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perf(runtime): avoid Kimi MLA projection layout copies #950

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36 changes: 11 additions & 25 deletions python/tokenspeed/runtime/models/deepseek_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -817,18 +817,11 @@ def forward_absorb_qkv_proj(
# latent_cache contains normalized kv_a and k_pe before rotate.
K = latent_cache.unsqueeze(1)
q_nope_out_view = Q[..., : self.kv_lora_rank]
if _is_amd:
q_nope_projected = torch.bmm(
q_nope.transpose(0, 1).contiguous(),
self.w_kc.contiguous(),
)
q_nope_out_view.copy_(q_nope_projected.transpose(0, 1))
else:
torch.bmm(
q_nope.transpose(0, 1),
self.w_kc,
out=q_nope_out_view.transpose(0, 1),
)
torch.bmm(
q_nope.transpose(0, 1),
self.w_kc,
out=q_nope_out_view.transpose(0, 1),
)
# Model-owned fused FP8 decode: RoPE + quantize + KV cache write
# all done here, so backend only needs to do attention.
k_scale = getattr(self.attn_mqa, "k_scale_float", 1.0)
Expand Down Expand Up @@ -957,19 +950,12 @@ def forward_absorb_attn_v_proj(
record_kv_cache=record_kv_cache,
)
attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
if _is_amd:
projected = torch.bmm(
attn_output.transpose(0, 1).contiguous(),
self.w_vc.contiguous(),
)
output.copy_(projected.transpose(0, 1).reshape_as(output))
else:
output_view = output.view(-1, self.num_local_heads, self.v_head_dim)
torch.bmm(
attn_output.transpose(0, 1),
self.w_vc,
out=output_view.transpose(0, 1),
)
output_view = output.view(-1, self.num_local_heads, self.v_head_dim)
torch.bmm(
attn_output.transpose(0, 1),
self.w_vc,
out=output_view.transpose(0, 1),
)
return output

def forward_normal_chunked(
Expand Down
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