forked from Dao-AILab/flash-attention
-
Notifications
You must be signed in to change notification settings - Fork 49
/
Copy pathsetup.py
554 lines (475 loc) · 24.2 KB
/
setup.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
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
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
# Copyright (c) 2023, Tri Dao.
import sys
import warnings
import os
import re
import ast
import glob
import shutil
from pathlib import Path
from packaging.version import parse, Version
import platform
from setuptools import setup, find_packages
import subprocess
import urllib.request
import urllib.error
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
import torch
from torch.utils.cpp_extension import (
BuildExtension,
CppExtension,
CUDAExtension,
CUDA_HOME,
ROCM_HOME,
IS_HIP_EXTENSION,
)
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
BUILD_TARGET = os.environ.get("BUILD_TARGET", "auto")
if BUILD_TARGET == "auto":
if IS_HIP_EXTENSION:
IS_ROCM = True
else:
IS_ROCM = False
else:
if BUILD_TARGET == "cuda":
IS_ROCM = False
elif BUILD_TARGET == "rocm":
IS_ROCM = True
PACKAGE_NAME = "flash_attn"
BASE_WHEEL_URL = (
"https://github.com/Dao-AILab/flash-attention/releases/download/{tag_name}/{wheel_name}"
)
# FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
# SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to copy over raw files, without any cuda compilation
FORCE_BUILD = os.getenv("FLASH_ATTENTION_FORCE_BUILD", "FALSE") == "TRUE"
SKIP_CUDA_BUILD = os.getenv("FLASH_ATTENTION_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("FLASH_ATTENTION_FORCE_CXX11_ABI", "FALSE") == "TRUE"
def get_platform():
"""
Returns the platform name as used in wheel filenames.
"""
if sys.platform.startswith("linux"):
return f'linux_{platform.uname().machine}'
elif sys.platform == "darwin":
mac_version = ".".join(platform.mac_ver()[0].split(".")[:2])
return f"macosx_{mac_version}_x86_64"
elif sys.platform == "win32":
return "win_amd64"
else:
raise ValueError("Unsupported platform: {}".format(sys.platform))
def get_cuda_bare_metal_version(cuda_dir):
raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
output = raw_output.split()
release_idx = output.index("release") + 1
bare_metal_version = parse(output[release_idx].split(",")[0])
return raw_output, bare_metal_version
def get_hip_version():
return parse(torch.version.hip.split()[-1].rstrip('-').replace('-', '+'))
def check_if_cuda_home_none(global_option: str) -> None:
if CUDA_HOME is not None:
return
# warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary
# in that case.
warnings.warn(
f"{global_option} was requested, but nvcc was not found. Are you sure your environment has nvcc available? "
"If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, "
"only images whose names contain 'devel' will provide nvcc."
)
def check_if_rocm_home_none(global_option: str) -> None:
if ROCM_HOME is not None:
return
# warn instead of error because user could be downloading prebuilt wheels, so hipcc won't be necessary
# in that case.
warnings.warn(
f"{global_option} was requested, but hipcc was not found."
)
def append_nvcc_threads(nvcc_extra_args):
nvcc_threads = os.getenv("NVCC_THREADS") or "4"
return nvcc_extra_args + ["--threads", nvcc_threads]
def rename_cpp_to_cu(cpp_files):
for entry in cpp_files:
shutil.copy(entry, os.path.splitext(entry)[0] + ".cu")
def validate_and_update_archs(archs):
# List of allowed architectures
allowed_archs = ["native", "gfx90a", "gfx940", "gfx941", "gfx942"]
# Validate if each element in archs is in allowed_archs
assert all(
arch in allowed_archs for arch in archs
), f"One of GPU archs of {archs} is invalid or not supported by Flash-Attention"
cmdclass = {}
ext_modules = []
# We want this even if SKIP_CUDA_BUILD because when we run python setup.py sdist we want the .hpp
# files included in the source distribution, in case the user compiles from source.
if IS_ROCM:
subprocess.run(["git", "submodule", "update", "--init", "csrc/composable_kernel"])
else:
subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"])
if not SKIP_CUDA_BUILD and not IS_ROCM:
print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
# Check, if ATen/CUDAGeneratorImpl.h is found, otherwise use ATen/cuda/CUDAGeneratorImpl.h
# See https://github.com/pytorch/pytorch/pull/70650
generator_flag = []
torch_dir = torch.__path__[0]
if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")):
generator_flag = ["-DOLD_GENERATOR_PATH"]
check_if_cuda_home_none("flash_attn")
# Check, if CUDA11 is installed for compute capability 8.0
cc_flag = []
if CUDA_HOME is not None:
_, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
if bare_metal_version < Version("11.7"):
raise RuntimeError(
"FlashAttention is only supported on CUDA 11.7 and above. "
"Note: make sure nvcc has a supported version by running nvcc -V."
