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
|
# Owner(s): ["module: inductor"]
import unittest
from typing import Any, Dict, List, Type
import sympy
import torch
import torch._inductor
from torch._inductor import config
from torch._inductor.choices import InductorChoices
from torch._inductor.codegen.simd_kernel_features import SIMDKernelFeatures
from torch._inductor.codegen.triton import FixedTritonConfig, TritonKernel
from torch._inductor.test_case import TestCase
from torch._inductor.utils import run_and_get_code
from torch.testing._internal.common_cuda import IS_SM89
from torch.testing._internal.common_utils import (
instantiate_parametrized_tests,
parametrize,
)
from torch.testing._internal.inductor_utils import HAS_CUDA
@config.patch(
{
"triton.cooperative_reductions": True,
"triton.force_cooperative_reductions": True,
}
)
@instantiate_parametrized_tests
class CooperativeReductionTests(TestCase):
def setUp(self):
super().setUp()
torch._inductor.metrics.generated_kernel_count = 0
torch._dynamo.reset()
def run_and_check(self, fn, args, *, expect_kernel_count=1):
expected = fn(*args)
fn = torch.compile(fn, fullgraph=True)
result, (source_code,) = run_and_get_code(fn, *args)
self.assertEqual(result, expected)
self.assertIn("@triton_heuristics.cooperative_reduction", source_code)
if "async_compile.multi_kernel" not in source_code:
self.assertEqual(
torch._inductor.metrics.generated_kernel_count, expect_kernel_count
)
return source_code
@parametrize(
"name",
[
"sum",
"mean",
"prod",
"amin",
"amax",
"min",
"max",
"var_mean",
"std",
"softmax",
],
)
@parametrize("dtype", [torch.float16, torch.float32, torch.float64])
def test_reduction_fns(self, name, dtype):
if IS_SM89 and dtype == torch.float64 and name in ["std", "var_mean"]:
raise unittest.SkipTest("Timeouts on SM89")
def fn(x, y):
return reduction_fn(x + y, dim=-1)
reduction_fn = getattr(torch, name)
args = [torch.randn(1, 1024**2, device="cuda", dtype=dtype) for _ in range(2)]
self.run_and_check(fn, args)
def test_bool_reduction_fns(self):
def fn(x, y):
return [
torch.any(x == y),
torch.all(x == y),
torch.any(x != y),
torch.all(x != y),
torch.any(x < y),
torch.all(x > y),
]
args = [torch.randn(1024, device="cuda") for _ in range(2)]
source_code = self.run_and_check(fn, args)
if "async_compile.multi_kernel" in source_code:
return
before, after = source_code.split("triton_helpers.x_grid_barrier")
self.assertEqual(before.count("if rsplit_id == ("), 0)
self.assertEqual(after.count("if rsplit_id == ("), 6)
@parametrize("bs", [1, 2, 5, 15])
@parametrize("count", [1024**2 + 1, 1024**2 - 1, 1024])
def test_non_power_of_2(self, bs, count):
def fn(x):
return x.mean(), x.std() + x.min()
args = [torch.randn([bs, count], device="cuda")]
self.run_and_check(fn, args)
def test_chained_reductions(self):
def fn(x):
for _ in range(8):
x = x + torch.softmax(x, 1)
return x
args = [torch.randn(4, 100000, device="cuda")]
source_code = self.run_and_check(fn, args)
if "async_compile.multi_kernel" in source_code:
return
self.assertEqual(source_code.count("triton_helpers.x_grid_barrier"), 16)
self.assertEqual(source_code.count("empty_strided_cuda"), 5)
def test_reduce_split(self):
def fn(a, b):
a1 = torch.linalg.vector_norm(a)
b1 = torch.sum(b, dim=0)
return a1, b1
inps = [
torch.rand(2048, 512, device="cuda"),
torch.rand(20, 20, device="cuda"),
]
self.run_and_check(fn, inps, expect_kernel_count=2)
@config.patch("triton.persistent_reductions", not config.triton.persistent_reductions)
class NoPersistCooperativeReductionTests(CooperativeReductionTests):
pass
@config.patch("triton.multi_kernel", int(not config.triton.multi_kernel))
class MultiKernelCooperativeReductionTests(CooperativeReductionTests):
pass
@config.patch(
{
"triton.cooperative_reductions": True,
}
)
@instantiate_parametrized_tests
class TestFixedConfigs(TestCase):
@parametrize(
"persistent,cooperative,cfg",
[
(False, False, {"XBLOCK": 1, "RBLOCK": 128}),
(False, False, {"XBLOCK": 2, "RBLOCK": 128}),
(True, False, {"XBLOCK": 1}),
(True, False, {"XBLOCK": 2}),
(False, True, {"XBLOCK": 1, "RBLOCK": 128, "RSPLIT": 16}),
(False, True, {"XBLOCK": 2, "RBLOCK": 128, "RSPLIT": 16}),
(True, True, {"XBLOCK": 1, "RSPLIT": 16}),
(True, True, {"XBLOCK": 2, "RSPLIT": 16}),
],
)
def test_fixed_configs(self, persistent, cooperative, cfg):
class MyHeuristics(InductorChoices):
def triton_kernel_kwargs(
self,
kernel_cls: Type[TritonKernel],
features: SIMDKernelFeatures,
groups: List[sympy.Expr],
kernel_kwargs: Dict[str, Any],
) -> Dict[str, Any]:
return {
**kernel_kwargs,
"override_cooperative_reduction": cooperative,
"override_persistent_reduction": persistent,
"fixed_config": FixedTritonConfig(cfg),
}
def fn(x):
return torch.softmax(x + 1, dim=-1) + x
args = [torch.randn(8, 8000, device="cuda")]
with torch._inductor.virtualized.V.set_choices_handler(MyHeuristics()):
expected = fn(*args)
fn = torch.compile(fn, fullgraph=True)
result, (source_code,) = run_and_get_code(fn, *args)
self.assertEqual(result, expected)
self.assertIn("@triton_heuristics.fixed_config(", source_code)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
if HAS_CUDA:
run_tests(needs="filelock")
|
