[Bugfix] Fix the attn_metadata is None (#5038)

### What this PR does / why we need it?
Fix the bug " TypeError: 'NoneType' object is not iterable' " in
vllm_ascend/compilation/acl_graph.py
The reason of that is the attn_metadata is none in the dummy_run of MTP.

- vLLM version: v0.12.0
- vLLM main:
vllm-project/vllm@ad32e3e

Signed-off-by: chenmenglong <[email protected]>

Author: dragondream-chen

vllm_ascend/spec_decode/eagle_proposer.py

@@ -117,7 +117,7 @@ def load_model(self, model: nn.Module) -> None:
     def dummy_run(self,
                   num_tokens: int,
                   with_prefill: bool = False,
-                  skip_attn: bool = False,
+                  in_graph_capturing: bool = False,
                   num_reqs: int = 0,
                   num_tokens_across_dp: Optional[torch.Tensor] = None,
                   aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,

vllm_ascend/spec_decode/interface.py

@@ -32,7 +32,7 @@ def load_model(self, model):
     def dummy_run(self,
                   num_tokens: int,
                   with_prefill: bool = False,
-                  skip_attn: bool = False,
+                  in_graph_capturing: bool = False,
                   num_reqs: int = 0,
                   num_tokens_across_dp: Optional[torch.Tensor] = None,
                   aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,

vllm_ascend/spec_decode/mtp_proposer.py

@@ -223,7 +223,7 @@ def load_model(self, model) -> None:
     def dummy_run(self,
                   num_tokens: int,
                   with_prefill: bool = False,
-                  skip_attn: bool = False,
+                  in_graph_capturing: bool = False,
                   num_reqs: int = 0,
                   num_tokens_across_dp=None,
                   aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
@@ -247,9 +247,7 @@ def dummy_run(self,
         moe_comm_type = (MoECommType.ALLTOALL if moe_comm_type
                          == MoECommType.FUSED_ALLTOALL else moe_comm_type)
 
-        if skip_attn:
-            attn_metadata = None
-        elif aclgraph_runtime_mode == CUDAGraphMode.FULL:
+        if aclgraph_runtime_mode == CUDAGraphMode.FULL:
             if len(self.runner.attn_groups) > 0:
                 num_computed_tokens_cpu = (
                     self.runner.input_batch.
@@ -294,7 +292,7 @@ def dummy_run(self,
         positions = self.positions[:num_tokens]
         previous_hidden_states = self.hidden_states[:num_tokens]
         for i in range(self.num_speculative_tokens):
-            if i > 0 and not skip_attn and aclgraph_runtime_mode == CUDAGraphMode.FULL:
+            if i > 0 and not in_graph_capturing and aclgraph_runtime_mode == CUDAGraphMode.FULL:
                 aclgraph_runtime_mode = CUDAGraphMode.NONE
             with set_ascend_forward_context(
                     attn_metadata,

vllm_ascend/spec_decode/ngram_proposer.py

@@ -22,7 +22,7 @@ def load_model(self, *args, **kwargs):
     def dummy_run(self,
                   num_tokens,
                   with_prefill=None,
-                  skip_attn=None,
+                  in_graph_capturing=None,
                   num_reqs=None,
                   num_tokens_across_dp=None,
                   aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,

vllm_ascend/spec_decode/suffix_proposer.py

@@ -22,7 +22,7 @@ def load_model(self, *args, **kwargs):
     def dummy_run(self,
                   num_tokens,
                   with_prefill=None,
-                  skip_attn=None,
+                  in_graph_capturing=None,
                   num_reqs=None,
                   num_tokens_across_dp=None,
                   aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,

vllm_ascend/worker/model_runner_v1.py

@@ -2296,7 +2296,7 @@ def dummy_drafter_compute_logits(hidden_states):
                     aclgraph_runtime_mode=aclgraph_runtime_mode,
                     batch_descriptor=batch_descriptor,
                     dummy_compute_logits=dummy_drafter_compute_logits,
-                    skip_attn=not force_attention)
+                    in_graph_capturing=not force_attention)
             if self.in_profile_run and self.dynamic_eplb:
                 self.model.clear_all_moe_loads()
             if not self.in_profile_run and self.dynamic_eplb: