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[Misc] Revert FA on ViT #12355 and #12435 #12445

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Merged
merged 2 commits into from
Jan 26, 2025
Merged

[Misc] Revert FA on ViT #12355 and #12435 #12445

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00f5c7a
revert
ywang96 Jan 26, 2025
8ea20ad
revert
ywang96 Jan 26, 2025
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41 changes: 4 additions & 37 deletions vllm/attention/layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,22 +210,19 @@ def __init__(
self.scale = scale
self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads

assert self.num_heads % self.num_kv_heads == 0
self.num_queries_per_kv = self.num_heads // self.num_kv_heads

dtype = torch.get_default_dtype()
attn_backend = get_attn_backend(head_size,
dtype,
kv_cache_dtype=None,
block_size=16,
is_attention_free=False)
backend = backend_name_to_enum(attn_backend.get_name())
if backend in {_Backend.FLASH_ATTN, _Backend.FLASH_ATTN_VLLM_V1}:
backend = _Backend.XFORMERS

self.attn_backend = backend if backend in {
_Backend.TORCH_SDPA,
_Backend.XFORMERS,
_Backend.FLASH_ATTN,
_Backend.FLASH_ATTN_VLLM_V1,
} else _Backend.TORCH_SDPA

def forward(
Expand All @@ -235,45 +232,15 @@ def forward(
value: torch.Tensor,
) -> torch.Tensor:
"""Input shape: batch_size x seq_len x hidden_size"""
# TODO(Isotr0py): Use existing backend implementations and support FA3
bsz, q_len, _ = query.size()
kv_len = key.size(1)

query = query.view(bsz, q_len, self.num_heads, self.head_size)
key = key.view(bsz, kv_len, self.num_kv_heads, self.head_size)
value = value.view(bsz, kv_len, self.num_kv_heads, self.head_size)

if (num_repeat := self.num_queries_per_kv) > 1:
# Handle MQA and GQA
key = torch.repeat_interleave(key, num_repeat, dim=2)
value = torch.repeat_interleave(value, num_repeat, dim=2)

if self.attn_backend in {
_Backend.FLASH_ATTN,
_Backend.FLASH_ATTN_VLLM_V1,
}:
from vllm.vllm_flash_attn import flash_attn_varlen_func

cu_seqlens_q = torch.arange(0, (bsz + 1) * q_len,
step=q_len,
dtype=torch.int32,
device=query.device)
cu_seqlens_k = torch.arange(0, (bsz + 1) * kv_len,
step=kv_len,
dtype=torch.int32,
device=key.device)

out = flash_attn_varlen_func(
query.flatten(0, 1),
key.flatten(0, 1),
value.flatten(0, 1),
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=q_len,
max_seqlen_k=kv_len,
softmax_scale=self.scale,
)
out = out.reshape(bsz, q_len, -1)
elif self.attn_backend == _Backend.XFORMERS:
if self.attn_backend == _Backend.XFORMERS:
from xformers import ops as xops

out = xops.memory_efficient_attention_forward(query,
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