mirror of
https://github.com/deepseek-ai/DeepGEMM
synced 2025-05-05 23:34:22 +00:00
feat: drop support for CUDA<12.3
Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com>
This commit is contained in:
Zihua Wu
parent
46762b6903
commit
f6198492cb
@ -14,7 +14,7 @@ import cuda.bindings.nvrtc as nvrtc
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from torch.utils.cpp_extension import CUDA_HOME
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from . import interleave_ffma
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from .runtime import Runtime, Fp8GemmRuntime, RuntimeCache, get_symbol
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from .runtime import Runtime, Fp8GemmRuntime, RuntimeCache
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runtime_cache = RuntimeCache()
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@ -115,7 +115,7 @@ class Compiler(abc.ABC):
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@classmethod
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@abc.abstractmethod
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def compile(cls, name: str, code: str, target_path: str, kernel_name_pattern: str) -> str:
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def compile(cls, name: str, code: str, target_path: str) -> str:
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pass
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@staticmethod
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@ -132,10 +132,9 @@ class Compiler(abc.ABC):
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return [get_jit_include_dir()]
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@classmethod
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def build(cls, name: str, code: str, kernel_name_pattern: str, runtime_cls: Type[Runtime] = Fp8GemmRuntime) -> Runtime:
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def build(cls, name: str, code: str, runtime_cls: Type[Runtime] = Fp8GemmRuntime) -> Runtime:
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# Compiler flags
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flags = cls.flags()
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include_dirs = cls.include_dirs()
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# Build signature
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enable_sass_opt = get_nvcc_compiler()[1] <= '12.8' and int(
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@ -158,7 +157,7 @@ class Compiler(abc.ABC):
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tmp_cubin_path = os.path.join(make_tmp_dir(), f'nvcc.tmp.{str(uuid.uuid4())}.{hash_to_hex(cubin_path)}.cubin')
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start_time = time.time()
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kernel_name = cls.compile(name, code, tmp_cubin_path, kernel_name_pattern)
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cls.compile(name, code, tmp_cubin_path)
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end_time = time.time()
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elapsed_time = end_time - start_time
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if os.getenv('DG_JIT_DEBUG', None):
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@ -169,15 +168,11 @@ class Compiler(abc.ABC):
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if enable_sass_opt:
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interleave_ffma.process(tmp_cubin_path)
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# Store kernel name
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put(f'{tmp_cubin_path}.name', kernel_name)
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# Atomic replace files
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os.replace(tmp_cubin_path, cubin_path)
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os.replace(f'{tmp_cubin_path}.name', f'{cubin_path}.name')
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# Put cache and return
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runtime = runtime_cls(path, kernel_name)
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runtime = runtime_cls(path)
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runtime_cache[path] = runtime
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return runtime
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@ -202,7 +197,7 @@ class NvccCompiler(Compiler):
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f'--compiler-options={",".join(cxx_flags)}']
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@classmethod
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def compile(cls, name: str, code: str, target_path: str, kernel_name_pattern: str) -> str:
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def compile(cls, name: str, code: str, target_path: str):
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# Write the code
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path = os.path.join(get_cache_dir(), name)
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src_path = os.path.join(path, 'kernel.cu')
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@ -221,9 +216,6 @@ class NvccCompiler(Compiler):
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assert result.returncode == 0, f'Failed to compile {src_path}'
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# NVCC needs to get the symbol name from the cubin file using `cuobjdump`
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return get_symbol(target_path, kernel_name_pattern)
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class NvrtcCompiler(Compiler):
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@staticmethod
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@ -238,7 +230,7 @@ class NvrtcCompiler(Compiler):
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def include_dirs() -> List[str]:
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if CUDA_HOME is None:
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raise RuntimeError('CUDA_HOME is required for NVRTC compilation')
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return [get_jit_include_dir(), os.path.join(CUDA_HOME, 'include'), os.path.join(CUDA_HOME, 'targets', 'x86_64-linux', 'include')]
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return [get_jit_include_dir(), os.path.join(CUDA_HOME, 'include')]
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@classmethod
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def flags(cls) -> List[str]:
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@ -251,20 +243,12 @@ class NvrtcCompiler(Compiler):
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return base_flags
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@classmethod
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def compile(cls, name: str, code: str, target_path: str, kernel_name_pattern: str) -> str:
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def compile(cls, name: str, code: str, target_path: str) -> str:
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code_bytes = bytes(code, 'utf-8')
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res, program = nvrtc.nvrtcCreateProgram(
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code_bytes, bytes(name, 'utf-8'), 0, [], [])
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to create program: {res}")
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kernel_regex = re.compile(kernel_name_pattern, re.MULTILINE)
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kernel_name = kernel_regex.search(code).group(
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0).replace('\n', '').replace(' ', '')
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res = nvrtc.nvrtcAddNameExpression(
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program, bytes(kernel_name, 'utf-8'))[0]
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to add name expression: {res}")
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raise Exception(f'Failed to create program: {res}')
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options = [bytes(flag, 'utf-8') for flag in cls.flags()]
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compile_res = nvrtc.nvrtcCompileProgram(
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@ -273,43 +257,35 @@ class NvrtcCompiler(Compiler):
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if os.getenv('DG_JIT_DEBUG', None):
