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# mypy: allow-untyped-defs
import functools
import logging
import os
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any, List, Optional
import sympy
import torch
from ... import config
from ...ir import Layout
from ...runtime.runtime_utils import cache_dir
from ...virtualized import V
from .cuda_env import get_cuda_arch, get_cuda_version
log = logging.getLogger(__name__)
def _rename_cutlass_import(content: str, cutlass_modules: List[str]) -> str:
for cutlass_module in cutlass_modules:
content = content.replace(
f"from {cutlass_module} import ",
f"from cutlass_library.{cutlass_module} import ",
)
return content
def _gen_cutlass_file(
file_name: str, cutlass_modules: List[str], src_dir: str, dst_dir: str
) -> None:
orig_full_path = os.path.abspath(os.path.join(src_dir, file_name))
text = ""
with open(orig_full_path) as f:
text = f.read()
text = _rename_cutlass_import(text, cutlass_modules)
dst_full_path = os.path.abspath(
os.path.join(
dst_dir,
file_name,
)
)
with open(dst_full_path, "w") as f:
f.write(text)
@functools.lru_cache(None)
def try_import_cutlass() -> bool:
if config.is_fbcode():
return True
# Copy CUTLASS python scripts to a temp dir and add the temp dir to Python search path.
# This is a temporary hack to avoid CUTLASS module naming conflicts.
# TODO(ipiszy): remove this hack when CUTLASS solves Python scripts packaging structure issues.
cutlass_py_full_path = os.path.abspath(
os.path.join(config.cuda.cutlass_dir, "python/cutlass_library")
)
tmp_cutlass_py_full_path = os.path.abspath(
os.path.join(cache_dir(), "torch_cutlass_library")
)
dst_link = os.path.join(tmp_cutlass_py_full_path, "cutlass_library")
if os.path.isdir(cutlass_py_full_path):
if tmp_cutlass_py_full_path not in sys.path:
if os.path.exists(dst_link):
assert os.path.islink(
dst_link
), f"{dst_link} is not a symlink. Try to remove {dst_link} manually and try again."
assert os.path.realpath(os.readlink(dst_link)) == os.path.realpath(
cutlass_py_full_path
), f"Symlink at {dst_link} does not point to {cutlass_py_full_path}"
else:
os.makedirs(tmp_cutlass_py_full_path, exist_ok=True)
os.symlink(cutlass_py_full_path, dst_link)
sys.path.append(tmp_cutlass_py_full_path)
try:
import cutlass_library.generator # noqa: F401
import cutlass_library.library # noqa: F401
import cutlass_library.manifest # noqa: F401
return True
except ImportError as e:
log.debug(
"Failed to import CUTLASS packages: %s, ignoring the CUTLASS backend.",
str(e),
)
else:
log.debug(
"Failed to import CUTLASS packages: CUTLASS repo does not exist: %s",
cutlass_py_full_path,
)
return False
def _normalize_cuda_arch(arch: str) -> str:
if int(arch) >= 90:
return "90"
elif int(arch) >= 80:
return "80"
elif int(arch) >= 75:
return "75"
elif int(arch) >= 70:
return "70"
else:
raise NotImplementedError(f"Unsupported cuda arch: {arch}")
@dataclass
class CUTLASSArgs:
"""
CUTLASS args used to initialize a CUTLASS Manifest.
"""
architectures: Optional[str] = None
cuda_version: Optional[str] = None
operations = "all"
build_dir = ""
curr_build_dir = ""
generator_target = ""
kernels = "all"
ignore_kernels = ""
# TODO: these three look dead?
kernel_filter_file: None = None
selected_kernel_list: None = None
interface_dir: None = None
filter_by_cc = True
disable_full_archs_compilation = False
def __post_init__(self):
if self.architectures is None or self.cuda_version is None:
raise RuntimeError(
f"{self.architectures=} or {self.cuda_version=} is None!"
)
self.architectures = _normalize_cuda_arch(self.architectures)
@functools.lru_cache(None)
def _gen_ops_cached(arch, version) -> List[Any]:
# Note: Cache needs to be specific for cuda architecture and version
# Import cutlass python scripts.
assert try_import_cutlass()
import cutlass_library.generator as cutlass_generator
import cutlass_library.manifest as cutlass_manifest
if arch is None or version is None:
log.error(
"Cannot detect cuda arch %s or cuda version %s. "
"Will discard all cutlass ops. "
"Please consider setting _inductor.cuda.arch and _inductor.cuda.version configs.",
arch,
version,
)
return []
arch = _normalize_cuda_arch(arch)
args = CUTLASSArgs(architectures=arch, cuda_version=version)
manifest = cutlass_manifest.Manifest(args)
if arch == "90":
cutlass_generator.GenerateSM90(manifest, args.cuda_version)
cutlass_generator.GenerateSM80(manifest, args.cuda_version)
else:
try:
func = getattr(cutlass_generator, "GenerateSM" + arch)
func(manifest, args.cuda_version)
except AttributeError as e:
raise NotImplementedError(
"Arch " + arch + " is not supported by current cutlass lib."
) from e
return manifest.operations
def gen_ops() -> List[Any]:
"""
Generates all supported CUTLASS operations.
