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Switch NCCL to build from open source (version 2.3.5-5) by default.

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Note to users manually patching ptxas from a later toolkit version:
Building NCCL requires the same version of ptxas and nvlink.

PiperOrigin-RevId: 215911973
This commit is contained in:
A. Unique TensorFlower 2018-10-05 08:46:54 -07:00 committed by TensorFlower Gardener
parent cea6b49591
commit 53faa313b7
8 changed files with 1565 additions and 1289 deletions
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17
configure.py
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@ -35,7 +35,6 @@ except ImportError:
_DEFAULT_CUDA_VERSION = '9.0'
_DEFAULT_CUDNN_VERSION = '7'
_DEFAULT_NCCL_VERSION = '2.2'
_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,7.0'
_DEFAULT_CUDA_PATH = '/usr/local/cuda'
_DEFAULT_CUDA_PATH_LINUX = '/opt/cuda'
@ -1109,18 +1108,17 @@ def set_tf_nccl_install_path(environ_cp):
raise ValueError('Currently NCCL is only supported on Linux platforms.')
ask_nccl_version = (
'Please specify the NCCL version you want to use. If NCCL %s is not '
'installed, then you can use version 1.3 that can be fetched '
'automatically but it may have worse performance with multiple GPUs. '
'[Default is %s]: ') % (_DEFAULT_NCCL_VERSION, _DEFAULT_NCCL_VERSION)
'Please specify the locally installed NCCL version you want to use. '
'[Default is to use https://github.com/nvidia/nccl]: ')
for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS):
tf_nccl_version = get_from_env_or_user_or_default(
environ_cp, 'TF_NCCL_VERSION', ask_nccl_version, _DEFAULT_NCCL_VERSION)
tf_nccl_version = reformat_version_sequence(str(tf_nccl_version), 1)
environ_cp, 'TF_NCCL_VERSION', ask_nccl_version, '')
if tf_nccl_version == '1':
break # No need to get install path, NCCL 1 is a GitHub repo.
if not tf_nccl_version:
break # No need to get install path, building the open source code.
tf_nccl_version = reformat_version_sequence(str(tf_nccl_version), 1)
# Look with ldconfig first if we can find the library in paths
# like /usr/lib/x86_64-linux-gnu and the header file in the corresponding
@ -1232,7 +1230,6 @@ def set_tf_nccl_install_path(environ_cp):
environ_cp['TF_NCCL_VERSION'] = tf_nccl_version
write_action_env_to_bazelrc('TF_NCCL_VERSION', tf_nccl_version)
def get_native_cuda_compute_capabilities(environ_cp):
"""Get native cuda compute capabilities.

10
tensorflow/workspace.bzl
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@ -585,12 +585,12 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
tf_http_archive(
name = "nccl_archive",
build_file = clean_dep("//third_party:nccl/nccl_archive.BUILD"),
sha256 = "2ca86fb6179ecbff789cc67c836139c1bbc0324ed8c04643405a30bf26325176",
strip_prefix = "nccl-03d856977ecbaac87e598c0c4bafca96761b9ac7",
build_file = clean_dep("//third_party:nccl/archive.BUILD"),
sha256 = "19132b5127fa8e02d95a09795866923f04064c8f1e0770b2b42ab551408882a4",
strip_prefix = "nccl-f93fe9bfd94884cec2ba711897222e0df5569a53",
urls = [
"https://mirror.bazel.build/github.com/nvidia/nccl/archive/03d856977ecbaac87e598c0c4bafca96761b9ac7.tar.gz",
"https://github.com/nvidia/nccl/archive/03d856977ecbaac87e598c0c4bafca96761b9ac7.tar.gz",
"https://mirror.bazel.build/github.com/nvidia/nccl/archive/f93fe9bfd94884cec2ba711897222e0df5569a53.tar.gz",
"https://github.com/nvidia/nccl/archive/f93fe9bfd94884cec2ba711897222e0df5569a53.tar.gz",
],
)

