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[PERF] Qwen3-next MTP speedup (change bool mask indexing to index_select / index_copy to reduce d2h) (#26437)

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Signed-off-by: Vadim Gimpelson <vadim.gimpelson@gmail.com>
This commit is contained in:
Vadim Gimpelson 2025-10-16 08:18:31 +04:00 committed by GitHub
parent f6cdc9a02f
commit 785d8b6410
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GPG Key ID: B5690EEEBB952194
3 changed files with 56 additions and 36 deletions
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2
vllm/model_executor/layers/fla/ops/utils.py
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@ -45,7 +45,7 @@ def tensor_cache(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]
"""
cache_entries: tuple[tuple | None, dict | None, Any] = []
cache_size = 4
cache_size = 8
@functools.wraps(fn)
def wrapper(*args: Any, **kwargs: Any) -> Any:

24
vllm/model_executor/models/qwen3_next.py
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@ -423,7 +423,7 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
(query, key),
)
value = rearrange(value, "l (h d) -> 1 l h d", d=self.head_v_dim)
return query, key, value
return query.contiguous(), key.contiguous(), value.contiguous()
def forward(
self,
@ -455,7 +455,8 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
spec_query_start_loc = attn_metadata.spec_query_start_loc
non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
spec_sequence_masks = attn_metadata.spec_sequence_masks
spec_token_masks = attn_metadata.spec_token_masks
spec_token_indx = attn_metadata.spec_token_indx
non_spec_token_indx = attn_metadata.non_spec_token_indx
spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor # noqa: E501
non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
self_kv_cache = self.kv_cache[forward_context.virtual_engine]
@ -463,8 +464,6 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
ssm_state = self_kv_cache[1]
num_actual_tokens = attn_metadata.num_actual_tokens
num_accepted_tokens = attn_metadata.num_accepted_tokens
if spec_token_masks is not None:
spec_token_masks = spec_token_masks[:num_actual_tokens]
# 1. Set up dimensions for reshapes later
projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states[:num_actual_tokens])
@ -487,8 +486,8 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
mixed_qkv_spec = mixed_qkv
mixed_qkv_non_spec = None
else:
mixed_qkv_spec = mixed_qkv[spec_token_masks]
mixed_qkv_non_spec = mixed_qkv[~spec_token_masks]
mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
else:
mixed_qkv_spec = None
mixed_qkv_non_spec = mixed_qkv
@ -558,10 +557,10 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
g_non_spec = None
beta_non_spec = None
else:
g_spec = g[:, spec_token_masks]
beta_spec = beta[:, spec_token_masks]
g_non_spec = g[:, ~spec_token_masks]
beta_non_spec = beta[:, ~spec_token_masks]
g_spec = g.index_select(1, spec_token_indx)
beta_spec = beta.index_select(1, spec_token_indx)
g_non_spec = g.index_select(1, non_spec_token_indx)
beta_non_spec = beta.index_select(1, non_spec_token_indx)
else:
g_spec = None
beta_spec = None
@ -638,8 +637,9 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
dtype=core_attn_out_non_spec.dtype,
device=core_attn_out_non_spec.device,
)
core_attn_out[:, spec_token_masks] = core_attn_out_spec
core_attn_out[:, ~spec_token_masks] = core_attn_out_non_spec
core_attn_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
core_attn_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
elif spec_sequence_masks is not None:
core_attn_out = core_attn_out_spec
else:

