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
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
|
# mypy: ignore-errors
from __future__ import annotations
import contextlib
import dataclasses
import sys
import threading
from typing import Any, Callable, Dict, Optional, Type, TYPE_CHECKING
from typing_extensions import override, Self
from unittest.mock import patch
from torch._inductor import config
from torch._inductor.remote_cache import RemoteCacheBackend
if TYPE_CHECKING:
from types import TracebackType
@dataclasses.dataclass
class Stats:
num_put: int = 0
num_get_hit: int = 0
num_get_miss: int = 0
def __iadd__(self, other: Stats) -> Self:
self.num_put += other.num_put
self.num_get_hit += other.num_get_hit
self.num_get_miss += other.num_get_miss
return self
def reset(self) -> None:
self.num_put = 0
self.num_get_hit = 0
self.num_get_miss = 0
def __str__(self) -> str:
return "".join(
(
f"puts: {self.num_put}, ",
f"misses: {self.num_get_miss}, ",
f"hits: {self.num_get_hit}, ",
)
)
def __eq__(self, other: object) -> bool:
# Dataclass's default __eq__ checks that the types are the same so can't
# be used with _GlobalItemStats.
return (
isinstance(other, (Stats, _GlobalItemStats))
and self.num_put == other.num_put
and self.num_get_hit == other.num_get_hit
and self.num_get_miss == other.num_get_miss
)
class _GlobalItemStats(Stats):
cache: Dict[str, object]
def __init__(self) -> None:
super().__init__()
self.cache = {}
def reset(self) -> None:
super().reset()
self.cache = {}
# The cache states are thread-local so if we're running multiple tests at once
# they won't cross contaminate. However - it needs to be "global" because we
# allow code to create new cache clients which refer to the same cache (because
# it's a remote cache).
class _GlobalStats(threading.local):
def __init__(self) -> None:
self.autotune_local = _GlobalItemStats()
self.autotune_remote = _GlobalItemStats()
self.bundled_autotune = _GlobalItemStats()
self.fx_graph = _GlobalItemStats()
self.triton = _GlobalItemStats()
self.aot_autograd = _GlobalItemStats()
self.dynamo_pgo = _GlobalItemStats()
def reset(self) -> None:
self.autotune_local.reset()
self.autotune_remote.reset()
self.bundled_autotune.reset()
self.fx_graph.reset()
self.triton.reset()
self.aot_autograd.reset()
self.dynamo_pgo.reset()
def get_stat(self, name: str) -> _GlobalItemStats:
return getattr(self, name)
def report(self):
subs = (
("autotune_local", self.autotune_local),
("autotune_remote", self.autotune_remote),
("bundled_autotune", self.bundled_autotune),
("fx_graph", self.fx_graph),
("triton", self.triton),
("aot_autograd", self.aot_autograd),
("dynamo_pgo", self.dynamo_pgo),
)
print("Cache Stats:", file=sys.stderr)
for name, sub in subs:
print(f" {name}: {sub}", file=sys.stderr)
print("Cache Entries:", file=sys.stderr)
for name, sub in subs:
if sub.cache:
print(f" {name}:", file=sys.stderr)
for k, v in sorted(sub.cache.items()):
v = repr(v)
if len(v) > 100:
v = v[:100] + "..."
print(f" {k!r}: {v}", file=sys.stderr)
global_stats = _GlobalStats()
class MockBackend(RemoteCacheBackend[Any]):
def __init__(self, name: str) -> None:
self._name = name
@staticmethod
def with_name(name: str) -> Callable[[], MockBackend]:
def wrapper() -> MockBackend:
return MockBackend(name)
return wrapper
@override
def _get(self, key: str) -> Optional[Any]:
stat = global_stats.get_stat(self._name)
if key in stat.cache:
stat += Stats(num_get_hit=1)
return stat.cache.get(key)
else:
stat += Stats(num_get_miss=1)
return None
@override
def _put(self, key: str, data: Any) -> None:
stat = global_stats.get_stat(self._name)
stat += Stats(num_put=1)
stat.cache[key] = data
# List of configs for each cache
_CACHE_CONFIG_EN = (
"fx_graph_cache",
"fx_graph_remote_cache",
"autotune_local_cache",
"autotune_remote_cache",
"bundled_autotune_remote_cache",
)
class PatchCaches(contextlib.AbstractContextManager):
@classmethod
def setUp(cls):
# If this test is using PatchCaches then disable all the caches by
# default, letting the tests turn them on explicitly. This is because
# tests using PatchCaches will often want to check stats explicitly.
cls._savedCacheState = {}
for name in _CACHE_CONFIG_EN:
if hasattr(config, name):
cls._savedCacheState[name] = getattr(config, name)
setattr(config, name, False)
@classmethod
def tearDown(cls):
# Restore cache defaults
for name in _CACHE_CONFIG_EN:
delattr(config, name)
if name in cls._savedCacheState:
setattr(config, name, cls._savedCacheState[name])
def __init__(self) -> None:
self._stack = contextlib.ExitStack()
def __enter__(self) -> Self:
global_stats.reset()
self._stack.__enter__()
ctx = patch(
"torch._inductor.runtime.autotune_cache.LocalAutotuneCache.backend_override_cls",
MockBackend.with_name("autotune_local"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.remote_cache.RemoteAutotuneCache.backend_override_cls",
MockBackend.with_name("autotune_remote"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.remote_cache.RemoteBundledAutotuneCache.backend_override_cls",
MockBackend.with_name("bundled_autotune"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.remote_cache.RemoteFxGraphCache.backend_override_cls",
MockBackend.with_name("fx_graph"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.remote_cache.RemoteAOTAutogradCache.backend_override_cls",
MockBackend.with_name("aot_autograd"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.remote_cache.RemoteDynamoPGOCache.backend_override_cls",
MockBackend.with_name("dynamo_pgo"),
)
self._stack.enter_context(ctx)
if config.is_fbcode():
ctx = patch(
"torch._inductor.fb.remote_cache.FbRemoteAutotuneCache.backend_override_cls",
MockBackend.with_name("autotune_remote"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.fb.remote_cache.FbRemoteBundledAutotuneCache.backend_override_cls",
MockBackend.with_name("bundled_autotune"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.fb.remote_cache.FbRemoteFxGraphCache.backend_override_cls",
MockBackend.with_name("fx_graph"),
)
self._stack.enter_context(ctx)
ctx = patch(
"triton.fb.fb_memcache.FbMemcacheRemoteKernelCache.backend_override_cls",
MockBackend.with_name("triton"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.fb.remote_cache.FbRemoteAOTAutogradCache.backend_override_cls",
MockBackend.with_name("aot_autograd"),
)
self._stack.enter_context(ctx)
ctx = patch(
"torch._inductor.fb.remote_cache.FbRemoteDynamoPGOCache.backend_override_cls",
MockBackend.with_name("dynamo_pgo"),
)
self._stack.enter_context(ctx)
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
self._stack.__exit__(exc_type, exc_value, traceback)
|
