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https://github.com/pytorch/pytorch.git
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4cc1745b13
This PR is a follow up on the pyupgrade series to convert more strings to use f-strings using `flynt`. - https://docs.python.org/3/reference/lexical_analysis.html#f-strings - https://pypi.org/project/flynt/ Command used: ``` flynt torch/ -ll 120 flynt scripts/ -ll 120 flynt tools/ -ll 120 ``` and excluded `collect_env.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105538 Approved by: https://github.com/ezyang, https://github.com/malfet
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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pytorch, 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(f"torch::gdb::tensor_repr({name})")
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print(f"Python-level repr of {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(f"(void)free({int(res)})")
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TensorRepr()
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