463
third_party/gpus/cuda_configure.bzl vendored
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225
third_party/nccl/LICENSE vendored
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@ -1,203 +1,30 @@
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179
third_party/nccl/archive.BUILD vendored Normal file
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@ -0,0 +1,179 @@
# NVIDIA NCCL 2
# A package of optimized primitives for collective multi-GPU communication.
licenses(["restricted"])
exports_files(["LICENSE.txt"])
load(
"@local_config_nccl//:build_defs.bzl",
"device_link",
"gen_nccl_h",
"nccl_library",
"rdc_copts",
)
load(
"@local_config_cuda//cuda:build_defs.bzl",
"cuda_default_copts",
)
# Generate the nccl.h header file.
gen_nccl_h(
name = "nccl_h",
output = "src/nccl.h",
template = "src/nccl.h.in",
)
nccl_library(
name = "src_hdrs",
hdrs = [
"src/nccl.h",
# src/include/common_coll.h #includes "collectives/collectives.h".
# All other #includes of collectives.h are patched in process_srcs.
"src/collectives/collectives.h",
],
strip_include_prefix = "src",
)
nccl_library(
name = "include_hdrs",
hdrs = glob(["src/include/*.h"]),
strip_include_prefix = "src/include",
)
filegroup(
name = "device_hdrs",
srcs = glob(["src/collectives/device/*.h"]),
)
filegroup(
name = "device_srcs",
srcs = [
"src/collectives/device/all_gather.cu",
"src/collectives/device/all_reduce.cu",
"src/collectives/device/broadcast.cu",
"src/collectives/device/reduce.cu",
"src/collectives/device/reduce_scatter.cu",
],
)
nccl_library(
name = "sum",
srcs = [
":device_hdrs",
":device_srcs",
],
copts = ["-DNCCL_OP=0"] + rdc_copts(),
prefix = "sum_",
deps = [
":src_hdrs",
":include_hdrs",
"@local_config_cuda//cuda:cuda_headers",
],
linkstatic = True,
)
nccl_library(
name = "prod",
srcs = [
":device_hdrs",
":device_srcs",
],
copts = ["-DNCCL_OP=1"] + rdc_copts(),
prefix = "_prod",
deps = [
":src_hdrs",
":include_hdrs",
"@local_config_cuda//cuda:cuda_headers",
],
linkstatic = True,
)
nccl_library(
name = "min",
srcs = [
":device_hdrs",
":device_srcs",
],
copts = ["-DNCCL_OP=2"] + rdc_copts(),
prefix = "min_",
deps = [
":src_hdrs",
":include_hdrs",
"@local_config_cuda//cuda:cuda_headers",
],
linkstatic = True,
)
nccl_library(
name = "max",
srcs = [
":device_hdrs",
":device_srcs",
],
copts = ["-DNCCL_OP=3"] + rdc_copts(),
prefix = "max_",
deps = [
":src_hdrs",
":include_hdrs",
"@local_config_cuda//cuda:cuda_headers",
],
linkstatic = True,
)
nccl_library(
name = "functions",
srcs = [
":device_hdrs",
"src/collectives/device/functions.cu",
],
copts = rdc_copts(),
deps = [
":src_hdrs",
":include_hdrs",
"@local_config_cuda//cuda:cuda_headers",
],
linkstatic = True,
)
device_link(
name = "device_code",
srcs = [
":functions",
":max",
":min",
":prod",
":sum",
],
)
# Primary NCCL target.
nccl_library(
name = "nccl",
srcs = glob(
include = ["src/**/*.cu"],
# Exclude device-library code.
exclude = ["src/collectives/device/**"],
) + [
# Required for header inclusion checking (see
# http://docs.bazel.build/versions/master/be/c-cpp.html#hdrs).
# Files in src/ which #include "nccl.h" load it from there rather than
# from the virtual includes directory.
"src/nccl.h",
],
hdrs = ["src/nccl.h"],
include_prefix = "third_party/nccl",
strip_include_prefix = "src",
copts = cuda_default_copts(),
deps = [
":device_code",
":functions",
":include_hdrs",
":max",
":min",
":prod",
":src_hdrs",
":sum",
],
visibility = ["//visibility:public"],
)

210
third_party/nccl/build_defs.bzl.tpl vendored Normal file
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@ -0,0 +1,210 @@
"""Repository rule for NCCL."""
