)
+ let cse_var_38: int32 = (cse_var_1 + 217)
+ let cse_var_37: int32 = (cse_var_1 + 216)
+ let cse_var_36: int32 = (cse_var_1 + 215)
+ let cse_var_35: int32 = (cse_var_1 + 229)
+ let cse_var_34: int32 = (cse_var_1 + 42)
+ let cse_var_33: int32 = (cse_var_1 + 40)
+ let cse_var_32: int32 = (cse_var_1 + 4)
+ let cse_var_31: int32 = (cse_var_1 + 39)
+ let cse_var_30: int32 = (cse_var_1 + 38)
+ let cse_var_29: int32 = (cse_var_1 + 37)
+ let cse_var_28: int32 = (cse_var_1 + 36)
+ let cse_var_27: int32 = (cse_var_1 + 35)
+ let cse_var_26: int32 = (cse_var_1 + 34)
+ let cse_var_25: int32 = (cse_var_1 + 33)
+ let cse_var_24: int32 = (cse_var_1 + 32)
+ let cse_var_23: int32 = (cse_var_1 + 31)
+ let cse_var_22: int32 = (cse_var_1 + 30)
+ let cse_var_21: int32 = (cse_var_1 + 3)
+ let cse_var_20: int32 = (cse_var_1 + 29)
+ let cse_var_19: int32 = (cse_var_1 + 28)
+ let cse_var_18: int32 = (cse_var_1 + 41)
+ let cse_var_17: int32 = (cse_var_1 + 245)
+ let cse_var_16: int32 = (cse_var_1 + 246)
+ let cse_var_15: int32 = (cse_var_1 + 247)
+ let cse_var_14: int32 = (cse_var_1 + 248)
+ let cse_var_13: int32 = (cse_var_1 + 249)
+ let cse_var_12: int32 = (cse_var_1 + 25)
+ let cse_var_11: int32 = (cse_var_1 + 250)
+ let cse_var_10: int32 = (cse_var_1 + 251)
+ let cse_var_9: int32 = (cse_var_1 + 252)
+ let cse_var_8: int32 = (cse_var_1 + 253)
+ let cse_var_7: int32 = (cse_var_1 + 254)
+ let cse_var_6: int32 = (cse_var_1 + 255)
+ let cse_var_5: int32 = (cse_var_1 + 26)
+ let cse_var_4: int32 = (cse_var_1 + 244)
+ let cse_var_3: int32 = (cse_var_1 + 27)
+ let cse_var_2: int32 = ((i0.outer*16384) + (i.outer.inner*4096))
+ {
+ compute_4[cse_var_1] = (compute_4[cse_var_1] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_164] = (compute_4[cse_var_164] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_195] = (compute_4[cse_var_195] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_21] = (compute_4[cse_var_21] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_32] = (compute_4[cse_var_32] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_107] = (compute_4[cse_var_107] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_118] = (compute_4[cse_var_118] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_129] = (compute_4[cse_var_129] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_76] = (compute_4[cse_var_76] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_87] = (compute_4[cse_var_87] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_165] = (compute_4[cse_var_165] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_176] = (compute_4[cse_var_176] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_187] = (compute_4[cse_var_187] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_134] = (compute_4[cse_var_134] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_145] = (compute_4[cse_var_145] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_156] = (compute_4[cse_var_156] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_231] = (compute_4[cse_var_231] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_242] = (compute_4[cse_var_242] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_253] = (compute_4[cse_var_253] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_200] = (compute_4[cse_var_200] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_212] = (compute_4[cse_var_212] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_223] = (compute_4[cse_var_223] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_41] = (compute_4[cse_var_41] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_52] = (compute_4[cse_var_52] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_63] = (compute_4[cse_var_63] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_20] = (compute_4[cse_var_20] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_22] = (compute_4[cse_var_22] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_23] = (compute_4[cse_var_23] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_24] = (compute_4[cse_var_24] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_25] = (compute_4[cse_var_25] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_26] = (compute_4[cse_var_26] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_27] = (compute_4[cse_var_27] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_28] = (compute_4[cse_var_28] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_29] = (compute_4[cse_var_29] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_30] = (compute_4[cse_var_30] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_31] = (compute_4[cse_var_31] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_33] = (compute_4[cse_var_33] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_34] = (compute_4[cse_var_34] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_100] = (compute_4[cse_var_100] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_101] = (compute_4[cse_var_101] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_102] = (compute_4[cse_var_102] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_103] = (compute_4[cse_var_103] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_104] = (compute_4[cse_var_104] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_105] = (compute_4[cse_var_105] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_106] = (compute_4[cse_var_106] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_108] = (compute_4[cse_var_108] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_109] = (compute_4[cse_var_109] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_110] = (compute_4[cse_var_110] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_111] = (compute_4[cse_var_111] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_112] = (compute_4[cse_var_112] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_113] = (compute_4[cse_var_113] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_114] = (compute_4[cse_var_114] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_99] = (compute_4[cse_var_99] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_116] = (compute_4[cse_var_116] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_117] = (compute_4[cse_var_117] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_119] = (compute_4[cse_var_119] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_120] = (compute_4[cse_var_120] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_121] = (compute_4[cse_var_121] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_122] = (compute_4[cse_var_122] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_123] = (compute_4[cse_var_123] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_124] = (compute_4[cse_var_124] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_125] = (compute_4[cse_var_125] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_126] = (compute_4[cse_var_126] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_127] = (compute_4[cse_var_127] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_128] = (compute_4[cse_var_128] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_130] = (compute_4[cse_var_130] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_83] = (compute_4[cse_var_83] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_68] = (compute_4[cse_var_68] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_69] = (compute_4[cse_var_69] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_70] = (compute_4[cse_var_70] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_71] = (compute_4[cse_var_71] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_72] = (compute_4[cse_var_72] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_73] = (compute_4[cse_var_73] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_74] = (compute_4[cse_var_74] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_75] = (compute_4[cse_var_75] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_77] = (compute_4[cse_var_77] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_78] = (compute_4[cse_var_78] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_79] = (compute_4[cse_var_79] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_80] = (compute_4[cse_var_80] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_81] = (compute_4[cse_var_81] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_82] = (compute_4[cse_var_82] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_67] = (compute_4[cse_var_67] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_84] = (compute_4[cse_var_84] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_85] = (compute_4[cse_var_85] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_86] = (compute_4[cse_var_86] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_88] = (compute_4[cse_var_88] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_89] = (compute_4[cse_var_89] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_90] = (compute_4[cse_var_90] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_91] = (compute_4[cse_var_91] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_92] = (compute_4[cse_var_92] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_93] = (compute_4[cse_var_93] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_94] = (compute_4[cse_var_94] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_95] = (compute_4[cse_var_95] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_96] = (compute_4[cse_var_96] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_97] = (compute_4[cse_var_97] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_166] = (compute_4[cse_var_166] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_167] = (compute_4[cse_var_167] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_168] = (compute_4[cse_var_168] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_169] = (compute_4[cse_var_169] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_170] = (compute_4[cse_var_170] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_171] = (compute_4[cse_var_171] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_172] = (compute_4[cse_var_172] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_173] = (compute_4[cse_var_173] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_174] = (compute_4[cse_var_174] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_175] = (compute_4[cse_var_175] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_177] = (compute_4[cse_var_177] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_178] = (compute_4[cse_var_178] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_163] = (compute_4[cse_var_163] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_180] = (compute_4[cse_var_180] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_181] = (compute_4[cse_var_181] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_182] = (compute_4[cse_var_182] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_183] = (compute_4[cse_var_183] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_184] = (compute_4[cse_var_184] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_185] = (compute_4[cse_var_185] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_186] = (compute_4[cse_var_186] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_188] = (compute_4[cse_var_188] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_189] = (compute_4[cse_var_189] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_190] = (compute_4[cse_var_190] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_191] = (compute_4[cse_var_191] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_192] = (compute_4[cse_var_192] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_193] = (compute_4[cse_var_193] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_194] = (compute_4[cse_var_194] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_147] = (compute_4[cse_var_147] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_132] = (compute_4[cse_var_132] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_133] = (compute_4[cse_var_133] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_135] = (compute_4[cse_var_135] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_136] = (compute_4[cse_var_136] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_137] = (compute_4[cse_var_137] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_138] = (compute_4[cse_var_138] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_139] = (compute_4[cse_var_139] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_140] = (compute_4[cse_var_140] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_141] = (compute_4[cse_var_141] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_142] = (compute_4[cse_var_142] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_143] = (compute_4[cse_var_143] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_144] = (compute_4[cse_var_144] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_146] = (compute_4[cse_var_146] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_131] = (compute_4[cse_var_131] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_148] = (compute_4[cse_var_148] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_149] = (compute_4[cse_var_149] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_150] = (compute_4[cse_var_150] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_151] = (compute_4[cse_var_151] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_152] = (compute_4[cse_var_152] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_153] = (compute_4[cse_var_153] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_154] = (compute_4[cse_var_154] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_155] = (compute_4[cse_var_155] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_157] = (compute_4[cse_var_157] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_158] = (compute_4[cse_var_158] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_159] = (compute_4[cse_var_159] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_160] = (compute_4[cse_var_160] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_161] = (compute_4[cse_var_161] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_162] = (compute_4[cse_var_162] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_179] = (compute_4[cse_var_179] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_228] = (compute_4[cse_var_228] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_229] = (compute_4[cse_var_229] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_230] = (compute_4[cse_var_230] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_232] = (compute_4[cse_var_232] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_233] = (compute_4[cse_var_233] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_234] = (compute_4[cse_var_234] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_235] = (compute_4[cse_var_235] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_236] = (compute_4[cse_var_236] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_237] = (compute_4[cse_var_237] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_238] = (compute_4[cse_var_238] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_239] = (compute_4[cse_var_239] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_240] = (compute_4[cse_var_240] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_241] = (compute_4[cse_var_241] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_227] = (compute_4[cse_var_227] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_244] = (compute_4[cse_var_244] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_245] = (compute_4[cse_var_245] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_246] = (compute_4[cse_var_246] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_247] = (compute_4[cse_var_247] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_248] = (compute_4[cse_var_248] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_249] = (compute_4[cse_var_249] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_250] = (compute_4[cse_var_250] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_251] = (compute_4[cse_var_251] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_252] = (compute_4[cse_var_252] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_254] = (compute_4[cse_var_254] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_255] = (compute_4[cse_var_255] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_256] = (compute_4[cse_var_256] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_257] = (compute_4[cse_var_257] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_258] = (compute_4[cse_var_258] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_211] = (compute_4[cse_var_211] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_196] = (compute_4[cse_var_196] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_197] = (compute_4[cse_var_197] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_198] = (compute_4[cse_var_198] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_199] = (compute_4[cse_var_199] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_201] = (compute_4[cse_var_201] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_202] = (compute_4[cse_var_202] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_203] = (compute_4[cse_var_203] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_204] = (compute_4[cse_var_204] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_205] = (compute_4[cse_var_205] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_206] = (compute_4[cse_var_206] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_207] = (compute_4[cse_var_207] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_208] = (compute_4[cse_var_208] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_209] = (compute_4[cse_var_209] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_210] = (compute_4[cse_var_210] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_213] = (compute_4[cse_var_213] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_214] = (compute_4[cse_var_214] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_215] = (compute_4[cse_var_215] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_216] = (compute_4[cse_var_216] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_217] = (compute_4[cse_var_217] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_218] = (compute_4[cse_var_218] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_219] = (compute_4[cse_var_219] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_220] = (compute_4[cse_var_220] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_221] = (compute_4[cse_var_221] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_222] = (compute_4[cse_var_222] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_224] = (compute_4[cse_var_224] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_225] = (compute_4[cse_var_225] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_226] = (compute_4[cse_var_226] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_115] = (compute_4[cse_var_115] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_243] = (compute_4[cse_var_243] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_36] = (compute_4[cse_var_36] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_37] = (compute_4[cse_var_37] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_38] = (compute_4[cse_var_38] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_39] = (compute_4[cse_var_39] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_40] = (compute_4[cse_var_40] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_42] = (compute_4[cse_var_42] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_43] = (compute_4[cse_var_43] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_44] = (compute_4[cse_var_44] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_45] = (compute_4[cse_var_45] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_46] = (compute_4[cse_var_46] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_47] = (compute_4[cse_var_47] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_48] = (compute_4[cse_var_48] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_49] = (compute_4[cse_var_49] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_50] = (compute_4[cse_var_50] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_35] = (compute_4[cse_var_35] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_53] = (compute_4[cse_var_53] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_54] = (compute_4[cse_var_54] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_55] = (compute_4[cse_var_55] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_56] = (compute_4[cse_var_56] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_57] = (compute_4[cse_var_57] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_58] = (compute_4[cse_var_58] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_59] = (compute_4[cse_var_59] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_60] = (compute_4[cse_var_60] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_61] = (compute_4[cse_var_61] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_62] = (compute_4[cse_var_62] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_64] = (compute_4[cse_var_64] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_65] = (compute_4[cse_var_65] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_66] = (compute_4[cse_var_66] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_51] = (compute_4[cse_var_51] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_4] = (compute_4[cse_var_4] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 128) {
- let cse_var_2: int32 = ((i0.inner*512) + (i0.outer.i1.outer.fused*16))
- compute[ramp(cse_var_2, 1, 16)] = max((compute_4[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_2, 1, 16)]), broadcast(0f32, 16))
+ for (i0.inner: int32, 0, 64) {
+ let cse_var_259: int32 = (((i0.outer*32768) + (i0.inner*512)) + (i1.outer*16))
+ compute[ramp(cse_var_259, 1, 16)] = max((compute_4[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_259, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -435,7 +1199,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.454 ms
+ Execution time of this operator: 3.337 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 d39a66a8c..f51ba922e 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:43.241** total execution time for **how_to_tune_with_autotvm** files:
+**00:43.418** total execution time for **how_to_tune_with_autotvm** files:
-- **00:42.422**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.217**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.203**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.201**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.198**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:42.622**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.209**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.202**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.192**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.192**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
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 e79b48e89..1cca2eb4c 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
@@ -859,8 +859,8 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, 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, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
- No: 6 GFLOPS: 96.08/96.08 result: MeasureResult(costs=(0.002409375229166667,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5743904113769531, timestamp=1650062645.8736827) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
- No: 7 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 103.50/103.50 result: MeasureResult(costs=(0.002236654708333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.592435359954834, timestamp=1650063815.6699162) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+ No: 7 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -983,7 +983,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
- No: 8 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1106,7 +1106,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
- No: 9 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1229,7 +1229,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
- No: 10 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/103.50 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
@@ -1247,7 +1247,7 @@ for this template
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
- No: 11 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1370,7 +1370,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
- No: 12 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1493,7 +1493,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
- No: 13 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1616,7 +1616,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
- No: 14 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1739,7 +1739,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
- No: 15 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1862,7 +1862,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
- No: 16 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1985,7 +1985,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
- No: 17 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2108,7 +2108,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, 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, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
- No: 18 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2231,7 +2231,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
- No: 19 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2319,7 +2319,7 @@ for this template
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007fa2da56afa2
+ 12: 0x00007fc4d562cfa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2384,7 +2384,7 @@ for this template
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
- No: 20 GFLOPS: 142.44/142.44 result: MeasureResult(costs=(0.0016252172580645161,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1265416145324707, timestamp=1650062671.247943) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+ No: 20 GFLOPS: 144.98/144.98 result: MeasureResult(costs=(0.0015968331,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4312255382537842, timestamp=1650063841.3437583) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
@@ -2437,7 +2437,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
- Time cost of this operator: 0.001975
+ Time cost of this operator: 0.001952
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 f9997ad40..0e6828c55 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
@@ -292,10 +292,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.9 98.747 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.0 0.959 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.92 0.294 (1, 1, 10, 10, 3) 1 1
- Total_time - 312.82 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.7 98.733 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.978 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.906 0.289 (1, 1, 10, 10, 3) 1 1
+ Total_time - 313.673 - - - -
@@ -357,10 +357,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 216.8 98.672 (1, 1, 10, 10, 6) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.992 0.907 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.925 0.421 (1, 3, 10, 10, 1) 1 1
- Total_time - 219.717 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 89.1 97.148 (1, 6, 10, 10, 1) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.715 1.87 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.901 0.982 (1, 1, 10, 10, 3) 1 1
+ Total_time - 91.716 - - - -
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 4c51f4fd7..696f424f0 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:43.558** total execution time for **how_to_work_with_microtvm** files:
+**00:43.326** total execution time for **how_to_work_with_microtvm** files:
-- **00:39.589**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.408**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.192**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
-- **00:00.189**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
-- **00:00.180**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:39.410**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.367**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.188**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+- **00:00.186**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
+- **00:00.176**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
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 56b4b6ebb..175ebd546 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
@@ -5,8 +5,8 @@
Computation times
=================
-**00:09.599** total execution time for **how_to_work_with_relay** files:
+**00:05.817** total execution time for **how_to_work_with_relay** files:
-- **00:07.073**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:02.323**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.203**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:04.046**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.566**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.205**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
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 93b320935..a89063969 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
@@ -5,13 +5,13 @@
Computation times
=================
-**00:05.501** total execution time for **how_to_work_with_schedules** files:
+**00:05.195** total execution time for **how_to_work_with_schedules** files:
-- **00:02.040**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.099**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.708**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.693**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.305**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.230**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.220**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.206**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.939**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:00.986**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.687**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.656**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.289**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.227**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.213**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.198**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
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 02441fa9a..a3275fdc0 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -314,7 +314,7 @@ The importing needs to happen before the tensorized GEMV being executed.
B: Buffer(B_2: Pointer(float32), float32, [32768], []),
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpch4fv22p/input0.cc'\nsource_filename = \"/tmp/tmpch4fv22p/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/tmphcc2ycl2/input0.cc'\nsource_filename = \"/tmp/tmphcc2ycl2/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 1a96993f9..cbd03957f 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
@@ -5,7 +5,7 @@
Computation times
=================
-**00:20.829** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:20.128** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:20.625**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.204**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:19.945**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.184**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
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 3117297b2..c5483c007 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -265,7 +265,7 @@ 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 21.73s!
+ resnet18_v1 inference graph built in 21.18s!
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 e0a1f3ea0..2ca6dbcdd 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -301,7 +301,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 15.12s!
+ yolov3-tiny inference graph built in 14.74s!
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 0a2e45d8f..cb0aa80e6 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
@@ -5,7 +5,7 @@
Computation times
=================
-**01:29.082** total execution time for **topic_vta_tutorials_frontend** files:
+**01:27.605** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:47.185**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:41.897**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:46.557**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:41.048**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
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 bdf4e216d..7ae7e7968 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
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.493** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.467** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:02.952**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.541**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.970**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.497**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
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 498a541f9..706574f47 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.968** total execution time for **topic_vta_tutorials** files:
+**00:00.907** total execution time for **topic_vta_tutorials** files:
-- **00:00.485**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.482**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.463**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.444**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 8177889fb..af00ae3aa 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -305,7 +305,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 92.988 ms
+ Execution time of this operator: 92.791 ms
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 7dd62dbd8..bf2e9df60 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -268,7 +268,7 @@ standard deviation.
.. code-block:: none
- {'mean': 491.0217237799998, 'median': 490.89573064999854, 'std': 0.4141502182556612}
+ {'mean': 490.400963600041, 'median': 490.2133211000546, 'std': 0.7120891974454429}
@@ -482,30 +482,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 19.66/ 19.66 GFLOPS | Progress: (4/10) | 5.02 s
[Task 1/25] Current/Best: 11.56/ 19.66 GFLOPS | Progress: (8/10) | 7.25 s
[Task 1/25] Current/Best: 14.73/ 19.66 GFLOPS | Progress: (10/10) | 8.83 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 15.87/ 15.87 GFLOPS | Progress: (4/10) | 2.26 s
[Task 2/25] Current/Best: 22.25/ 22.25 GFLOPS | Progress: (8/10) | 4.11 s
[Task 2/25] Current/Best: 6.83/ 22.25 GFLOPS | Progress: (10/10) | 4.79 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 11.27/ 17.16 GFLOPS | Progress: (4/10) | 2.72 s
[Task 3/25] Current/Best: 13.70/ 23.00 GFLOPS | Progress: (8/10) | 5.35 s
[Task 3/25] Current/Best: 17.35/ 23.00 GFLOPS | Progress: (10/10) | 6.63 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 19.95/ 19.95 GFLOPS | Progress: (4/10) | 2.66 s
[Task 4/25] Current/Best: 14.96/ 19.95 GFLOPS | Progress: (8/10) | 8.79 s
[Task 4/25] Current/Best: 11.97/ 19.95 GFLOPS | Progress: (10/10) | 10.70 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 12.86/ 16.17 GFLOPS | Progress: (4/10) | 3.01 s
[Task 5/25] Current/Best: 16.73/ 18.70 GFLOPS | Progress: (8/10) | 5.06 s
[Task 5/25] Current/Best: 12.05/ 18.70 GFLOPS | Progress: (10/10) | 5.90 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 3.26/ 15.88 GFLOPS | Progress: (4/10) | 3.68 s
[Task 6/25] Current/Best: 11.23/ 23.36 GFLOPS | Progress: (8/10) | 8.36 s
[Task 6/25] Current/Best: 3.97/ 23.36 GFLOPS | Progress: (10/10) | 9.63 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 21.56/ 21.56 GFLOPS | Progress: (4/10) | 2.75 s
[Task 7/25] Current/Best: 13.01/ 21.56 GFLOPS | Progress: (8/10) | 5.34 s
[Task 7/25] Current/Best: 14.52/ 21.56 GFLOPS | Progress: (10/10) | 6.49 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 9.66/ 11.37 GFLOPS | Progress: (4/10) | 6.10 s
[Task 8/25] Current/Best: 11.69/ 12.29 GFLOPS | Progress: (8/10) | 12.53 s
[Task 8/25] Current/Best: 19.40/ 19.40 GFLOPS | Progress: (10/10) | 13.25 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 9.03/ 16.57 GFLOPS | Progress: (4/10) | 5.99 s
[Task 9/25] Current/Best: 9.85/ 16.57 GFLOPS | Progress: (8/10) | 8.80 s
[Task 9/25] Current/Best: 11.10/ 16.57 GFLOPS | Progress: (10/10) | 13.02 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 18.83/ 19.46 GFLOPS | Progress: (4/10) | 2.36 s
[Task 10/25] Current/Best: 8.25/ 19.46 GFLOPS | Progress: (8/10) | 3.80 s
[Task 10/25] Current/Best: 8.85/ 19.46 GFLOPS | Progress: (10/10) | 4.66 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 24.04/ 24.04 GFLOPS | Progress: (4/10) | 2.85 s
[Task 11/25] Current/Best: 24.20/ 24.20 GFLOPS | Progress: (8/10) | 4.93 s
[Task 11/25] Current/Best: 1.59/ 24.20 GFLOPS | Progress: (10/10) | 7.23 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 4.86/ 23.55 GFLOPS | Progress: (4/10) | 2.95 s
[Task 12/25] Current/Best: 14.93/ 23.55 GFLOPS | Progress: (8/10) | 4.89 s
[Task 12/25] Current/Best: 21.46/ 23.55 GFLOPS | Progress: (10/10) | 5.70 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 17.61/ 20.08 GFLOPS | Progress: (4/10) | 3.11 s
[Task 13/25] Current/Best: 6.25/ 20.08 GFLOPS | Progress: (8/10) | 7.53 s
[Task 13/25] Current/Best: 18.83/ 20.08 GFLOPS | Progress: (10/10) | 8.84 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 16.53/ 16.53 GFLOPS | Progress: (4/10) | 5.66 s
[Task 14/25] Current/Best: 10.74/ 19.73 GFLOPS | Progress: (8/10) | 7.89 s
[Task 14/25] Current/Best: 12.11/ 19.73 GFLOPS | Progress: (10/10) | 13.52 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 19.95/ 19.95 GFLOPS | Progress: (4/10) | 3.13 s
[Task 15/25] Current/Best: 22.97/ 23.97 GFLOPS | Progress: (8/10) | 5.34 s
[Task 15/25] Current/Best: 7.18/ 23.97 GFLOPS | Progress: (10/10) | 6.00 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 15.97/ 18.38 GFLOPS | Progress: (4/10) | 2.32 s
[Task 16/25] Current/Best: 15.12/ 18.38 GFLOPS | Progress: (8/10) | 3.58 s
[Task 16/25] Current/Best: 12.66/ 18.38 GFLOPS | Progress: (10/10) | 4.20 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 7.10/ 18.26 GFLOPS | Progress: (4/10) | 3.24 s
[Task 17/25] Current/Best: 9.42/ 18.86 GFLOPS | Progress: (8/10) | 5.57 s
[Task 17/25] Current/Best: 12.27/ 18.86 GFLOPS | Progress: (10/10) | 6.59 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 10.30/ 11.61 GFLOPS | Progress: (4/10) | 6.76 s
[Task 18/25] Current/Best: 7.03/ 17.26 GFLOPS | Progress: (8/10) | 12.33 s
[Task 18/25] Current/Best: 9.72/ 17.26 GFLOPS | Progress: (10/10) | 15.94 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 20.57/ 20.57 GFLOPS | Progress: (4/10) | 3.31 s
[Task 19/25] Current/Best: 19.11/ 20.57 GFLOPS | Progress: (8/10) | 5.22 s
[Task 19/25] Current/Best: 11.18/ 20.57 GFLOPS | Progress: (10/10) | 7.16 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 11.40/ 15.23 GFLOPS | Progress: (4/10) | 3.83 s
[Task 20/25] Current/Best: 8.04/ 15.23 GFLOPS | Progress: (8/10) | 5.96 s
[Task 20/25] Current/Best: 5.26/ 16.60 GFLOPS | Progress: (10/10) | 7.02 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 12.98/ 20.75 GFLOPS | Progress: (4/10) | 2.71 s
[Task 21/25] Current/Best: 12.21/ 20.75 GFLOPS | Progress: (8/10) | 4.54 s
[Task 21/25] Current/Best: 11.67/ 20.75 GFLOPS | Progress: (10/10) | 5.18 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 18.50/ 18.50 GFLOPS | Progress: (4/10) | 3.15 s
[Task 22/25] Current/Best: 9.11/ 18.50 GFLOPS | Progress: (8/10) | 4.52 s
[Task 22/25] Current/Best: 15.66/ 20.88 GFLOPS | Progress: (10/10) | 5.17 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 10.39/ 19.59 GFLOPS | Progress: (4/10) | 4.55 s Done.
-
[Task 23/25] Current/Best: 13.64/ 19.59 GFLOPS | Progress: (8/10) | 7.90 s
[Task 23/25] Current/Best: 23.42/ 23.42 GFLOPS | Progress: (10/10) | 8.75 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 5.69/ 9.32 GFLOPS | Progress: (4/10) | 6.17 s
[Task 24/25] Current/Best: 1.10/ 9.32 GFLOPS | Progress: (8/10) | 84.91 s
[Task 24/25] Current/Best: 4.58/ 9.32 GFLOPS | Progress: (10/10) | 88.48 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 25/25] Current/Best: 9.30/ 9.36 GFLOPS | Progress: (4/10) | 16.17 s
[Task 25/25] Current/Best: 5.97/ 9.36 GFLOPS | Progress: (8/10) | 18.73 s
[Task 25/25] Current/Best: 7.32/ 9.36 GFLOPS | Progress: (10/10) | 319.46 s
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 17.88/ 17.88 GFLOPS | Progress: (4/10) | 5.42 s
[Task 1/25] Current/Best: 16.82/ 18.32 GFLOPS | Progress: (8/10) | 9.04 s
[Task 1/25] Current/Best: 15.25/ 18.32 GFLOPS | Progress: (10/10) | 10.29 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 5.85/ 16.32 GFLOPS | Progress: (4/10) | 2.41 s
[Task 2/25] Current/Best: 15.50/ 16.85 GFLOPS | Progress: (8/10) | 3.77 s
[Task 2/25] Current/Best: 17.75/ 20.05 GFLOPS | Progress: (10/10) | 4.39 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 8.11/ 14.04 GFLOPS | Progress: (4/10) | 3.37 s
[Task 3/25] Current/Best: 12.35/ 17.13 GFLOPS | Progress: (8/10) | 4.97 s
[Task 3/25] Current/Best: 13.29/ 17.13 GFLOPS | Progress: (10/10) | 6.06 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 18.37/ 18.37 GFLOPS | Progress: (4/10) | 2.65 s
[Task 4/25] Current/Best: 4.72/ 20.28 GFLOPS | Progress: (8/10) | 7.83 s
[Task 4/25] Current/Best: 12.64/ 20.28 GFLOPS | Progress: (10/10) | 9.09 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 21.00/ 21.46 GFLOPS | Progress: (4/10) | 2.46 s
[Task 5/25] Current/Best: 7.65/ 21.46 GFLOPS | Progress: (8/10) | 3.98 s
[Task 5/25] Current/Best: 16.54/ 21.46 GFLOPS | Progress: (10/10) | 4.89 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 11.72/ 18.02 GFLOPS | Progress: (4/10) | 4.66 s
[Task 6/25] Current/Best: 14.79/ 18.02 GFLOPS | Progress: (8/10) | 7.22 s
[Task 6/25] Current/Best: 16.45/ 18.02 GFLOPS | Progress: (10/10) | 8.08 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 6.33/ 15.80 GFLOPS | Progress: (4/10) | 3.70 s
[Task 7/25] Current/Best: 9.05/ 15.80 GFLOPS | Progress: (8/10) | 6.42 s
[Task 7/25] Current/Best: 17.16/ 17.16 GFLOPS | Progress: (10/10) | 7.20 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 5.38/ 12.06 GFLOPS | Progress: (4/10) | 7.40 s
[Task 8/25] Current/Best: 9.89/ 17.30 GFLOPS | Progress: (8/10) | 12.55 s
[Task 8/25] Current/Best: 10.60/ 17.30 GFLOPS | Progress: (10/10) | 15.53 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 12.41/ 14.43 GFLOPS | Progress: (4/10) | 10.41 s
[Task 9/25] Current/Best: 17.91/ 22.62 GFLOPS | Progress: (8/10) | 11.70 s
[Task 9/25] Current/Best: 17.47/ 22.62 GFLOPS | Progress: (10/10) | 12.76 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 3.00/ 18.14 GFLOPS | Progress: (4/10) | 3.02 s
[Task 10/25] Current/Best: 19.79/ 19.79 GFLOPS | Progress: (8/10) | 5.23 s
[Task 10/25] Current/Best: 9.55/ 19.79 GFLOPS | Progress: (10/10) | 5.87 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 6.20/ 16.52 GFLOPS | Progress: (4/10) | 4.90 s
[Task 11/25] Current/Best: 15.74/ 17.53 GFLOPS | Progress: (8/10) | 6.84 s
[Task 11/25] Current/Best: 18.58/ 18.58 GFLOPS | Progress: (10/10) | 7.95 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 10.68/ 14.09 GFLOPS | Progress: (4/10) | 3.16 s
[Task 12/25] Current/Best: 15.10/ 15.32 GFLOPS | Progress: (8/10) | 4.78 s
[Task 12/25] Current/Best: 10.08/ 15.32 GFLOPS | Progress: (10/10) | 6.55 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 18.01/ 18.01 GFLOPS | Progress: (4/10) | 5.41 s
[Task 13/25] Current/Best: 3.11/ 18.01 GFLOPS | Progress: (8/10) | 8.19 s
[Task 13/25] Current/Best: 14.66/ 20.23 GFLOPS | Progress: (10/10) | 9.10 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 12.97/ 12.97 GFLOPS | Progress: (4/10) | 7.04 s
[Task 14/25] Current/Best: 13.36/ 15.10 GFLOPS | Progress: (8/10) | 9.23 s
[Task 14/25] Current/Best: 9.44/ 15.10 GFLOPS | Progress: (10/10) | 10.40 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 15.71/ 24.03 GFLOPS | Progress: (4/10) | 3.67 s
[Task 15/25] Current/Best: 5.99/ 24.03 GFLOPS | Progress: (8/10) | 4.86 s
[Task 15/25] Current/Best: 10.84/ 24.03 GFLOPS | Progress: (10/10) | 6.22 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 6.30/ 15.06 GFLOPS | Progress: (4/10) | 2.83 s
[Task 16/25] Current/Best: 14.64/ 18.77 GFLOPS | Progress: (8/10) | 4.36 s
[Task 16/25] Current/Best: 16.19/ 18.77 GFLOPS | Progress: (10/10) | 5.69 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 18.16/ 18.16 GFLOPS | Progress: (4/10) | 3.94 s
[Task 17/25] Current/Best: 21.02/ 21.02 GFLOPS | Progress: (8/10) | 5.37 s
[Task 17/25] Current/Best: 15.53/ 23.93 GFLOPS | Progress: (10/10) | 6.77 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 11.67/ 19.96 GFLOPS | Progress: (4/10) | 2.43 s
[Task 18/25] Current/Best: 11.79/ 20.65 GFLOPS | Progress: (8/10) | 8.02 s
[Task 18/25] Current/Best: 14.76/ 20.65 GFLOPS | Progress: (10/10) | 9.38 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 10.68/ 10.68 GFLOPS | Progress: (4/10) | 5.60 s
[Task 19/25] Current/Best: 16.43/ 20.13 GFLOPS | Progress: (8/10) | 8.72 s
[Task 19/25] Current/Best: 10.97/ 20.13 GFLOPS | Progress: (10/10) | 10.19 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 10.14/ 10.28 GFLOPS | Progress: (4/10) | 4.21 s
[Task 20/25] Current/Best: 4.00/ 21.25 GFLOPS | Progress: (8/10) | 6.61 s
[Task 20/25] Current/Best: 20.10/ 21.25 GFLOPS | Progress: (10/10) | 7.63 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 14.90/ 21.97 GFLOPS | Progress: (4/10) | 2.66 s
[Task 21/25] Current/Best: 1.63/ 21.97 GFLOPS | Progress: (8/10) | 4.66 s
[Task 21/25] Current/Best: 9.70/ 21.97 GFLOPS | Progress: (10/10) | 5.74 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+ Done.
+
[Task 22/25] Current/Best: 9.84/ 16.59 GFLOPS | Progress: (4/10) | 2.95 s
[Task 22/25] Current/Best: 7.24/ 16.79 GFLOPS | Progress: (8/10) | 4.53 s
[Task 22/25] Current/Best: 19.38/ 19.38 GFLOPS | Progress: (10/10) | 6.70 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 18.45/ 23.04 GFLOPS | Progress: (4/10) | 3.22 s
[Task 23/25] Current/Best: 3.09/ 23.04 GFLOPS | Progress: (8/10) | 5.78 s
[Task 23/25] Current/Best: 9.02/ 23.04 GFLOPS | Progress: (10/10) | 7.11 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 6.01/ 6.16 GFLOPS | Progress: (4/10) | 3.65 s
[Task 24/25] Current/Best: 4.02/ 10.00 GFLOPS | Progress: (8/10) | 5.83 s
[Task 24/25] Current/Best: 7.13/ 10.00 GFLOPS | Progress: (10/10) | 7.21 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 25/25] Current/Best: 9.67/ 9.67 GFLOPS | Progress: (4/10) | 2.34 s
[Task 25/25] Current/Best: 8.43/ 10.39 GFLOPS | Progress: (8/10) | 34.56 s
[Task 25/25] Current/Best: 1.53/ 10.39 GFLOPS | Progress: (10/10) | 50.42 s
The output from this tuning process will look something like this:
@@ -563,6 +564,14 @@ model using optimized operators to speed up our computations.
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ Done.
+
Verify that the optimized model runs and produces the same results:
@@ -593,8 +602,8 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
- class='n02123045 tabby, tabby cat' with probability=0.621103
- class='n02123159 tiger cat' with probability=0.356379
+ class='n02123045 tabby, tabby cat' with probability=0.621104
+ class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -647,8 +656,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 446.0708166700033, 'median': 445.9797331499999, 'std': 0.3701649082768512}
- unoptimized: {'mean': 491.0217237799998, 'median': 490.89573064999854, 'std': 0.4141502182556612}
+ optimized: {'mean': 443.517296980026, 'median': 443.01024564992986, 'std': 1.563976552930658}
+ unoptimized: {'mean': 490.400963600041, 'median': 490.2133211000546, 'std': 0.7120891974454429}
@@ -668,7 +677,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 4.464 seconds)
+ **Total running time of the script:** ( 7 minutes 15.308 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 135fdb4a6..a01177d45 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.239e-07 secs/op
+ 1.233e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index b2de581fd..b46ea6db0 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -230,7 +230,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x224a2b30)), stage(b, placeholder(b, 0xbd6d670)), 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, 0x2342b560)), stage(b, placeholder(b, 0x28d18ad0)), 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(mi [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index a6cb0ee1f..2113ed072 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,17 +5,17 @@
Computation times
=================
-**15:42.252** total execution time for **tutorial** files:
+**09:47.352** total execution time for **tutorial** files:
-- **13:04.464**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **01:01.698**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:51.686**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:26.259**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:15.812**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:01.294**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.699**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.205**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.037**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
-- **00:00.035**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **07:15.308**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **00:58.544**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:49.313**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **00:25.816**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:16.820**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:00.700**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.539**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.190**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.033**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
- **00:00.032**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.030**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.029**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
+- **00:00.028**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 13122053b..c402939c3 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -436,10 +436,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 8.047240000905732e-06 1.0
- naive 5.8546e-06 0.7275289415179681
- parallel 6.08e-06 0.7555385448073731
- vector 2.4551599999999997e-05 3.0509342330086677
+ numpy 8.072379969235045e-06 1.0
+ naive 5.8386e-06 0.7232811168765236
+ parallel 6.2619999999999995e-06 0.7757315715892149
+ vector 2.4645900000000004e-05 3.0531144586762435
@@ -828,7 +828,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018668
+ Numpy running time: 0.018119
@@ -884,7 +884,7 @@ optimizations.
.. code-block:: none
- none: 3.468946
+ none: 3.231856
@@ -982,7 +982,7 @@ schedule.
.. code-block:: none
- blocking: 0.303051
+ blocking: 0.307331
@@ -1073,7 +1073,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.334796
+ vectorization: 0.337173
@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], []),
@@ -1144,7 +1144,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.118738
+ loop permutation: 0.112953
@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], []),
@@ -1240,7 +1240,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.111174
+ array packing: 0.108143
@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], []),
@@ -1330,7 +1330,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.110921
+ block caching: 0.111776
@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], []),
@@ -1413,7 +1413,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.145220
+ parallelization: 0.143978
@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], []),
@@ -1491,13 +1491,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.4689456389999997 1.0
- blocking 0.3030505159 0.08736098729623247
- vectorization 0.3347961647 0.09651236990744899
- loop permutation 0.1187383554 0.03422894670503656
- array packing 0.1111739621 0.03204834369559286
- block caching 0.1109211023 0.03197545128783726
- parallelization 0.1452196773 0.04186277111620082
+ none 3.2318558358 1.0
+ blocking 0.3073305725 0.0950941465567955
+ vectorization 0.33717297030000004 0.10432797359494152
+ loop permutation 0.112953436 0.0349500230637732
+ array packing 0.1081428521 0.033461533432920215
+ block caching 0.11177630140000001 0.03458579437913925
+ parallelization 0.1439777031 0.04454954379620722
@@ -1532,11 +1532,6 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 1.698 seconds)
-
-
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index 7ea61bf95..4e0cd7761 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-365fcc832d1d5916947e50643f388b34194d44b7
+351f31b51cd85648b66f2b344b96a7460052760b
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index cfd165fd7..9bc23ec4a 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -400,7 +400,7 @@
</div>
<img alt="../../_images/sphx_glr_from_mxnet_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_from_mxnet_001.png" />
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip803b0a3a-6035-4765-a06c-8581bc5909be from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip67db9577-e96a-4c5c-8273-04062e07c5aa 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_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 12f8eeb8f..eaa92c64f 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -463,7 +463,7 @@ A quick solution is</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.440 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.500 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 2e6493eff..24722fb34 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -386,10 +386,10 @@ 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
0%| | 0.00/44.7M [00:00<?, ?B/s]
- 10%|# | 4.58M/44.7M [00:00<00:00, 42.7MB/s]
- 24%|##3 | 10.7M/44.7M [00:00<00:00, 54.4MB/s]
- 70%|######9 | 31.2M/44.7M [00:00<00:00, 126MB/s]
-100%|##########| 44.7M/44.7M [00:00<00:00, 127MB/s]
+ 1%|1 | 472k/44.7M [00:00<00:09, 4.80MB/s]
+ 10%|# | 4.68M/44.7M [00:00<00:01, 27.9MB/s]
+ 70%|####### | 31.4M/44.7M [00:00<00:00, 143MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 135MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 751587097..a1761f0a1 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -300,17 +300,17 @@
<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>04:41.387</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>04:39.864</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:04.440</strong>: <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></li>
-<li><p><strong>00:59.564</strong>: <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></li>
-<li><p><strong>00:56.244</strong>: <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></li>
-<li><p><strong>00:25.459</strong>: <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></li>
-<li><p><strong>00:21.232</strong>: <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></li>
-<li><p><strong>00:20.937</strong>: <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></li>
-<li><p><strong>00:18.923</strong>: <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></li>
-<li><p><strong>00:12.131</strong>: <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></li>
-<li><p><strong>00:02.457</strong>: <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></li>
+<li><p><strong>01:04.500</strong>: <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></li>
+<li><p><strong>00:59.553</strong>: <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></li>
+<li><p><strong>00:55.423</strong>: <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></li>
+<li><p><strong>00:25.357</strong>: <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></li>
+<li><p><strong>00:20.788</strong>: <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></li>
+<li><p><strong>00:20.742</strong>: <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></li>
+<li><p><strong>00:18.969</strong>: <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></li>
+<li><p><strong>00:12.078</strong>: <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></li>
+<li><p><strong>00:02.454</strong>: <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></li>
</ul>
</div>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 0e35c4671..02348dda1 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -622,7 +622,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.7567 15.7586 15.8629 15.6674 0.0521
+ 15.6234 15.6349 15.7135 15.5296 0.0604
</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 aea5993ec..01b20dbe3 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,14 +409,14 @@ 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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/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').
