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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43898 Adding with_source parameter to enable tracking source code (filename and line) in profiler for eager, torchscript and autograd modes Test Plan: python test/test_profiler.py ``` Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Source Location ----------------------------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------------------------------------- ts_method_1 10.43% 235.364us 36.46% 822.920us 822.920us 1 test/test_profiler.py(70): test_source aten::add 7.52% 169.833us 8.88% 200.439us 200.439us 1 test/test_profiler.py(69): test_source aten::normal_ 6.26% 141.380us 6.26% 141.380us 141.380us 1 test/test_profiler.py(67): test_source aten::add 5.80% 130.830us 8.41% 189.800us 63.267us 3 test/test_profiler.py(72): test_source aten::sum 5.02% 113.340us 8.39% 189.475us 189.475us 1 test/test_profiler.py(64): ts_method_1 aten::add 4.58% 103.346us 6.33% 142.847us 142.847us 1 test/test_profiler.py(62): ts_method_1 aten::mul 4.05% 91.498us 9.62% 217.113us 217.113us 1 test/test_profiler.py(71): test_source aten::add 4.03% 90.880us 5.60% 126.405us 126.405us 1 test/test_profiler.py(58): ts_method_2 aten::empty 3.49% 78.735us 3.49% 78.735us 19.684us 4 test/test_profiler.py(72): test_source ``` Reviewed By: ngimel Differential Revision: D23432664 Pulled By: ilia-cher fbshipit-source-id: 83ad7ebe0c2502494d3b48c4e687802db9c77615 |
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| contrib | ||
| core | ||
| cuda_rtc | ||
| db | ||
| distributed | ||
| experiments | ||
| ideep | ||
| image | ||
| mobile | ||
| mpi | ||
| observers | ||
| onnx | ||
| operators | ||
| opt | ||
| perfkernels | ||
| predictor | ||
| proto | ||
| python | ||
| quantization | ||
| queue | ||
| serialize | ||
| sgd | ||
| share | ||
| test | ||
| transforms | ||
| utils | ||
| video | ||
| .clang-format | ||
| __init__.py | ||
| c2_aten_srcs.bzl | ||
| CMakeLists.txt | ||
| README.md | ||
| release-notes.md | ||
| requirements.txt | ||
| VERSION_NUMBER | ||
Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
Questions and Feedback
Please use GitHub issues (https://github.com/pytorch/pytorch/issues) to ask questions, report bugs, and request new features.