[Feat] Support async_scheduler and disable_padded_drafter_batch in eagle (#4893)

### What this PR does / why we need it?
We refactored the eagle_proposer.py to adapt the framework of eagle.py
in vllm-v0.12.0, to support the logit of padded drafter batch and
async-scheduler.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: drslark <slarksblood@qq.com>

vllm_ascend/attention/attention_v1.py

@@ -730,6 +730,9 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
             slots = attn_metadata.slot_mapping
             if get_ascend_device_type() == AscendDeviceType._910_95:
+                # TODO: Once eagle running to here, it may has error because of the 0 dim of slot_mapping.
+                # Should check if the 0 dim of slot_mapping must equal to the 0 dim of key.
+                # If it's necessary, the slots should be sliced.
                 torch_npu.npu_scatter_pa_kv_cache(
                     key=key[:attn_metadata.num_actual_tokens],
                     value=value[:attn_metadata.num_actual_tokens].contiguous(),
@@ -742,7 +745,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
                     value=value[:attn_metadata.num_actual_tokens],
                     key_cache=self.key_cache,
                     value_cache=self.value_cache,
-                    slot_indices=slots)
+                    slot_indices=slots[:attn_metadata.num_actual_tokens])
         return key, value
 
     def forward_impl(

vllm_ascend/attention/utils.py

@@ -119,6 +119,35 @@ class AscendCommonAttentionMetadata:
     prefill_context_parallel_metadata: Optional[
         AscendPrefillContextParallelMetadata] = None
 
+    # TODO: Remove it when vLLM no longer uses this function.
+    def unpadded(self, num_actual_tokens: int,
+                 num_actual_reqs: int) -> "AscendCommonAttentionMetadata":
+        # This only use to eagle now. It will be use to enforce_eager in future.
+        return AscendCommonAttentionMetadata(
+            query_start_loc=self.query_start_loc[:num_actual_reqs + 1],
+            query_start_loc_cpu=self.query_start_loc_cpu[:num_actual_reqs + 1],
+            seq_lens=self.seq_lens[:num_actual_reqs],
+            seq_lens_cpu=self.seq_lens_cpu[:num_actual_reqs],
+            num_computed_tokens_cpu=self.
+            num_computed_tokens_cpu[:num_actual_reqs],
+            num_reqs=num_actual_reqs,
+            num_actual_tokens=num_actual_tokens,
+            max_query_len=self.max_query_len,
+            decode_token_per_req=self.decode_token_per_req,
+            block_table_tensor=self.block_table_tensor[:num_actual_reqs],
+            slot_mapping=self.slot_mapping[:num_actual_tokens],
+            actual_seq_lengths_q=self.actual_seq_lengths_q[:num_actual_tokens],
+            positions=self.positions[:num_actual_tokens],
+            attn_mask=self.attn_mask,
+            spec_attn_mask=self.spec_attn_mask,
+            attn_state=self.attn_state,
+            is_only_prefill=self.is_only_prefill,
+            graph_pad_size=-1,  # It should be -1 when not run in fullgraph mode.
+            num_input_tokens=num_actual_tokens,
+            prefill_context_parallel_metadata=self.
+            prefill_context_parallel_metadata,
+        )
+
 
 def filter_chunked_req_indices(
     seq_len: torch.Tensor,