@@ -509,7 +509,7 @@ 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 2.481 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 58.290 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download 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 e6945f9e8..534a3f3e5 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,7 +450,7 @@ 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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</pre></div>
</div>
</div>
@@ -539,7 +539,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
<p class="sphx-glr-script-out">Out:</p>
<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)
- 90.2895 90.1853 92.5166 89.9874 0.3500
+ 90.1806 90.1495 91.6179 89.9756 0.1878
</pre></div>
</div>
<div class="admonition note">
@@ -578,7 +578,7 @@ 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 5.050 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.795 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download 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 4025505d9..f4da12a4b 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -540,7 +540,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 119.9716 119.9055 121.8111 118.9488 0.3938
+ 118.9514 118.9186 121.8738 118.0731 0.4844
</pre></div>
</div>
<div class="admonition note">
@@ -568,7 +568,7 @@ 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 53.883 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 57.873 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download 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 ce90c84bc..a7b863220 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -480,7 +480,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 9.391 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
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<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 90fec33d5..00958a576 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,24 +415,25 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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</div>
<p>Create TVM runtime and do inference
@@ -472,7 +473,7 @@ Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from h
</pre></div>
</div>
<img alt="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 25.474 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 21.103 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
<div class="sphx-glr-download 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 5a93863d5..db368dbab 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:27.080</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:19.589</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>03:02.481</strong>: <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></li>
-<li><p><strong>02:25.474</strong>: <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></li>
-<li><p><strong>01:53.883</strong>: <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></li>
-<li><p><strong>01:11.117</strong>: <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></li>
-<li><p><strong>01:05.050</strong>: <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></li>
-<li><p><strong>00:27.508</strong>: <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></li>
-<li><p><strong>00:21.368</strong>: <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></li>
-<li><p><strong>00:00.197</strong>: <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></li>
+<li><p><strong>02:58.290</strong>: <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></li>
+<li><p><strong>02:21.103</strong>: <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></li>
+<li><p><strong>01:57.873</strong>: <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></li>
+<li><p><strong>01:09.391</strong>: <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></li>
+<li><p><strong>01:03.795</strong>: <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></li>
+<li><p><strong>00:27.182</strong>: <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></li>
+<li><p><strong>00:21.776</strong>: <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></li>
+<li><p><strong>00:00.179</strong>: <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></li>
</ul>
</div>
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 bd1c6ce80..8c71c419b 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -588,7 +588,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipcc3bc45d-40fd-49a6-9ec8-1b9e57b89287 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.zip87c00332-7f47-42da-8a75-970612d26974 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>
@@ -650,7 +650,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
</pre></div>
</div>
<p>When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index ab838d335..f51cb4fd5 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -300,12 +300,12 @@
<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>00:38.214</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:37.555</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:34.706</strong>: <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></li>
-<li><p><strong>00:02.247</strong>: <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></li>
-<li><p><strong>00:01.060</strong>: <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></li>
-<li><p><strong>00:00.202</strong>: <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></li>
+<li><p><strong>00:34.169</strong>: <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></li>
+<li><p><strong>00:02.187</strong>: <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></li>
+<li><p><strong>00:01.007</strong>: <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></li>
+<li><p><strong>00:00.192</strong>: <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></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 7eef9d416..6e0c92fc0 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -486,10 +486,10 @@ profile the execution time of each passes.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6287us [6287us] (45.51%; 45.51%)
-FoldScaleAxis: 7529us [2us] (54.49%; 54.49%)
- FoldConstant: 7527us [1591us] (54.48%; 99.97%)
- InferType: 5936us [5936us] (42.96%; 78.87%)
+InferType: 5881us [5881us] (44.91%; 44.91%)
+FoldScaleAxis: 7213us [2us] (55.09%; 55.09%)
+ FoldConstant: 7211us [1499us] (55.07%; 99.97%)
+ InferType: 5712us [5712us] (43.62%; 79.21%)
</pre></div>
</div>
</div>
@@ -512,10 +512,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6044us [6044us] (44.57%; 44.57%)
-FoldScaleAxis: 7517us [2us] (55.43%; 55.43%)
- FoldConstant: 7515us [1551us] (55.42%; 99.97%)
- InferType: 5964us [5964us] (43.98%; 79.36%)
+InferType: 5735us [5735us] (44.33%; 44.33%)
+FoldScaleAxis: 7201us [2us] (55.67%; 55.67%)
+ FoldConstant: 7199us [1533us] (55.65%; 99.97%)
+ InferType: 5666us [5666us] (43.80%; 78.70%)
</pre></div>
</div>
<p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index 859174b67..e51998719 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -534,7 +534,7 @@ latency of convolution.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.264198 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 33.608237 ms
</pre></div>
</div>
<div class="sphx-glr-footer class 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 218ba8460..1d81eab1a 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -876,7 +876,7 @@ be able to run on our build server</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 7.099950 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 8.191855 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 bb5aaff2e..2baabde11 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,8 +431,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018656
-Baseline: 3.459400
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018082
+Baseline: 3.226418
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -493,7 +493,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.295284
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.302598
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -561,7 +561,7 @@ vastly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.333298
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.333762
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -623,7 +623,7 @@ the access pattern for A matrix is more cache friendly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118302
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.115119
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -707,7 +707,7 @@ flattening.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110819
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.112020
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -794,7 +794,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111621
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111046
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -885,7 +885,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.144608
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.144175
</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 79d44922d..0b7335022 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
<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>00:34.975</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:34.203</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:32.346</strong>: <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></li>
-<li><p><strong>00:01.418</strong>: <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></li>
-<li><p><strong>00:01.211</strong>: <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></li>
+<li><p><strong>00:31.709</strong>: <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></li>
+<li><p><strong>00:01.346</strong>: <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></li>
+<li><p><strong>00:01.148</strong>: <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></li>
</ul>
</div>
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 26a245917..17d403de3 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -300,14 +300,14 @@
<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>04:53.342</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>04:56.539</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:21.888</strong>: <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></li>
-<li><p><strong>01:18.235</strong>: <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></li>
-<li><p><strong>00:39.453</strong>: <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></li>
-<li><p><strong>00:16.968</strong>: <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></li>
-<li><p><strong>00:08.615</strong>: <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></li>
-<li><p><strong>00:08.184</strong>: <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></li>
+<li><p><strong>02:17.771</strong>: <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></li>
+<li><p><strong>01:19.538</strong>: <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></li>
+<li><p><strong>00:39.1000</strong>: <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></li>
+<li><p><strong>00:22.188</strong>: <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></li>
+<li><p><strong>00:08.630</strong>: <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></li>
+<li><p><strong>00:08.413</strong>: <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></li>
</ul>
</div>
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 03b2eb765..a08906be1 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
@@ -469,136 +469,118 @@ cooperative fetching, unrolling and operator fusion.</p>
bias: Buffer(bias_2: Pointer(float32), float32, [512], []),
compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
- 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, [36]), storage_scope = shared;
- allocate(kernel.shared: Pointer(shared float32), float32, [1536]), storage_scope = shared;
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=8)[0] = 0f32
- conv2d_nchw_1[2] = 0f32
- conv2d_nchw_1[4] = 0f32
- conv2d_nchw_1[6] = 0f32
- conv2d_nchw_1[8] = 0f32
- conv2d_nchw_1[10] = 0f32
- conv2d_nchw_1[12] = 0f32
- conv2d_nchw_1[1] = 0f32
- conv2d_nchw_1[3] = 0f32
- conv2d_nchw_1[5] = 0f32
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 32;
+ allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
+ allocate(pad_temp.shared: Pointer(shared float32), float32, [252]), storage_scope = shared;
+ allocate(kernel.shared: Pointer(shared float32), float32, [192]), storage_scope = shared;
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28 {
+ conv2d_nchw_1: Buffer(conv2d_nchw, float32, [49], [], scope="local", align=16)[0] = 0f32
conv2d_nchw_1[7] = 0f32
+ conv2d_nchw_1[14] = 0f32
+ conv2d_nchw_1[21] = 0f32
+ conv2d_nchw_1[1] = 0f32
+ conv2d_nchw_1[8] = 0f32
+ conv2d_nchw_1[15] = 0f32
+ conv2d_nchw_1[22] = 0f32
+ conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[9] = 0f32
+ conv2d_nchw_1[16] = 0f32
+ conv2d_nchw_1[23] = 0f32
+ conv2d_nchw_1[3] = 0f32
+ conv2d_nchw_1[10] = 0f32
+ conv2d_nchw_1[17] = 0f32
+ conv2d_nchw_1[24] = 0f32
+ conv2d_nchw_1[4] = 0f32
conv2d_nchw_1[11] = 0f32
+ conv2d_nchw_1[18] = 0f32
+ conv2d_nchw_1[25] = 0f32
+ conv2d_nchw_1[5] = 0f32
+ conv2d_nchw_1[12] = 0f32
+ conv2d_nchw_1[19] = 0f32
+ conv2d_nchw_1[26] = 0f32
+ conv2d_nchw_1[6] = 0f32
conv2d_nchw_1[13] = 0f32
+ conv2d_nchw_1[20] = 0f32
+ conv2d_nchw_1[27] = 0f32
for (rc.outer.outer: int32, 0, 128) {
- for (ry.outer.outer: int32, 0, 3) {
- let cse_var_2: int32 = (rc.outer.outer*36)
- let cse_var_1: int32 = (ry.outer.outer*3)
+ for (rx.outer.outer: int32, 0, 3) {
+ let cse_var_2: int32 = (rc.outer.outer*196)
+ let cse_var_1: int32 = (rc.outer.outer*36)
{
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- if @tir.likely((threadIdx.x_1 < 12), dtype=bool) {
- pad_temp.shared_1: Buffer(pad_temp.shared, float32, [36], [], scope="shared")[(threadIdx.x_1*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, 3))), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 8)], 0f32, dtype=float32)
- pad_temp.shared_1[((threadIdx.x_1*3) + 1)] = @tir.if_then_else(((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 7)], 0f32, dtype=float32)
- pad_temp.shared_1[((threadIdx.x_1*3) + 2)] = @tir.if_then_else((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (floormod(threadIdx.x_1, 3) < 2)), data[((((((rc.outer.outer*196) + (floordiv(threadIdx.x_1, 3)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + (floormod(threadIdx.x_1, 3)*3)) - 6)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1: Buffer(pad_temp.shared, float32, [252], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((7 <= threadIdx.x_1) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((cse_var_2 + threadIdx.x_1) + rx.outer.outer) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 28)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[((((cse_var_2 + ((floordiv(threadIdx.x_1, 7) + 4)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 8), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod(thre [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 84)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 12), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 16), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod(th [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 140)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 20), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 24), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod(th [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else(((1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 28), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else((((floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), data[(((((cse_var_2 + (floordiv((floordiv(threadIdx.x_1, 7) + 32), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1: Buffer(kernel.shared, float32, [192], [], scope="shared")[threadIdx.x_2] = kernel[(((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 28)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 7), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 4), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 14), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 84)] = kernel[((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 32256)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 28), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 4), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ kernel.shared_1[(threadIdx.x_2 + 140)] = kernel[(((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 35), 3)*4608)) + cse_var_1) + (floormod((threadIdx.x_2 + 8), 12)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
+ if @tir.likely((threadIdx.x_2 < 24), dtype=bool) {
+ kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_1) + (floormod(threadIdx.x_2, 12)*3)) + rx.outer.outer) + 64512)]
}
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared_1: Buffer(kernel.shared, float32, [1536], [], scope="shared")[threadIdx.x_2] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 12)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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((floordiv(threadIdx.x_2, 4) + 16), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 64), 12), 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((floordiv(threadIdx.x_2, 4) + 32), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 128), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 64), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 256), 12), 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((floordiv(threadIdx.x_2, 4) + 80), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 320), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 448)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 112), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 448), 12), 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((floordiv(threadIdx.x_2, 4) + 128), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 512), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 640)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 160), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 640), 12), 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((floordiv(threadIdx.x_2, 4) + 176), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 704), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 832)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 208), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 832), 12), 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((floordiv(threadIdx.x_2, 4) + 224), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 896), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1024)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 256), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1024), 12), 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((floordiv(threadIdx.x_2, 4) + 272), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1088), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1216)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 304), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1216), 12), 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((floordiv(threadIdx.x_2, 4) + 320), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1280), 12), 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(floordiv(threadIdx.x_2, 4), 3)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 12), 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 + 1408)] = kernel[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((floordiv(threadIdx.x_2, 4) + 352), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1408), 12), 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((floordiv(threadIdx.x_2, 4) + 368), 3)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 1472), 12), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
- for (ff.outer.inner: int32, 0, 2) {
+ for (ry.outer.inner: int32, 0, 3) {
for (rc.inner: int32, 0, 4) {
- let cse_var_16: int32 = (rc.inner*9)
- let cse_var_15: int32 = (ff.outer.inner + 8)
- let cse_var_14: int32 = (ff.outer.inner + 6)
- let cse_var_13: int32 = (ff.outer.inner + 4)
- let cse_var_12: int32 = (ff.outer.inner + 2)
- let cse_var_11: int32 = (ff.outer.inner + 12)
- let cse_var_10: int32 = (ff.outer.inner + 10)
- let cse_var_9: int32 = (cse_var_16 + 1)
- let cse_var_8: int32 = (cse_var_16 + 2)
- let cse_var_7: int32 = (cse_var_16 + 3)
- let cse_var_6: int32 = (cse_var_16 + 4)
- let cse_var_5: int32 = (cse_var_16 + 5)
- let cse_var_4: int32 = (cse_var_16 + 6)
- let cse_var_3: int32 = (cse_var_16 + 7)
- {
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_16]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_9]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[(((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3))]))
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_9]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[cse_var_3]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 1)]))
- conv2d_nchw_1[ff.outer.inner] = (conv2d_nchw_1[ff.outer.inner] + (pad_temp.shared_1[cse_var_8]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_12] = (conv2d_nchw_1[cse_var_12] + (pad_temp.shared_1[cse_var_7]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_13] = (conv2d_nchw_1[cse_var_13] + (pad_temp.shared_1[cse_var_6]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_14] = (conv2d_nchw_1[cse_var_14] + (pad_temp.shared_1[cse_var_5]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_15] = (conv2d_nchw_1[cse_var_15] + (pad_temp.shared_1[cse_var_4]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_10] = (conv2d_nchw_1[cse_var_10] + (pad_temp.shared_1[cse_var_3]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- conv2d_nchw_1[cse_var_11] = (conv2d_nchw_1[cse_var_11] + (pad_temp.shared_1[(cse_var_16 + 8)]*kernel.shared_1[((((threadIdx.x*24) + (ff.outer.inner*12)) + (rc.inner*3)) + 2)]))
- }
+ conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[(((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7))]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 7)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 14)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 21)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 28)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 35)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
+ conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner)]))
+ conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 48)]))
+ conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 96)]))
+ conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((((rc.inner*63) + (ry.outer.inner*7)) + floormod(threadIdx.x, 7)) + 42)]*kernel.shared_1[((((floordiv(threadIdx.x, 7)*12) + (rc.inner*3)) + ry.outer.inner) + 144)]))
}
}
}
}
}
- for (i1.inner: int32, 0, 2) {
- compute[((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[i1.inner] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[(i1.inner + 2)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[(i1.inner + 6)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[(i1.inner + 10)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- compute[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
+ for (i2.inner: int32, 0, 7) {
+ compute[((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[i2.inner] + bias[((blockIdx.x*16) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 196)] = max((conv2d_nchw_1[(i2.inner + 7)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 4)]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 392)] = max((conv2d_nchw_1[(i2.inner + 14)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 8)]), 0f32)
+ compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7)) + 588)] = max((conv2d_nchw_1[(i2.inner + 21)] + bias[(((blockIdx.x*16) + floordiv(threadIdx.x, 7)) + 12)]), 0f32)
}
}
}
@@ -636,7 +618,7 @@ cooperative fetching, unrolling and operator fusion.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.448 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.351 ms
</pre></div>
</div>
</div>
@@ -667,36 +649,36 @@ conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o
conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
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_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
+conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_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=4)
+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=4)
+conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=7)
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_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=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=7)
+conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
+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=4)
conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
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_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=3)
+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=1)
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 [...]
