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opt flashinfer mla cat #5822

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69840db
opt flashinfer mla cat
Apr 27, 2025
1fce849
Merge branch 'main' into flashinfer_cat_opt
ispobock Apr 28, 2025
ba29ddf
Merge branch 'main' into flashinfer_cat_opt
xu-yfei Apr 28, 2025
def4b9d
Merge branch 'main' into flashinfer_cat_opt
zhyncs Apr 29, 2025
5421f6b
Merge branch 'main' into flashinfer_cat_opt
xu-yfei Apr 29, 2025
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73 changes: 60 additions & 13 deletions python/sglang/srt/layers/attention/flashinfer_mla_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,22 +332,38 @@ def forward_extend(
layer: RadixAttention,
forward_batch: ForwardBatch,
save_kv_cache: bool = True,
q_rope: Optional[torch.Tensor] = None,
k_rope: Optional[torch.Tensor] = None,
):

cache_loc = forward_batch.out_cache_loc
logits_soft_cap = layer.logit_cap
prefill_wrapper_paged = self.forward_metadata.prefill_wrapper
qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
k_buf = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)

# Save kv cache
if save_kv_cache and k is not None:
assert v is not None
if save_kv_cache:
forward_batch.token_to_kv_pool.set_kv_buffer(layer, cache_loc, k, v)
if k_rope is not None:
forward_batch.token_to_kv_pool.set_mla_kv_buffer(
layer, cache_loc, k, k_rope
)
else:
forward_batch.token_to_kv_pool.set_kv_buffer(layer, cache_loc, k, v)
if q_rope is not None:
q = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
q_rope = q_rope.view(
-1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
)

if self.forward_metadata.use_ragged:
# ragged prefill
if q_rope is not None:
q = torch.cat([q, q_rope], dim=-1)
qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
if k_rope is not None:
k = torch.cat([k, k_rope], dim=-1)
o, _ = self.prefill_wrapper_ragged.forward_return_lse(
qall,
k.view(-1, layer.tp_k_head_num, layer.head_dim),
Expand All @@ -358,11 +374,19 @@ def forward_extend(
)
else:
# mla paged prefill
if q_rope is None:
qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
q, q_rope = (
qall[:, :, : layer.v_head_dim],
qall[:, :, layer.v_head_dim :],
)
o = q.new_empty(q.shape)
o = prefill_wrapper_paged.run(
qall[:, :, : layer.v_head_dim],
qall[:, :, layer.v_head_dim :],
q,
q_rope,
k_buf[:, :, : layer.v_head_dim],
k_buf[:, :, layer.v_head_dim :],
out=o,
)

return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
Expand All @@ -375,27 +399,50 @@ def forward_decode(
layer: RadixAttention,
forward_batch: ForwardBatch,
save_kv_cache: bool = True,
# For multi-head latent attention
q_rope: Optional[torch.Tensor] = None,
k_rope: Optional[torch.Tensor] = None,
):
decode_wrapper = self.forward_metadata.decode_wrapper
cache_loc = forward_batch.out_cache_loc

if k is not None:
assert v is not None
if save_kv_cache:
forward_batch.token_to_kv_pool.set_kv_buffer(
layer,
cache_loc,
k,
v,
)
reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
if k_rope is not None:
forward_batch.token_to_kv_pool.set_mla_kv_buffer(
layer,
cache_loc,
k,
k_rope,
)
else:
forward_batch.token_to_kv_pool.set_kv_buffer(
layer,
cache_loc,
k,
v,
)

if q_rope is not None:
q_nope = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
q_rope = q_rope.view(
-1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
)
else:
reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
q_nope = reshaped_q[:, :, : layer.v_head_dim]
q_rope = reshaped_q[:, :, layer.v_head_dim :]

k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
reshaped_k = k_buffer.view(-1, 1, layer.head_dim)
o = q_nope.new_empty(q_nope.shape)
o = decode_wrapper.run(
reshaped_q[:, :, : layer.v_head_dim],
reshaped_q[:, :, layer.v_head_dim :],
q_nope,
q_rope,
reshaped_k[:, :, : layer.v_head_dim],
reshaped_k[:, :, layer.v_head_dim :],
out=o,
)

return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
Expand Down
2 changes: 1 addition & 1 deletion python/sglang/srt/models/deepseek_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -757,7 +757,7 @@ def forward_absorb(

q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)

if self.attention_backend == "fa3":
if self.attention_backend == "fa3" or self.attention_backend == "flashinfer":
attn_output = self.attn_mqa(
q_nope_out, k_nope, k_nope, forward_batch, q_rope=q_pe, k_rope=k_pe
)
Expand Down