def _cvt_to_def_str(obj):
# bool
if isinstance(obj, bool):
return str(int(obj))
# tensorflow type
if fw.has_tensorflow():
if isinstance(obj, fw.tensorflow.DType):
return {fw.tensorflow.int8: 'char',
fw.tensorflow.int16: 'short',
fw.tensorflow.int32: 'int',
fw.tensorflow.int64: 'long',
fw.tensorflow.float16: 'half',
fw.tensorflow.float32: 'float',
fw.tensorflow.float64: 'double'}[obj]
# torch type
elif fw.has_torch():
if isinstance(obj, fw.torch.dtype):
return {fw.torch.int8: 'char',
fw.torch.int16: 'short',
fw.torch.int32: 'int',
fw.torch.int64: 'long',
fw.torch.float16: 'half',
fw.torch.float32: 'float',
fw.torch.float64: 'double'}[obj]
else:
assert False
# default
return str(obj)
def _make_framework_src(src, out, tmp, grid):
if fw.has_tensorflow():
return libtriton.make_tensorflow_src(src, out, tmp, grid)
elif fw.has_torch:
return libtriton.make_torch_src(src, out, tmp, grid)
else:
assert False
def apply(cls, *args, **kwargs):
if fw.has_tensorflow():
return cls.apply_tensorflow(*args, **kwargs)
elif fw.has_torch():
return cls.apply_torch(*args, **kwargs)
else:
assert False
def shape(A):
if fw.has_tensorflow():
return A.shape.as_list()
elif fw.has_torch():
return A.shape
else:
assert False
def _get_key(key):
if fw.has_tensorflow():
if isinstance(key, fw.tensorflow.Tensor):
key = id(key.op)
if fw.has_torch():
if isinstance(key, fw.torch.Tensor):
key = id(key)
return key
def empty(shape, dtype):
if fw.has_tensorflow():
shape = [fw.tensorflow.constant(x) for x in shape]
shape = fw.tensorflow.stack(shape)
return tf_empty_proxy(shape, dtype)
#return fw.tf_extra_ops.alloc_empty(args, T = dtype)
elif fw.has_torch():
return fw.torch.empty(shape, dtype=dtype, device='cuda:0')
def _make_framework_op(src, outputs, tmp, options):
src, name = _make_framework_src(src, outputs, tmp, options)
cache_path = _make_cache_path(src)
cpp, so = _write_bindings(src, cache_path)
_build(cpp, cache_path)
if fw.has_tensorflow():
return fw.tensorflow.load_op_library(so).__dict__[name]
elif fw.has_torch():
fw.torch.ops.load_library(so)
return getattr(fw.torch.ops.triton, name)
else:
assert False
def _write_bindings(src, root):
if fw.has_tensorflow():
name = 'tensorflow'
elif fw.has_torch():
name = 'torch'
else:
assert False
cpp = os.path.join(root, '{name}.cpp'.format(name=name))
suffix = sysconfig.get_config_var('EXT_SUFFIX')
so = os.path.join(root, '{name}{suffix}'.format(name=name, suffix=suffix))
recompile = False
# recompile if .so does not exist
if not os.path.exists(cpp) or not os.path.exists(so):
recompile = True
# recompile if cpp was modified after .so
elif max(cpp, so, key=os.path.getctime) == cpp:
recompile = True
# write cpp file
if recompile:
with open(cpp, 'w+') as handle:
handle.writelines(src)
# return path of cpp file
return (cpp, so)
def _build(src, path):
ccdir = os.path.join(libtriton.__file__, os.path.pardir)
ccdir = os.path.realpath(ccdir)
# include directories
triton_include_dirs = [os.path.join(ccdir, 'include')]
include_dirs = triton_include_dirs
# library directories
triton_library_dirs = [ccdir]
library_dirs = triton_library_dirs
# libraries
libraries = ['triton']
# add framework
extra_compile_args = []
if fw.has_tensorflow():
library_dirs += [fw.tensorflow.sysconfig.get_lib()]
include_dirs += [fw.tensorflow.sysconfig.get_include()]
include_dirs += ['/usr/local/cuda/include/']
libraries += [fw.tensorflow.sysconfig.get_link_flags()[1].replace('-l', '')]
abi = fw.tensorflow.__cxx11_abi_flag__ if "__cxx11_abi_flag__" in fw.tensorflow.__dict__ else 0
extra_compile_args += ['-D_GLIBCXX_USE_CXX11_ABI={abi}'.format(abi=abi)]
name = 'tensorflow'
elif fw.has_torch():
prefix = os.path.dirname(fw.torch.__file__)
library_dirs += [os.path.join(prefix, 'lib')]
include_dirs += ['/usr/local/cuda/include/',
os.path.join(prefix, 'lib', 'include'),
