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ColossalAI/extensions/flash_attention/flash_attention_npu.py

63 lines
1.9 KiB

from ..base_extension import _Extension
class FlashAttentionNpuExtension(_Extension):
def __init__(self):
super().__init__(name="flash_attention_npu", support_aot=False, support_jit=False)
def is_available(self) -> bool:
try:
import torch_npu
return hasattr(torch_npu, "npu_fusion_attention")
except:
return False
def assert_compatible(self) -> bool:
pass
def build_aot(self) -> None:
raise NotImplementedError(
"Flash Attention NPU does not require ahead-of-time compilation. Please use it by installing torch_npu."
)
def build_jit(self) -> None:
raise NotImplementedError(
"Flash Attention NPU does not require just-in-time compilation. Please use it by installing torch_npu."
)
def load(self):
from typing import Optional
import torch
import torch_npu
def flash_attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
dropout_p: float = 0.0,
scale: Optional[float] = None,
attention_mask: Optional[torch.Tensor] = None,
is_causal: bool = False,
cu_seqlens_q: Optional[torch.Tensor] = None,
cu_seqlens_kv: Optional[torch.Tensor] = None,
max_seqlen_q: Optional[int] = None,
max_seqlen_kv: Optional[int] = None,
q_indices: Optional[torch.Tensor] = None,
kv_indices: Optional[torch.Tensor] = None,
):
num_heads = q.size(1)
return torch_npu.npu_fusion_attention(
q,
k,
v,
num_heads,
"BNSD",
atten_mask=attention_mask.bool(),
scale=scale,
keep_prob=1 - dropout_p,
)[0]
return flash_attention