load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts")
def _gen_nccl_h_impl(ctx):
"""Creates nccl.h from a template."""
ctx.actions.expand_template(
output = ctx.outputs.output,
template = ctx.file.template,
substitutions = {
"${nccl:Major}": "2",
"${nccl:Minor}": "3",
"${nccl:Patch}": "5",
"${nccl:Suffix}": "",
"${nccl:Version}": "2305",
},
)
gen_nccl_h = rule(
implementation = _gen_nccl_h_impl,
attrs = {
"template": attr.label(allow_single_file = True),
"output": attr.output(),
},
)
"""Creates the NCCL header file."""
def _process_srcs_impl(ctx):
"""Appends .cc to .cu files, patches include directives."""
files = []
for src in ctx.files.srcs:
if not src.is_source:
# Process only once, specifically "src/nccl.h".
files.append(src)
continue
name = src.basename
if src.extension == "cu":
name = ctx.attr.prefix + name + ".cc"
file = ctx.actions.declare_file(name, sibling = src)
ctx.actions.expand_template(
output = file,
template = src,
substitutions = {
"\"collectives.h": "\"collectives/collectives.h",
"\"../collectives.h": "\"collectives/collectives.h",
"#if __CUDACC_VER_MAJOR__":
"#if defined __CUDACC_VER_MAJOR__ && __CUDACC_VER_MAJOR__",
# Substitutions are applied in order.
"std::nullptr_t": "nullptr_t",
"nullptr_t": "std::nullptr_t",
},
)
files.append(file)
return [DefaultInfo(files = depset(files))]
_process_srcs = rule(
implementation = _process_srcs_impl,
attrs = {
"srcs": attr.label_list(allow_files = True),
"prefix": attr.string(default = ""),
},
)
"""Processes the NCCL srcs so they can be compiled with bazel and clang."""
def nccl_library(name, srcs=None, hdrs=None, prefix=None, **kwargs):
"""Processes the srcs and hdrs and creates a cc_library."""
_process_srcs(
name = name + "_srcs",
srcs = srcs,
prefix = prefix,
)
_process_srcs(
name = name + "_hdrs",
srcs = hdrs,
)
native.cc_library(
name = name,
srcs = [name + "_srcs"] if srcs else [],
hdrs = [name + "_hdrs"] if hdrs else [],
**kwargs
)
def rdc_copts():
"""Returns copts for compiling relocatable device code."""
# The global functions can not have a lower register count than the
# device functions. This is enforced by setting a fixed register count.
# https://github.com/NVIDIA/nccl/blob/f93fe9bfd94884cec2ba711897222e0df5569a53/makefiles/common.mk#L48
maxrregcount = "-maxrregcount=96"
return cuda_default_copts() + select({
"@local_config_cuda//cuda:using_nvcc": [
"-nvcc_options",
"relocatable-device-code=true",
"-nvcc_options",
"ptxas-options=" + maxrregcount,
],
"@local_config_cuda//cuda:using_clang": [
"-fcuda-rdc",
"-Xcuda-ptxas",
maxrregcount,
],
"//conditions:default": [],
}) + ["-fvisibility=hidden"]
def _filter_impl(ctx):
suffix = ctx.attr.suffix
files = [src for src in ctx.files.srcs if src.path.endswith(suffix)]
return [DefaultInfo(files = depset(files))]
_filter = rule(
implementation = _filter_impl,
attrs = {
"srcs": attr.label_list(allow_files = True),
"suffix": attr.string(),
},
)
"""Filters the srcs to the ones ending with suffix."""
def _gen_link_src_impl(ctx):
ctx.actions.expand_template(
output = ctx.outputs.output,
template = ctx.file.template,
substitutions = {
"REGISTERLINKBINARYFILE": '"%s"' % ctx.file.register_hdr.short_path,
"FATBINFILE": '"%s"' % ctx.file.fatbin_hdr.short_path,
},
)
_gen_link_src = rule(
implementation = _gen_link_src_impl,
attrs = {
"register_hdr": attr.label(allow_single_file = True),
"fatbin_hdr": attr.label(allow_single_file = True),
"template": attr.label(allow_single_file = True),
"output": attr.output(),
},
)
"""Patches the include directives for the link.stub file."""
def device_link(name, srcs):
"""Links seperately compiled relocatable device code into a cc_library."""
