[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/medusa_proposer.py

@@ -1,36 +1,20 @@
 import torch
-from vllm.config import CUDAGraphMode, VllmConfig
+from vllm.config import CUDAGraphMode
 from vllm.logger import init_logger
 from vllm.v1.sample.metadata import SamplingMetadata
-from vllm.v1.spec_decode.medusa import MedusaProposer as VllmMedusaProposer
+from vllm.v1.spec_decode.medusa import MedusaProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 
 from vllm_ascend.ascend_forward_context import set_ascend_forward_context
-from vllm_ascend.spec_decode.interface import SpecDcodeType
 
 logger = init_logger(__name__)
 
 
-class MedusaProposer(VllmMedusaProposer):
+class AscendMedusaProposer(MedusaProposer):
     """
     Medusa proposer class for generating token sequences
     """
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        device: torch.device,
-        runner,
-    ):
-        # Save config parameters
-        self.name = SpecDcodeType.MEDUSA
-        self.vllm_config = vllm_config
-        self.device = device
-        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
-        self.hidden_size = vllm_config.speculative_config.draft_model_config.get_hidden_size()
-        self.dtype = vllm_config.model_config.dtype
-        self.runner = runner
-
     @torch.inference_mode()
     def dummy_run(
         self,
@@ -62,14 +46,12 @@ class MedusaProposer(VllmMedusaProposer):
             self.model(hidden_states)
             dummy_compute_logits(hidden_states)
 
-    def generate_token_ids(
+    def propose(
         self,
         valid_sampled_token_ids: list[list[int]],
         sampling_metadata: SamplingMetadata,
         spec_decode_metadata: SpecDecodeMetadata,
         sample_hidden_states: torch.Tensor,
-        *args,
-        **kwargs,
     ):
         if sample_hidden_states.shape[0] == len(valid_sampled_token_ids):
             # The input to the target model does not include draft tokens.
@@ -84,7 +66,7 @@ class MedusaProposer(VllmMedusaProposer):
             indices = offsets + num_accepted_tokens - 1
             hidden_states = sample_hidden_states[indices]
 
-        spec_token_ids = self.propose(
+        spec_token_ids = super().propose(
             target_hidden_states=hidden_states,
             sampling_metadata=sampling_metadata,
         )