[Refactor] refactor spec decode

vllm_ascend/spec_decode/__init__.py

@@ -0,0 +1,33 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
+#
+from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
+from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
+from vllm_ascend.spec_decode.ngram_proposer import NgramProposer
+
+
+def get_spec_decode_method(method, vllm_config, device, runner):
+    if method == "ngram":
+        return NgramProposer(vllm_config, device, runner)
+    elif method in ["eagle", "eagle3"]:
+        return EagleProposer(vllm_config, device, runner)
+    elif method == 'deepseek_mtp':
+        return MtpProposer(vllm_config, device, runner)
+    else:
+        raise ValueError("Unknown speculative decoding method: "
+                         f"{method}")

vllm_ascend/spec_decode/interface.py

@@ -0,0 +1,51 @@
+import enum
+from typing import Optional
+
+import torch
+from vllm.config import VllmConfig
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+
+
+class SpecDcodeType(enum.Enum):
+    NGRAM = 0
+    EAGLE = 1
+    EAGLE3 = 2
+    MTP = 4
+
+
+class Proposer:
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 device: torch.device = None,
+                 runner=None):
+        pass
+
+    def load_model(self, model):
+        """Called by load_model in model_runner"""
+        raise NotImplementedError
+
+    @torch.inference_mode()
+    def dummy_run(self,
+                  num_tokens: int,
+                  with_prefill: bool = False,
+                  skip_attn: bool = False,
+                  num_reqs: int = 0,
+                  num_tokens_across_dp: Optional[torch.Tensor] = None):
+        """Called by dummy_run in modle_runner"""
+        raise NotImplementedError
+
+    def generate_token_ids(self,
+                           valid_sampled_token_ids: list[list[int]],
+                           sampling_metadata: SamplingMetadata = None,
+                           scheduler_output: SchedulerOutput = None,
+                           spec_decode_metadata: SpecDecodeMetadata = None,
+                           positions: torch.Tensor = None,
+                           num_scheduled_tokens: int = 0,
+                           hidden_states: torch.Tensor = None,
+                           attn_metadata=None,
+                           aux_hidden_states: torch.Tensor = None):
+        """Called by execute_model in model_runner"""
+        raise NotImplementedError