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_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
+compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=4)
+compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=4)
+compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=7)
compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
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=1)
-compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
-compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
+compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
+compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -715,12 +697,12 @@ s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread
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)
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=28)
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=3)
+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=28)
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", 64)
s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
@@ -740,92 +722,99 @@ 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[36];
- __shared__ float kernel_shared[1536];
+extern "C" __global__ void __launch_bounds__(28) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+ float conv2d_nchw[28];
+ __shared__ float pad_temp_shared[252];
+ __shared__ float kernel_shared[192];
conv2d_nchw[0] = 0.000000e+00f;
- conv2d_nchw[2] = 0.000000e+00f;
- conv2d_nchw[4] = 0.000000e+00f;
- conv2d_nchw[6] = 0.000000e+00f;
- conv2d_nchw[8] = 0.000000e+00f;
- conv2d_nchw[10] = 0.000000e+00f;
- conv2d_nchw[12] = 0.000000e+00f;
- conv2d_nchw[1] = 0.000000e+00f;
- conv2d_nchw[3] = 0.000000e+00f;
- conv2d_nchw[5] = 0.000000e+00f;
conv2d_nchw[7] = 0.000000e+00f;
+ conv2d_nchw[14] = 0.000000e+00f;
+ conv2d_nchw[21] = 0.000000e+00f;
+ conv2d_nchw[1] = 0.000000e+00f;
+ conv2d_nchw[8] = 0.000000e+00f;
+ conv2d_nchw[15] = 0.000000e+00f;
+ conv2d_nchw[22] = 0.000000e+00f;
+ conv2d_nchw[2] = 0.000000e+00f;
conv2d_nchw[9] = 0.000000e+00f;
+ conv2d_nchw[16] = 0.000000e+00f;
+ conv2d_nchw[23] = 0.000000e+00f;
+ conv2d_nchw[3] = 0.000000e+00f;
+ conv2d_nchw[10] = 0.000000e+00f;
+ conv2d_nchw[17] = 0.000000e+00f;
+ conv2d_nchw[24] = 0.000000e+00f;
+ conv2d_nchw[4] = 0.000000e+00f;
conv2d_nchw[11] = 0.000000e+00f;
+ conv2d_nchw[18] = 0.000000e+00f;
+ conv2d_nchw[25] = 0.000000e+00f;
+ conv2d_nchw[5] = 0.000000e+00f;
+ conv2d_nchw[12] = 0.000000e+00f;
+ conv2d_nchw[19] = 0.000000e+00f;
+ conv2d_nchw[26] = 0.000000e+00f;
+ conv2d_nchw[6] = 0.000000e+00f;
conv2d_nchw[13] = 0.000000e+00f;
+ conv2d_nchw[20] = 0.000000e+00f;
+ conv2d_nchw[27] = 0.000000e+00f;
for (int rc_outer_outer = 0; rc_outer_outer < 128; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
__syncthreads();
- if (((int)threadIdx.x) < 12) {
- pad_temp_shared[(((int)threadIdx.x) * 3)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((int)threadIdx.x) % 3))) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 8)] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) * 3) + 1)] = (((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 7)] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) * 3) + 2)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && ((((int)threadIdx.x) % 3) < 2)) ? data[((((((rc_outer_outer * 196) + ((((int)threadIdx.x) / 3) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) % 3) * 3)) - 6)] : 0.000000e+00f);
+ pad_temp_shared[((int)threadIdx.x)] = ((((7 <= ((int)threadIdx.x)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((rc_outer_outer * 196) + ((int)threadIdx.x)) + rx_outer_outer) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 28)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((rc_outer_outer * 196) + ((int)threadIdx.x)) + rx_outer_outer) + 20)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 56) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 84)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 84) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 3) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 112) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 140)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 140) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 2) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 168)] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 168) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 196)] = (((1 <= (rx_outer_outer + (((int)threadIdx.x) % 7))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 196) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 1) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ pad_temp_shared[(((int)threadIdx.x) + 224)] = ((((((int)threadIdx.x) < 21) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[((((((rc_outer_outer * 196) + (((((int)threadIdx.x) + 224) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 5) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8)] : 0.000000e+00f);
+ kernel_shared[((int)threadIdx.x)] = kernel[(((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 28)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 28) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 4) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 56)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 56) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 8) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 84)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 32256)];
+ kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 112) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 4) % 12) * 3)) + rx_outer_outer)];
+ kernel_shared[(((int)threadIdx.x) + 140)] = kernel[(((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 140) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) + 8) % 12) * 3)) + rx_outer_outer)];
+ if (((int)threadIdx.x) < 24) {
+ kernel_shared[(((int)threadIdx.x) + 168)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((int)threadIdx.x) % 12) * 3)) + rx_outer_outer) + 64512)];
}
- kernel_shared[((int)threadIdx.x)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 73728)];
- kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 256) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 147456)];
- kernel_shared[(((int)threadIdx.x) + 448)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 448) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 221184)];
- kernel_shared[(((int)threadIdx.x) + 640)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 640) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 294912)];
- kernel_shared[(((int)threadIdx.x) + 832)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 832) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 368640)];
- kernel_shared[(((int)threadIdx.x) + 1024)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1024) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 442368)];
- kernel_shared[(((int)threadIdx.x) + 1216)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1216) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + (((((int)threadIdx.x) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 516096)];
- kernel_shared[(((int)threadIdx.x) + 1408)] = kernel[(((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1408) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 4) % 12) / 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) / 12) * 4608)) + (rc_outer_outer * 36)) + ((((((int)threadIdx.x) + 8) % 12) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
__syncthreads();
- for (int ff_outer_inner = 0; ff_outer_inner < 2; ++ff_outer_inner) {
+ for (int ry_outer_inner = 0; ry_outer_inner < 3; ++ry_outer_inner) {
for (int rc_inner = 0; rc_inner < 4; ++rc_inner) {
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[(rc_inner * 9)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 1)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[(((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3))]));
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[((rc_inner * 9) + 1)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 7)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 1)]));
- conv2d_nchw[ff_outer_inner] = (conv2d_nchw[ff_outer_inner] + (pad_temp_shared[((rc_inner * 9) + 2)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 2)] = (conv2d_nchw[(ff_outer_inner + 2)] + (pad_temp_shared[((rc_inner * 9) + 3)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 4)] = (conv2d_nchw[(ff_outer_inner + 4)] + (pad_temp_shared[((rc_inner * 9) + 4)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 6)] = (conv2d_nchw[(ff_outer_inner + 6)] + (pad_temp_shared[((rc_inner * 9) + 5)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 8)] = (conv2d_nchw[(ff_outer_inner + 8)] + (pad_temp_shared[((rc_inner * 9) + 6)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 10)] = (conv2d_nchw[(ff_outer_inner + 10)] + (pad_temp_shared[((rc_inner * 9) + 7)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
- conv2d_nchw[(ff_outer_inner + 12)] = (conv2d_nchw[(ff_outer_inner + 12)] + (pad_temp_shared[((rc_inner * 9) + 8)] * kernel_shared[((((((int)threadIdx.x) * 24) + (ff_outer_inner * 12)) + (rc_inner * 3)) + 2)]));
+ conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[(((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 7)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 14)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 21)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 28)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 35)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
+ conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner)]));
+ conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 48)]));
+ conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 96)]));
+ conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((((rc_inner * 63) + (ry_outer_inner * 7)) + (((int)threadIdx.x) % 7)) + 42)] * kernel_shared[(((((((int)threadIdx.x) / 7) * 12) + (rc_inner * 3)) + ry_outer_inner) + 144)]));
}
}
}
}
- for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- compute[(((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[i1_inner] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[(i1_inner + 2)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[(i1_inner + 4)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[(i1_inner + 6)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[(i1_inner + 8)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[(i1_inner + 10)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
- compute[((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[(i1_inner + 12)] + bias[((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner)]), 0.000000e+00f);
+ for (int i2_inner = 0; i2_inner < 7; ++i2_inner) {
+ compute[((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[i2_inner] + bias[((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7))]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 196)] = max((conv2d_nchw[(i2_inner + 7)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 4)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 392)] = max((conv2d_nchw[(i2_inner + 14)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 8)]), 0.000000e+00f);
+ compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 49)) + (i2_inner * 7)) + (((int)threadIdx.x) % 7)) + 588)] = max((conv2d_nchw[(i2_inner + 21)] + bias[(((((int)blockIdx.x) * 16) + (((int)threadIdx.x) / 7)) + 12)]), 0.000000e+00f);
}
}
</pre></div>
@@ -863,7 +852,7 @@ 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> ( 2 minutes 21.888 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 17.771 seconds)</p>
<div class="sphx-glr-footer class 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 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 361d4099b..1609842cb 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -876,7 +876,7 @@ 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)
- 10.0732 10.0926 10.1119 10.0150 0.0419
+ 9.6715 9.6668 9.6970 9.6506 0.0193
</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 ee3524d0e..1e113a707 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -895,7 +895,7 @@ 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)
- 740.6048 739.1237 745.2095 737.4813 3.3243
+ 762.0476 761.2159 765.8830 759.0438 2.8534
</pre></div>
</div>
</div>
@@ -917,7 +917,7 @@ 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 18.235 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.538 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download 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 d6b7f3270..83dc60614 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,26 +600,790 @@ 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} {
- for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
- allocate(compute_3: Pointer(global float32), float32, [2048]), storage_scope = global {
- for (i.outer.inner: int32, 0, 32) {
- for (i.inner.init: int32, 0, 4) {
- for (j.init: int32, 0, 16) {
- compute_4: Buffer(compute_3, float32, [2048], [])[(((i.outer.inner*64) + (i.inner.init*16)) + j.init)] = 0f32
- }
- }
- for (elem_idx: int32, 0, (placeholder_3[(i0.outer.i1.outer.fused + 1)] - placeholder_3[i0.outer.i1.outer.fused])) {
- for (i.inner: int32, 0, 4) {
- for (j: int32, 0, 16) {
- let cse_var_1: int32 = (((i.outer.inner*64) + (i.inner*16)) + j)
- compute_4[cse_var_1] = (compute_4[cse_var_1] + (placeholder_1[(((placeholder_3[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + j)]*max(placeholder[(((i.outer.inner*1024) + (i.inner*256)) + placeholder_2[(placeholder_3[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ for (i0.outer: int32, 0, 2) "parallel" {
+ allocate(compute_3: Pointer(global float32), float32, [1024]), storage_scope = global;
+ for (i1.outer: int32, 0, 32) {
+ for (i.outer.inner: int32, 0, 4) {
+ let cse_var_1: int32 = (i.outer.inner*256)
+ {
+ compute_4: Buffer(compute_3, float32, [1024], [])[cse_var_1] = 0f32
+ compute_4[(cse_var_1 + 1)] = 0f32
+ compute_4[(cse_var_1 + 2)] = 0f32
+ compute_4[(cse_var_1 + 3)] = 0f32
+ compute_4[(cse_var_1 + 4)] = 0f32
+ compute_4[(cse_var_1 + 5)] = 0f32
+ compute_4[(cse_var_1 + 6)] = 0f32
+ compute_4[(cse_var_1 + 7)] = 0f32
+ compute_4[(cse_var_1 + 8)] = 0f32
+ compute_4[(cse_var_1 + 9)] = 0f32
+ compute_4[(cse_var_1 + 10)] = 0f32
+ compute_4[(cse_var_1 + 11)] = 0f32
+ compute_4[(cse_var_1 + 12)] = 0f32
+ compute_4[(cse_var_1 + 13)] = 0f32
+ compute_4[(cse_var_1 + 14)] = 0f32
+ compute_4[(cse_var_1 + 15)] = 0f32
+ compute_4[(cse_var_1 + 16)] = 0f32
+ compute_4[(cse_var_1 + 17)] = 0f32
+ compute_4[(cse_var_1 + 18)] = 0f32
+ compute_4[(cse_var_1 + 19)] = 0f32
+ compute_4[(cse_var_1 + 20)] = 0f32
+ compute_4[(cse_var_1 + 21)] = 0f32
+ compute_4[(cse_var_1 + 22)] = 0f32
+ compute_4[(cse_var_1 + 23)] = 0f32
+ compute_4[(cse_var_1 + 24)] = 0f32
+ compute_4[(cse_var_1 + 25)] = 0f32
+ compute_4[(cse_var_1 + 26)] = 0f32
+ compute_4[(cse_var_1 + 27)] = 0f32
+ compute_4[(cse_var_1 + 28)] = 0f32
+ compute_4[(cse_var_1 + 29)] = 0f32
+ compute_4[(cse_var_1 + 30)] = 0f32
+ compute_4[(cse_var_1 + 31)] = 0f32
+ compute_4[(cse_var_1 + 32)] = 0f32
+ compute_4[(cse_var_1 + 33)] = 0f32
+ compute_4[(cse_var_1 + 34)] = 0f32
+ compute_4[(cse_var_1 + 35)] = 0f32
+ compute_4[(cse_var_1 + 36)] = 0f32
+ compute_4[(cse_var_1 + 37)] = 0f32
+ compute_4[(cse_var_1 + 38)] = 0f32
+ compute_4[(cse_var_1 + 39)] = 0f32
+ compute_4[(cse_var_1 + 40)] = 0f32
+ compute_4[(cse_var_1 + 41)] = 0f32
+ compute_4[(cse_var_1 + 42)] = 0f32
+ compute_4[(cse_var_1 + 43)] = 0f32
+ compute_4[(cse_var_1 + 44)] = 0f32
+ compute_4[(cse_var_1 + 45)] = 0f32
+ compute_4[(cse_var_1 + 46)] = 0f32
+ compute_4[(cse_var_1 + 47)] = 0f32
+ compute_4[(cse_var_1 + 48)] = 0f32
+ compute_4[(cse_var_1 + 49)] = 0f32
+ compute_4[(cse_var_1 + 50)] = 0f32
+ compute_4[(cse_var_1 + 51)] = 0f32
+ compute_4[(cse_var_1 + 52)] = 0f32
+ compute_4[(cse_var_1 + 53)] = 0f32
+ compute_4[(cse_var_1 + 54)] = 0f32
+ compute_4[(cse_var_1 + 55)] = 0f32
+ compute_4[(cse_var_1 + 56)] = 0f32
+ compute_4[(cse_var_1 + 57)] = 0f32
+ compute_4[(cse_var_1 + 58)] = 0f32
+ compute_4[(cse_var_1 + 59)] = 0f32
+ compute_4[(cse_var_1 + 60)] = 0f32
+ compute_4[(cse_var_1 + 61)] = 0f32
+ compute_4[(cse_var_1 + 62)] = 0f32
+ compute_4[(cse_var_1 + 63)] = 0f32
+ compute_4[(cse_var_1 + 64)] = 0f32
+ compute_4[(cse_var_1 + 65)] = 0f32
+ compute_4[(cse_var_1 + 66)] = 0f32
+ compute_4[(cse_var_1 + 67)] = 0f32
+ compute_4[(cse_var_1 + 68)] = 0f32
+ compute_4[(cse_var_1 + 69)] = 0f32
+ compute_4[(cse_var_1 + 70)] = 0f32
+ compute_4[(cse_var_1 + 71)] = 0f32
+ compute_4[(cse_var_1 + 72)] = 0f32
+ compute_4[(cse_var_1 + 73)] = 0f32
+ compute_4[(cse_var_1 + 74)] = 0f32
+ compute_4[(cse_var_1 + 75)] = 0f32
+ compute_4[(cse_var_1 + 76)] = 0f32
+ compute_4[(cse_var_1 + 77)] = 0f32
+ compute_4[(cse_var_1 + 78)] = 0f32
+ compute_4[(cse_var_1 + 79)] = 0f32
+ compute_4[(cse_var_1 + 80)] = 0f32
+ compute_4[(cse_var_1 + 81)] = 0f32
+ compute_4[(cse_var_1 + 82)] = 0f32
+ compute_4[(cse_var_1 + 83)] = 0f32
+ compute_4[(cse_var_1 + 84)] = 0f32
+ compute_4[(cse_var_1 + 85)] = 0f32
+ compute_4[(cse_var_1 + 86)] = 0f32
+ compute_4[(cse_var_1 + 87)] = 0f32
+ compute_4[(cse_var_1 + 88)] = 0f32
+ compute_4[(cse_var_1 + 89)] = 0f32
+ compute_4[(cse_var_1 + 90)] = 0f32
+ compute_4[(cse_var_1 + 91)] = 0f32
+ compute_4[(cse_var_1 + 92)] = 0f32
+ compute_4[(cse_var_1 + 93)] = 0f32
+ compute_4[(cse_var_1 + 94)] = 0f32
+ compute_4[(cse_var_1 + 95)] = 0f32
+ compute_4[(cse_var_1 + 96)] = 0f32
+ compute_4[(cse_var_1 + 97)] = 0f32
+ compute_4[(cse_var_1 + 98)] = 0f32
+ compute_4[(cse_var_1 + 99)] = 0f32
+ compute_4[(cse_var_1 + 100)] = 0f32
+ compute_4[(cse_var_1 + 101)] = 0f32
+ compute_4[(cse_var_1 + 102)] = 0f32
+ compute_4[(cse_var_1 + 103)] = 0f32
+ compute_4[(cse_var_1 + 104)] = 0f32
+ compute_4[(cse_var_1 + 105)] = 0f32
+ compute_4[(cse_var_1 + 106)] = 0f32
+ compute_4[(cse_var_1 + 107)] = 0f32
+ compute_4[(cse_var_1 + 108)] = 0f32
+ compute_4[(cse_var_1 + 109)] = 0f32
+ compute_4[(cse_var_1 + 110)] = 0f32
+ compute_4[(cse_var_1 + 111)] = 0f32
+ compute_4[(cse_var_1 + 112)] = 0f32
+ compute_4[(cse_var_1 + 113)] = 0f32
+ compute_4[(cse_var_1 + 114)] = 0f32
+ compute_4[(cse_var_1 + 115)] = 0f32
+ compute_4[(cse_var_1 + 116)] = 0f32
+ compute_4[(cse_var_1 + 117)] = 0f32
+ compute_4[(cse_var_1 + 118)] = 0f32
+ compute_4[(cse_var_1 + 119)] = 0f32
+ compute_4[(cse_var_1 + 120)] = 0f32
+ compute_4[(cse_var_1 + 121)] = 0f32
+ compute_4[(cse_var_1 + 122)] = 0f32
+ compute_4[(cse_var_1 + 123)] = 0f32
+ compute_4[(cse_var_1 + 124)] = 0f32
+ compute_4[(cse_var_1 + 125)] = 0f32
+ compute_4[(cse_var_1 + 126)] = 0f32
+ compute_4[(cse_var_1 + 127)] = 0f32
+ compute_4[(cse_var_1 + 128)] = 0f32
+ compute_4[(cse_var_1 + 129)] = 0f32
+ compute_4[(cse_var_1 + 130)] = 0f32
+ compute_4[(cse_var_1 + 131)] = 0f32
+ compute_4[(cse_var_1 + 132)] = 0f32
+ compute_4[(cse_var_1 + 133)] = 0f32
+ compute_4[(cse_var_1 + 134)] = 0f32
+ compute_4[(cse_var_1 + 135)] = 0f32
+ compute_4[(cse_var_1 + 136)] = 0f32
+ compute_4[(cse_var_1 + 137)] = 0f32
+ compute_4[(cse_var_1 + 138)] = 0f32
+ compute_4[(cse_var_1 + 139)] = 0f32
+ compute_4[(cse_var_1 + 140)] = 0f32
+ compute_4[(cse_var_1 + 141)] = 0f32
+ compute_4[(cse_var_1 + 142)] = 0f32
+ compute_4[(cse_var_1 + 143)] = 0f32
+ compute_4[(cse_var_1 + 144)] = 0f32
+ compute_4[(cse_var_1 + 145)] = 0f32
+ compute_4[(cse_var_1 + 146)] = 0f32
+ compute_4[(cse_var_1 + 147)] = 0f32
+ compute_4[(cse_var_1 + 148)] = 0f32
+ compute_4[(cse_var_1 + 149)] = 0f32
+ compute_4[(cse_var_1 + 150)] = 0f32
+ compute_4[(cse_var_1 + 151)] = 0f32
+ compute_4[(cse_var_1 + 152)] = 0f32
+ compute_4[(cse_var_1 + 153)] = 0f32
+ compute_4[(cse_var_1 + 154)] = 0f32
+ compute_4[(cse_var_1 + 155)] = 0f32
+ compute_4[(cse_var_1 + 156)] = 0f32
+ compute_4[(cse_var_1 + 157)] = 0f32
+ compute_4[(cse_var_1 + 158)] = 0f32
+ compute_4[(cse_var_1 + 159)] = 0f32
+ compute_4[(cse_var_1 + 160)] = 0f32
+ compute_4[(cse_var_1 + 161)] = 0f32
+ compute_4[(cse_var_1 + 162)] = 0f32
+ compute_4[(cse_var_1 + 163)] = 0f32
+ compute_4[(cse_var_1 + 164)] = 0f32
+ compute_4[(cse_var_1 + 165)] = 0f32
+ compute_4[(cse_var_1 + 166)] = 0f32
+ compute_4[(cse_var_1 + 167)] = 0f32
+ compute_4[(cse_var_1 + 168)] = 0f32
+ compute_4[(cse_var_1 + 169)] = 0f32
+ compute_4[(cse_var_1 + 170)] = 0f32
+ compute_4[(cse_var_1 + 171)] = 0f32
+ compute_4[(cse_var_1 + 172)] = 0f32
+ compute_4[(cse_var_1 + 173)] = 0f32
+ compute_4[(cse_var_1 + 174)] = 0f32
+ compute_4[(cse_var_1 + 175)] = 0f32
+ compute_4[(cse_var_1 + 176)] = 0f32
+ compute_4[(cse_var_1 + 177)] = 0f32
+ compute_4[(cse_var_1 + 178)] = 0f32
+ compute_4[(cse_var_1 + 179)] = 0f32
+ compute_4[(cse_var_1 + 180)] = 0f32
+ compute_4[(cse_var_1 + 181)] = 0f32
+ compute_4[(cse_var_1 + 182)] = 0f32
+ compute_4[(cse_var_1 + 183)] = 0f32
+ compute_4[(cse_var_1 + 184)] = 0f32
+ compute_4[(cse_var_1 + 185)] = 0f32
+ compute_4[(cse_var_1 + 186)] = 0f32
+ compute_4[(cse_var_1 + 187)] = 0f32
+ compute_4[(cse_var_1 + 188)] = 0f32
+ compute_4[(cse_var_1 + 189)] = 0f32
+ compute_4[(cse_var_1 + 190)] = 0f32
+ compute_4[(cse_var_1 + 191)] = 0f32
+ compute_4[(cse_var_1 + 192)] = 0f32
+ compute_4[(cse_var_1 + 193)] = 0f32
+ compute_4[(cse_var_1 + 194)] = 0f32
+ compute_4[(cse_var_1 + 195)] = 0f32
+ compute_4[(cse_var_1 + 196)] = 0f32
+ compute_4[(cse_var_1 + 197)] = 0f32
+ compute_4[(cse_var_1 + 198)] = 0f32
+ compute_4[(cse_var_1 + 199)] = 0f32
+ compute_4[(cse_var_1 + 200)] = 0f32
+ compute_4[(cse_var_1 + 201)] = 0f32
+ compute_4[(cse_var_1 + 202)] = 0f32
+ compute_4[(cse_var_1 + 203)] = 0f32
+ compute_4[(cse_var_1 + 204)] = 0f32
+ compute_4[(cse_var_1 + 205)] = 0f32
+ compute_4[(cse_var_1 + 206)] = 0f32
+ compute_4[(cse_var_1 + 207)] = 0f32
+ compute_4[(cse_var_1 + 208)] = 0f32
+ compute_4[(cse_var_1 + 209)] = 0f32
+ compute_4[(cse_var_1 + 210)] = 0f32
+ compute_4[(cse_var_1 + 211)] = 0f32
+ compute_4[(cse_var_1 + 212)] = 0f32
+ compute_4[(cse_var_1 + 213)] = 0f32
+ compute_4[(cse_var_1 + 214)] = 0f32
+ compute_4[(cse_var_1 + 215)] = 0f32
+ compute_4[(cse_var_1 + 216)] = 0f32
+ compute_4[(cse_var_1 + 217)] = 0f32
+ compute_4[(cse_var_1 + 218)] = 0f32
+ compute_4[(cse_var_1 + 219)] = 0f32
+ compute_4[(cse_var_1 + 220)] = 0f32
+ compute_4[(cse_var_1 + 221)] = 0f32
+ compute_4[(cse_var_1 + 222)] = 0f32
+ compute_4[(cse_var_1 + 223)] = 0f32
+ compute_4[(cse_var_1 + 224)] = 0f32
+ compute_4[(cse_var_1 + 225)] = 0f32
+ compute_4[(cse_var_1 + 226)] = 0f32
+ compute_4[(cse_var_1 + 227)] = 0f32
+ compute_4[(cse_var_1 + 228)] = 0f32
+ compute_4[(cse_var_1 + 229)] = 0f32
+ compute_4[(cse_var_1 + 230)] = 0f32
+ compute_4[(cse_var_1 + 231)] = 0f32
+ compute_4[(cse_var_1 + 232)] = 0f32
+ compute_4[(cse_var_1 + 233)] = 0f32
+ compute_4[(cse_var_1 + 234)] = 0f32
+ compute_4[(cse_var_1 + 235)] = 0f32
+ compute_4[(cse_var_1 + 236)] = 0f32
+ compute_4[(cse_var_1 + 237)] = 0f32
+ compute_4[(cse_var_1 + 238)] = 0f32
+ compute_4[(cse_var_1 + 239)] = 0f32
+ compute_4[(cse_var_1 + 240)] = 0f32
+ compute_4[(cse_var_1 + 241)] = 0f32
+ compute_4[(cse_var_1 + 242)] = 0f32
+ compute_4[(cse_var_1 + 243)] = 0f32
+ compute_4[(cse_var_1 + 244)] = 0f32
+ compute_4[(cse_var_1 + 245)] = 0f32
+ compute_4[(cse_var_1 + 246)] = 0f32
+ compute_4[(cse_var_1 + 247)] = 0f32
+ compute_4[(cse_var_1 + 248)] = 0f32
+ compute_4[(cse_var_1 + 249)] = 0f32
+ compute_4[(cse_var_1 + 250)] = 0f32
+ compute_4[(cse_var_1 + 251)] = 0f32
+ compute_4[(cse_var_1 + 252)] = 0f32
+ compute_4[(cse_var_1 + 253)] = 0f32
+ compute_4[(cse_var_1 + 254)] = 0f32
+ compute_4[(cse_var_1 + 255)] = 0f32
+ for (elem_idx: int32, 0, (placeholder_3[(i1.outer + 1)] - placeholder_3[i1.outer])) {
+ let cse_var_258: int32 = (cse_var_1 + 184)
+ let cse_var_257: int32 = (cse_var_1 + 183)
+ let cse_var_256: int32 = (cse_var_1 + 182)
+ let cse_var_255: int32 = (cse_var_1 + 181)
+ let cse_var_254: int32 = (cse_var_1 + 180)
+ let cse_var_253: int32 = (cse_var_1 + 18)
+ let cse_var_252: int32 = (cse_var_1 + 179)
+ let cse_var_251: int32 = (cse_var_1 + 178)
+ let cse_var_250: int32 = (cse_var_1 + 177)
+ let cse_var_249: int32 = (cse_var_1 + 176)
+ let cse_var_248: int32 = (cse_var_1 + 175)
+ let cse_var_247: int32 = (cse_var_1 + 174)
+ let cse_var_246: int32 = (cse_var_1 + 173)
+ let cse_var_245: int32 = (cse_var_1 + 172)
+ let cse_var_244: int32 = (cse_var_1 + 171)
+ let cse_var_243: int32 = (cse_var_1 + 214)
+ let cse_var_242: int32 = (cse_var_1 + 17)
+ let cse_var_241: int32 = (cse_var_1 + 169)
+ let cse_var_240: int32 = (cse_var_1 + 168)
+ let cse_var_239: int32 = (cse_var_1 + 167)
+ let cse_var_238: int32 = (cse_var_1 + 166)
+ let cse_var_237: int32 = (cse_var_1 + 165)
+ let cse_var_236: int32 = (cse_var_1 + 164)
+ let cse_var_235: int32 = (cse_var_1 + 163)
+ let cse_var_234: int32 = (cse_var_1 + 162)
+ let cse_var_233: int32 = (cse_var_1 + 161)
+ let cse_var_232: int32 = (cse_var_1 + 160)
+ let cse_var_231: int32 = (cse_var_1 + 16)
+ let cse_var_230: int32 = (cse_var_1 + 159)
+ let cse_var_229: int32 = (cse_var_1 + 158)
+ let cse_var_228: int32 = (cse_var_1 + 157)
+ let cse_var_227: int32 = (cse_var_1 + 170)
+ let cse_var_226: int32 = (cse_var_1 + 212)
+ let cse_var_225: int32 = (cse_var_1 + 211)
+ let cse_var_224: int32 = (cse_var_1 + 210)
+ let cse_var_223: int32 = (cse_var_1 + 21)
+ let cse_var_222: int32 = (cse_var_1 + 209)
+ let cse_var_221: int32 = (cse_var_1 + 208)
+ let cse_var_220: int32 = (cse_var_1 + 207)
+ let cse_var_219: int32 = (cse_var_1 + 206)
+ let cse_var_218: int32 = (cse_var_1 + 205)
+ let cse_var_217: int32 = (cse_var_1 + 204)
+ let cse_var_216: int32 = (cse_var_1 + 203)
+ let cse_var_215: int32 = (cse_var_1 + 202)
+ let cse_var_214: int32 = (cse_var_1 + 201)
+ let cse_var_213: int32 = (cse_var_1 + 200)
+ let cse_var_212: int32 = (cse_var_1 + 20)
+ let cse_var_211: int32 = (cse_var_1 + 185)
+ let cse_var_210: int32 = (cse_var_1 + 199)
+ let cse_var_209: int32 = (cse_var_1 + 198)
+ let cse_var_208: int32 = (cse_var_1 + 197)
+ let cse_var_207: int32 = (cse_var_1 + 196)
+ let cse_var_206: int32 = (cse_var_1 + 195)
+ let cse_var_205: int32 = (cse_var_1 + 194)
+ let cse_var_204: int32 = (cse_var_1 + 193)
+ let cse_var_203: int32 = (cse_var_1 + 192)
+ let cse_var_202: int32 = (cse_var_1 + 191)
+ let cse_var_201: int32 = (cse_var_1 + 190)
+ let cse_var_200: int32 = (cse_var_1 + 19)
+ let cse_var_199: int32 = (cse_var_1 + 189)
+ let cse_var_198: int32 = (cse_var_1 + 188)
+ let cse_var_197: int32 = (cse_var_1 + 187)
+ let cse_var_196: int32 = (cse_var_1 + 186)
+ let cse_var_195: int32 = (cse_var_1 + 2)
+ let cse_var_194: int32 = (cse_var_1 + 126)
+ let cse_var_193: int32 = (cse_var_1 + 125)
+ let cse_var_192: int32 = (cse_var_1 + 124)
+ let cse_var_191: int32 = (cse_var_1 + 123)
+ let cse_var_190: int32 = (cse_var_1 + 122)
+ let cse_var_189: int32 = (cse_var_1 + 121)
+ let cse_var_188: int32 = (cse_var_1 + 120)
+ let cse_var_187: int32 = (cse_var_1 + 12)
+ let cse_var_186: int32 = (cse_var_1 + 119)
+ let cse_var_185: int32 = (cse_var_1 + 118)
+ let cse_var_184: int32 = (cse_var_1 + 117)
+ let cse_var_183: int32 = (cse_var_1 + 116)
+ let cse_var_182: int32 = (cse_var_1 + 115)
+ let cse_var_181: int32 = (cse_var_1 + 114)
+ let cse_var_180: int32 = (cse_var_1 + 113)
+ let cse_var_179: int32 = (cse_var_1 + 156)
+ let cse_var_178: int32 = (cse_var_1 + 111)
+ let cse_var_177: int32 = (cse_var_1 + 110)
+ let cse_var_176: int32 = (cse_var_1 + 11)
+ let cse_var_175: int32 = (cse_var_1 + 109)
+ let cse_var_174: int32 = (cse_var_1 + 108)
+ let cse_var_173: int32 = (cse_var_1 + 107)
+ let cse_var_172: int32 = (cse_var_1 + 106)
+ let cse_var_171: int32 = (cse_var_1 + 105)
+ let cse_var_170: int32 = (cse_var_1 + 104)
+ let cse_var_169: int32 = (cse_var_1 + 103)
+ let cse_var_168: int32 = (cse_var_1 + 102)
+ let cse_var_167: int32 = (cse_var_1 + 101)
+ let cse_var_166: int32 = (cse_var_1 + 100)
+ let cse_var_165: int32 = (cse_var_1 + 10)
+ let cse_var_164: int32 = (cse_var_1 + 1)
+ let cse_var_163: int32 = (cse_var_1 + 112)
+ let cse_var_162: int32 = (cse_var_1 + 155)
+ let cse_var_161: int32 = (cse_var_1 + 154)
+ let cse_var_160: int32 = (cse_var_1 + 153)
+ let cse_var_159: int32 = (cse_var_1 + 152)
+ let cse_var_158: int32 = (cse_var_1 + 151)
+ let cse_var_157: int32 = (cse_var_1 + 150)
+ let cse_var_156: int32 = (cse_var_1 + 15)
+ let cse_var_155: int32 = (cse_var_1 + 149)
+ let cse_var_154: int32 = (cse_var_1 + 148)
+ let cse_var_153: int32 = (cse_var_1 + 147)
+ let cse_var_152: int32 = (cse_var_1 + 146)
+ let cse_var_151: int32 = (cse_var_1 + 145)
+ let cse_var_150: int32 = (cse_var_1 + 144)
+ let cse_var_149: int32 = (cse_var_1 + 143)
+ let cse_var_148: int32 = (cse_var_1 + 142)
+ let cse_var_147: int32 = (cse_var_1 + 127)
+ let cse_var_146: int32 = (cse_var_1 + 140)
+ let cse_var_145: int32 = (cse_var_1 + 14)
+ let cse_var_144: int32 = (cse_var_1 + 139)
+ let cse_var_143: int32 = (cse_var_1 + 138)
+ let cse_var_142: int32 = (cse_var_1 + 137)
+ let cse_var_141: int32 = (cse_var_1 + 136)
+ let cse_var_140: int32 = (cse_var_1 + 135)
+ let cse_var_139: int32 = (cse_var_1 + 134)
+ let cse_var_138: int32 = (cse_var_1 + 133)
+ let cse_var_137: int32 = (cse_var_1 + 132)
+ let cse_var_136: int32 = (cse_var_1 + 131)
+ let cse_var_135: int32 = (cse_var_1 + 130)
+ let cse_var_134: int32 = (cse_var_1 + 13)
+ let cse_var_133: int32 = (cse_var_1 + 129)
+ let cse_var_132: int32 = (cse_var_1 + 128)
+ let cse_var_131: int32 = (cse_var_1 + 141)
+ let cse_var_130: int32 = (cse_var_1 + 70)
+ let cse_var_129: int32 = (cse_var_1 + 7)
+ let cse_var_128: int32 = (cse_var_1 + 69)
+ let cse_var_127: int32 = (cse_var_1 + 68)
+ let cse_var_126: int32 = (cse_var_1 + 67)
+ let cse_var_125: int32 = (cse_var_1 + 66)
+ let cse_var_124: int32 = (cse_var_1 + 65)
+ let cse_var_123: int32 = (cse_var_1 + 64)
+ let cse_var_122: int32 = (cse_var_1 + 63)
+ let cse_var_121: int32 = (cse_var_1 + 62)
+ let cse_var_120: int32 = (cse_var_1 + 61)
+ let cse_var_119: int32 = (cse_var_1 + 60)
+ let cse_var_118: int32 = (cse_var_1 + 6)
+ let cse_var_117: int32 = (cse_var_1 + 59)
+ let cse_var_116: int32 = (cse_var_1 + 58)
+ let cse_var_115: int32 = (cse_var_1 + 213)
+ let cse_var_114: int32 = (cse_var_1 + 56)
+ let cse_var_113: int32 = (cse_var_1 + 55)
+ let cse_var_112: int32 = (cse_var_1 + 54)
+ let cse_var_111: int32 = (cse_var_1 + 53)
+ let cse_var_110: int32 = (cse_var_1 + 52)
+ let cse_var_109: int32 = (cse_var_1 + 51)
+ let cse_var_108: int32 = (cse_var_1 + 50)
+ let cse_var_107: int32 = (cse_var_1 + 5)
+ let cse_var_106: int32 = (cse_var_1 + 49)
+ let cse_var_105: int32 = (cse_var_1 + 48)
+ let cse_var_104: int32 = (cse_var_1 + 47)
+ let cse_var_103: int32 = (cse_var_1 + 46)
+ let cse_var_102: int32 = (cse_var_1 + 45)
+ let cse_var_101: int32 = (cse_var_1 + 44)
+ let cse_var_100: int32 = (cse_var_1 + 43)
+ let cse_var_99: int32 = (cse_var_1 + 57)
+ let cse_var_98: int32 = (elem_idx*16)
+ let cse_var_97: int32 = (cse_var_1 + 99)
+ let cse_var_96: int32 = (cse_var_1 + 98)
+ let cse_var_95: int32 = (cse_var_1 + 97)
+ let cse_var_94: int32 = (cse_var_1 + 96)
+ let cse_var_93: int32 = (cse_var_1 + 95)
+ let cse_var_92: int32 = (cse_var_1 + 94)
+ let cse_var_91: int32 = (cse_var_1 + 93)
+ let cse_var_90: int32 = (cse_var_1 + 92)
+ let cse_var_89: int32 = (cse_var_1 + 91)
+ let cse_var_88: int32 = (cse_var_1 + 90)
+ let cse_var_87: int32 = (cse_var_1 + 9)
+ let cse_var_86: int32 = (cse_var_1 + 89)
+ let cse_var_85: int32 = (cse_var_1 + 88)
+ let cse_var_84: int32 = (cse_var_1 + 87)
+ let cse_var_83: int32 = (cse_var_1 + 71)
+ let cse_var_82: int32 = (cse_var_1 + 85)
+ let cse_var_81: int32 = (cse_var_1 + 84)
+ let cse_var_80: int32 = (cse_var_1 + 83)
+ let cse_var_79: int32 = (cse_var_1 + 82)
+ let cse_var_78: int32 = (cse_var_1 + 81)
+ let cse_var_77: int32 = (cse_var_1 + 80)
+ let cse_var_76: int32 = (cse_var_1 + 8)
+ let cse_var_75: int32 = (cse_var_1 + 79)
+ let cse_var_74: int32 = (cse_var_1 + 78)
+ let cse_var_73: int32 = (cse_var_1 + 77)
+ let cse_var_72: int32 = (cse_var_1 + 76)
+ let cse_var_71: int32 = (cse_var_1 + 75)
+ let cse_var_70: int32 = (cse_var_1 + 74)
+ let cse_var_69: int32 = (cse_var_1 + 73)
+ let cse_var_68: int32 = (cse_var_1 + 72)
+ let cse_var_67: int32 = (cse_var_1 + 86)
+ let cse_var_66: int32 = (cse_var_1 + 242)
+ let cse_var_65: int32 = (cse_var_1 + 241)
+ let cse_var_64: int32 = (cse_var_1 + 240)
+ let cse_var_63: int32 = (cse_var_1 + 24)
+ let cse_var_62: int32 = (cse_var_1 + 239)
+ let cse_var_61: int32 = (cse_var_1 + 238)
+ let cse_var_60: int32 = (cse_var_1 + 237)
+ let cse_var_59: int32 = (cse_var_1 + 236)
+ let cse_var_58: int32 = (cse_var_1 + 235)
+ let cse_var_57: int32 = (cse_var_1 + 234)
+ let cse_var_56: int32 = (cse_var_1 + 233)
+ let cse_var_55: int32 = (cse_var_1 + 232)
+ let cse_var_54: int32 = (cse_var_1 + 231)
+ let cse_var_53: int32 = (cse_var_1 + 230)
+ let cse_var_52: int32 = (cse_var_1 + 23)
+ let cse_var_51: int32 = (cse_var_1 + 243)
+ let cse_var_50: int32 = (cse_var_1 + 228)
+ let cse_var_49: int32 = (cse_var_1 + 227)
+ let cse_var_48: int32 = (cse_var_1 + 226)
+ let cse_var_47: int32 = (cse_var_1 + 225)
+ let cse_var_46: int32 = (cse_var_1 + 224)
+ let cse_var_45: int32 = (cse_var_1 + 223)
+ let cse_var_44: int32 = (cse_var_1 + 222)
+ let cse_var_43: int32 = (cse_var_1 + 221)
+ let cse_var_42: int32 = (cse_var_1 + 220)
+ let cse_var_41: int32 = (cse_var_1 + 22)
+ let cse_var_40: int32 = (cse_var_1 + 219)
+ let cse_var_39: int32 = (cse_var_1 + 218)
+ let cse_var_38: int32 = (cse_var_1 + 217)
+ let cse_var_37: int32 = (cse_var_1 + 216)
+ let cse_var_36: int32 = (cse_var_1 + 215)
+ let cse_var_35: int32 = (cse_var_1 + 229)
+ let cse_var_34: int32 = (cse_var_1 + 42)
+ let cse_var_33: int32 = (cse_var_1 + 40)
+ let cse_var_32: int32 = (cse_var_1 + 4)
+ let cse_var_31: int32 = (cse_var_1 + 39)
+ let cse_var_30: int32 = (cse_var_1 + 38)
+ let cse_var_29: int32 = (cse_var_1 + 37)
+ let cse_var_28: int32 = (cse_var_1 + 36)
+ let cse_var_27: int32 = (cse_var_1 + 35)
+ let cse_var_26: int32 = (cse_var_1 + 34)
+ let cse_var_25: int32 = (cse_var_1 + 33)
+ let cse_var_24: int32 = (cse_var_1 + 32)
+ let cse_var_23: int32 = (cse_var_1 + 31)
+ let cse_var_22: int32 = (cse_var_1 + 30)
+ let cse_var_21: int32 = (cse_var_1 + 3)
+ let cse_var_20: int32 = (cse_var_1 + 29)
+ let cse_var_19: int32 = (cse_var_1 + 28)
+ let cse_var_18: int32 = (cse_var_1 + 41)
+ let cse_var_17: int32 = (cse_var_1 + 245)
+ let cse_var_16: int32 = (cse_var_1 + 246)
+ let cse_var_15: int32 = (cse_var_1 + 247)
+ let cse_var_14: int32 = (cse_var_1 + 248)
+ let cse_var_13: int32 = (cse_var_1 + 249)
+ let cse_var_12: int32 = (cse_var_1 + 25)
+ let cse_var_11: int32 = (cse_var_1 + 250)
+ let cse_var_10: int32 = (cse_var_1 + 251)
+ let cse_var_9: int32 = (cse_var_1 + 252)
+ let cse_var_8: int32 = (cse_var_1 + 253)
+ let cse_var_7: int32 = (cse_var_1 + 254)
+ let cse_var_6: int32 = (cse_var_1 + 255)
+ let cse_var_5: int32 = (cse_var_1 + 26)
+ let cse_var_4: int32 = (cse_var_1 + 244)
+ let cse_var_3: int32 = (cse_var_1 + 27)
+ let cse_var_2: int32 = ((i0.outer*16384) + (i.outer.inner*4096))
+ {
+ compute_4[cse_var_1] = (compute_4[cse_var_1] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_164] = (compute_4[cse_var_164] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_195] = (compute_4[cse_var_195] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_21] = (compute_4[cse_var_21] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_32] = (compute_4[cse_var_32] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_107] = (compute_4[cse_var_107] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_118] = (compute_4[cse_var_118] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_129] = (compute_4[cse_var_129] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_76] = (compute_4[cse_var_76] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_87] = (compute_4[cse_var_87] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_165] = (compute_4[cse_var_165] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_176] = (compute_4[cse_var_176] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_187] = (compute_4[cse_var_187] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_134] = (compute_4[cse_var_134] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_145] = (compute_4[cse_var_145] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_156] = (compute_4[cse_var_156] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[(cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)])], 0f32)))
+ compute_4[cse_var_231] = (compute_4[cse_var_231] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_242] = (compute_4[cse_var_242] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_253] = (compute_4[cse_var_253] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_200] = (compute_4[cse_var_200] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_212] = (compute_4[cse_var_212] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_223] = (compute_4[cse_var_223] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_41] = (compute_4[cse_var_41] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_52] = (compute_4[cse_var_52] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_63] = (compute_4[cse_var_63] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_20] = (compute_4[cse_var_20] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_22] = (compute_4[cse_var_22] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_23] = (compute_4[cse_var_23] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 256)], 0f32)))
+ compute_4[cse_var_24] = (compute_4[cse_var_24] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_25] = (compute_4[cse_var_25] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_26] = (compute_4[cse_var_26] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_27] = (compute_4[cse_var_27] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_28] = (compute_4[cse_var_28] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_29] = (compute_4[cse_var_29] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_30] = (compute_4[cse_var_30] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_31] = (compute_4[cse_var_31] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_33] = (compute_4[cse_var_33] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_34] = (compute_4[cse_var_34] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_100] = (compute_4[cse_var_100] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_101] = (compute_4[cse_var_101] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_102] = (compute_4[cse_var_102] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_103] = (compute_4[cse_var_103] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_104] = (compute_4[cse_var_104] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 512)], 0f32)))
+ compute_4[cse_var_105] = (compute_4[cse_var_105] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_106] = (compute_4[cse_var_106] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_108] = (compute_4[cse_var_108] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_109] = (compute_4[cse_var_109] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_110] = (compute_4[cse_var_110] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_111] = (compute_4[cse_var_111] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_112] = (compute_4[cse_var_112] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_113] = (compute_4[cse_var_113] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_114] = (compute_4[cse_var_114] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_99] = (compute_4[cse_var_99] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_116] = (compute_4[cse_var_116] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_117] = (compute_4[cse_var_117] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_119] = (compute_4[cse_var_119] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_120] = (compute_4[cse_var_120] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_121] = (compute_4[cse_var_121] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_122] = (compute_4[cse_var_122] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 768)], 0f32)))
+ compute_4[cse_var_123] = (compute_4[cse_var_123] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_124] = (compute_4[cse_var_124] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_125] = (compute_4[cse_var_125] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_126] = (compute_4[cse_var_126] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_127] = (compute_4[cse_var_127] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_128] = (compute_4[cse_var_128] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_130] = (compute_4[cse_var_130] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_83] = (compute_4[cse_var_83] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_68] = (compute_4[cse_var_68] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_69] = (compute_4[cse_var_69] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_70] = (compute_4[cse_var_70] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_71] = (compute_4[cse_var_71] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_72] = (compute_4[cse_var_72] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_73] = (compute_4[cse_var_73] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_74] = (compute_4[cse_var_74] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_75] = (compute_4[cse_var_75] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1024)], 0f32)))
+ compute_4[cse_var_77] = (compute_4[cse_var_77] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_78] = (compute_4[cse_var_78] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_79] = (compute_4[cse_var_79] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_80] = (compute_4[cse_var_80] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_81] = (compute_4[cse_var_81] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_82] = (compute_4[cse_var_82] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_67] = (compute_4[cse_var_67] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_84] = (compute_4[cse_var_84] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_85] = (compute_4[cse_var_85] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_86] = (compute_4[cse_var_86] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_88] = (compute_4[cse_var_88] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_89] = (compute_4[cse_var_89] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_90] = (compute_4[cse_var_90] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_91] = (compute_4[cse_var_91] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_92] = (compute_4[cse_var_92] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_93] = (compute_4[cse_var_93] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1280)], 0f32)))
+ compute_4[cse_var_94] = (compute_4[cse_var_94] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_95] = (compute_4[cse_var_95] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_96] = (compute_4[cse_var_96] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_97] = (compute_4[cse_var_97] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_166] = (compute_4[cse_var_166] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_167] = (compute_4[cse_var_167] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_168] = (compute_4[cse_var_168] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_169] = (compute_4[cse_var_169] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_170] = (compute_4[cse_var_170] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_171] = (compute_4[cse_var_171] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_172] = (compute_4[cse_var_172] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_173] = (compute_4[cse_var_173] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_174] = (compute_4[cse_var_174] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_175] = (compute_4[cse_var_175] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_177] = (compute_4[cse_var_177] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_178] = (compute_4[cse_var_178] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1536)], 0f32)))
+ compute_4[cse_var_163] = (compute_4[cse_var_163] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_180] = (compute_4[cse_var_180] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_181] = (compute_4[cse_var_181] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_182] = (compute_4[cse_var_182] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_183] = (compute_4[cse_var_183] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_184] = (compute_4[cse_var_184] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_185] = (compute_4[cse_var_185] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_186] = (compute_4[cse_var_186] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_188] = (compute_4[cse_var_188] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_189] = (compute_4[cse_var_189] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_190] = (compute_4[cse_var_190] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_191] = (compute_4[cse_var_191] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_192] = (compute_4[cse_var_192] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_193] = (compute_4[cse_var_193] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_194] = (compute_4[cse_var_194] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_147] = (compute_4[cse_var_147] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 1792)], 0f32)))
+ compute_4[cse_var_132] = (compute_4[cse_var_132] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_133] = (compute_4[cse_var_133] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_135] = (compute_4[cse_var_135] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_136] = (compute_4[cse_var_136] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_137] = (compute_4[cse_var_137] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_138] = (compute_4[cse_var_138] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_139] = (compute_4[cse_var_139] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_140] = (compute_4[cse_var_140] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_141] = (compute_4[cse_var_141] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_142] = (compute_4[cse_var_142] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_143] = (compute_4[cse_var_143] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_144] = (compute_4[cse_var_144] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_146] = (compute_4[cse_var_146] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_131] = (compute_4[cse_var_131] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_148] = (compute_4[cse_var_148] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_149] = (compute_4[cse_var_149] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2048)], 0f32)))
+ compute_4[cse_var_150] = (compute_4[cse_var_150] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_151] = (compute_4[cse_var_151] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_152] = (compute_4[cse_var_152] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_153] = (compute_4[cse_var_153] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_154] = (compute_4[cse_var_154] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_155] = (compute_4[cse_var_155] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_157] = (compute_4[cse_var_157] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_158] = (compute_4[cse_var_158] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_159] = (compute_4[cse_var_159] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_160] = (compute_4[cse_var_160] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_161] = (compute_4[cse_var_161] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_162] = (compute_4[cse_var_162] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_179] = (compute_4[cse_var_179] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_228] = (compute_4[cse_var_228] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_229] = (compute_4[cse_var_229] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_230] = (compute_4[cse_var_230] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2304)], 0f32)))
+ compute_4[cse_var_232] = (compute_4[cse_var_232] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_233] = (compute_4[cse_var_233] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_234] = (compute_4[cse_var_234] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_235] = (compute_4[cse_var_235] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_236] = (compute_4[cse_var_236] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_237] = (compute_4[cse_var_237] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_238] = (compute_4[cse_var_238] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_239] = (compute_4[cse_var_239] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_240] = (compute_4[cse_var_240] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_241] = (compute_4[cse_var_241] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_227] = (compute_4[cse_var_227] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_244] = (compute_4[cse_var_244] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_245] = (compute_4[cse_var_245] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_246] = (compute_4[cse_var_246] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_247] = (compute_4[cse_var_247] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_248] = (compute_4[cse_var_248] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2560)], 0f32)))
+ compute_4[cse_var_249] = (compute_4[cse_var_249] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_250] = (compute_4[cse_var_250] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_251] = (compute_4[cse_var_251] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_252] = (compute_4[cse_var_252] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_254] = (compute_4[cse_var_254] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_255] = (compute_4[cse_var_255] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_256] = (compute_4[cse_var_256] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_257] = (compute_4[cse_var_257] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_258] = (compute_4[cse_var_258] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_211] = (compute_4[cse_var_211] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_196] = (compute_4[cse_var_196] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_197] = (compute_4[cse_var_197] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_198] = (compute_4[cse_var_198] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_199] = (compute_4[cse_var_199] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_201] = (compute_4[cse_var_201] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_202] = (compute_4[cse_var_202] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 2816)], 0f32)))
+ compute_4[cse_var_203] = (compute_4[cse_var_203] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_204] = (compute_4[cse_var_204] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_205] = (compute_4[cse_var_205] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_206] = (compute_4[cse_var_206] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_207] = (compute_4[cse_var_207] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_208] = (compute_4[cse_var_208] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_209] = (compute_4[cse_var_209] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_210] = (compute_4[cse_var_210] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_213] = (compute_4[cse_var_213] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_214] = (compute_4[cse_var_214] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_215] = (compute_4[cse_var_215] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_216] = (compute_4[cse_var_216] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_217] = (compute_4[cse_var_217] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_218] = (compute_4[cse_var_218] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_219] = (compute_4[cse_var_219] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_220] = (compute_4[cse_var_220] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3072)], 0f32)))
+ compute_4[cse_var_221] = (compute_4[cse_var_221] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_222] = (compute_4[cse_var_222] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_224] = (compute_4[cse_var_224] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_225] = (compute_4[cse_var_225] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_226] = (compute_4[cse_var_226] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_115] = (compute_4[cse_var_115] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_243] = (compute_4[cse_var_243] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_36] = (compute_4[cse_var_36] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_37] = (compute_4[cse_var_37] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_38] = (compute_4[cse_var_38] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_39] = (compute_4[cse_var_39] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_40] = (compute_4[cse_var_40] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_42] = (compute_4[cse_var_42] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_43] = (compute_4[cse_var_43] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_44] = (compute_4[cse_var_44] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_45] = (compute_4[cse_var_45] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3328)], 0f32)))
+ compute_4[cse_var_46] = (compute_4[cse_var_46] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_47] = (compute_4[cse_var_47] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_48] = (compute_4[cse_var_48] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_49] = (compute_4[cse_var_49] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_50] = (compute_4[cse_var_50] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_35] = (compute_4[cse_var_35] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_53] = (compute_4[cse_var_53] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_54] = (compute_4[cse_var_54] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_55] = (compute_4[cse_var_55] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_56] = (compute_4[cse_var_56] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_57] = (compute_4[cse_var_57] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_58] = (compute_4[cse_var_58] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_59] = (compute_4[cse_var_59] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_60] = (compute_4[cse_var_60] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_61] = (compute_4[cse_var_61] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_62] = (compute_4[cse_var_62] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3584)], 0f32)))
+ compute_4[cse_var_64] = (compute_4[cse_var_64] + (placeholder_1[((placeholder_3[i1.outer]*16) + cse_var_98)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_65] = (compute_4[cse_var_65] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 1)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_66] = (compute_4[cse_var_66] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 2)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_51] = (compute_4[cse_var_51] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 3)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_4] = (compute_4[cse_var_4] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 4)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 5)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 6)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 7)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 8)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 9)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 10)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 11)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 12)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 13)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 14)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
+ compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[i1.outer]*16) + cse_var_98) + 15)]*max(placeholder[((cse_var_2 + placeholder_2[(placeholder_3[i1.outer] + elem_idx)]) + 3840)], 0f32)))
}
}
}
}
- for (i0.inner: int32, 0, 128) {
- let cse_var_2: int32 = ((i0.inner*512) + (i0.outer.i1.outer.fused*16))
- compute[ramp(cse_var_2, 1, 16)] = max((compute_4[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_2, 1, 16)]), broadcast(0f32, 16))
+ for (i0.inner: int32, 0, 64) {
+ let cse_var_259: int32 = (((i0.outer*32768) + (i0.inner*512)) + (i1.outer*16))
+ compute[ramp(cse_var_259, 1, 16)] = max((compute_4[ramp((i0.inner*16), 1, 16)] + placeholder_4[ramp(cse_var_259, 1, 16)]), broadcast(0f32, 16))
}
}
}
@@ -658,7 +1422,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.454 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 3.337 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 1f7a95f26..c658425fd 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<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>00:43.241</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:43.418</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:42.422</strong>: <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></li>
-<li><p><strong>00:00.217</strong>: <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></li>
-<li><p><strong>00:00.203</strong>: <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></li>
-<li><p><strong>00:00.201</strong>: <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></li>
-<li><p><strong>00:00.198</strong>: <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></li>
+<li><p><strong>00:42.622</strong>: <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></li>
+<li><p><strong>00:00.209</strong>: <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></li>
+<li><p><strong>00:00.202</strong>: <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></li>
+<li><p><strong>00:00.192</strong>: <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></li>
+<li><p><strong>00:00.192</strong>: <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></li>
</ul>
</div>
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 dffc9104f..fa79b4712 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -1142,8 +1142,8 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, 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, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
-No: 6 GFLOPS: 96.08/96.08 result: MeasureResult(costs=(0.002409375229166667,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5743904113769531, timestamp=1650062645.8736827) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-No: 7 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 6 GFLOPS: 103.50/103.50 result: MeasureResult(costs=(0.002236654708333333,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.592435359954834, timestamp=1650063815.6699162) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+No: 7 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1266,7 +1266,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
-No: 8 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1389,7 +1389,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
-No: 9 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1512,7 +1512,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
-No: 10 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/103.50 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
@@ -1530,7 +1530,7 @@ No: 10 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
-No: 11 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1653,7 +1653,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
-No: 12 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1776,7 +1776,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
-No: 13 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1899,7 +1899,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
-No: 14 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2022,7 +2022,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
-No: 15 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2145,7 +2145,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
-No: 16 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2268,7 +2268,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
-No: 17 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2391,7 +2391,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, 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, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
-No: 18 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2514,7 +2514,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
-No: 19 GFLOPS: 0.00/96.08 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/103.50 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2602,7 +2602,7 @@ tvm._ffi.base.TVMError: Traceback (most recent call last):
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007fa2da56afa2
+ 12: 0x00007fc4d562cfa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2667,7 +2667,7 @@ Traceback (most recent call last):
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
-No: 20 GFLOPS: 142.44/142.44 result: MeasureResult(costs=(0.0016252172580645161,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1265416145324707, timestamp=1650062671.247943) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+No: 20 GFLOPS: 144.98/144.98 result: MeasureResult(costs=(0.0015968331,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4312255382537842, timestamp=1650063841.3437583) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
</pre></div>
</div>
<p>Finally we can inspect the best config from log file, check correctness,
@@ -2706,7 +2706,7 @@ and measure running time.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
-Time cost of this operator: 0.001975
+Time cost of this operator: 0.001952
</pre></div>
</div>
<div class="sphx-glr-footer class 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 871550489..8ad31a996 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -553,10 +553,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 308.9 98.747 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.0 0.959 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.92 0.294 (1, 1, 10, 10, 3) 1 1
-Total_time - 312.82 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.7 98.733 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.067 0.978 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.906 0.289 (1, 1, 10, 10, 3) 1 1
+Total_time - 313.673 - - - -
</pre></div>
</div>
</div>
@@ -608,10 +608,10 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 216.8 98.672 (1, 1, 10, 10, 6) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.992 0.907 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.925 0.421 (1, 3, 10, 10, 1) 1 1
-Total_time - 219.717 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 89.1 97.148 (1, 6, 10, 10, 1) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.715 1.87 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.901 0.982 (1, 1, 10, 10, 3) 1 1
+Total_time - 91.716 - - - -
</pre></div>
</div>
<div class="sphx-glr-footer class 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/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 3c334e814..12d6ceddc 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<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>00:43.558</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:43.326</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:39.589</strong>: <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></li>
-<li><p><strong>00:03.408</strong>: <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></li>
-<li><p><strong>00:00.192</strong>: <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</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
-<li><p><strong>00:00.189</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
-<li><p><strong>00:00.180</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:39.410</strong>: <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></li>
+<li><p><strong>00:03.367</strong>: <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></li>
+<li><p><strong>00:00.188</strong>: <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</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
+<li><p><strong>00:00.186</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
+<li><p><strong>00:00.176</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
</ul>
</div>
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 20a8bbec7..f39a927b7 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -300,11 +300,11 @@
<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>00:09.599</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:05.817</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:07.073</strong>: <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></li>
-<li><p><strong>00:02.323</strong>: <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></li>
-<li><p><strong>00:00.203</strong>: <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></li>
+<li><p><strong>00:04.046</strong>: <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></li>
+<li><p><strong>00:01.566</strong>: <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></li>
+<li><p><strong>00:00.205</strong>: <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></li>
</ul>
</div>
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 9ac2f3523..2ea39a854 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -300,16 +300,16 @@
<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>00:05.501</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.195</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.040</strong>: <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></li>
-<li><p><strong>00:01.099</strong>: <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></li>
-<li><p><strong>00:00.708</strong>: <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></li>
-<li><p><strong>00:00.693</strong>: <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></li>
-<li><p><strong>00:00.305</strong>: <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></li>
-<li><p><strong>00:00.230</strong>: <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></li>
-<li><p><strong>00:00.220</strong>: <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></li>
-<li><p><strong>00:00.206</strong>: <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></li>
+<li><p><strong>00:01.939</strong>: <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></li>
+<li><p><strong>00:00.986</strong>: <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></li>
+<li><p><strong>00:00.687</strong>: <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></li>
+<li><p><strong>00:00.656</strong>: <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></li>
+<li><p><strong>00:00.289</strong>: <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></li>
+<li><p><strong>00:00.227</strong>: <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></li>
+<li><p><strong>00:00.213</strong>: <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></li>
+<li><p><strong>00:00.198</strong>: <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></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 28e8355d5..c2783957d 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -548,7 +548,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
B: Buffer(B_2: Pointer(float32), float32, [32768], []),
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpch4fv22p/input0.cc'\nsource_filename = \"/tmp/tmpch4fv22p/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/tmphcc2ycl2/input0.cc'\nsource_filename = \"/tmp/tmphcc2ycl2/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/reference/api/python/auto_scheduler.html b/docs/reference/api/python/auto_scheduler.html
index 3e03275c7..ca2da0eab 100644
--- a/docs/reference/api/python/auto_scheduler.html
+++ b/docs/reference/api/python/auto_scheduler.html
@@ -1713,7 +1713,7 @@ Can be the a function or the function name.</p></li>
<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">
@@ -1750,7 +1750,7 @@ the initial naive schedule (state).</p>
<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>
diff --git a/docs/reference/api/typedoc/classes/bytestreamreader.html b/docs/reference/api/typedoc/classes/bytestreamreader.html
index 283492fb3..47c9ec134 100644
--- a/docs/reference/api/typedoc/classes/bytestreamreader.html
+++ b/docs/reference/api/typedoc/classes/bytestreamreader.html
@@ -119,7 +119,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
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<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/365fcc832/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L43">rpc_server.ts:43</a></li>
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<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/365fcc832/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
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@@ -185,7 +185,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
</ul>
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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 2f380e7e7..4fc6a3c8c 100644
--- a/docs/reference/api/typedoc/classes/cachedcallstack.html
+++ b/docs/reference/api/typedoc/classes/cachedcallstack.html
@@ -144,7 +144,7 @@
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L223">memory.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L223">memory.ts:223</a></li>
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<h4 class="tsd-parameters-title">Parameters</h4>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L208">memory.ts:208</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L208">memory.ts:208</a></li>
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L312">memory.ts:312</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L284">memory.ts:284</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L388">memory.ts:388</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L388">memory.ts:388</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L376">memory.ts:376</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L376">memory.ts:376</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L267">memory.ts:267</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L267">memory.ts:267</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L243">memory.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L243">memory.ts:243</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L321">memory.ts:321</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L321">memory.ts:321</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L252">memory.ts:252</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L252">memory.ts:252</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L359">memory.ts:359</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L359">memory.ts:359</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L342">memory.ts:342</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L342">memory.ts:342</a></li>
</ul>
</aside>
<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/365fcc832/web/src/memory.ts#L350">memory.ts:350</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L326">memory.ts:326</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L363">memory.ts:363</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L346">memory.ts:346</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L334">memory.ts:334</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L334">memory.ts:334</a></li>
</ul>
</aside>
<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 fdfbabf0b..c6770c89b 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/365fcc832/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L260">runtime.ts:260</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L258">runtime.ts:258</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L262">runtime.ts:262</a></li>
</ul>
</aside>
<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/365fcc832/web/src/runtime.ts#L279">runtime.ts:279</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L279">runtime.ts:279</a></li>
</ul>
</aside>
<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/365fcc832/web/src/runtime.ts#L270">runtime.ts:270</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 ca141098f..9197daf9f 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/365fcc832/web/src/runtime.ts#L202">runtime.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L200">runtime.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L198">runtime.ts:198</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L223">runtime.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L230">runtime.ts:230</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 0a00821d8..f34e056d6 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/365fcc832/web/src/environment.ts#L86">environment.ts:86</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/environment.ts#L70">environment.ts:70</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/environment.ts#L70">environment.ts:70</a></li>
</ul>
</aside>
</section>
@@ -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/365fcc832/web/src/environment.ts#L69">environment.ts:69</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/environment.ts#L78">environment.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/environment.ts#L84">environment.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/environment.ts#L105">environment.ts:105</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/environment.ts#L105">environment.ts:105</a></li>
</ul>
</aside>
<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 1d236d9a2..ba230786c 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/365fcc832/web/src/runtime.ts#L49">runtime.ts:49</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L46">runtime.ts:46</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L45">runtime.ts:45</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L44">runtime.ts:44</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L47">runtime.ts:47</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L76">runtime.ts:76</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L66">runtime.ts:66</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L84">runtime.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L95">runtime.ts:95</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L72">runtime.ts:72</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 5d9222be3..0c9772682 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/365fcc832/web/src/runtime.ts#L583">runtime.ts:583</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L579">runtime.ts:579</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L654">runtime.ts:654</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L597">runtime.ts:597</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L631">runtime.ts:631</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L644">runtime.ts:644</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L621">runtime.ts:621</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L609">runtime.ts:609</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 b72fac180..4b5b90940 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/365fcc832/web/src/runtime.ts#L692">runtime.ts:692</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L684">runtime.ts:684</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L683">runtime.ts:683</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L932">runtime.ts:932</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L994">runtime.ts:994</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L924">runtime.ts:924</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L732">runtime.ts:732</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L952">runtime.ts:952</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L816">runtime.ts:816</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L846">runtime.ts:846</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L750">runtime.ts:750</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L789">runtime.ts:789</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L914">runtime.ts:914</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L1134">runtime.ts:1134</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/365fcc832/web/src/runtime.ts#L740">runtime.ts:740</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L868">runtime.ts:868</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L857">runtime.ts:857</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L940">runtime.ts:940</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L940">runtime.ts:940</a></li>
