[feat][spec decode]Unified draft parallel (#6766)

### What this PR does / why we need it?
Implement a unified parallelized speculative decoding in VLLM
Ascend，which can simultaneously support parallel speculative inference
schemes such as Pard, P-Eagle, etc. refer to
https://github.com/vllm-project/vllm-ascend/pull/6565 and
https://github.com/vllm-project/vllm-ascend/pull/4078

### How was this patch tested?

run with parallel drafting script:
export target=/model/Llama-3.1-8B-Instruct
export draft=/model/PARD-Llama-3.2-1B
export CUDA_VISIBLE_DEVICES=6
export ASCEND_RT_VISIBLE_DEVICES=6
vllm serve $target \
  --tensor-parallel-size 1 \
  --max-model-len 4096 \
  --no-enable-prefix-caching \
  --port 8811 \
--speculative-config '{"model": "/model/PARD-Llama-3.2-1B", "method":
"draft_model", "num_speculative_tokens": 8, "parallel_drafting": true}'

base script:
export target=/model/Llama-3.1-8B-Instruct
export draft=/model/PARD-Llama-3.2-1B
export CUDA_VISIBLE_DEVICES=6
export ASCEND_RT_VISIBLE_DEVICES=6
vllm serve $target \
  --tensor-parallel-size 1 \
  --max-model-len 4096 \
  --no-enable-prefix-caching \
  --port 8811

benchmark script:
MAX_CONCURRENCY=1
NUM_PROMPTS=80
vllm bench serve --port 8811 \
    --temperature 0 \
    --model /model/Llama-3.1-8B-Instruct \
    --backend openai-chat \
    --endpoint /v1/chat/completions \
    --dataset-name hf \
    --dataset-path philschmid/mt-bench \
    --num-prompts ${NUM_PROMPTS} \
    --max-concurrency ${MAX_CONCURRENCY} \
    --seed 1234

test results :
base(without spec decode): TTFT 79.46ms TPOT 26.99ms
output_tokens_throughput 36.75 tok/s
this pr(with parallel drafting): TTFT 72.24ms TPOT 13.45ms
output_tokens_throughput 72.98 tok/s
per-position acceptance(from position 0 to 7):
79.48%、56.93%、40%、27.90%、19.79%、14.25%、10.57%、7.61%.

----------------------------------------------------------------------
run on qwen3 model script ：
export target=/model/Qwen3-1.7B
export draft=/model/PARD-Qwen3-0.6B
export CUDA_VISIBLE_DEVICES=1
export ASCEND_RT_VISIBLE_DEVICES=1

vllm serve $target \
  --tensor-parallel-size 1 \
  --max-model-len 4096 \
  --no-enable-prefix-caching \
  --port 8811 \
--speculative-config '{"model": "/model/PARD-Qwen3-0.6B", "method":
"draft_model", "num_speculative_tokens": 8, "parallel_drafting": true}'

cc  @NickJudyHvv
- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: 01267596 <xiongkai123@cmbchina.com>
Signed-off-by: kx <1670186653@qq.com>
Signed-off-by: HF-001 <1670186653@qq.com>
Co-authored-by: 01267596 <xiongkai123@cmbchina.com>

vllm_ascend/spec_decode/draft_proposer.py

@@ -0,0 +1,71 @@
+import torch
+import torch.nn as nn
+from typing_extensions import override
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader import get_model
+from vllm.v1.spec_decode.utils import create_vllm_config_for_draft_model
+
+from vllm_ascend.spec_decode.eagle_proposer import SpecDecodeBaseProposer
+
+logger = init_logger(__name__)
+
+
+class AscendDraftModelProposer(SpecDecodeBaseProposer):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        runner=None,
+    ):
+        super().__init__(
+            vllm_config=vllm_config,
+            device=device,
+            pass_hidden_states_to_model=False,
+            runner=runner,
+        )
+        self._raise_if_vocab_size_mismatch()
+        self._raise_if_draft_tp_mismatch()
+
+    def _raise_if_vocab_size_mismatch(self):
+        self.speculative_config.verify_equal_vocab_size_if_draft_model()
+
+    def _raise_if_draft_tp_mismatch(self):
+        # Note(Tomas Ruiz) If we run the target model with TP > 1 and
+        # the draft model with TP = 1, then the different TP ranks collide.
+        # Specifically when all ranks compile the draft model on rank 0
+        # (because TP=1), then the torch compile cache is overwritten and corrupted.
+        # We need a mechanism like this: https://github.com/vllm-project/vllm/pull/5414
+        # To prevent this error, we assert that both TP sizes must be the same.
+        spec_cfg = self.speculative_config
+        tgt_tp = spec_cfg.target_parallel_config.tensor_parallel_size
+        draft_tp = spec_cfg.draft_parallel_config.tensor_parallel_size
+        if draft_tp != tgt_tp:
+            raise ValueError(
+                f"Currently, 'draft_tensor_parallel_size' and 'tensor_parallel_size' "
+                f"must be the same. Got {draft_tp} and {tgt_tp}. "
+                "Please pass 'draft_tensor_parallel_size' in the speculative_config."
+            )
+
+    def _get_model(self) -> nn.Module:
+        # Draft models may be quantized or on different parallelism,
+        # so we load them with a modified vllm config
+        from vllm.compilation.backends import set_model_tag
+
+        temp_vllm_config = create_vllm_config_for_draft_model(self.vllm_config)
+        with set_model_tag("draft_model"):
+            model = get_model(
+                vllm_config=temp_vllm_config,
+                prefix="draft_model",
+            )
+        return model
+
+    @override
+    def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
+        # Draft models don't share embeddings with the target model
+        pass
+
+    @override
+    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
+        # Draft models don't share lm_head with the target model
+        pass