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res, log_size = nvrtc.nvrtcGetProgramLogSize(program)
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to get program log size: {res}")
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raise Exception(f'Failed to get program log size: {res}')
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log_bytes = bytes(log_size)
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res = nvrtc.nvrtcGetProgramLog(program, log_bytes)[0]
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to get program log: {res}")
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raise Exception(f'Failed to get program log: {res}')
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log_str = log_bytes.decode('utf-8')
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print(log_str)
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if compile_res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to compile program: {compile_res}")
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# NVRTC can directly get the lowered name
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res, lowered_name = nvrtc.nvrtcGetLoweredName(
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program, bytes(kernel_name, 'utf-8'))
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to get lowered name: {res}")
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raise Exception(f'Failed to compile program: {compile_res}')
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res, cubin_size = nvrtc.nvrtcGetCUBINSize(program)
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to get CUBIN size: {res}")
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raise Exception(f'Failed to get CUBIN size: {res}')
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cubin_bytes = bytes(cubin_size)
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res = nvrtc.nvrtcGetCUBIN(program, cubin_bytes)[0]
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to get CUBIN: {res}")
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raise Exception(f'Failed to get CUBIN: {res}')
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put(target_path, cubin_bytes)
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res = nvrtc.nvrtcDestroyProgram(program)[0]
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if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
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raise Exception(f"Failed to destroy program: {res}")
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return lowered_name.decode('utf-8')
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raise Exception(f'Failed to destroy program: {res}')
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def build(name: str, code: str) -> Runtime:
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def build(name: str, code: str, runtime_cls: Type[Runtime] = Fp8GemmRuntime) -> Runtime:
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if os.getenv('DG_JIT_USE_NVRTC', '0') in ['1', 'true', 'True']:
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return NvrtcCompiler.build(name, code, kernel_name_pattern=r'fp8_gemm_kernel<[\S\s]*?>')
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return NvrtcCompiler.build(name, code, runtime_cls=runtime_cls)
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else:
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return NvccCompiler.build(name, code, kernel_name_pattern='fp8_gemm_kernel')
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return NvccCompiler.build(name, code, runtime_cls=runtime_cls)
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@ -1,31 +1,12 @@
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import os
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import platform
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import time
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import subprocess
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from typing import Any, Callable, Dict, List, Optional, Type
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import cuda.bindings.driver as cuda
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from torch.utils.cpp_extension import CUDA_HOME
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from .utils import run_gemm
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def get_symbol(file_path: str, pattern: str) -> Optional[str]:
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if CUDA_HOME is None:
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raise Exception("CUDA_HOME is not set")
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cuobjdump_bin = 'cuobjdump.exe' if platform.system() == 'Windows' else 'cuobjdump'
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command = [os.path.join(CUDA_HOME, 'bin', cuobjdump_bin),
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'-symbols', file_path]
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result = subprocess.run(command, stdout=subprocess.PIPE,
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stderr=subprocess.PIPE, text=True)
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assert result.returncode == 0
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for line in result.stdout.splitlines():
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if pattern in line:
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return line.split()[-1]
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return None
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class Runtime:
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def __init__(self, path: str, kernel_name: str, caller: Callable[..., cuda.CUresult], args: List[str]) -> None:
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self.path = path
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@ -43,7 +24,7 @@ class Runtime:
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return False
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# Contains all necessary files
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files = ['kernel.cubin', 'kernel.cubin.name']
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files = ['kernel.cubin']
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return all(os.path.exists(os.path.join(path, file)) for file in files)
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def __call__(self, **kwargs: Dict[str, Any]) -> cuda.CUresult:
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@ -53,18 +34,28 @@ class Runtime:
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res, lib = cuda.cuLibraryLoadFromFile(
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bytes(os.path.join(self.path, 'kernel.cubin'), 'utf-8'), [], [], 0, [], [], 0)
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f"Failed to load library: {res}")
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raise Exception(f'Failed to load library: {res}')
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res, kernel = cuda.cuLibraryGetKernel(
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lib, bytes(self.kernel_name, encoding='utf-8'))
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res, kernel_count = cuda.cuLibraryGetKernelCount(lib)
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f"Failed to get kernel: {res}")
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raise Exception(f'Failed to get kernel count: {res}')
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res, kernels = cuda.cuLibraryEnumerateKernels(kernel_count, lib)
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f'Failed to enumerate kernels: {res}')
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for kernel in kernels:
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res, kernel_name = cuda.cuKernelGetName(kernel)
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f'Failed to get kernel name: {res}')
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if bytes(self.kernel_name, encoding='utf-8') in kernel_name:
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self.kernel = kernel
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break
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if self.kernel is not None:
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self.lib = lib
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else:
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raise Exception("Failed to find kernel")