</ul>
</aside>
<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 d8227c09c..30f9567ee 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/365fcc832/web/src/memory.ts#L40">memory.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L32">memory.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L33">memory.ts:33</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L154">memory.ts:154</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L90">memory.ts:90</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L97">memory.ts:97</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L74">memory.ts:74</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L81">memory.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L104">memory.ts:104</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L104">memory.ts:104</a></li>
</ul>
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<h4 class="tsd-parameters-title">Parameters</h4>
@@ -325,7 +325,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L132">memory.ts:132</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L132">memory.ts:132</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -362,7 +362,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L145">memory.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L145">memory.ts:145</a></li>
</ul>
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<div class="tsd-comment tsd-typography">
@@ -393,7 +393,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L60">memory.ts:60</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L60">memory.ts:60</a></li>
</ul>
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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/365fcc832/web/src/memory.ts#L67">memory.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L67">memory.ts:67</a></li>
</ul>
</aside>
<h4 class="tsd-parameters-title">Parameters</h4>
@@ -439,7 +439,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L53">memory.ts:53</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/memory.ts#L114">memory.ts:114</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/memory.ts#L124">memory.ts:124</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L124">memory.ts:124</a></li>
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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/365fcc832/web/src/memory.ts#L175">memory.ts:175</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/memory.ts#L175">memory.ts:175</a></li>
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diff --git a/docs/reference/api/typedoc/classes/module.html b/docs/reference/api/typedoc/classes/module.html
index 0ebdc0883..e7c139c04 100644
--- a/docs/reference/api/typedoc/classes/module.html
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@@ -124,7 +124,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L504">runtime.ts:504</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L504">runtime.ts:504</a></li>
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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>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L502">runtime.ts:502</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L502">runtime.ts:502</a></li>
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@@ -187,7 +187,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L516">runtime.ts:516</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L530">runtime.ts:530</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L530">runtime.ts:530</a></li>
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<div class="tsd-comment tsd-typography">
@@ -236,7 +236,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L561">runtime.ts:561</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L561">runtime.ts:561</a></li>
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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 2a0c28529..55c4c21d1 100644
--- a/docs/reference/api/typedoc/classes/ndarray.html
+++ b/docs/reference/api/typedoc/classes/ndarray.html
@@ -130,7 +130,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L304">runtime.ts:304</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L304">runtime.ts:304</a></li>
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<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/365fcc832/web/src/runtime.ts#L297">runtime.ts:297</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L297">runtime.ts:297</a></li>
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<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/365fcc832/web/src/runtime.ts#L293">runtime.ts:293</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L293">runtime.ts:293</a></li>
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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/365fcc832/web/src/runtime.ts#L289">runtime.ts:289</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L289">runtime.ts:289</a></li>
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<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/365fcc832/web/src/runtime.ts#L291">runtime.ts:291</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L291">runtime.ts:291</a></li>
</ul>
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<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/365fcc832/web/src/runtime.ts#L295">runtime.ts:295</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L295">runtime.ts:295</a></li>
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<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/365fcc832/web/src/runtime.ts#L370">runtime.ts:370</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L370">runtime.ts:370</a></li>
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<div class="tsd-comment tsd-typography">
@@ -273,7 +273,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L414">runtime.ts:414</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L414">runtime.ts:414</a></li>
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<div class="tsd-comment tsd-typography">
@@ -305,7 +305,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L355">runtime.ts:355</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L355">runtime.ts:355</a></li>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -322,7 +322,7 @@
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<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L474">runtime.ts:474</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L474">runtime.ts:474</a></li>
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<div class="tsd-comment tsd-typography">
@@ -346,7 +346,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L443">runtime.ts:443</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 2b1197ae7..063097767 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/365fcc832/web/src/runtime.ts#L158">runtime.ts:158</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L158">runtime.ts:158</a></li>
</ul>
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<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/365fcc832/web/src/runtime.ts#L157">runtime.ts:157</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L157">runtime.ts:157</a></li>
</ul>
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@@ -164,7 +164,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L165">runtime.ts:165</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L165">runtime.ts:165</a></li>
</ul>
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<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 22f72729c..28618e272 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/365fcc832/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
</ul>
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@@ -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/365fcc832/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 28a20216c..387fa888b 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/365fcc832/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L143">runtime.ts:143</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 278bff33a..235923c14 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/365fcc832/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
</ul>
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<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/365fcc832/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
</ul>
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@@ -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/365fcc832/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
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@@ -172,7 +172,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
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<div class="tsd-comment tsd-typography">
@@ -209,7 +209,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
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<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
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<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 a57c45987..78fe5d523 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/365fcc832/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
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@@ -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/365fcc832/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
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@@ -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/365fcc832/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
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</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/365fcc832/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 845f5af78..acade5434 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/365fcc832/web/src/runtime.ts#L676">runtime.ts:676</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L675">runtime.ts:675</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 37bd5d1f2..2032cd9eb 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/365fcc832/web/src/runtime.ts#L242">runtime.ts:242</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L240">runtime.ts:240</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L243">runtime.ts:243</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/runtime.ts#L241">runtime.ts:241</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 359161a04..aa420567c 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/365fcc832/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 bc4013c39..be560e1a9 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/365fcc832/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 a852079e4..b5236ca6f 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/365fcc832/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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/365fcc832/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1169,7 +1169,7 @@
<div class="tsd-signature tsd-kind-icon">Packed<wbr>Func<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>args<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol"> & </span><a href="interfaces/disp [...]
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L36">runtime.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L36">runtime.ts:36</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1184,7 +1184,7 @@
<div class="tsd-signature tsd-kind-icon">Pointer<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/365fcc832/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1199,7 +1199,7 @@
<div class="tsd-signature tsd-kind-icon">Ptr<wbr>Offset<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/365fcc832/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1217,7 +1217,7 @@
<div class="tsd-signature tsd-kind-icon">RPC_<wbr>MAGIC<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">1045105</span><span class="tsd-signature-symbol"> = 1045105</span></div>
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1239,7 +1239,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/support.ts#L25">support.ts:25</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/support.ts#L25">support.ts:25</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1271,7 +1271,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/support.ts#L39">support.ts:39</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/support.ts#L39">support.ts:39</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1300,7 +1300,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/support.ts#L52">support.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/support.ts#L52">support.ts:52</a></li>
</ul>
</aside>
<div class="tsd-comment tsd-typography">
@@ -1337,7 +1337,7 @@
<li class="tsd-description">
<aside class="tsd-sources">
<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/compact.ts#L38">compact.ts:38</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/compact.ts#L38">compact.ts:38</a></li>
</ul>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L247">runtime.ts:247</a></li>
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<ul>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L175">runtime.ts:175</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L176">runtime.ts:176</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L180">runtime.ts:180</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L179">runtime.ts:179</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L183">runtime.ts:183</a></li>
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<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1649,7 +1649,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L186">runtime.ts:186</a></li>
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@@ -1659,7 +1659,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L184">runtime.ts:184</a></li>
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@@ -1669,7 +1669,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L185">runtime.ts:185</a></li>
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@@ -1679,7 +1679,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L189">runtime.ts:189</a></li>
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@@ -1689,7 +1689,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L187">runtime.ts:187</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L188">runtime.ts:188</a></li>
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@@ -1709,7 +1709,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/runtime.ts#L190">runtime.ts:190</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/runtime.ts#L190">runtime.ts:190</a></li>
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diff --git a/docs/reference/api/typedoc/interfaces/disposable.html b/docs/reference/api/typedoc/interfaces/disposable.html
index 4cf984a68..6e339f2be 100644
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@@ -113,7 +113,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/types.ts#L52">types.ts:52</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/types.ts#L52">types.ts:52</a></li>
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<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 30d2f75df..34c75ae81 100644
--- a/docs/reference/api/typedoc/interfaces/functioninfo.html
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@@ -95,7 +95,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
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- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/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 312c08115..1fad83476 100644
--- a/docs/reference/api/typedoc/interfaces/libraryprovider.html
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@@ -112,7 +112,7 @@
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<ul>
- <li>Defined in <a href="https://github.com/apache/tvm/blob/365fcc832/web/src/types.ts#L34">types.ts:34</a></li>
+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/types.ts#L34">types.ts:34</a></li>
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<div class="tsd-comment tsd-typography">
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<ul>
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+ <li>Defined in <a href="https://github.com/apache/tvm/blob/351f31b51/web/src/types.ts#L39">types.ts:39</a></li>
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<div class="tsd-comment tsd-typography">
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 6662e99df..0f9ba2653 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 fa549b293..051f65f51 100644
--- a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
@@ -300,10 +300,10 @@
<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>00:20.829</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
+<p><strong>00:20.128</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:20.625</strong>: <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></li>
-<li><p><strong>00:00.204</strong>: <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></li>
+<li><p><strong>00:19.945</strong>: <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></li>
+<li><p><strong>00:00.184</strong>: <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></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_classification.html b/docs/topic/vta/tutorials/frontend/deploy_classification.html
index 3a00a665d..12159ce7d 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_classification.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_classification.html
@@ -539,7 +539,7 @@ 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 21.73s!
+resnet18_v1 inference graph built in 21.18s!
</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 df90bb277..fdd45cbb5 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -557,7 +557,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
-yolov3-tiny inference graph built in 15.12s!
+yolov3-tiny inference graph built in 14.74s!
</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 2827d5e71..3ac4862ed 100644
--- a/docs/topic/vta/tutorials/frontend/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
@@ -300,10 +300,10 @@
<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>01:29.082</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:27.605</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:47.185</strong>: <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></li>
-<li><p><strong>00:41.897</strong>: <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></li>
+<li><p><strong>00:46.557</strong>: <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></li>
+<li><p><strong>00:41.048</strong>: <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></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/optimize/sg_execution_times.html b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
index aad97a1ec..5ef5ecb5a 100644
--- a/docs/topic/vta/tutorials/optimize/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
@@ -300,10 +300,10 @@
<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>00:03.493</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.467</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.952</strong>: <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></li>
-<li><p><strong>00:00.541</strong>: <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></li>
+<li><p><strong>00:02.970</strong>: <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></li>
+<li><p><strong>00:00.497</strong>: <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></li>
</ul>
</div>
diff --git a/docs/topic/vta/tutorials/sg_execution_times.html b/docs/topic/vta/tutorials/sg_execution_times.html
index bcb979105..077ef7576 100644
--- a/docs/topic/vta/tutorials/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -300,10 +300,10 @@
<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>00:00.968</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
+<p><strong>00:00.907</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:00.485</strong>: <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></li>
-<li><p><strong>00:00.482</strong>: <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></li>
+<li><p><strong>00:00.463</strong>: <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></li>
+<li><p><strong>00:00.444</strong>: <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></li>
</ul>
</div>
diff --git a/docs/tutorial/auto_scheduler_matmul_x86.html b/docs/tutorial/auto_scheduler_matmul_x86.html
index 361a8e00f..72d2ba56b 100644
--- a/docs/tutorial/auto_scheduler_matmul_x86.html
+++ b/docs/tutorial/auto_scheduler_matmul_x86.html
@@ -544,7 +544,7 @@ operator fusion.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 92.988 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 92.791 ms
</pre></div>
</div>
</div>
diff --git a/docs/tutorial/autotvm_relay_x86.html b/docs/tutorial/autotvm_relay_x86.html
index 8cfbe885b..b89bfce2e 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -513,7 +513,7 @@ standard deviation.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 491.0217237799998, 'median': 490.89573064999854, 'std': 0.4141502182556612}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 490.400963600041, 'median': 490.2133211000546, 'std': 0.7120891974454429}
</pre></div>
</div>
</div>
@@ -667,128 +667,129 @@ depending on the specifics of the model and the target platform.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 1/25] Current/Best: 19.66/ 19.66 GFLOPS | Progress: (4/10) | 5.02 s
-[Task 1/25] Current/Best: 11.56/ 19.66 GFLOPS | Progress: (8/10) | 7.25 s
-[Task 1/25] Current/Best: 14.73/ 19.66 GFLOPS | Progress: (10/10) | 8.83 s Done.
+[Task 1/25] Current/Best: 17.88/ 17.88 GFLOPS | Progress: (4/10) | 5.42 s
+[Task 1/25] Current/Best: 16.82/ 18.32 GFLOPS | Progress: (8/10) | 9.04 s
+[Task 1/25] Current/Best: 15.25/ 18.32 GFLOPS | Progress: (10/10) | 10.29 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 2/25] Current/Best: 15.87/ 15.87 GFLOPS | Progress: (4/10) | 2.26 s
-[Task 2/25] Current/Best: 22.25/ 22.25 GFLOPS | Progress: (8/10) | 4.11 s
-[Task 2/25] Current/Best: 6.83/ 22.25 GFLOPS | Progress: (10/10) | 4.79 s Done.
+[Task 2/25] Current/Best: 5.85/ 16.32 GFLOPS | Progress: (4/10) | 2.41 s
+[Task 2/25] Current/Best: 15.50/ 16.85 GFLOPS | Progress: (8/10) | 3.77 s
+[Task 2/25] Current/Best: 17.75/ 20.05 GFLOPS | Progress: (10/10) | 4.39 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 3/25] Current/Best: 11.27/ 17.16 GFLOPS | Progress: (4/10) | 2.72 s
-[Task 3/25] Current/Best: 13.70/ 23.00 GFLOPS | Progress: (8/10) | 5.35 s
-[Task 3/25] Current/Best: 17.35/ 23.00 GFLOPS | Progress: (10/10) | 6.63 s Done.
+[Task 3/25] Current/Best: 8.11/ 14.04 GFLOPS | Progress: (4/10) | 3.37 s
+[Task 3/25] Current/Best: 12.35/ 17.13 GFLOPS | Progress: (8/10) | 4.97 s
+[Task 3/25] Current/Best: 13.29/ 17.13 GFLOPS | Progress: (10/10) | 6.06 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 4/25] Current/Best: 19.95/ 19.95 GFLOPS | Progress: (4/10) | 2.66 s
-[Task 4/25] Current/Best: 14.96/ 19.95 GFLOPS | Progress: (8/10) | 8.79 s
-[Task 4/25] Current/Best: 11.97/ 19.95 GFLOPS | Progress: (10/10) | 10.70 s Done.
+[Task 4/25] Current/Best: 18.37/ 18.37 GFLOPS | Progress: (4/10) | 2.65 s
+[Task 4/25] Current/Best: 4.72/ 20.28 GFLOPS | Progress: (8/10) | 7.83 s
+[Task 4/25] Current/Best: 12.64/ 20.28 GFLOPS | Progress: (10/10) | 9.09 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 5/25] Current/Best: 12.86/ 16.17 GFLOPS | Progress: (4/10) | 3.01 s
-[Task 5/25] Current/Best: 16.73/ 18.70 GFLOPS | Progress: (8/10) | 5.06 s
-[Task 5/25] Current/Best: 12.05/ 18.70 GFLOPS | Progress: (10/10) | 5.90 s Done.
+[Task 5/25] Current/Best: 21.00/ 21.46 GFLOPS | Progress: (4/10) | 2.46 s
+[Task 5/25] Current/Best: 7.65/ 21.46 GFLOPS | Progress: (8/10) | 3.98 s
+[Task 5/25] Current/Best: 16.54/ 21.46 GFLOPS | Progress: (10/10) | 4.89 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 6/25] Current/Best: 3.26/ 15.88 GFLOPS | Progress: (4/10) | 3.68 s
-[Task 6/25] Current/Best: 11.23/ 23.36 GFLOPS | Progress: (8/10) | 8.36 s
-[Task 6/25] Current/Best: 3.97/ 23.36 GFLOPS | Progress: (10/10) | 9.63 s Done.
+[Task 6/25] Current/Best: 11.72/ 18.02 GFLOPS | Progress: (4/10) | 4.66 s
+[Task 6/25] Current/Best: 14.79/ 18.02 GFLOPS | Progress: (8/10) | 7.22 s
+[Task 6/25] Current/Best: 16.45/ 18.02 GFLOPS | Progress: (10/10) | 8.08 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 7/25] Current/Best: 21.56/ 21.56 GFLOPS | Progress: (4/10) | 2.75 s
-[Task 7/25] Current/Best: 13.01/ 21.56 GFLOPS | Progress: (8/10) | 5.34 s
-[Task 7/25] Current/Best: 14.52/ 21.56 GFLOPS | Progress: (10/10) | 6.49 s Done.
+[Task 7/25] Current/Best: 6.33/ 15.80 GFLOPS | Progress: (4/10) | 3.70 s
+[Task 7/25] Current/Best: 9.05/ 15.80 GFLOPS | Progress: (8/10) | 6.42 s
+[Task 7/25] Current/Best: 17.16/ 17.16 GFLOPS | Progress: (10/10) | 7.20 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 8/25] Current/Best: 9.66/ 11.37 GFLOPS | Progress: (4/10) | 6.10 s
-[Task 8/25] Current/Best: 11.69/ 12.29 GFLOPS | Progress: (8/10) | 12.53 s
-[Task 8/25] Current/Best: 19.40/ 19.40 GFLOPS | Progress: (10/10) | 13.25 s Done.
