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
|
# Owner(s): ["oncall: pt2"]
import functools
import itertools
import os
import sys
import textwrap
import unittest
import torch
import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools
from torch._inductor import config
from torch._inductor.codecache import HalideCodeCache
from torch._inductor.runtime.hints import HalideInputSpec, HalideMeta
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import parallel_num_threads
from torch.testing._internal.common_utils import IS_CI, IS_MACOS, IS_WINDOWS
from torch.testing._internal.inductor_utils import HAS_CPU
from torch.utils._triton import has_triton
if IS_WINDOWS and IS_CI:
sys.stderr.write(
"Windows CI does not have necessary dependencies for test_torchinductor_dynamic_shapes yet\n"
)
if __name__ == "__main__":
sys.exit(0)
raise unittest.SkipTest("requires sympy/functorch/filelock")
try:
import halide # @manual
HAS_HALIDE = halide is not None
except ImportError:
HAS_HALIDE = False
try:
from . import test_torchinductor
except ImportError:
import test_torchinductor # @manual=fbcode//caffe2/test/inductor:test_inductor-library
make_halide = config.patch(
{
"halide.scan_kernels": True,
"cpu_backend": "halide",
"cuda_backend": "halide",
}
)
@unittest.skipUnless(HAS_HALIDE, "requires halide")
class HalideTests(TestCase):
def test_codecache(self):
fn = HalideCodeCache.generate_halide(
HalideMeta(
argtypes=[
HalideInputSpec(
ctype="float*",
name="in_ptr0",
shape=["1024L"],
stride=["1L"],
offset="0",
),
HalideInputSpec(
ctype="float*",
name="in_ptr1",
shape=["1024L"],
stride=["1L"],
offset="0",
),
HalideInputSpec(
ctype="float*",
name="out_ptr0",
shape=["1024L"],
stride=["1L"],
offset="0",
),
],
target="host-no_runtime",
scheduler="Mullapudi2016",
scheduler_flags={
"parallelism": parallel_num_threads(),
},
),
textwrap.dedent(
"""
import halide as hl
@hl.generator(name="kernel")
class Kernel:
in_ptr0 = hl.InputBuffer(hl.Float(32), 1)
in_ptr1 = hl.InputBuffer(hl.Float(32), 1)
out_ptr0 = hl.OutputBuffer(hl.Float(32), 1)
def generate(g):
in_ptr0 = g.in_ptr0
in_ptr1 = g.in_ptr1
out_ptr0 = g.out_ptr0
xindex = hl.Var('xindex')
x0 = xindex
tmp0 = hl.Func()
tmp0[xindex] = in_ptr0[x0]
tmp1 = hl.Func()
tmp1[xindex] = in_ptr1[x0]
tmp2 = hl.Func()
tmp2[xindex] = tmp0[xindex] + tmp1[xindex]
out_ptr0[x0] = tmp2[xindex]
assert g.using_autoscheduler()
in_ptr0.set_estimates([hl.Range(1024, 1024)])
in_ptr1.set_estimates([hl.Range(1024, 1024)])
out_ptr0.set_estimates([hl.Range(1024, 1024)])
__name__ == '__main__' and hl.main()
"""
),
)
a = torch.randn(1024)
b = torch.randn(1024)
c = torch.randn(1024)
fn(a, b, c)
self.assertEqual(c, a + b)
def test_manual_schedule(self):
fn = HalideCodeCache.generate_halide(
HalideMeta(
argtypes=[
HalideInputSpec(
ctype="float*",
name="in_ptr0",
shape=["1024L"],
stride=["1L"],
offset="0",
),
HalideInputSpec(
ctype="float*",
name="in_ptr1",
shape=["1024L"],
stride=["1L"],
offset="0",
),
HalideInputSpec(
ctype="float*",
name="out_ptr0",
shape=["1024L"],
stride=["1L"],
offset="0",
),
],
target="host-no_runtime",
scheduler=None,
),
textwrap.dedent(
"""
import halide as hl
@hl.generator(name="kernel")
class Kernel:
in_ptr0 = hl.InputBuffer(hl.Float(32), 1)
in_ptr1 = hl.InputBuffer(hl.Float(32), 1)
out_ptr0 = hl.OutputBuffer(hl.Float(32), 1)
def generate(g):
in_ptr0 = g.in_ptr0
in_ptr1 = g.in_ptr1
out_ptr0 = g.out_ptr0
xindex = hl.Var('xindex')
x0 = xindex
tmp0 = hl.Func()
tmp0[xindex] = in_ptr0[x0]
tmp1 = hl.Func()
tmp1[xindex] = in_ptr1[x0]
tmp2 = hl.Func()
tmp2[xindex] = tmp0[xindex] + tmp1[xindex]
out_ptr0[x0] = tmp2[xindex]
assert not g.using_autoscheduler()
i = hl.Var()
j = hl.Var()
out_ptr0.compute_root()
out_ptr0.split(xindex, i, j, 32)
out_ptr0.parallel(i)
out_ptr0.vectorize(j)
tmp2.compute_at(out_ptr0, i)
tmp2.store_at(out_ptr0, i)
tmp1.compute_inline()
__name__ == '__main__' and hl.main()
"""
),
)
a = torch.randn(1024)
b = torch.randn(1024)
c = torch.randn(1024)
fn(a, b, c)
self.assertEqual(c, a + b)
@unittest.skipUnless(has_triton(), "requires triton")
def test_random_consistency(self):
seed = 1234
shape = (3, 3)
dtype = torch.float32
for (rand_fn,) in itertools.product(
(
functools.partial(torch.rand, shape, dtype=dtype, device="cuda"),
functools.partial(torch.randn, shape, dtype=dtype, device="cuda"),
functools.partial(
torch.randint,
-1000,
1000,
size=shape,
dtype=torch.int64,
device="cuda",
),
)
):
@torch.compile(backend="inductor", options={"cuda_backend": "halide"})
def get_rand_halide():
return rand_fn()
@torch.compile(backend="inductor", options={"cuda_backend": "triton"})
def get_rand_triton():
return rand_fn()
torch.manual_seed(seed)
halide_output = get_rand_halide()
torch.manual_seed(seed)
triton_output = get_rand_triton()
self.assertEqual(halide_output, triton_output)
if test_torchinductor.HAS_CPU and HAS_HALIDE:
SweepInputsCpuHalideTest = make_halide(test_torchinductor.SweepInputsCpuTest)
CpuHalideTests = make_halide(test_torchinductor.CpuTests)
if (
test_torchinductor.HAS_GPU
and HAS_HALIDE
and os.environ.get("TEST_HALIDE_GPU") == "1"
):
SweepInputsGPUHalideTest = make_halide(test_torchinductor.SweepInputsGPUTest)
GPUHalideTests = make_halide(test_torchinductor.GPUTests)
if __name__ == "__main__":
if HAS_CPU and not IS_MACOS and HAS_HALIDE:
run_tests(needs="filelock")
|
