[Feat] Merge the multi eagle graphs to one graph (#5940)

### What this PR does / why we need it?
This PR merge all steps of draft model in fullgraph mode, to avoid the
synchronize between each graph, reduce the bubble time.

#### Key ideas:
- The "model forward" of the step 0 (first step) and remaining steps are
captured together as a "Callable", rather than capturing each model
individually.
- "update_attn_params" is moved outside the entire graph, meaning that
all "attn_metadata" required by all steps are constructed before
"replay", and the "attn_params" of all steps are updated at once.
- Remove synchronization between the main model graph and draft model
graph.

#### Key params/functions:
- params: draft_attn_metadatas, attn_metadata_multi_steps,
slot_mapping_group
- functions: _run_merged_draft, attn_update_stack_num_spec_norm,
update_attn_params, _propose, dummy_run

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
11b6af5280

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>

vllm_ascend/compilation/acl_graph.py

@@ -191,7 +191,15 @@ class ACLGraphWrapper:
         # before the grph replay of iteration i-1.
         # To ensure proper ordering, we must call synchronize here before replaying,
         # so that update_attn_params only executes after the previous graph replay has fully completed.
-        torch.npu.synchronize()
+        # If we do not in main model and in full-graph mode when using merge-eagle-graph,
+        # we do not need to synchronize.
+        use_eagle = (
+            self.vllm_config.speculative_config.method in ("eagle", "eagle3")
+            if self.vllm_config.speculative_config
+            else False
+        )
+        if self.runtime_mode != CUDAGraphMode.FULL or not forward_context.is_draft_model or not use_eagle:
+            torch.npu.synchronize()
         entry.aclgraph.replay()
         return entry.output
 
@@ -247,18 +255,31 @@ def _update_attn_pa_params(update_stream, forward_context, runtime_shape):
             event.record(update_stream)
 
 
-def _update_attn_fia_params(update_stream, forward_context, runtime_shape):
+def _update_attn_fia_params(update_stream, forward_context, runtime_shape, draft_attn_metadatas=None):
     if forward_context.is_draft_model:
         graph_params = get_draft_graph_params()
+        attn_metadata = draft_attn_metadatas
+        attn_keys = list(attn_metadata[0].keys())
     else:
         graph_params = get_graph_params()
+        attn_metadata = forward_context.attn_metadata
+        attn_keys = list(attn_metadata.keys())
     # For Qwen3-next, since the kv_cache_config has already categorized
     # linear_attn and self_attn, the attn_metadata is first arranged with
     # self_attn followed by linear_attn. Therefore, using zip directly
     # filters out the update operations for linear_attn.
+    # TODO: We use a new variable `attn_keys` to ensure the loop count is
+    # correct after get by `zip` because of the new structure of the attn_metadata
+    # when running with the merged full eagle-graph. Should check it with Qwen3-next.
+    num_layers = len(attn_keys)
+    if num_layers == 0:
+        return
+    if forward_context.is_draft_model:
+        attn_keys = attn_keys * (len(graph_params.attn_params[runtime_shape]) // num_layers)
+    attn_count = 0
     with torch.npu.stream(update_stream):
         for key, param, handle, event in zip(
-            forward_context.attn_metadata,
+            attn_keys,
             graph_params.attn_params[runtime_shape],
             graph_params.handles[runtime_shape],
             graph_params.events[runtime_shape],
@@ -279,8 +300,15 @@ def _update_attn_fia_params(update_stream, forward_context, runtime_shape):
                 softmax_lse,
             ) = param
 
-            seq_lens = forward_context.attn_metadata[key].seq_lens_list
-            actual_seq_lengths_q = forward_context.attn_metadata[key].actual_seq_lengths_q
+            if forward_context.is_draft_model:
+                draft_step = attn_count // num_layers
+                seq_lens = attn_metadata[draft_step][key].seq_lens_list
+                actual_seq_lengths_q = attn_metadata[draft_step][key].actual_seq_lengths_q
+                attn_count = attn_count + 1
+            else:
+                seq_lens = attn_metadata[key].seq_lens_list
+                actual_seq_lengths_q = attn_metadata[key].actual_seq_lengths_q
+
             torch.npu.graph_task_update_begin(update_stream, handle)
             torch_npu.npu_fused_infer_attention_score.out(
                 query=query,
@@ -304,11 +332,11 @@ def _update_attn_fia_params(update_stream, forward_context, runtime_shape):
             event.record(update_stream)
 
 
-def update_attn_params(update_stream, forward_context, runtime_shape, vllm_config):
+def update_attn_params(update_stream, forward_context, runtime_shape, vllm_config, draft_attn_metadatas=None):
     if using_paged_attention(runtime_shape, vllm_config):
         _update_attn_pa_params(update_stream, forward_context, runtime_shape)
     else:
-        _update_attn_fia_params(update_stream, forward_context, runtime_shape)
+        _update_attn_fia_params(update_stream, forward_context, runtime_shape, draft_attn_metadatas)
 
 
 def update_mla_attn_params(update_stream, forward_context, runtime_shape, speculative_config):