forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
pytorch-gdb.py
58 lines (45 loc) · 1.8 KB
/
pytorch-gdb.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
import textwrap
from typing import Any
import gdb # type: ignore[import]
class DisableBreakpoints:
"""
Context-manager to temporarily disable all gdb breakpoints, useful if
there is a risk to hit one during the evaluation of one of our custom
commands
"""
def __enter__(self) -> None:
self.disabled_breakpoints = []
for b in gdb.breakpoints():
if b.enabled:
b.enabled = False
self.disabled_breakpoints.append(b)
def __exit__(self, etype: Any, evalue: Any, tb: Any) -> None:
for b in self.disabled_breakpoints:
b.enabled = True
class TensorRepr(gdb.Command): # type: ignore[misc, no-any-unimported]
"""
Print a human readable representation of the given at::Tensor.
Usage: torch-tensor-repr EXP
at::Tensor instances do not have a C++ implementation of a repr method: in
pytoch, this is done by pure-Python code. As such, torch-tensor-repr
internally creates a Python wrapper for the given tensor and call repr()
on it.
"""
__doc__ = textwrap.dedent(__doc__).strip()
def __init__(self) -> None:
gdb.Command.__init__(
self, "torch-tensor-repr", gdb.COMMAND_USER, gdb.COMPLETE_EXPRESSION
)
def invoke(self, args: str, from_tty: bool) -> None:
args = gdb.string_to_argv(args)
if len(args) != 1:
print("Usage: torch-tensor-repr EXP")
return
name = args[0]
with DisableBreakpoints():
res = gdb.parse_and_eval("torch::gdb::tensor_repr(%s)" % name)
print("Python-level repr of %s:" % name)
print(res.string())
# torch::gdb::tensor_repr returns a malloc()ed buffer, let's free it
gdb.parse_and_eval("(void)free(%s)" % int(res))
TensorRepr()