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raise Exception('Failed to find required kernel')
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end_time = time.time_ns()
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elapsed_time = (end_time - start_time) / 1000
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@ -81,12 +72,12 @@ class Runtime:
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if self.lib is not None:
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res = cuda.cuLibraryUnload(self.lib)[0]
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f"Failed to unload library {self.path}: {res}")
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raise Exception(f'Failed to unload library {self.path}: {res}')
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class Fp8GemmRuntime(Runtime):
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def __init__(self, path: str, kernel_name: str) -> None:
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super().__init__(path, kernel_name, run_gemm, [
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def __init__(self, path: str) -> None:
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super().__init__(path, 'fp8_gemm', run_gemm, [
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'NUM_TMA_MULTICAST',
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'M',
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'BLOCK_M',
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@ -117,9 +108,7 @@ class RuntimeCache:
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# Already compiled
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if os.path.exists(path) and Runtime.is_path_valid(path):
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kernel_name = open(os.path.join(
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path, 'kernel.cubin.name'), 'r').read()
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runtime = runtime_cls(path, kernel_name)
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runtime = runtime_cls(path)
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self.cache[path] = runtime
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return runtime
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return None
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@ -24,7 +24,6 @@ def generate(**kwargs: Dict[str, Any]) -> str:
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using namespace deep_gemm;
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#ifndef NVRTC_JIT_COMPILATION
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__global__ void dummy_kernel() {{
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void *ptr = (void *)&fp8_gemm_kernel<
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{kwargs['N']},
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@ -43,7 +42,6 @@ __global__ void dummy_kernel() {{
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GemmType::{kwargs['GEMM_TYPE']}
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>;
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}}
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#endif
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'''
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# Debug print
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@ -53,7 +53,7 @@ def get_num_math_warpgroups(block_m: int) -> int:
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return 1 if block_m == 64 else 2
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def get_num_threads_per_sm(num_tma_threads: int, num_math_threads_per_group: int, block_m: int) -> int:
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assert num_math_threads_per_group == 128, "Only support 128 threads per math group"
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assert num_math_threads_per_group == 128, 'Only support 128 threads per math group'
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return get_num_math_warpgroups(block_m) * num_math_threads_per_group + num_tma_threads
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@ -74,7 +74,7 @@ def make_2d_tma_copy_desc(global_address: torch.Tensor, gmem_dim: Tuple[cuda.cuu
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)
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f"Failed to encode tensor map: {res}")
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raise Exception(f'Failed to encode tensor map: {res}')
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return tensor_map
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@ -118,7 +118,7 @@ def run_gemm(kernel: cuda.CUkernel, num_tma_multicast: int, shape_m: int, block_
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res = cuda.cuKernelSetAttribute(cuda.CUfunction_attribute.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, smem_size, kernel, cuda.CUdevice(gmem_d.device.index))[0]
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if res != cuda.CUresult.CUDA_SUCCESS:
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raise Exception(f"Failed to set max dynamic shared memory size: {res}")
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raise Exception(f'Failed to set max dynamic shared memory size: {res}')
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attr_val = cuda.CUlaunchAttributeValue()
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attr_val.clusterDim.x = num_tma_multicast
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@ -62,17 +62,15 @@ __global__ void vector_add(T* a, T* b, T* c, uint32_t N) {{
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}}
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}}
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#ifndef NVRTC_JIT_COMPILATION
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__global__ void dummy_kernel() {{
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void *ptr = (void *)&vector_add<{kwargs['T']}>;
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}}
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#endif
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"""
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class VectorAddRuntime(jit.Runtime):
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def __init__(self, path: str, kernel_name: str) -> None:
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super().__init__(path, kernel_name, run_vector_add, [
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def __init__(self, path: str) -> None:
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super().__init__(path, 'vector_add', run_vector_add, [
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'A',
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'B',
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'C',
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@ -87,7 +85,7 @@ if __name__ == '__main__':
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code = generate_vector_add(T='float')
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print(code)
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print('Building ...')
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func = jit.NvccCompiler.build('test_func', code, 'vector_add', VectorAddRuntime)
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func = jit.NvccCompiler.build('test_func', code, VectorAddRuntime)
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a = torch.randn((1024, ), dtype=torch.float32, device='cuda')
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b = torch.randn((1024, ), dtype=torch.float32, device='cuda')
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@ -105,7 +103,7 @@ if __name__ == '__main__':
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code = generate_vector_add(T='__nv_bfloat16')
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print(code)
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print('Building ...')
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func = jit.NvrtcCompiler.build('test_func', code, r'vector_add<[\S\s]*?>', VectorAddRuntime)
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func = jit.NvrtcCompiler.build('test_func', code, VectorAddRuntime)
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a = torch.randn((1024, ), dtype=torch.bfloat16, device='cuda')
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b = torch.randn((1024, ), dtype=torch.bfloat16, device='cuda')
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