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init v0.11.0rc0

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luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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51
vllm_ascend/attention/attention_mask.py
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@@ -39,11 +39,22 @@ class AttentionMaskBuilder:
self,
max_seq_len: int,
dtype: torch.dtype,
device: torch.device = None,
):
# NOTE: The device argument specifies the target NPU
# to be used for the newly added FIA operator.
# Only pass this parameter when using the new FIA operator.
attn_mask = _generate_attn_mask(max_seq_len, dtype)
self._seq_len_cached = attn_mask.shape[0]
self.attn_mask_cache = attn_mask
self.device = device
if torch.version.cann.startswith("8.3"):
assigned_mask_dim = 2048
self.chunked_prefill_attn_mask = torch.triu(
torch.ones(assigned_mask_dim, assigned_mask_dim),
diagonal=1).to(torch.int8).to(device)
@staticmethod
def get_mask_scale_factor(dtype: torch.dtype = torch.float16):
@@ -62,28 +73,32 @@ class AttentionMaskBuilder:
device: torch.device):
self._update_attn_cache(max_seq_len, dtype)
return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous(
).to(device)
).to(device, non_blocking=True)
def get_splitfuse_attn_mask(
self,
seq_lens: torch.Tensor,
position: torch.Tensor,
dtype: torch.dtype,
device: torch.device,
seq_lens: torch.Tensor = None,
position: torch.Tensor = None,
dtype: torch.dtype = None,
device: torch.device = None,
) -> torch.Tensor:
if dtype not in [torch.float16, torch.bfloat16]:
raise ValueError(
"splitfuse_attn_mask now only supports bf16 and fp16")
max_seq_len = max(seq_lens, default=0)
self._update_attn_cache(max_seq_len, dtype)
# FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
# is not the same. Fix this in the future when kernel is ready.
mask_scale_factor = AttentionMaskBuilder.get_mask_scale_factor(dtype)
attn_mask = torch.index_select(self.attn_mask_cache,
dim=0,
index=position)[:, :max_seq_len]
attn_mask *= mask_scale_factor
return attn_mask.contiguous().to(device, non_blocking=True)
if torch.version.cann.startswith("8.3"):
return self.chunked_prefill_attn_mask
else:
if dtype not in [torch.float16, torch.bfloat16]:
raise ValueError(
"splitfuse_attn_mask now only supports bf16 and fp16")
max_seq_len = max(seq_lens, default=0)
self._update_attn_cache(max_seq_len, dtype)
# FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
# is not the same. Fix this in the future when kernel is ready.
mask_scale_factor = AttentionMaskBuilder.get_mask_scale_factor(
dtype)
attn_mask = torch.index_select(self.attn_mask_cache,
dim=0,
index=position)[:, :max_seq_len]
attn_mask *= mask_scale_factor
return attn_mask.contiguous().to(device, non_blocking=True)
def _update_attn_cache(self, seqlen: int, dtype: torch.dtype):
if seqlen > self._seq_len_cached: