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https://github.com/saymrwulf/pytorch.git
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bbe7f53218
Summary: et_replay uses random data to run operators, however, the operators using index tensor to access memory won't work with random data. It usually ran into two exceptions: 1. illegal memory access since index is out of range, it has been fixed with the environment variable ENABLE_PYTORCH_EXECUTION_TRACE_SAVE_INTEGRAL_TENSOR_RANGE to record the min/max value of index tensors. 2. unaligned memory access, FBGEMM ops have speical requirements for the memory layout. To fix the second execption, ENABLE_PYTORCH_EXECUTION_TRACE_SAVE_INTEGRAL_TENSOR is added to allow user to specify the node names, separated by comma, so ET will save the integral tensor data for these nodes. The saved data will be used in et_replay. Be careful to turn on this option since it will use more space to save the extra data. Test Plan: buck2 run mode/opt caffe2/test:test_profiler_cuda -- profiler.test_execution_trace.TestExecutionTraceCUDA.test_execution_trace_record_integral_tensor_data_cuda Differential Revision: D67989856 Pull Request resolved: https://github.com/pytorch/pytorch/pull/144508 Approved by: https://github.com/briancoutinho |
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| .. | ||
| profiler_utils_mock_events.json | ||
| test_cpp_thread.cpp | ||
| test_cpp_thread.py | ||
| test_cpp_thread_lib.pyi | ||
| test_execution_trace.py | ||
| test_kineto.py | ||
| test_memory_profiler.py | ||
| test_profiler.py | ||
| test_profiler_tree.py | ||
| test_record_function.py | ||
| test_torch_tidy.py | ||