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MLA prefill w/o weight absorption (#2349)

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This commit is contained in:
Ke Bao
2024-12-05 01:50:28 +08:00
committed by GitHub
parent eb0c1f5373
commit ec52464dde
8 changed files with 166 additions and 36 deletions
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71
python/sglang/srt/models/deepseek_v2.py
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@@ -453,7 +453,7 @@ class DeepseekV2AttentionMLA(nn.Module):
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
self.attn = RadixAttention(
self.attn_mqa = RadixAttention(
self.num_local_heads,
self.kv_lora_rank + self.qk_rope_head_dim,
self.scaling,
@@ -462,6 +462,15 @@ class DeepseekV2AttentionMLA(nn.Module):
v_head_dim=self.kv_lora_rank,
)
self.attn_mha = RadixAttention(
self.num_local_heads,
self.qk_nope_head_dim + self.qk_rope_head_dim,
self.scaling,
num_kv_heads=self.num_local_heads,
layer_id=layer_id,
v_head_dim=self.v_head_dim,
)
self.w_kc = None
self.w_vc = None
self.w_scale = None
@@ -471,6 +480,63 @@ class DeepseekV2AttentionMLA(nn.Module):
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
# Use normal computation for prefill and use weight absorption for extend/decode
if (
forward_batch.forward_mode.is_extend()
and forward_batch.extend_prefix_lens.sum() == 0
):
return self.forward_normal(positions, hidden_states, forward_batch)
else:
return self.forward_absorb(positions, hidden_states, forward_batch)
def forward_normal(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
if self.q_lora_rank is not None:
q = self.q_a_proj(hidden_states)[0]
q = self.q_a_layernorm(q)
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
else:
q = self.q_proj(hidden_states)[0].view(
-1, self.num_local_heads, self.qk_head_dim
)
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
latent_cache = latent_cache.unsqueeze(1)
kv_a = self.kv_a_layernorm(kv_a.contiguous())
kv = self.kv_b_proj(kv_a)[0]
kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope = kv[..., : self.qk_nope_head_dim]
v = kv[..., self.qk_nope_head_dim :]
k_pe = latent_cache[:, :, self.kv_lora_rank :]
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
q[..., self.qk_nope_head_dim :] = q_pe
k = torch.empty_like(q)
k[..., : self.qk_nope_head_dim] = k_nope
k[..., self.qk_nope_head_dim :] = k_pe
latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
latent_cache[:, :, self.kv_lora_rank :] = k_pe
# Save latent cache
forward_batch.token_to_kv_pool.set_kv_buffer(
self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
)
attn_output = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False)
attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim)
output, _ = self.o_proj(attn_output)
return output
def forward_absorb(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
q_len = hidden_states.shape[0]
q_input = hidden_states.new_empty(
@@ -508,7 +574,7 @@ class DeepseekV2AttentionMLA(nn.Module):
q_input[..., self.kv_lora_rank :] = q_pe
k_input[..., self.kv_lora_rank :] = k_pe
attn_output = self.attn(q_input, k_input, v_input, forward_batch)
attn_output = self.attn_mqa(q_input, k_input, v_input, forward_batch)
attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
if self.w_vc.dtype == torch.float8_e4m3fn:
@@ -835,7 +901,6 @@ class DeepseekV2ForCausalLM(nn.Module):
self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
if hasattr(self_attn.kv_b_proj, "weight_scale"):
self_attn.w_scale = self_attn.kv_b_proj.weight_scale
del self_attn.kv_b_proj
EntryClass = DeepseekV2ForCausalLM