66
vllm/v1/attention/backends/gdn_attn.py
View File

@ -47,9 +47,9 @@ class GDNAttentionMetadata:
None # shape: [batch - num_spec_decodes,]
)
spec_sequence_masks: torch.Tensor | None = None # shape: [batch,]
spec_token_masks: torch.Tensor | None = (
None # shape: [num_prefill_tokens + num_decode_tokens,]
)
spec_token_indx: torch.Tensor | None = None
non_spec_token_indx: torch.Tensor | None = None
num_accepted_tokens: torch.Tensor | None = None # shape: [batch,]
# The following attributes are for triton implementation of causal_conv1d
@ -105,9 +105,14 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
dtype=torch.bool,
device=device,
)
self.spec_token_masks = torch.empty(
self.spec_token_indx = torch.empty(
(self.decode_cudagraph_max_bs * (self.num_spec + 1),),
dtype=torch.bool,
dtype=torch.int32,
device=device,
)
self.non_spec_token_indx = torch.empty(
(self.decode_cudagraph_max_bs * (self.num_spec + 1),),
dtype=torch.int32,
device=device,
)
self.spec_query_start_loc = torch.empty(
@ -166,7 +171,8 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
split_decodes_and_prefills(m, decode_threshold=1)
)
num_spec_decode_tokens = 0
spec_token_masks = None
spec_token_indx = None
non_spec_token_indx = None
spec_state_indices_tensor = None
non_spec_state_indices_tensor = m.block_table_tensor[:, 0]
spec_query_start_loc = None
@ -180,18 +186,23 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
num_prefills = non_spec_query_lens.size(0) - num_decodes
num_decode_tokens = num_decodes
num_prefill_tokens = non_spec_query_lens.sum().item() - num_decode_tokens
num_spec_decode_tokens = (
query_lens.sum().item() - num_prefill_tokens - num_decode_tokens
)
if num_prefills == 0 and num_decodes == 0:
spec_token_masks = torch.ones(
(
min(
num_spec_decodes * (self.num_spec + 1),
query_start_loc[-1].item(),
)
),
dtype=torch.bool,
spec_token_size = min(
num_spec_decodes * (self.num_spec + 1),
query_start_loc[-1].item(),
)
spec_token_indx = torch.arange(
spec_token_size,
dtype=torch.int32,
device=query_start_loc.device,
)
non_spec_token_indx = torch.empty(
0, dtype=torch.int32, device=query_start_loc.device
)
spec_state_indices_tensor = m.block_table_tensor[:, : self.num_spec + 1]
non_spec_state_indices_tensor = None
spec_query_start_loc = query_start_loc
@ -200,6 +211,11 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
spec_token_masks = torch.repeat_interleave(
spec_sequence_masks, query_lens
)
index = torch.argsort(spec_token_masks)
num_non_spec_tokens = num_prefill_tokens + num_decode_tokens
non_spec_token_indx = index[:num_non_spec_tokens]
spec_token_indx = index[num_non_spec_tokens:]
spec_state_indices_tensor = m.block_table_tensor[
spec_sequence_masks, : self.num_spec + 1
]
@ -226,9 +242,6 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
out=non_spec_query_start_loc[1:],
)
num_spec_decode_tokens = (
query_lens.sum().item() - num_prefill_tokens - num_decode_tokens
)
assert num_accepted_tokens is not None
num_accepted_tokens = num_accepted_tokens[spec_sequence_masks]
@ -274,12 +287,18 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
spec_sequence_masks = self.spec_sequence_masks[:batch_size]
spec_sequence_masks[num_spec_decodes:].fill_(False)
assert spec_token_masks is not None
self.spec_token_masks[: spec_token_masks.size(0)].copy_(
spec_token_masks, non_blocking=True
assert non_spec_token_indx is not None and spec_token_indx is not None
self.non_spec_token_indx[: non_spec_token_indx.size(0)].copy_(
non_spec_token_indx, non_blocking=True
)
spec_token_masks = self.spec_token_masks[:num_actual_tokens]
spec_token_masks[spec_token_masks.size(0) :].fill_(False)
non_spec_token_indx = self.non_spec_token_indx[
: non_spec_token_indx.size(0)
]
self.spec_token_indx[: spec_token_indx.size(0)].copy_(
spec_token_indx, non_blocking=True
)
spec_token_indx = self.spec_token_indx[: spec_token_indx.size(0)]
self.spec_query_start_loc[: num_spec_decodes + 1].copy_(
spec_query_start_loc, non_blocking=True
@ -332,7 +351,8 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
spec_state_indices_tensor=spec_state_indices_tensor,
non_spec_state_indices_tensor=non_spec_state_indices_tensor,
spec_sequence_masks=spec_sequence_masks,
spec_token_masks=spec_token_masks,
spec_token_indx=spec_token_indx,
non_spec_token_indx=non_spec_token_indx,
num_accepted_tokens=num_accepted_tokens,
nums_dict=nums_dict,
batch_ptr=batch_ptr,