setup.py 14.2 KB
Newer Older
Tri Dao's avatar
Tri Dao committed
1
2
3
4
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import sys
import warnings
import os
5
6
import re
import ast
Tri Dao's avatar
Tri Dao committed
7
from pathlib import Path
Tri Dao's avatar
Tri Dao committed
8
from packaging.version import parse, Version
9
import platform
Tri Dao's avatar
Tri Dao committed
10
11
12
13

from setuptools import setup, find_packages
import subprocess

Pierce Freeman's avatar
Pierce Freeman committed
14
15
import urllib.request
import urllib.error
Tri Dao's avatar
Tri Dao committed
16
17
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

Tri Dao's avatar
Tri Dao committed
18
19
20
21
22
23
24
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME


with open("README.md", "r", encoding="utf-8") as fh:
    long_description = fh.read()

Tri Dao's avatar
Tri Dao committed
25
26
27
28

# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))

29
PACKAGE_NAME = "flash_attn"
Tri Dao's avatar
Tri Dao committed
30

31
BASE_WHEEL_URL = "https://github.com/Dao-AILab/flash-attention/releases/download/{tag_name}/{wheel_name}"
32
33
34
35
36

# 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"
Tri Dao's avatar
Tri Dao committed
37
38
# 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"
39
40
# For CI, we want the option to not add "--threads 4" to nvcc, since the runner can OOM
FORCE_SINGLE_THREAD = os.getenv("FLASH_ATTENTION_FORCE_SINGLE_THREAD", "FALSE") == "TRUE"
41
42


43
44
def get_platform():
    """
45
    Returns the platform name as used in wheel filenames.
46
47
48
49
    """
    if sys.platform.startswith('linux'):
        return 'linux_x86_64'
    elif sys.platform == 'darwin':
50
51
        mac_version = '.'.join(platform.mac_ver()[0].split('.')[:2])
        return f'macosx_{mac_version}_x86_64'
52
53
54
55
56
    elif sys.platform == 'win32':
        return 'win_amd64'
    else:
        raise ValueError('Unsupported platform: {}'.format(sys.platform))

Tri Dao's avatar
Tri Dao committed
57
58
59
60
61

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
Tri Dao's avatar
Tri Dao committed
62
    bare_metal_version = parse(output[release_idx].split(",")[0])
Tri Dao's avatar
Tri Dao committed
63

Tri Dao's avatar
Tri Dao committed
64
    return raw_output, bare_metal_version
Tri Dao's avatar
Tri Dao committed
65
66
67


def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
Tri Dao's avatar
Tri Dao committed
68
69
    raw_output, bare_metal_version = get_cuda_bare_metal_version(cuda_dir)
    torch_binary_version = parse(torch.version.cuda)
Tri Dao's avatar
Tri Dao committed
70
71
72
73

    print("\nCompiling cuda extensions with")
    print(raw_output + "from " + cuda_dir + "/bin\n")

Tri Dao's avatar
Tri Dao committed
74
    if (bare_metal_version != torch_binary_version):
Tri Dao's avatar
Tri Dao committed
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
        raise RuntimeError(
            "Cuda extensions are being compiled with a version of Cuda that does "
            "not match the version used to compile Pytorch binaries.  "
            "Pytorch binaries were compiled with Cuda {}.\n".format(torch.version.cuda)
            + "In some cases, a minor-version mismatch will not cause later errors:  "
            "https://github.com/NVIDIA/apex/pull/323#discussion_r287021798.  "
            "You can try commenting out this check (at your own risk)."
        )


def raise_if_cuda_home_none(global_option: str) -> None:
    if CUDA_HOME is not None:
        return
    raise RuntimeError(
        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 append_nvcc_threads(nvcc_extra_args):
96
    if not FORCE_SINGLE_THREAD:
Tri Dao's avatar
Tri Dao committed
97
98
99
100
101
102
103
104
105
106
107
        return nvcc_extra_args + ["--threads", "4"]
    return nvcc_extra_args


if not torch.cuda.is_available():
    # https://github.com/NVIDIA/apex/issues/486
    # Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query torch.cuda.get_device_capability(),
    # which will fail if you are compiling in an environment without visible GPUs (e.g. during an nvidia-docker build command).
    print(
        "\nWarning: Torch did not find available GPUs on this system.\n",
        "If your intention is to cross-compile, this is not an error.\n"
Tri Dao's avatar
Tri Dao committed
108
109
        "By default, FlashAttention will cross-compile for Ampere (compute capability 8.0, 8.6, "
        "8.9), and, if the CUDA version is >= 11.8, Hopper (compute capability 9.0).\n"
Tri Dao's avatar
Tri Dao committed
110
111
112
        "If you wish to cross-compile for a single specific architecture,\n"
        'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n',
    )
Tri Dao's avatar
Tri Dao committed
113
114
115
    if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None and CUDA_HOME is not None:
        _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
        if bare_metal_version >= Version("11.8"):
Tri Dao's avatar
Tri Dao committed
116
117
118
            os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6;9.0"
        elif bare_metal_version >= Version("11.4"):
            os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
Tri Dao's avatar
Tri Dao committed
119
        else:
Tri Dao's avatar
Tri Dao committed
120
            os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
Tri Dao's avatar
Tri Dao committed
121

Tri Dao's avatar
Tri Dao committed
122
123
124
cmdclass = {}
ext_modules = []

