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347b036350
With ufmt in place https://github.com/pytorch/pytorch/pull/81157, we can now use it to gradually format all files. I'm breaking this down into multiple smaller batches to avoid too many merge conflicts later on. This batch (as copied from the current BLACK linter config): * `tools/**/*.py` Upcoming batchs: * `torchgen/**/*.py` * `torch/package/**/*.py` * `torch/onnx/**/*.py` * `torch/_refs/**/*.py` * `torch/_prims/**/*.py` * `torch/_meta_registrations.py` * `torch/_decomp/**/*.py` * `test/onnx/**/*.py` Once they are all formatted, BLACK linter will be removed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/81285 Approved by: https://github.com/suo
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import textwrap
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from typing import Any
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import gdb # type: ignore[import]
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class DisableBreakpoints:
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"""
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Context-manager to temporarily disable all gdb breakpoints, useful if
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there is a risk to hit one during the evaluation of one of our custom
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commands
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"""
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def __enter__(self) -> None:
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self.disabled_breakpoints = []
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for b in gdb.breakpoints():
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if b.enabled:
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b.enabled = False
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self.disabled_breakpoints.append(b)
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def __exit__(self, etype: Any, evalue: Any, tb: Any) -> None:
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for b in self.disabled_breakpoints:
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b.enabled = True
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class TensorRepr(gdb.Command): # type: ignore[misc, no-any-unimported]
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"""
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Print a human readable representation of the given at::Tensor.
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Usage: torch-tensor-repr EXP
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at::Tensor instances do not have a C++ implementation of a repr method: in
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pytoch, this is done by pure-Python code. As such, torch-tensor-repr
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internally creates a Python wrapper for the given tensor and call repr()
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on it.
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"""
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__doc__ = textwrap.dedent(__doc__).strip()
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def __init__(self) -> None:
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gdb.Command.__init__(
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self, "torch-tensor-repr", gdb.COMMAND_USER, gdb.COMPLETE_EXPRESSION
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)
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def invoke(self, args: str, from_tty: bool) -> None:
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args = gdb.string_to_argv(args)
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if len(args) != 1:
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print("Usage: torch-tensor-repr EXP")
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return
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name = args[0]
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with DisableBreakpoints():
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res = gdb.parse_and_eval("torch::gdb::tensor_repr(%s)" % name)
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print("Python-level repr of %s:" % name)
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print(res.string())
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# torch::gdb::tensor_repr returns a malloc()ed buffer, let's free it
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gdb.parse_and_eval("(void)free(%s)" % int(res))
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TensorRepr()
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