mirror of https://github.com/hpcaitech/ColossalAI
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import warnings
|
|
from typing import Optional
|
|
|
|
import torch
|
|
|
|
|
|
def is_ampere_or_better_gpu():
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda")
|
|
properties = torch.cuda.get_device_properties(device)
|
|
if properties.major >= 8: # Ampere GPUs or newer
|
|
return True
|
|
return False
|
|
|
|
|
|
# "Check Ampere GPUs or newer"
|
|
HAS_FLASH_ATTN = False
|
|
if is_ampere_or_better_gpu():
|
|
HAS_FLASH_ATTN = True
|
|
else:
|
|
warnings.warn('FlashAttention only supports Ampere GPUs or newer.')
|
|
HAS_FLASH_ATTN = False
|
|
try:
|
|
from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
|
|
HAS_FLASH_ATTN = True
|
|
except ImportError:
|
|
warnings.warn('please install flash_attn from https://github.com/HazyResearch/flash-attention')
|
|
HAS_FLASH_ATTN = False
|
|
|
|
if HAS_FLASH_ATTN:
|
|
from einops import rearrange
|
|
|
|
from .utils import SeqLenInfo
|
|
|
|
def flash_attention(q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
seq_len_info_q: SeqLenInfo,
|
|
seq_len_info_kv: SeqLenInfo,
|
|
bias: Optional[torch.Tensor] = None,
|
|
dropout_p: float = 0.,
|
|
scale: float = None,
|
|
causal: bool = False,
|
|
padded: bool = False):
|
|
"""
|
|
Arguments:
|
|
q: (batch, q_seqlen, nheads, headdim)
|
|
k: (batch, kv_seqlen, nheads, headdim)
|
|
v: (batch, kv_seqlen, nheads, headdim)
|
|
batch_size: int.
|
|
seq_len: int.
|
|
dropout_p: float. Dropout probability.
|
|
sm_scale: float. The scaling of QK^T before applying softmax.
|
|
Default to 1 / sqrt(headdim).
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
|
|
Return:
|
|
attn_out: (batch, q_seqlen, nheads, headdim).
|
|
"""
|
|
if padded:
|
|
if seq_len_info_kv == None:
|
|
seq_len_info_kv = seq_len_info_q
|
|
|
|
attn_out = flash_attn_varlen_func(q, k, v, seq_len_info_q.cu_seqlens, seq_len_info_kv.cu_seqlens,
|
|
seq_len_info_q.max_seqlen, seq_len_info_kv.max_seqlen, dropout_p, scale,
|
|
causal)
|
|
else:
|
|
attn_out = flash_attn_func(q, k, v, dropout_p=dropout_p, softmax_scale=scale, causal=causal)
|
|
return attn_out
|