Tri Dao's avatar
Tri Dao committed
125
126
127
128
# 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.
subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"])

129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
if not SKIP_CUDA_BUILD:
    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"]

    raise_if_cuda_home_none("flash_attn")
    # Check, if CUDA11 is installed for compute capability 8.0
    cc_flag = []
    _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
Tri Dao's avatar
Tri Dao committed
145
146
    if bare_metal_version < Version("11.4"):
        raise RuntimeError("FlashAttention is only supported on CUDA 11.4 and above")
147
148
    # cc_flag.append("-gencode")
    # cc_flag.append("arch=compute_75,code=sm_75")
Tri Dao's avatar
Tri Dao committed
149
    cc_flag.append("-gencode")
150
151
152
153
154
    cc_flag.append("arch=compute_80,code=sm_80")
    if bare_metal_version >= Version("11.8"):
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_90,code=sm_90")

Tri Dao's avatar
Tri Dao committed
155
156
157
158
159
    # 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
160
161
    ext_modules.append(
        CUDAExtension(
162
            name="flash_attn_2_cuda",
163
            sources=[
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
                "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_hdim224_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim224_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_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_hdim224_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim224_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu",
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
            ],
            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",
212
                        # "--ptxas-options=-O2",
213
214
215
216
217
218
219
220
221
                        "-lineinfo"
                    ]
                    + generator_flag
                    + cc_flag
                ),
            },
            include_dirs=[
                Path(this_dir) / 'csrc' / 'flash_attn',
                Path(this_dir) / 'csrc' / 'flash_attn' / 'src',
222
                Path(this_dir) / 'csrc' / 'cutlass' / 'include',
223
224
            ],
        )
Tri Dao's avatar
Tri Dao committed
225
    )
Tri Dao's avatar
Tri Dao committed
226

Tri Dao's avatar
Tri Dao committed
227

228
229
230
231
232
233
234
235
236
237
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)

Tri Dao's avatar
Tri Dao committed
238

239
class CachedWheelsCommand(_bdist_wheel):
Tri Dao's avatar
Tri Dao committed
240
241
242
243
244
245
246
    """
    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):
247
        if FORCE_BUILD:
Pierce Freeman's avatar
Pierce Freeman committed
248
            return super().run()
249
250

        # Determine the version numbers that will be used to determine the correct wheel
Tri Dao's avatar
Tri Dao committed
251
252
253
        # 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)
254
255
256
257
        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()
Tri Dao's avatar
Tri Dao committed
258
259
260
261
        # cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}"
        cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
        torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
        cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()
262
263

        # Determine wheel URL based on CUDA version, torch version, python version and OS
Tri Dao's avatar
Tri Dao committed
264
        wheel_filename = f'{PACKAGE_NAME}-{flash_version}+cu{cuda_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl'
265
266
267
268
269
        wheel_url = BASE_WHEEL_URL.format(
            tag_name=f"v{flash_version}",
            wheel_name=wheel_filename
        )
        print("Guessing wheel URL: ", wheel_url)
270

271
272
        try:
            urllib.request.urlretrieve(wheel_url, wheel_filename)
273
274
275
276
277
278
279
280
281

            # 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}"
282

283
284
285
            wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
            print("Raw wheel path", wheel_path)
            os.rename(wheel_filename, wheel_path)
286
287
288
        except urllib.error.HTTPError:
            print("Precompiled wheel not found. Building from source...")
            # If the wheel could not be downloaded, build from source
289
            super().run()
290
291


Tri Dao's avatar
Tri Dao committed
292
setup(
293
    name=PACKAGE_NAME,
294
    version=get_package_version(),
Tri Dao's avatar
Tri Dao committed
295
296
297
298
    packages=find_packages(
        exclude=("build", "csrc", "include", "tests", "dist", "docs", "benchmarks", "flash_attn.egg-info",)
    ),
    author="Tri Dao",
Tri Dao's avatar
Tri Dao committed
299
    author_email="trid@cs.stanford.edu",
Tri Dao's avatar
Tri Dao committed
300
301
302
    description="Flash Attention: Fast and Memory-Efficient Exact Attention",
    long_description=long_description,
    long_description_content_type="text/markdown",
Tri Dao's avatar
Tri Dao committed
303
    url="https://github.com/Dao-AILab/flash-attention",
Tri Dao's avatar
Tri Dao committed
304
305
    classifiers=[
        "Programming Language :: Python :: 3",
306
        "License :: OSI Approved :: BSD License",
Phil Wang's avatar
Phil Wang committed
307
        "Operating System :: Unix",
Tri Dao's avatar
Tri Dao committed
308
    ],
Tri Dao's avatar
Tri Dao committed
309
    ext_modules=ext_modules,
310
    cmdclass={
311
        'bdist_wheel': CachedWheelsCommand,
312
313
        "build_ext": BuildExtension
    } if ext_modules else {
314
        'bdist_wheel': CachedWheelsCommand,
315
    },
Gustaf's avatar
Gustaf committed
316
317
318
319
    python_requires=">=3.7",
    install_requires=[
        "torch",
        "einops",
Pavel Shvets's avatar
Pavel Shvets committed
320
        "packaging",
321
        "ninja",
Gustaf's avatar
Gustaf committed
322
    ],
Tri Dao's avatar
Tri Dao committed
323
)