)
# cc_flag.append("-gencode")
# cc_flag.append("arch=compute_75,code=sm_75")
cc_flag.append("-gencode")
cc_flag.append("arch=compute_80,code=sm_80")
if CUDA_HOME is not None:
if bare_metal_version >= Version("11.8"):
cc_flag.append("-gencode")
cc_flag.append("arch=compute_90,code=sm_90")
# HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
# torch._C._GLIBCXX_USE_CXX11_ABI
# https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
if FORCE_CXX11_ABI:
torch._C._GLIBCXX_USE_CXX11_ABI = True
ext_modules.append(
CUDAExtension(
name="flash_attn_2_cuda",
sources=[
"csrc/flash_attn/flash_api.cpp",
"csrc/flash_attn/src/flash_fwd_hdim32_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim32_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim64_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim64_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim96_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim96_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim128_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim128_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim160_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim160_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim192_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim192_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim256_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim256_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim32_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim32_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim64_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim64_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim96_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim96_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim128_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim128_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim160_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim160_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim192_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim192_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim256_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_hdim256_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim32_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim32_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim64_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim64_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim96_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim96_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim128_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim128_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim160_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim160_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim192_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim192_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim32_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim32_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim64_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim64_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim96_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim96_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim128_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim128_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim160_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim160_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim192_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim192_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim256_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_bwd_hdim256_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim128_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim128_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim160_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim160_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim192_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim192_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim128_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim128_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim160_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim160_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim192_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim192_bf16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_causal_sm80.cu",
"csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_causal_sm80.cu",
],
extra_compile_args={
"cxx": ["-O3", "-std=c++17"] + generator_flag,
"nvcc": append_nvcc_threads(
[
"-O3",
"-std=c++17",
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"-U__CUDA_NO_HALF2_OPERATORS__",
"-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
"--expt-relaxed-constexpr",
"--expt-extended-lambda",
"--use_fast_math",
# "--ptxas-options=-v",
# "--ptxas-options=-O2",
# "-lineinfo",
# "-DFLASHATTENTION_DISABLE_BACKWARD",
# "-DFLASHATTENTION_DISABLE_DROPOUT",
# "-DFLASHATTENTION_DISABLE_ALIBI",
# "-DFLASHATTENTION_DISABLE_SOFTCAP",
# "-DFLASHATTENTION_DISABLE_UNEVEN_K",
# "-DFLASHATTENTION_DISABLE_LOCAL",
]