"""
arch = get_cuda_arch()
version = get_cuda_version()
return _gen_ops_cached(arch, version)
def torch_dtype_to_cutlass_type(
torch_dtype: torch.dtype,
) -> "cutlass_library.library.DataType": # type: ignore[name-defined] # noqa: F821
# Import cutlass python scripts.
assert try_import_cutlass()
import cutlass_library # type: ignore[import]
if torch_dtype == torch.float:
return cutlass_library.library.DataType.f32
elif torch_dtype == torch.half:
return cutlass_library.library.DataType.f16
elif torch_dtype == torch.bfloat16:
return cutlass_library.library.DataType.bf16
else:
raise NotImplementedError(f"Unsupported data type: {torch_dtype=}")
def dtype_match(
torch_dtype: Optional[torch.dtype],
cutlass_dtype: "cutlass_library.library.DataType", # type: ignore[name-defined] # noqa: F821
) -> bool:
# Import cutlass python scripts.
assert try_import_cutlass()
import cutlass_library
if torch_dtype == torch.float:
return (
cutlass_dtype == cutlass_library.library.DataType.f32
or cutlass_dtype == cutlass_library.library.DataType.tf32
)
elif torch_dtype == torch.half:
return cutlass_dtype == cutlass_library.library.DataType.f16
elif torch_dtype == torch.bfloat16:
return cutlass_dtype == cutlass_library.library.DataType.bf16
elif torch_dtype == torch.int8:
return cutlass_dtype == cutlass_library.library.DataType.s8
elif torch_dtype == torch.uint8:
return cutlass_dtype == cutlass_library.library.DataType.u8
elif torch_dtype == torch.int32:
return cutlass_dtype == cutlass_library.library.DataType.s32
else:
return False
def get_accumulator_dtype(
input_torch_dtypes: List[torch.dtype],
) -> Optional[torch.dtype]:
"""
Given a pair of input torch dtypes, returns the inferred accumulator torch dtype.
"""
if len(input_torch_dtypes) != 2:
return None
torch_dtype = None
if input_torch_dtypes[0] == input_torch_dtypes[1]:
torch_dtype = input_torch_dtypes[0]
else:
size0 = torch.tensor([], dtype=input_torch_dtypes[0]).element_size()
size1 = torch.tensor([], dtype=input_torch_dtypes[1]).element_size()
if size0 > size1:
dtype0, dtype1 = input_torch_dtypes
else:
dtype1, dtype0 = input_torch_dtypes
if dtype0 in [torch.half, torch.bfloat16] and dtype1 in [
torch.int8,
torch.uint8,
]:
torch_dtype = dtype0
if torch_dtype == torch.half:
if torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction:
return torch_dtype
else:
return torch.float
if torch_dtype in {torch.bfloat16, torch.float}:
return torch.float
if torch_dtype == torch.int8:
return torch.int32
raise NotImplementedError(f"Unsupported data types: {input_torch_dtypes=}")
def get_alignments(torch_dtype: torch.dtype) -> List[int]:
"""
Returns all possible valid CUTLASS alignments in terms of the number of elements for a given dtype.
CUTLASS gemm / conv SM80 APIs support 16 bytes max alignment, and 2 bytes min alignment.
"""
if torch_dtype in (torch.half, torch.bfloat16):
return [8, 4, 2, 1]
elif torch_dtype == torch.float:
return [4, 2, 1]
elif torch_dtype in (torch.uint8, torch.int8):
return [16, 8, 4, 2]
elif torch_dtype == torch.int32:
return [4, 2, 1]
else:
raise NotImplementedError(f"unsupported {torch_dtype=} for alignments")
def get_max_alignment(inductor_layout: Layout) -> int:
"""
Returns the max alignment (in terms of number of elements) for a given Inductor Layout.
"""
dtype = inductor_layout.dtype
size = inductor_layout.size
offset = inductor_layout.offset
def is_static_int(number):
return isinstance(number, (int, sympy.Integer))
def a_factor_of(x, alignment):
if is_static_int(x) and is_static_int(alignment):
return x % alignment == 0
rem = sympy.Mod(x, alignment)
return V.graph.sizevars.evaluate_expr(sympy.Eq(rem, 0))
try:
contiguous_dim = inductor_layout.stride.index(1)
except ValueError:
# No dim with stride 1 found, return 1
return 1
alignments = get_alignments(dtype)
for alignment in alignments:
if not a_factor_of(size[contiguous_dim], alignment) or not a_factor_of(
offset, alignment
):
continue
if all(
(dim == contiguous_dim)
or a_factor_of(inductor_layout.stride[dim], alignment)
for dim in range(len(size))
):
return alignment
return 1
class CUDACompileSourceCapturingContext:
# Helper class for Benchmarking and Testing CUTLASS Kernels in isolation.
# Can be used to capture the sourcecode passed to CUDACodeCache.compile
def __init__(self):
self.sources = []
self._compile_patch = None
def __enter__(self, *args, **kwargs):
import unittest.mock as mock
import torch._inductor.codecache
_compile_method_orig = torch._inductor.codecache.CUDACodeCache.compile
def my_compile(source_code, dst_file_ext):
self.sources.append(source_code)
return _compile_method_orig(source_code, dst_file_ext)
self._compile_patch = mock.patch(
"torch._inductor.codecache.CUDACodeCache.compile", my_compile
)
return self._compile_patch.__enter__(*args, **kwargs) # type: ignore[union-attr]
def __exit__(self, *args, **kwargs):
return self._compile_patch.__exit__(*args, **kwargs) # type: ignore[union-attr]
def cuda_standalone_runner_compile_command(srcpath: Path, exepath: Path):
# returns command string to compile a (captured) CUDA GEMM Kernel source to a standalone executable that's ready to run
# Passes the correct preprocessor define to nvcc to ensure the standalone runner is enabled.
from torch._inductor.codecache import cuda_compile_command
extra_args = ["-DGENERATE_STANDALONE_RUNNER=1", "-DCUTLASS_DEBUG_TRACE_LEVEL=1"]
compile_command = cuda_compile_command(
[str(srcpath)], str(exepath), "exe", extra_args=extra_args
)
return compile_command
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