os.path.join(prefix, 'lib', 'include', 'torch', 'csrc', 'api', 'include'),
os.path.join(prefix, 'include'),
os.path.join(prefix, 'include', 'torch', 'csrc', 'api', 'include')]
libraries += ['torch']
abi = fw.torch._C._GLIBCXX_USE_CXX11_ABI
extra_compile_args += ['-D_GLIBCXX_USE_CXX11_ABI={abi}'.format(abi=abi)]
name = 'torch'
else:
assert False
# extra arguments
extra_link_args = []
# dependences
depends = [os.path.realpath(libtriton.__file__)]
# create extension module
ext = setuptools.Extension(
name = name,
language = 'c++',
sources = [src],
include_dirs = include_dirs,
extra_compile_args = extra_compile_args + ['-g0'],
extra_link_args = extra_link_args,
library_dirs = library_dirs,
libraries = libraries,
depends = depends
)
# build extension module
args = ['build_ext']
tmp = tempfile.mkdtemp()
args.append('--build-temp=' + tmp)
args.append('--build-lib=' + path)
args.append('-q')
args = dict(
name = name,
ext_modules = [ext],
script_args = args,
)
with quiet():
setuptools.setup(**args)
shutil.rmtree(tmp)
def __call__(self, *args, **kwargs):
########################
# keyword arguments
########################
num_warps = kwargs['num_warps'] if 'num_warps' in kwargs else [2, 4, 8]
defines = kwargs['defines'] if 'defines' in kwargs else dict()
bench = kwargs['bench'] if 'bench' in kwargs else 0
if 'grid' not in kwargs:
raise RuntimeError('Must provide grid for kernel launch')
grid = kwargs['grid']
#########################
# cache
########################
# create a new framework op when defines are different
key = '-'.join(['{key}-{val}'.format(key=key, val=val) for key, val in defines.items()])
if key not in self.fw_id.keys():
# code generation options
macros = []
for k, v in defines.items():
cvt = lambda x: _cvt_to_def_str(x)
if(isinstance(v, list)):
values = list(map(cvt, v))
else:
values = [cvt(v)]
macros.append((k, values))
opt = libtriton.options_space()
opt.defines = macros
opt.num_warps = [2, 4, 8]
# create unique id for this op
op_id = libtriton.make_op_id()
self.fw_id[key] = op_id
# register function
libtriton.register_fn(op_id, self.src, opt)
for name, value in self.cst.items():
libtriton.register_cst(op_id, name, value)
if self.fw_op is None:
self.fw_op = _make_framework_op(self.src, self.outputs, self.tmp, opt)
########################
# initialize
########################
op_id = self.fw_id[key]
libtriton.register_grid(op_id, grid)
bench_id = libtriton.make_scalar_id() if bench > 0 else -1
#########################
# call framework function
#########################
if fw.has_tensorflow():
empty = [x for x in args if isinstance(x, triton.utils.tf_empty_proxy)]
if len(empty) != len(self.outputs):
raise ValueError('Number of empty arguments does not much number of outputs provided')
# operands
operands = [x.shape if isinstance(x, triton.utils.tf_empty_proxy) else x for x in args]
# output data types
kwargs = {'id': op_id, 'bench': bench, 'bench_id': bench_id}
for i, x in enumerate(args):
if isinstance(x, triton.utils.tf_empty_proxy):
kwargs['T' + str(i)] = x.dtype
# launch
ret = self.fw_op(*operands, **kwargs)
ret = [ret] if isinstance(ret, fw.tensorflow.Tensor) else ret
op_def = ret[0].op.op_def
# fill empty tensors with corresponding values
for j, y in enumerate(op_def.output_arg):
found = False
for i, x in enumerate(op_def.input_arg):
if y.name + '_shape' == x.name:
args[i].tensor = ret[j]
found = True
assert found
# store timing information
if bench > 0:
for y in ret:
bench_registry[y] = triton.utils.id_dict.lazy_entry(bench_id)
############################
# call torch function
############################
elif fw.has_torch():
args = [x if isinstance(x, fw.torch.Tensor) else x for x in args]
ret = self.fw_op(op_id, bench, bench_id, *args)
if bench > 0:
bench_registry[ret] = libtriton.retrieve_scalar(bench_id)
else:
assert False