+ generator_flag
+ cc_flag
),
},
include_dirs=[
Path(this_dir) / "csrc" / "flash_attn",
Path(this_dir) / "csrc" / "flash_attn" / "src",
Path(this_dir) / "csrc" / "cutlass" / "include",
],
)
)
elif not SKIP_CUDA_BUILD and IS_ROCM:
ck_dir = "csrc/composable_kernel"
#use codegen get code dispatch
if not os.path.exists("./build"):
os.makedirs("build")
os.system(f"{sys.executable} {ck_dir}/example/ck_tile/01_fmha/generate.py -d fwd --output_dir build --receipt 2")
os.system(f"{sys.executable} {ck_dir}/example/ck_tile/01_fmha/generate.py -d fwd_appendkv --output_dir build --receipt 2")
os.system(f"{sys.executable} {ck_dir}/example/ck_tile/01_fmha/generate.py -d fwd_splitkv --output_dir build --receipt 2")
os.system(f"{sys.executable} {ck_dir}/example/ck_tile/01_fmha/generate.py -d bwd --output_dir build --receipt 2")
print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
# Check, if ATen/CUDAGeneratorImpl.h is found, otherwise use ATen/cuda/CUDAGeneratorImpl.h
# See https://github.com/pytorch/pytorch/pull/70650
generator_flag = []
torch_dir = torch.__path__[0]
if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")):
generator_flag = ["-DOLD_GENERATOR_PATH"]
check_if_rocm_home_none("flash_attn")
archs = os.getenv("GPU_ARCHS", "native").split(";")
validate_and_update_archs(archs)
cc_flag = [f"--offload-arch={arch}" for arch in archs]
# HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
# torch._C._GLIBCXX_USE_CXX11_ABI
# https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
if FORCE_CXX11_ABI:
torch._C._GLIBCXX_USE_CXX11_ABI = True
sources = ["csrc/flash_attn_ck/flash_api.cpp",
"csrc/flash_attn_ck/flash_common.cpp",
"csrc/flash_attn_ck/mha_bwd.cpp",
"csrc/flash_attn_ck/mha_fwd_kvcache.cpp",
"csrc/flash_attn_ck/mha_fwd.cpp",
"csrc/flash_attn_ck/mha_varlen_bwd.cpp",
"csrc/flash_attn_ck/mha_varlen_fwd.cpp"] + glob.glob(
f"build/fmha_*wd*.cpp"
)
sources+=glob.glob(f"csrc/composable_kernel/example/ck_tile/01_fmha/hsaco/*.cpp")
rename_cpp_to_cu(sources)
renamed_sources = ["csrc/flash_attn_ck/flash_api.cu",
"csrc/flash_attn_ck/flash_common.cu",
"csrc/flash_attn_ck/mha_bwd.cu",
"csrc/flash_attn_ck/mha_fwd_kvcache.cu",
"csrc/flash_attn_ck/mha_fwd.cu",
"csrc/flash_attn_ck/mha_varlen_bwd.cu",
"csrc/flash_attn_ck/mha_varlen_fwd.cu"] + glob.glob(f"build/fmha_*wd*.cu")
renamed_sources+=glob.glob(f"csrc/composable_kernel/example/ck_tile/01_fmha/hsaco/*.cu")
cc_flag += ["-O3","-std=c++17",
"-DCK_TILE_FMHA_FWD_FAST_EXP2=1",
"-fgpu-flush-denormals-to-zero",
"-DCK_ENABLE_BF16",
"-DCK_ENABLE_BF8",
"-DCK_ENABLE_FP16",
"-DCK_ENABLE_FP32",
"-DCK_ENABLE_FP64",
"-DCK_ENABLE_FP8",
"-DCK_ENABLE_INT8",
"-DCK_USE_XDL",
"-DUSE_PROF_API=1",
# "-DFLASHATTENTION_DISABLE_BACKWARD",
"-D__HIP_PLATFORM_HCC__=1"]
cc_flag += [f"-DCK_TILE_FLOAT_TO_BFLOAT16_DEFAULT={os.environ.get('CK_TILE_FLOAT_TO_BFLOAT16_DEFAULT', 3)}"]
# Imitate https://github.com/ROCm/composable_kernel/blob/c8b6b64240e840a7decf76dfaa13c37da5294c4a/CMakeLists.txt#L190-L214
hip_version = get_hip_version()
if hip_version > Version('5.7.23302'):
cc_flag += ["-fno-offload-uniform-block"]
if hip_version > Version('6.1.40090'):
cc_flag += ["-mllvm", "-enable-post-misched=0"]
if hip_version > Version('6.2.41132'):
cc_flag += ["-mllvm", "-amdgpu-early-inline-all=true",
"-mllvm", "-amdgpu-function-calls=false"]
if hip_version > Version('6.2.41133') and hip_version < Version('6.3.00000'):
cc_flag += ["-mllvm", "-amdgpu-coerce-illegal-types=1"]
extra_compile_args = {
"cxx": ["-O3", "-std=c++17"] + generator_flag,
"nvcc": cc_flag + generator_flag,
}
include_dirs = [
Path(this_dir) / "csrc" / "composable_kernel" / "include",
Path(this_dir) / "csrc" / "composable_kernel" / "library" / "include",
Path(this_dir) / "csrc" / "composable_kernel" / "example" / "ck_tile" / "01_fmha",
]
ext_modules.append(
CUDAExtension(
name="flash_attn_2_cuda",
sources=renamed_sources,
extra_compile_args=extra_compile_args,
include_dirs=include_dirs,
)
)
def get_package_version():
with open(Path(this_dir) / "flash_attn" / "__init__.py", "r") as f:
version_match = re.search(r"^__version__\s*=\s*(.*)$", f.read(), re.MULTILINE)
public_version = ast.literal_eval(version_match.group(1))
local_version = os.environ.get("FLASH_ATTN_LOCAL_VERSION")
if local_version:
return f"{public_version}+{local_version}"
else:
return str(public_version)
def get_wheel_url():
torch_version_raw = parse(torch.__version__)
python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
platform_name = get_platform()
flash_version = get_package_version()
torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()
if IS_ROCM:
torch_hip_version = get_hip_version()
hip_version = f"{torch_hip_version.major}{torch_hip_version.minor}"
wheel_filename = f"{PACKAGE_NAME}-{flash_version}+rocm{hip_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl"
else:
# Determine the version numbers that will be used to determine the correct wheel
# We're using the CUDA version used to build torch, not the one currently installed
# _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
torch_cuda_version = parse(torch.version.cuda)