# From .a and .pic.a archives, just use the latter.
_filter(
name = name + "_pic_a",
srcs = srcs,
suffix = ".pic.a",
)
# Device-link to cubins for each architecture.
images = []
cubins = []
for arch in %{gpu_architectures}:
cubin = "%s_%s.cubin" % (name, arch)
register_hdr = "%s_%s.h" % (name, arch)
nvlink = "@local_config_nccl//:nvlink"
cmd = ("$(location %s) --cpu-arch=X86_64 " % nvlink +
"--arch=%s $(SRCS) " % arch +
"--register-link-binaries=$(location %s) " % register_hdr +
"--output-file=$(location %s)" % cubin)
native.genrule(
name = "%s_%s" % (name, arch),
outs = [register_hdr, cubin],
srcs = [name + "_pic_a"],
cmd = cmd,
tools = [nvlink],
)
images.append("--image=profile=%s,file=$(location %s)" % (arch, cubin))
cubins.append(cubin)
# Generate fatbin header from all cubins.
fatbin_hdr = name + ".fatbin.h"
fatbinary = "@local_config_nccl//:cuda/bin/fatbinary"
cmd = ("PATH=$$CUDA_TOOLKIT_PATH/bin:$$PATH " + # for bin2c
"$(location %s) -64 --cmdline=--compile-only --link " % fatbinary +
"--compress-all %s --create=%%{name}.fatbin " % " ".join(images) +
"--embedded-fatbin=$@")
native.genrule(
name = name + "_fatbin_h",
outs = [fatbin_hdr],
srcs = cubins,
cmd = cmd,
tools = [fatbinary],
)
# Generate the source file #including the headers generated above.
_gen_link_src(
name = name + "_cc",
# Include just the last one, they are equivalent.
register_hdr = register_hdr,
fatbin_hdr = fatbin_hdr,
template = "@local_config_nccl//:cuda/bin/crt/link.stub",
output = name + ".cc",
)
# Compile the source file into the cc_library.
native.cc_library(
name = name,
srcs = [name + "_cc"],
textual_hdrs = [register_hdr, fatbin_hdr],
deps = [
"@local_config_cuda//cuda:cuda_headers",
"@local_config_cuda//cuda:cudart_static",
],
)

68
third_party/nccl/nccl_archive.BUILD vendored
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@ -1,68 +0,0 @@
# NVIDIA nccl
# A package of optimized primitives for collective multi-GPU communication.
licenses(["notice"]) # BSD
exports_files(["LICENSE.txt"])
load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts", "if_cuda")
SRCS = [
"src/all_gather.cu",
"src/all_reduce.cu",
"src/broadcast.cu",
"src/core.cu",
"src/libwrap.cu",
"src/reduce.cu",
"src/reduce_scatter.cu",
]