+[Task 8/25] Current/Best: 5.38/ 12.06 GFLOPS | Progress: (4/10) | 7.40 s
+[Task 8/25] Current/Best: 9.89/ 17.30 GFLOPS | Progress: (8/10) | 12.55 s
+[Task 8/25] Current/Best: 10.60/ 17.30 GFLOPS | Progress: (10/10) | 15.53 s Done.
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 9/25] Current/Best: 9.03/ 16.57 GFLOPS | Progress: (4/10) | 5.99 s
-[Task 9/25] Current/Best: 9.85/ 16.57 GFLOPS | Progress: (8/10) | 8.80 s
-[Task 9/25] Current/Best: 11.10/ 16.57 GFLOPS | Progress: (10/10) | 13.02 s Done.
+[Task 9/25] Current/Best: 12.41/ 14.43 GFLOPS | Progress: (4/10) | 10.41 s
+[Task 9/25] Current/Best: 17.91/ 22.62 GFLOPS | Progress: (8/10) | 11.70 s
+[Task 9/25] Current/Best: 17.47/ 22.62 GFLOPS | Progress: (10/10) | 12.76 s Done.
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 10/25] Current/Best: 18.83/ 19.46 GFLOPS | Progress: (4/10) | 2.36 s
-[Task 10/25] Current/Best: 8.25/ 19.46 GFLOPS | Progress: (8/10) | 3.80 s
-[Task 10/25] Current/Best: 8.85/ 19.46 GFLOPS | Progress: (10/10) | 4.66 s Done.
+[Task 10/25] Current/Best: 3.00/ 18.14 GFLOPS | Progress: (4/10) | 3.02 s
+[Task 10/25] Current/Best: 19.79/ 19.79 GFLOPS | Progress: (8/10) | 5.23 s
+[Task 10/25] Current/Best: 9.55/ 19.79 GFLOPS | Progress: (10/10) | 5.87 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 11/25] Current/Best: 24.04/ 24.04 GFLOPS | Progress: (4/10) | 2.85 s
-[Task 11/25] Current/Best: 24.20/ 24.20 GFLOPS | Progress: (8/10) | 4.93 s
-[Task 11/25] Current/Best: 1.59/ 24.20 GFLOPS | Progress: (10/10) | 7.23 s Done.
+[Task 11/25] Current/Best: 6.20/ 16.52 GFLOPS | Progress: (4/10) | 4.90 s
+[Task 11/25] Current/Best: 15.74/ 17.53 GFLOPS | Progress: (8/10) | 6.84 s
+[Task 11/25] Current/Best: 18.58/ 18.58 GFLOPS | Progress: (10/10) | 7.95 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 12/25] Current/Best: 4.86/ 23.55 GFLOPS | Progress: (4/10) | 2.95 s
-[Task 12/25] Current/Best: 14.93/ 23.55 GFLOPS | Progress: (8/10) | 4.89 s
-[Task 12/25] Current/Best: 21.46/ 23.55 GFLOPS | Progress: (10/10) | 5.70 s Done.
+[Task 12/25] Current/Best: 10.68/ 14.09 GFLOPS | Progress: (4/10) | 3.16 s
+[Task 12/25] Current/Best: 15.10/ 15.32 GFLOPS | Progress: (8/10) | 4.78 s
+[Task 12/25] Current/Best: 10.08/ 15.32 GFLOPS | Progress: (10/10) | 6.55 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 13/25] Current/Best: 17.61/ 20.08 GFLOPS | Progress: (4/10) | 3.11 s
-[Task 13/25] Current/Best: 6.25/ 20.08 GFLOPS | Progress: (8/10) | 7.53 s
-[Task 13/25] Current/Best: 18.83/ 20.08 GFLOPS | Progress: (10/10) | 8.84 s Done.
+[Task 13/25] Current/Best: 18.01/ 18.01 GFLOPS | Progress: (4/10) | 5.41 s
+[Task 13/25] Current/Best: 3.11/ 18.01 GFLOPS | Progress: (8/10) | 8.19 s
+[Task 13/25] Current/Best: 14.66/ 20.23 GFLOPS | Progress: (10/10) | 9.10 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 14/25] Current/Best: 16.53/ 16.53 GFLOPS | Progress: (4/10) | 5.66 s
-[Task 14/25] Current/Best: 10.74/ 19.73 GFLOPS | Progress: (8/10) | 7.89 s
-[Task 14/25] Current/Best: 12.11/ 19.73 GFLOPS | Progress: (10/10) | 13.52 s Done.
-
+[Task 14/25] Current/Best: 12.97/ 12.97 GFLOPS | Progress: (4/10) | 7.04 s
+[Task 14/25] Current/Best: 13.36/ 15.10 GFLOPS | Progress: (8/10) | 9.23 s
+[Task 14/25] Current/Best: 9.44/ 15.10 GFLOPS | Progress: (10/10) | 10.40 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 15/25] Current/Best: 19.95/ 19.95 GFLOPS | Progress: (4/10) | 3.13 s
-[Task 15/25] Current/Best: 22.97/ 23.97 GFLOPS | Progress: (8/10) | 5.34 s
-[Task 15/25] Current/Best: 7.18/ 23.97 GFLOPS | Progress: (10/10) | 6.00 s Done.
+[Task 15/25] Current/Best: 15.71/ 24.03 GFLOPS | Progress: (4/10) | 3.67 s
+[Task 15/25] Current/Best: 5.99/ 24.03 GFLOPS | Progress: (8/10) | 4.86 s
+[Task 15/25] Current/Best: 10.84/ 24.03 GFLOPS | Progress: (10/10) | 6.22 s Done.
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 16/25] Current/Best: 15.97/ 18.38 GFLOPS | Progress: (4/10) | 2.32 s
-[Task 16/25] Current/Best: 15.12/ 18.38 GFLOPS | Progress: (8/10) | 3.58 s
-[Task 16/25] Current/Best: 12.66/ 18.38 GFLOPS | Progress: (10/10) | 4.20 s Done.
+[Task 16/25] Current/Best: 6.30/ 15.06 GFLOPS | Progress: (4/10) | 2.83 s
+[Task 16/25] Current/Best: 14.64/ 18.77 GFLOPS | Progress: (8/10) | 4.36 s
+[Task 16/25] Current/Best: 16.19/ 18.77 GFLOPS | Progress: (10/10) | 5.69 s Done.
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 17/25] Current/Best: 7.10/ 18.26 GFLOPS | Progress: (4/10) | 3.24 s
-[Task 17/25] Current/Best: 9.42/ 18.86 GFLOPS | Progress: (8/10) | 5.57 s
-[Task 17/25] Current/Best: 12.27/ 18.86 GFLOPS | Progress: (10/10) | 6.59 s Done.
+[Task 17/25] Current/Best: 18.16/ 18.16 GFLOPS | Progress: (4/10) | 3.94 s
+[Task 17/25] Current/Best: 21.02/ 21.02 GFLOPS | Progress: (8/10) | 5.37 s
+[Task 17/25] Current/Best: 15.53/ 23.93 GFLOPS | Progress: (10/10) | 6.77 s Done.
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 18/25] Current/Best: 10.30/ 11.61 GFLOPS | Progress: (4/10) | 6.76 s
-[Task 18/25] Current/Best: 7.03/ 17.26 GFLOPS | Progress: (8/10) | 12.33 s
-[Task 18/25] Current/Best: 9.72/ 17.26 GFLOPS | Progress: (10/10) | 15.94 s Done.
+[Task 18/25] Current/Best: 11.67/ 19.96 GFLOPS | Progress: (4/10) | 2.43 s
+[Task 18/25] Current/Best: 11.79/ 20.65 GFLOPS | Progress: (8/10) | 8.02 s
+[Task 18/25] Current/Best: 14.76/ 20.65 GFLOPS | Progress: (10/10) | 9.38 s Done.
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 19/25] Current/Best: 20.57/ 20.57 GFLOPS | Progress: (4/10) | 3.31 s
-[Task 19/25] Current/Best: 19.11/ 20.57 GFLOPS | Progress: (8/10) | 5.22 s
-[Task 19/25] Current/Best: 11.18/ 20.57 GFLOPS | Progress: (10/10) | 7.16 s Done.
+[Task 19/25] Current/Best: 10.68/ 10.68 GFLOPS | Progress: (4/10) | 5.60 s
+[Task 19/25] Current/Best: 16.43/ 20.13 GFLOPS | Progress: (8/10) | 8.72 s
+[Task 19/25] Current/Best: 10.97/ 20.13 GFLOPS | Progress: (10/10) | 10.19 s Done.
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 20/25] Current/Best: 11.40/ 15.23 GFLOPS | Progress: (4/10) | 3.83 s
-[Task 20/25] Current/Best: 8.04/ 15.23 GFLOPS | Progress: (8/10) | 5.96 s
-[Task 20/25] Current/Best: 5.26/ 16.60 GFLOPS | Progress: (10/10) | 7.02 s Done.
+[Task 20/25] Current/Best: 10.14/ 10.28 GFLOPS | Progress: (4/10) | 4.21 s
+[Task 20/25] Current/Best: 4.00/ 21.25 GFLOPS | Progress: (8/10) | 6.61 s
+[Task 20/25] Current/Best: 20.10/ 21.25 GFLOPS | Progress: (10/10) | 7.63 s Done.
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 21/25] Current/Best: 12.98/ 20.75 GFLOPS | Progress: (4/10) | 2.71 s
-[Task 21/25] Current/Best: 12.21/ 20.75 GFLOPS | Progress: (8/10) | 4.54 s
-[Task 21/25] Current/Best: 11.67/ 20.75 GFLOPS | Progress: (10/10) | 5.18 s
-[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 22/25] Current/Best: 18.50/ 18.50 GFLOPS | Progress: (4/10) | 3.15 s
-[Task 22/25] Current/Best: 9.11/ 18.50 GFLOPS | Progress: (8/10) | 4.52 s
-[Task 22/25] Current/Best: 15.66/ 20.88 GFLOPS | Progress: (10/10) | 5.17 s Done.
+[Task 21/25] Current/Best: 14.90/ 21.97 GFLOPS | Progress: (4/10) | 2.66 s
+[Task 21/25] Current/Best: 1.63/ 21.97 GFLOPS | Progress: (8/10) | 4.66 s
+[Task 21/25] Current/Best: 9.70/ 21.97 GFLOPS | Progress: (10/10) | 5.74 s
+[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+ Done.
-[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 23/25] Current/Best: 10.39/ 19.59 GFLOPS | Progress: (4/10) | 4.55 s Done.
+[Task 22/25] Current/Best: 9.84/ 16.59 GFLOPS | Progress: (4/10) | 2.95 s
+[Task 22/25] Current/Best: 7.24/ 16.79 GFLOPS | Progress: (8/10) | 4.53 s
+[Task 22/25] Current/Best: 19.38/ 19.38 GFLOPS | Progress: (10/10) | 6.70 s Done.
-[Task 23/25] Current/Best: 13.64/ 19.59 GFLOPS | Progress: (8/10) | 7.90 s
-[Task 23/25] Current/Best: 23.42/ 23.42 GFLOPS | Progress: (10/10) | 8.75 s Done.
+[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
+[Task 23/25] Current/Best: 18.45/ 23.04 GFLOPS | Progress: (4/10) | 3.22 s
+[Task 23/25] Current/Best: 3.09/ 23.04 GFLOPS | Progress: (8/10) | 5.78 s
+[Task 23/25] Current/Best: 9.02/ 23.04 GFLOPS | Progress: (10/10) | 7.11 s Done.
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 24/25] Current/Best: 5.69/ 9.32 GFLOPS | Progress: (4/10) | 6.17 s
-[Task 24/25] Current/Best: 1.10/ 9.32 GFLOPS | Progress: (8/10) | 84.91 s
-[Task 24/25] Current/Best: 4.58/ 9.32 GFLOPS | Progress: (10/10) | 88.48 s
+[Task 24/25] Current/Best: 6.01/ 6.16 GFLOPS | Progress: (4/10) | 3.65 s
+[Task 24/25] Current/Best: 4.02/ 10.00 GFLOPS | Progress: (8/10) | 5.83 s
+[Task 24/25] Current/Best: 7.13/ 10.00 GFLOPS | Progress: (10/10) | 7.21 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 25/25] Current/Best: 9.30/ 9.36 GFLOPS | Progress: (4/10) | 16.17 s
-[Task 25/25] Current/Best: 5.97/ 9.36 GFLOPS | Progress: (8/10) | 18.73 s
-[Task 25/25] Current/Best: 7.32/ 9.36 GFLOPS | Progress: (10/10) | 319.46 s
+[Task 25/25] Current/Best: 9.67/ 9.67 GFLOPS | Progress: (4/10) | 2.34 s
+[Task 25/25] Current/Best: 8.43/ 10.39 GFLOPS | Progress: (8/10) | 34.56 s
+[Task 25/25] Current/Best: 1.53/ 10.39 GFLOPS | Progress: (10/10) | 50.42 s
</pre></div>
</div>
<p>The output from this tuning process will look something like this:</p>
@@ -835,6 +836,10 @@ model using optimized operators to speed up our computations.</p>
<span class="n">module</span> <span class="o">=</span> <a href="../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="View documentation for tvm.contrib.graph_executor.GraphModule"><span class="n">graph_executor</span><span class="o">.</span><span class="n">GraphModule</span></a><span class="p">(</span><span class="n">lib</span><span class="p">[</span><span class="s2">"default"</span><span class="p">](</span><span class="n">dev</span><span c [...]
</pre></div>
</div>
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Done.
+</pre></div>
+</div>
<p>Verify that the optimized model runs and produces the same results:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dtype</span> <span class="o">=</span> <span class="s2">"float32"</span>
<span class="n">module</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">input_name</span><span class="p">,</span> <span class="n">img_data</span><span class="p">)</span>
@@ -850,8 +855,8 @@ model using optimized operators to speed up our computations.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class='n02123045 tabby, tabby cat' with probability=0.621103
-class='n02123159 tiger cat' with probability=0.356379
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class='n02123045 tabby, tabby cat' with probability=0.621104
+class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
@@ -889,8 +894,8 @@ improvement in comparing the optimized model to the unoptimized model.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 446.0708166700033, 'median': 445.9797331499999, 'std': 0.3701649082768512}
-unoptimized: {'mean': 491.0217237799998, 'median': 490.89573064999854, 'std': 0.4141502182556612}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 443.517296980026, 'median': 443.01024564992986, 'std': 1.563976552930658}
+unoptimized: {'mean': 490.400963600041, 'median': 490.2133211000546, 'std': 0.7120891974454429}
</pre></div>
</div>
</div>
@@ -904,7 +909,7 @@ 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> ( 13 minutes 4.464 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes 15.308 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-autotvm-relay-x86-py">
<div class="sphx-glr-download 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 b0911fbfb..dd8349c9e 100644
--- a/docs/tutorial/cross_compilation_and_rpc.html
+++ b/docs/tutorial/cross_compilation_and_rpc.html
@@ -496,7 +496,7 @@ device and returns the measured cost. Network overhead is excluded.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.239e-07 secs/op
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.233e-07 secs/op
</pre></div>
</div>
</div>
diff --git a/docs/tutorial/intro_topi.html b/docs/tutorial/intro_topi.html
index 8e0d5fd7d..c2d796999 100644
--- a/docs/tutorial/intro_topi.html
+++ b/docs/tutorial/intro_topi.html
@@ -458,7 +458,7 @@ we can schedule the following series of operations ending with <code class="code
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x224a2b30)), stage(b, placeholder(b, 0xbd6d670)), 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=[i [...]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x2342b560)), stage(b, placeholder(b, 0x28d18ad0)), 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=[ [...]
</pre></div>
</div>
<p>We can test the correctness by comparing with <code class="code docutils literal notranslate"><span class="pre">numpy</span></code> result as follows</p>
diff --git a/docs/tutorial/sg_execution_times.html b/docs/tutorial/sg_execution_times.html
index ab818fa8b..8415e259a 100644
--- a/docs/tutorial/sg_execution_times.html
+++ b/docs/tutorial/sg_execution_times.html
@@ -300,20 +300,20 @@
<div class="section" id="computation-times">
<span id="sphx-glr-tutorial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>15:42.252</strong> total execution time for <strong>tutorial</strong> files:</p>
+<p><strong>09:47.352</strong> total execution time for <strong>tutorial</strong> files:</p>
<ul class="simple">
-<li><p><strong>13:04.464</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
-<li><p><strong>01:01.698</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
-<li><p><strong>00:51.686</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:26.259</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
-<li><p><strong>00:15.812</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:01.294</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
-<li><p><strong>00:00.699</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
-<li><p><strong>00:00.205</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
-<li><p><strong>00:00.037</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
-<li><p><strong>00:00.035</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
+<li><p><strong>07:15.308</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
+<li><p><strong>00:58.544</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
+<li><p><strong>00:49.313</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>00:25.816</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
+<li><p><strong>00:16.820</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>00:00.700</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
+<li><p><strong>00:00.539</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
+<li><p><strong>00:00.190</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
+<li><p><strong>00:00.033</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
<li><p><strong>00:00.032</strong>: <a class="reference internal" href="introduction.html#sphx-glr-tutorial-introduction-py"><span class="std std-ref">Introduction</span></a> (<code class="docutils literal notranslate"><span class="pre">introduction.py</span></code>)</p></li>
-<li><p><strong>00:00.030</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
+<li><p><strong>00:00.029</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
+<li><p><strong>00:00.028</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/tutorial/tensor_expr_get_started.html b/docs/tutorial/tensor_expr_get_started.html
index 75f81f4c2..7a835996c 100644
--- a/docs/tutorial/tensor_expr_get_started.html
+++ b/docs/tutorial/tensor_expr_get_started.html
@@ -631,10 +631,10 @@ factor to be the number of threads on your CPU.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Operator Timing Performance
- numpy 8.047240000905732e-06 1.0
- naive 5.8546e-06 0.7275289415179681
-parallel 6.08e-06 0.7555385448073731
- vector 2.4551599999999997e-05 3.0509342330086677
+ numpy 8.072379969235045e-06 1.0
+ naive 5.8386e-06 0.7232811168765236
+parallel 6.2619999999999995e-06 0.7757315715892149
+ vector 2.4645900000000004e-05 3.0531144586762435
</pre></div>
</div>
<div class="admonition-code-specialization admonition">
@@ -952,7 +952,7 @@ matrix multiplication.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018668
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018119
</pre></div>
</div>
<p>Now we write a basic matrix multiplication using TVM TE and verify that it
@@ -994,7 +994,7 @@ optimizations.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.468946
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.231856
</pre></div>
</div>
<p>Let’s take a look at the intermediate representation of the operator and
@@ -1060,7 +1060,7 @@ schedule.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.303051
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.307331
</pre></div>
</div>
<p>By reordering the computation to take advantage of caching, you should see a
@@ -1120,7 +1120,7 @@ already cache friendly from our previous optimizations.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.334796
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.337173
@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], []),
@@ -1175,7 +1175,7 @@ more cache friendly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.118738
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.112953
@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], []),
@@ -1251,7 +1251,7 @@ optimized schedule.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.111174
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.108143
@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], []),
@@ -1325,7 +1325,7 @@ to `C</cite> when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.110921
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.111776
@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], []),
@@ -1392,7 +1392,7 @@ of thread-level parallelization.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.145220
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.143978
@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], []),
@@ -1454,13 +1454,13 @@ working, we can compare the results.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> Operator Timing Performance
- none 3.4689456389999997 1.0
- blocking 0.3030505159 0.08736098729623247
- vectorization 0.3347961647 0.09651236990744899
-loop permutation 0.1187383554 0.03422894670503656
- array packing 0.1111739621 0.03204834369559286
- block caching 0.1109211023 0.03197545128783726
- parallelization 0.1452196773 0.04186277111620082
+ none 3.2318558358 1.0
+ blocking 0.3073305725 0.0950941465567955
+ vectorization 0.33717297030000004 0.10432797359494152
+loop permutation 0.112953436 0.0349500230637732
+ array packing 0.1081428521 0.033461533432920215
+ block caching 0.11177630140000001 0.03458579437913925
+ parallelization 0.1439777031 0.04454954379620722
</pre></div>
</div>
<p>Note that the outputs on the web page reflect the running times on a
@@ -1492,7 +1492,6 @@ is</p>
you can build generic templates of the matrix multiplication and other
operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.698 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-tensor-expr-get-started-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.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">tensor_expr_get_started.py</span></code></a></p>