mirror of
https://github.com/deepseek-ai/DeepGEMM
synced 2025-05-05 13:54:22 +00:00
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
import os
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import time
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import subprocess
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from typing import Any, Dict, Optional
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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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command = [os.path.join(CUDA_HOME, 'bin', 'cuobjdump'), '-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) -> None:
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self.path = path
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self.lib = None
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self.kernel = None
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self.kernel_name = kernel_name
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assert self.is_path_valid(self.path)
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@staticmethod
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def is_path_valid(path: str) -> bool:
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# Exists and is a directory
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if not os.path.exists(path) or not os.path.isdir(path):
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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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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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# Load CUBIN
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if self.kernel is None:
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start_time = time.time_ns()
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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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res, kernel = cuda.cuLibraryGetKernel(
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lib, bytes(self.kernel_name, encoding='utf-8'))
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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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self.kernel = kernel
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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 fp8 gemm 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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if os.getenv('DG_JIT_DEBUG', None):
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print(
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f'Loading JIT runtime {self.path} took {elapsed_time:.2f} us.')
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return run_gemm(
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self.kernel,
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kwargs['NUM_TMA_MULTICAST'],
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kwargs['M'],
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kwargs['BLOCK_M'],
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kwargs['GMEM_D'],
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kwargs['SCALES_B'],
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kwargs['GROUPED_LAYOUT'],
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kwargs['NUM_SMS'],
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kwargs['SMEM_SIZE'],
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kwargs['TENSOR_MAP_A'],
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kwargs['TENSOR_MAP_B'],
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kwargs['TENSOR_MAP_SCALES_A'],
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kwargs['TENSOR_MAP_D'],
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kwargs['STREAM'],
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)
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def __del__(self) -> None:
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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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class RuntimeCache:
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def __init__(self) -> None:
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self.cache = {}
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def __getitem__(self, path: str) -> Optional[Runtime]:
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# In Python runtime
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if path in self.cache:
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return self.cache[path]
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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(path, 'kernel.cubin.name'), 'r').read()
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runtime = Runtime(path, kernel_name)
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self.cache[path] = runtime
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return runtime
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return None
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def __setitem__(self, path, runtime) -> None:
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self.cache[path] = runtime
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