[WIP] Build C++ & Python tracing support for PyTorch/XLA ops #3348
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…intercept and generate traces
miladm
changed the title
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Sample output of |
JackCaoG
reviewed
Feb 7, 2022
| @@ -0,0 +1,36 @@ | |||
| import torch | |||
| import torch_xla | |||
| #torch_xla._XLAC._ltc_init_ts_backend() #TOREMOVE | |||
| stack = traceback.format_list(stack) | ||
| if (len(stack) > 1): | ||
| second_frame = str(stack[-2]).replace( | ||
| "/usr/local/google/home/miladmo/anaconda3/envs/pytorch1/bin/ipython", |
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If you have to hardcode the path, maybe use something like /tmp instead of your home dir.
| } | ||
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| void XLATensorImpl::SetupSizeProperties() { | ||
| if (getPythonPrinter() && !disablePrinter()) { |
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so if I understand correctly, this will ensure that whenever tensor.size() is called, we log the python frame and C++ frame if printer is set. I think it is a useful tool but I am not sure if it is a good idea for us to merge the change in this file into the master. I think it make a lot of sense of add the binding and documents, but this part to me feel like a debugging feature for developer.
Unless we are at a stage where we expect user to dump this info and do debugging, I felt like it might be better to leave this in a separate branch?
| }; | ||
| getPythonPrinter() = printer; | ||
| }); | ||
| m.def("_print_text", |
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what's the use of this function?
miladm
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This PR is a software tool to enable tracing PyTorch/XLA ops at Python and C++ levels combined.
As an immediate use case, the
sizeop is traced to showcase the approach. Understanding the utility ofsizeat a detailed level is an important aspect of supporting Dynamic Shape in PyTorch / XLA and in Lazy Tensor Core (LTC).