# Copy .cu to .cu.cc so they can be in srcs of cc_library.
[
genrule(
name = "gen_" + src,
srcs = [src],
outs = [src + ".cc"],
cmd = "cp $(location " + src + ") $(location " + src + ".cc)",
)
for src in SRCS
]
SRCS_CU_CC = [src + ".cc" for src in SRCS]
cc_library(
name = "nccl",
srcs = if_cuda(SRCS_CU_CC + glob(["src/*.h"])),
hdrs = if_cuda(["src/nccl.h"]),
copts = [
"-DCUDA_MAJOR=0",
"-DCUDA_MINOR=0",
"-DNCCL_MAJOR=0",
"-DNCCL_MINOR=0",
"-DNCCL_PATCH=0",
"-Iexternal/nccl_archive/src",
"-O3",
] + cuda_default_copts(),
include_prefix = "third_party/nccl",
linkopts = select({
"@org_tensorflow//tensorflow:android": [
"-pie",
],
"@org_tensorflow//tensorflow:darwin": [
"-Wl,-framework",
"-Wl,CoreFoundation",
"-Wl,-framework",
"-Wl,Security",
],
"@org_tensorflow//tensorflow:ios": [],
"@org_tensorflow//tensorflow:windows": [
"-DEFAULTLIB:ws2_32.lib",
],
"//conditions:default": [
"-lrt",
],
}),
strip_include_prefix = "src",
visibility = ["//visibility:public"],
deps = ["@local_config_cuda//cuda:cuda_headers"],
)

108
third_party/nccl/nccl_configure.bzl vendored
View File

@ -11,12 +11,16 @@
load(
"//third_party/gpus:cuda_configure.bzl",
"auto_configure_fail",
"compute_capabilities",
"cuda_toolkit_path",
"find_cuda_define",
"matches_version",
)
_NCCL_INSTALL_PATH = "NCCL_INSTALL_PATH"
_CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH"
_NCCL_HDR_PATH = "NCCL_HDR_PATH"
_NCCL_INSTALL_PATH = "NCCL_INSTALL_PATH"
_TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
_TF_NCCL_VERSION = "TF_NCCL_VERSION"
_TF_NCCL_CONFIG_REPO = "TF_NCCL_CONFIG_REPO"
@ -37,6 +41,12 @@ cc_library(
"""
_NCCL_ARCHIVE_BUILD_CONTENT = """
exports_files([
"cuda/bin/crt/link.stub",
"cuda/bin/fatbinary",
"nvlink",
])
filegroup(
name = "LICENSE",
data = ["@nccl_archive//:LICENSE.txt"],
@ -50,9 +60,8 @@ alias(
)
"""
# Local build results in dynamic link and the license should not be included.
_NCCL_REMOTE_BUILD_TEMPLATE = Label("//third_party/nccl:remote.BUILD.tpl")
_NCCL_LOCAL_BUILD_TEMPLATE = Label("//third_party/nccl:system.BUILD.tpl")
def _label(file):
return Label("//third_party/nccl:{}".format(file))
def _find_nccl_header(repository_ctx, nccl_install_path):
"""Finds the NCCL header on the system.
@ -69,13 +78,13 @@ def _find_nccl_header(repository_ctx, nccl_install_path):
auto_configure_fail("Cannot find %s" % str(header_path))
return header_path
def _check_nccl_version(repository_ctx, nccl_install_path, nccl_hdr_path, nccl_version):
"""Checks whether the header file matches the specified version of NCCL.
Args:
repository_ctx: The repository context.
nccl_install_path: The NCCL library install directory.
nccl_hdr_path: The NCCL header path.
nccl_version: The expected NCCL version.
Returns:
@ -85,38 +94,31 @@ def _check_nccl_version(repository_ctx, nccl_install_path, nccl_hdr_path, nccl_v
if not header_path.exists:
header_path = _find_nccl_header(repository_ctx, nccl_install_path)
header_dir = str(header_path.realpath.dirname)
major_version = find_cuda_define(repository_ctx, header_dir, "nccl.h",
_DEFINE_NCCL_MAJOR)
minor_version = find_cuda_define(repository_ctx, header_dir, "nccl.h",
_DEFINE_NCCL_MINOR)
patch_version = find_cuda_define(repository_ctx, header_dir, "nccl.h",
_DEFINE_NCCL_PATCH)
major_version = find_cuda_define(
repository_ctx,
header_dir,
"nccl.h",
_DEFINE_NCCL_MAJOR,
)
minor_version = find_cuda_define(
repository_ctx,
header_dir,
"nccl.h",
_DEFINE_NCCL_MINOR,
)
patch_version = find_cuda_define(
repository_ctx,
header_dir,
"nccl.h",
_DEFINE_NCCL_PATCH,
)
header_version = "%s.%s.%s" % (major_version, minor_version, patch_version)
if not matches_version(nccl_version, header_version):
auto_configure_fail(
("NCCL library version detected from %s/nccl.h (%s) does not match " +
"TF_NCCL_VERSION (%s). To fix this rerun configure again.") %
(header_dir, header_version, nccl_version))
def _find_nccl_lib(repository_ctx, nccl_install_path, nccl_version):
"""Finds the given NCCL library on the system.
