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21a9a93eb4
Summary:
This is something which I wrote because it was useful during my debugging sessions, but I think it might be generally useful to other people as well so I took the liberty of proposing an official `pytorch-gdb` extension.
`pytorch-gdb` is a gdb script written in python. Currently, it contains only one command: `torch-tensor-repr`, which prints a human-readable repr of an `at::Tensor` object. Example:
```
Breakpoint 1, at::native::neg (self=...) at [...]/pytorch/aten/src/ATen/native/UnaryOps.cpp:520
520 Tensor neg(const Tensor& self) { return unary_op_impl(self, at::neg_out); }
(gdb) # the default repr of 'self' is not very useful
(gdb) p self
$1 = (const at::Tensor &) 0x7ffff72ed780: {impl_ = {target_ = 0x5555559df6e0}}
(gdb) torch-tensor-repr self
Python-level repr of self:
tensor([1., 2., 3., 4.], dtype=torch.float64)
```
The idea is that by having an official place where to put these things, `pytorch-gdb` will slowly grow other useful features and make the pytorch debugging experience nicer and faster.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54339
Reviewed By: bdhirsh
Differential Revision: D27253674
Pulled By: ezyang
fbshipit-source-id: dba219e126cc2fe66b2d26740f3a8e3b886e56f5
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import gdb
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import textwrap
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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):
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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, evalue, tb):
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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):
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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):
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gdb.Command.__init__(self, 'torch-tensor-repr',
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gdb.COMMAND_USER, gdb.COMPLETE_EXPRESSION)
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def invoke(self, args, from_tty):
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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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