[Refactor] Refactor Spec Decode (#2668)

### What this PR does / why we need it?
Refactor spec decode

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.


- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Icey <1790571317@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/spec_decode/ngram_proposer.py

@@ -0,0 +1,65 @@
+import torch
+from vllm.v1.spec_decode.ngram_proposer import \
+    NgramProposer as VllmNgramProposer
+
+from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
+
+
+class NgramProposer(VllmNgramProposer, Proposer):
+
+    def __init__(self, vllm_config, device, runner):
+        super().__init__(vllm_config)
+        self.name = SpecDcodeType.NGRAM
+        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,
+                  with_prefill=None,
+                  skip_attn=None,
+                  num_reqs=None,
+                  num_tokens_across_dp=None):
+        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,
+                           attn_metadata=None,
+                           aux_hidden_states=None) -> list[list[int]]:
+        # TODO(woosuk): Optimize.
+        draft_token_ids: list[list[int]] = []
+        for i, sampled_ids in enumerate(valid_sampled_token_ids):
+            num_sampled_ids = len(sampled_ids)
+            if not num_sampled_ids:
+                # Skip speculative decoding.
+                draft_token_ids.append([])
+                continue
+
+            # Skip requests that require top-p, top-k, etc.
+            req_id = self.runner.input_batch.req_ids[i]
+            if req_id in self.runner.input_batch.spec_decode_unsupported_reqs:
+                draft_token_ids.append([])
+                continue
+
+            # Add sampled_token_ids to token_ids_cpu.
+            start_idx = self.runner.input_batch.num_tokens_no_spec[i]
+            end_idx = start_idx + num_sampled_ids
+            self.runner.input_batch.token_ids_cpu[
+                i, start_idx:end_idx] = sampled_ids
+            drafter_output = self.propose(
+                self.runner.input_batch.token_ids_cpu[i, :end_idx])
+            if drafter_output is None or len(drafter_output) == 0:
+                draft_token_ids.append([])
+            else:
+                draft_token_ids.append(drafter_output.tolist())
+        return draft_token_ids