Enable trtllm mla prefix extend (#10526)

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -553,7 +553,7 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
         save_kv_cache: bool = True,
         q_rope: Optional[torch.Tensor] = None,
         k_rope: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    ):
         if (
             forward_batch.forward_mode.is_target_verify()
             or forward_batch.forward_mode.is_draft_extend()
@@ -591,10 +591,45 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
                 return_lse=forward_batch.mha_return_lse,
             )
         else:
-            # replace with trtllm ragged attention once accuracy is resolved.
+            if not (
+                forward_batch.attn_attend_prefix_cache is not None
+                and forward_batch.mha_return_lse
+            ):
                 output = super().forward_extend(
                     q, k, v, layer, forward_batch, save_kv_cache, q_rope, k_rope
                 )
+            else:
+                # MHA for chunked prefix kv cache when running model with MLA
+                assert forward_batch.prefix_chunk_idx is not None
+                assert forward_batch.prefix_chunk_cu_seq_lens is not None
+                assert q_rope is None
+                assert k_rope is None
+                chunk_idx = forward_batch.prefix_chunk_idx
+
+                q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
+                k = k.view(-1, layer.tp_k_head_num, layer.head_dim).to(q.dtype)
+                v = v.view(-1, layer.tp_k_head_num, layer.v_head_dim).to(q.dtype)
+                output_shape = (q.shape[0], layer.tp_q_head_num, layer.v_head_dim)
+                output = flashinfer.prefill.trtllm_ragged_attention_deepseek(
+                    query=q,
+                    key=k,
+                    value=v,
+                    workspace_buffer=self.workspace_buffer,
+                    seq_lens=forward_batch.prefix_chunk_seq_lens[chunk_idx],
+                    max_q_len=self.forward_prefill_metadata.max_seq_len,
+                    max_kv_len=forward_batch.prefix_chunk_max_seq_lens[chunk_idx],
+                    bmm1_scale=layer.scaling,
+                    bmm2_scale=1.0,
+                    o_sf_scale=-1.0,
+                    batch_size=forward_batch.batch_size,
+                    window_left=-1,
+                    cum_seq_lens_q=self.forward_prefill_metadata.cum_seq_lens,
+                    cum_seq_lens_kv=forward_batch.prefix_chunk_cu_seq_lens[chunk_idx],
+                    enable_pdl=False,
+                    is_causal=False,
+                    return_lse=True,
+                    out=torch.zeros(*output_shape, dtype=q.dtype, device=q.device),
+                )
         return output