[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/suffix_proposer.py

@@ -1,22 +1,11 @@
-import torch
-from vllm.config import CUDAGraphMode
-from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer as VllmSuffixDecodingProposer
-
-from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
+from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
 
 
-class SuffixDecodingProposer(VllmSuffixDecodingProposer, Proposer):
-    def __init__(self, vllm_config, device, runner):
+class AscendSuffixDecodingProposer(SuffixDecodingProposer):
+    def __init__(self, vllm_config, runner):
         super().__init__(vllm_config)
-        self.name = SpecDcodeType.SUFFIX
-        self.device = device
         self.runner = runner
 
-    def load_model(self, *args, **kwargs):
-        # No model to load.
-        pass
-
-    @torch.inference_mode()
     def dummy_run(
         self,
         num_tokens,
@@ -24,23 +13,12 @@ class SuffixDecodingProposer(VllmSuffixDecodingProposer, 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(
-        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,
-    ) -> list[list[int]]:
-        draft_token_ids = self.propose(self.runner.input_batch, valid_sampled_token_ids)
-        return draft_token_ids
+    def propose(self, valid_sampled_token_ids):
+        return super().propose(self.runner.input_batch, valid_sampled_token_ids)