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
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
|
from typing import Any
import lldb # type: ignore[import]
def get_target() -> Any:
target = lldb.debugger.GetSelectedTarget()
if not target:
print("[-] error: no target available. please add a target to lldb.")
return None
return target
class DisableBreakpoints:
"""
Context-manager to temporarily disable all lldb breakpoints, useful if
there is a risk to hit one during the evaluation of one of our custom
commands
"""
def __enter__(self) -> None:
target = get_target()
if target.DisableAllBreakpoints() is False:
print("[-] error: failed to disable all breakpoints.")
def __exit__(self, etype: Any, evalue: Any, tb: Any) -> None:
target = get_target()
if target.EnableAllBreakpoints() is False:
print("[-] error: failed to enable all breakpoints.")
def IntArrayRef_summary(valobj: Any, internal_dict: Any, options: Any) -> str:
"""Print human readable representation of c10::IntArrayRef"""
with DisableBreakpoints():
target = get_target()
tensor = valobj.GetName()
result = target.EvaluateExpression(
f"torch::gdb::int_array_ref_string({tensor})"
)
str_result = str(result)
str_result = str_result[str_result.find('"') + 1 : -1]
return str_result
def DispatchKeyset_summary(valobj: Any, internal_dict: Any, options: Any) -> str:
"""Print human readable representation of c10::DispatchKeyset"""
with DisableBreakpoints():
target = get_target()
keyset = valobj.GetName()
result = target.EvaluateExpression(
f"torch::gdb::dispatch_keyset_string({keyset})"
)
str_result = str(result)
str_result = str_result[str_result.find('"') + 1 : -1]
return str_result
def Tensor_summary(valobj: Any, internal_dict: Any, options: Any) -> str:
"""Print a human readable representation of the given at::Tensor.
at::Tensor instances do not have a C++ implementation of a repr method: in
pytorch, this is done by pure-Python code. As such, print <tensor>
internally creates a Python wrapper for the given tensor and call repr()
on it.
Usage:
print self
"""
with DisableBreakpoints():
target = get_target()
tensor = valobj.GetName()
result = target.EvaluateExpression(f"torch::gdb::tensor_repr({tensor})")
str_result = str(result)
target.EvaluateExpression(f"(void)free({result.GetValue()})")
str_result = "\n" + str_result[str_result.find("tensor") : -1]
return str_result
# And the initialization code to add your commands
def __lldb_init_module(debugger: Any, internal_dict: Any) -> Any:
debugger.HandleCommand(
"type summary add c10::IntArrayRef -F pytorch_lldb.IntArrayRef_summary -w torch"
)
debugger.HandleCommand(
"type summary add c10::DispatchKeySet -F pytorch_lldb.DispatchKeyset_summary -w torch"
)
debugger.HandleCommand(
"type summary add at::Tensor -F pytorch_lldb.Tensor_summary -w torch"
)
print(
"Pretty Printing lldb summary for PyTorch AT types has been installed and is ready for use. "
"This category is enabled by default. To disable run: `type category disable torch`"
)
print(
"Usage:\n\tprint <at::tensor>\n\tprint <c10::IntArrayRef>\n\tprint <c10::DispatchKeySet>"
)
|