# For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.3
# to save CI time. Minor versions should be compatible.
torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.3")
# cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}"
cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
# Determine wheel URL based on CUDA version, torch version, python version and OS
wheel_filename = f"{PACKAGE_NAME}-{flash_version}+cu{cuda_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl"
wheel_url = BASE_WHEEL_URL.format(tag_name=f"v{flash_version}", wheel_name=wheel_filename)
return wheel_url, wheel_filename
class CachedWheelsCommand(_bdist_wheel):
"""
The CachedWheelsCommand plugs into the default bdist wheel, which is ran by pip when it cannot
find an existing wheel (which is currently the case for all flash attention installs). We use
the environment parameters to detect whether there is already a pre-built version of a compatible
wheel available and short-circuits the standard full build pipeline.
"""
def run(self):
if FORCE_BUILD:
return super().run()
wheel_url, wheel_filename = get_wheel_url()
print("Guessing wheel URL: ", wheel_url)
try:
urllib.request.urlretrieve(wheel_url, wheel_filename)
# Make the archive
# Lifted from the root wheel processing command
# https://github.com/pypa/wheel/blob/cf71108ff9f6ffc36978069acb28824b44ae028e/src/wheel/bdist_wheel.py#LL381C9-L381C85
if not os.path.exists(self.dist_dir):
os.makedirs(self.dist_dir)
impl_tag, abi_tag, plat_tag = self.get_tag()
archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
print("Raw wheel path", wheel_path)
os.rename(wheel_filename, wheel_path)
except (urllib.error.HTTPError, urllib.error.URLError):
print("Precompiled wheel not found. Building from source...")
# If the wheel could not be downloaded, build from source
super().run()
class NinjaBuildExtension(BuildExtension):
def __init__(self, *args, **kwargs) -> None:
# do not override env MAX_JOBS if already exists
if not os.environ.get("MAX_JOBS"):
import psutil
# calculate the maximum allowed NUM_JOBS based on cores
max_num_jobs_cores = max(1, os.cpu_count() // 2)
# calculate the maximum allowed NUM_JOBS based on free memory
free_memory_gb = psutil.virtual_memory().available / (1024 ** 3) # free memory in GB
max_num_jobs_memory = int(free_memory_gb / 9) # each JOB peak memory cost is ~8-9GB when threads = 4
# pick lower value of jobs based on cores vs memory metric to minimize oom and swap usage during compilation
max_jobs = max(1, min(max_num_jobs_cores, max_num_jobs_memory))
os.environ["MAX_JOBS"] = str(max_jobs)
super().__init__(*args, **kwargs)
setup(
name=PACKAGE_NAME,
version=get_package_version(),
packages=find_packages(
exclude=(
"build",
"csrc",
"include",
"tests",
"dist",
"docs",
"benchmarks",
"flash_attn.egg-info",
)
),
author="Tri Dao",
author_email="tri@tridao.me",
description="Flash Attention: Fast and Memory-Efficient Exact Attention",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/Dao-AILab/flash-attention",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: BSD License",
"Operating System :: Unix",
],
ext_modules=ext_modules,
cmdclass={"bdist_wheel": CachedWheelsCommand, "build_ext": NinjaBuildExtension}
if ext_modules
else {
"bdist_wheel": CachedWheelsCommand,
},
python_requires=">=3.8",
install_requires=[
"torch",
"einops",
],
setup_requires=[
"packaging",
"psutil",
"ninja",
],
)