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- """
- Copyright 2020 The Microsoft DeepSpeed Team
- """
- import warnings
- from .builder import OpBuilder
- class SparseAttnBuilder(OpBuilder):
- BUILD_VAR = "DS_BUILD_SPARSE_ATTN"
- NAME = "sparse_attn"
- def __init__(self):
- super().__init__(name=self.NAME)
- def absolute_name(self):
- return f'deepspeed.ops.sparse_attention.{self.NAME}_op'
- def sources(self):
- return ['csrc/sparse_attention/utils.cpp']
- def cxx_args(self):
- return ['-O2', '-fopenmp']
- def is_compatible(self):
- # Check to see if llvm and cmake are installed since they are dependencies
- #required_commands = ['llvm-config|llvm-config-9', 'cmake']
- #command_status = list(map(self.command_exists, required_commands))
- #deps_compatible = all(command_status)
- try:
- import torch
- except ImportError:
- self.warning(f"unable to import torch, please install it first")
- return False
- # torch-cpu will not have a cuda version
- if torch.version.cuda is None:
- cuda_compatible = False
- self.warning(f"{self.NAME} cuda is not available from torch")
- else:
- major, minor = torch.version.cuda.split('.')[:2]
- cuda_compatible = (int(major) == 10
- and int(minor) >= 1) or (int(major) >= 11)
- if not cuda_compatible:
- self.warning(f"{self.NAME} requires CUDA version 10.1+")
- TORCH_MAJOR = int(torch.__version__.split('.')[0])
- TORCH_MINOR = int(torch.__version__.split('.')[1])
- torch_compatible = TORCH_MAJOR == 1 and TORCH_MINOR >= 5
- if not torch_compatible:
- self.warning(
- f'{self.NAME} requires a torch version >= 1.5 but detected {TORCH_MAJOR}.{TORCH_MINOR}'
- )
- return super().is_compatible() and torch_compatible and cuda_compatible
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