Args:
repository_ctx: The repository context.
nccl_install_path: The NCCL library installation directory.
nccl_version: The version of NCCL library files as returned
by _nccl_version.
Returns:
The path to the NCCL library.
"""
lib_path = repository_ctx.path("%s/lib/libnccl.so.%s" % (nccl_install_path,
nccl_version))
if not lib_path.exists:
auto_configure_fail("Cannot find NCCL library %s" % str(lib_path))
return lib_path
(header_dir, header_version, nccl_version),
)
def _nccl_configure_impl(repository_ctx):
"""Implementation of the nccl_configure repository rule."""
@ -127,36 +129,56 @@ def _nccl_configure_impl(repository_ctx):
if _TF_NCCL_CONFIG_REPO in repository_ctx.os.environ:
# Forward to the pre-configured remote repository.
repository_ctx.template("BUILD", _NCCL_REMOTE_BUILD_TEMPLATE, {
repository_ctx.template("BUILD", _label("remote.BUILD.tpl"), {
"%{target}": repository_ctx.os.environ[_TF_NCCL_CONFIG_REPO],
})
return
nccl_version = repository_ctx.os.environ[_TF_NCCL_VERSION].strip()
if matches_version("1", nccl_version):
# Alias to GitHub target from @nccl_archive.
if not matches_version(nccl_version, "1.3"):
auto_configure_fail(
"NCCL from GitHub must use version 1.3 (got %s)" % nccl_version)
if nccl_version == "":
# Alias to open source build from @nccl_archive.
repository_ctx.file("BUILD", _NCCL_ARCHIVE_BUILD_CONTENT)
# TODO(csigg): implement and reuse in cuda_configure.bzl.
gpu_architectures = [
"sm_" + capability.replace(".", "")
for capability in compute_capabilities(repository_ctx)
]
# Round-about way to make the list unique.
gpu_architectures = dict(zip(gpu_architectures, gpu_architectures)).keys()
repository_ctx.template("build_defs.bzl", _label("build_defs.bzl.tpl"), {
"%{gpu_architectures}": str(gpu_architectures),
})
repository_ctx.symlink(cuda_toolkit_path(repository_ctx), "cuda")
# Temporary work-around for setups which symlink ptxas to a newer
# version. The versions of nvlink and ptxas need to agree, so we find
# nvlink next to the real location of ptxas. This is only temporary and
# will be removed again soon.
nvlink_dir = repository_ctx.path("cuda/bin/ptxas").realpath.dirname
repository_ctx.symlink(nvlink_dir.get_child("nvlink"), "nvlink")
else:
# Create target for locally installed NCCL.
nccl_install_path = repository_ctx.os.environ[_NCCL_INSTALL_PATH].strip()
nccl_hdr_path = repository_ctx.os.environ[_NCCL_HDR_PATH].strip()
_check_nccl_version(repository_ctx, nccl_install_path, nccl_hdr_path, nccl_version)
repository_ctx.template("BUILD", _NCCL_LOCAL_BUILD_TEMPLATE, {
repository_ctx.template("BUILD", _label("system.BUILD.tpl"), {
"%{version}": nccl_version,
"%{install_path}": nccl_install_path,
"%{hdr_path}": nccl_hdr_path,
})
nccl_configure = repository_rule(
implementation=_nccl_configure_impl,
environ=[
_NCCL_INSTALL_PATH,
implementation = _nccl_configure_impl,
environ = [
_CUDA_TOOLKIT_PATH,
_NCCL_HDR_PATH,
_NCCL_INSTALL_PATH,
_TF_NCCL_VERSION,
_TF_CUDA_COMPUTE_CAPABILITIES,
_TF_NCCL_CONFIG_REPO,
],
)
"""Detects and configures the NCCL configuration.