[Spec Decode]clean up spec decode interface (#6947)

This pull request refactors the speculative decoding proposer interface
to align with upstream vLLM, removing the local `Proposer` interface and
renaming methods to `propose`.

This is the first step. In the future we should remove the class
register and just add few Ascend specified method once the arch in vLLM
is ready.

- vLLM version: v0.16.0
- vLLM main:
15d76f74e2

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/spec_decode/ngram_proposer.py

@@ -1,16 +1,11 @@
 import torch
-from vllm.config import CUDAGraphMode
-from vllm.v1.spec_decode.ngram_proposer import NgramProposer as VllmNgramProposer
-
-from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
+from vllm.v1.spec_decode.ngram_proposer import NgramProposer
 
 
-class NgramProposer(VllmNgramProposer, Proposer):
-    def __init__(self, vllm_config, device, runner):
-        super().__init__(vllm_config)
-        self.name = SpecDcodeType.NGRAM
-        self.device = device
+class AscendNgramProposer(NgramProposer):
+    def __init__(self, vllm_config, runner):
         self.runner = runner
+        super().__init__(vllm_config)
 
     def load_model(self, *args, **kwargs):
         # No model to load.
@@ -24,26 +19,22 @@ class NgramProposer(VllmNgramProposer, Proposer):
         in_graph_capturing=None,
         num_reqs=None,
         num_tokens_across_dp=None,
-        aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+        aclgraph_runtime_mode=None,
         batch_descriptor=None,
         dummy_compute_logits=lambda hidden_states: None,
         is_profile=False,
     ):
         pass
 
-    def generate_token_ids(
+    def propose(
         self,
-        valid_sampled_token_ids,
-        sampling_metadata=None,
-        scheduler_output=None,
-        spec_decode_metadata=None,
-        positions=None,
-        num_scheduled_tokens=None,
-        hidden_states=None,
-        aux_hidden_states=None,
+        sampled_token_ids: list[list[int]],
+        num_tokens_no_spec=None,
+        token_ids_cpu=None,
+        slot_masks: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None = None,
     ) -> list[list[int]]:
         valid_ngram_requests = []
-        for i, sampled_ids in enumerate(valid_sampled_token_ids):
+        for i, sampled_ids in enumerate(sampled_token_ids):
             num_sampled_ids = len(sampled_ids)
             if not num_sampled_ids:
                 continue
@@ -64,7 +55,7 @@ class NgramProposer(VllmNgramProposer, Proposer):
             valid_ngram_requests.append(i)
 
         draft_token_ids = self.batch_propose(
-            len(valid_sampled_token_ids),
+            len(sampled_token_ids),
             valid_ngram_requests,
             self.runner.input_batch.num_tokens_no_spec,
             self.runner.input_batch.token_ids_cpu,