[Speculative decoding] Adding configuration object for speculative decoding (#3706)

Co-authored-by: Lily Liu <lilyliupku@gmail.com>

tests/spec_decode/e2e/conftest.py

+import pytest
+
+from tests.conftest import cleanup
+from vllm import LLM
+from vllm.model_executor.utils import set_random_seed
+
+
+@pytest.fixture
+def baseline_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                           baseline_llm_kwargs, seed):
+    return create_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                                baseline_llm_kwargs, seed)
+
+
+@pytest.fixture
+def test_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                       test_llm_kwargs, seed):
+    return create_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                                test_llm_kwargs, seed)
+
+
+def create_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
+                         distinct_llm_kwargs, seed):
+    kwargs = {
+        **common_llm_kwargs,
+        **per_test_common_llm_kwargs,
+        **distinct_llm_kwargs,
+    }
+
+    def generator_inner():
+        llm = LLM(**kwargs)
+
+        set_random_seed(seed)
+
+        yield llm
+        del llm
+        cleanup()
+
+    for llm in generator_inner():
+        yield llm
+        del llm

tests/spec_decode/e2e/test_correctness.py

+import pytest
+
+from vllm import SamplingParams
+
+
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        # Use a small model for a fast test.
+        "model": "facebook/opt-125m",
+        "speculative_model": "facebook/opt-125m",
+        "num_speculative_tokens": 5,
+
+        # Required for spec decode.
+        "use_v2_block_manager": True
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("test_llm_kwargs", [{}])
+@pytest.mark.parametrize("seed", [1])
+def test_spec_decode_config(test_llm_generator):
+    output_len = 1024
+    temperature = 0.0
+
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(
+        max_tokens=output_len,
+        ignore_eos=True,
+        temperature=temperature,
+    )
+
+    with pytest.raises(
+            AssertionError,
+            match="Speculative decoding not yet supported for GPU backend"):
+        get_token_ids_from_llm_generator(test_llm_generator, prompts,
+                                         sampling_params)
+
+
+def get_token_ids_from_llm_generator(llm_generator, prompts, sampling_params):
+    for llm in llm_generator:
+        outputs = llm.generate(prompts, sampling_params, use_tqdm=True)
+        token_ids = [output.outputs[0].token_ids for output in outputs]
+        del llm
+
+    return token_ids

tests/spec_decode/utils.py

@@ -107,18 +107,16 @@ def create_worker(cls: type,
         block_size=block_size,
         enforce_eager=enforce_eager,
     )
-
-    (model_config, cache_config, parallel_config, scheduler_config,
-     device_config, _, _) = engine_args.create_engine_configs()
+    engine_config = engine_args.create_engine_config()
 
     distributed_init_method = get_distributed_init_method(
         get_ip(), get_open_port())
 
     worker = cls(
-        model_config=model_config,
-        parallel_config=parallel_config,
-        scheduler_config=scheduler_config,
-        device_config=device_config,
+        model_config=engine_config.model_config,
+        parallel_config=engine_config.parallel_config,
+        scheduler_config=engine_config.scheduler_config,
+        device_config=engine_config.device_config,
         local_rank=0,
         rank=0,
         distributed_init_method=distributed_init_method,
@@ -128,9 +126,9 @@ def create_worker(cls: type,
     worker.init_device()
     worker.load_model()
 
-    cache_config.num_gpu_blocks = num_gpu_blocks
-    cache_config.num_cpu_blocks = 0
-    worker.init_cache_engine(cache_config)
+    engine_config.cache_config.num_gpu_blocks = num_gpu_blocks
+    engine_config.cache_config.num_cpu_blocks = 0
+    worker.init_cache_engine(engine_config.cache_config)
     worker.warm_up_model()
 
     return worker

tests/worker/test_swap.py

@@ -10,19 +10,18 @@ def test_swap() -> None:
     engine_args = EngineArgs(model="facebook/opt-125m",
                              dtype="half",
                              load_format="dummy")
-    (model_config, cache_config, parallel_config, scheduler_config,
-     device_config, _, _) = engine_args.create_engine_configs()
-    cache_config.num_gpu_blocks = 100
-    cache_config.num_cpu_blocks = 100
+    engine_config = engine_args.create_engine_config()
+    engine_config.cache_config.num_gpu_blocks = 100
+    engine_config.cache_config.num_cpu_blocks = 100
 
     # Create the worker.
     distributed_init_method = get_distributed_init_method(
         get_ip(), get_open_port())
     worker = Worker(
-        model_config=model_config,
-        parallel_config=parallel_config,
-        scheduler_config=scheduler_config,
-        device_config=device_config,
+        model_config=engine_config.model_config,
+        parallel_config=engine_config.parallel_config,
+        scheduler_config=engine_config.scheduler_config,
+        device_config=engine_config.device_config,
         local_rank=0,
         rank=0,
         distributed_init_method=distributed_init_method,
@@ -32,7 +31,7 @@ def test_swap() -> None:
     # Initialize the worker.
     worker.init_device()
     worker.load_model()
-    worker.init_cache_engine(cache_config)
+    worker.init_cache_engine(engine_config.cache_config)
     worker.warm_up_model()
 
     # Randomly initialize the cache.

vllm/config.py

 import enum
 import json
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, fields
 from typing import TYPE_CHECKING, ClassVar, Optional, Union
 
 import torch
@@ -617,6 +617,159 @@ class DeviceConfig:
             self.device = torch.device(self.device_type)
 
 
+class SpeculativeConfig:
+    """Configuration for speculative decoding.
+
+    The configuration is currently specialized to draft-model speculative
+    decoding with top-1 proposals.
+    """
+
+    @staticmethod
+    def maybe_create_spec_config(
+        target_model_config: ModelConfig,
+        target_parallel_config: ParallelConfig,
+        target_dtype: str,
+        speculative_model: Optional[str],
+        num_speculative_tokens: Optional[int],
+    ) -> Optional["SpeculativeConfig"]:
+        """Create a SpeculativeConfig if possible, else return None.
+
+        This function attempts to create a SpeculativeConfig object based on the
+        provided parameters. If the necessary conditions are met, it returns an
+        instance of SpeculativeConfig. Otherwise, it returns None.
+
+        Args:
+            target_model_config (ModelConfig): The configuration of the target
+                model.
+            target_parallel_config (ParallelConfig): The parallel configuration
+                for the target model.
+            target_dtype (str): The data type used for the target model.
+            speculative_model (Optional[str]): The name of the speculative
+                model, if provided.
+            num_speculative_tokens (Optional[int]): The number of speculative
+                tokens, if provided.
+
+        Returns:
+            Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if
+                the necessary conditions are met, else None.
+        """
+
+        if (speculative_model is None and num_speculative_tokens is None):
+            return None
+
+        if speculative_model is not None and num_speculative_tokens is None:
+            raise ValueError(
+                "Expected both speculative_model and "
+                "num_speculative_tokens to be provided, but found "
+                f"{speculative_model=} and {num_speculative_tokens=}.")
+
+        # TODO: The user should be able to specify revision/quantization/max
+        # model len for the draft model. It is not currently supported.
+        draft_revision = None
+        draft_code_revision = None
+        draft_quantization = None
+        draft_max_model_len = None
+
+        draft_model_config = ModelConfig(
+            model=speculative_model,
+            tokenizer=target_model_config.tokenizer,
+            tokenizer_mode=target_model_config.tokenizer_mode,
+            trust_remote_code=target_model_config.trust_remote_code,
+            download_dir=target_model_config.download_dir,
+            load_format=target_model_config.load_format,
+            dtype=target_model_config.dtype,
+            seed=target_model_config.seed,
+            revision=draft_revision,
+            code_revision=draft_code_revision,
+            tokenizer_revision=target_model_config.tokenizer_revision,
+            max_model_len=draft_max_model_len,
+            quantization=draft_quantization,
+            enforce_eager=target_model_config.enforce_eager,
+            max_context_len_to_capture=target_model_config.
+            max_context_len_to_capture,
+            max_logprobs=target_model_config.max_logprobs,
+        )
+
+        draft_parallel_config = (
+            SpeculativeConfig.create_draft_parallel_config(
+                target_parallel_config))
+
+        return SpeculativeConfig(
+            draft_model_config,
+            draft_parallel_config,
+            num_speculative_tokens,
+        )
+
+    @staticmethod
+    def create_draft_parallel_config(
+            target_parallel_config: ParallelConfig) -> ParallelConfig:
+        """Create a parallel config for use by the draft worker.
+
+        This is mostly a copy of the target parallel config. In the future the
+        draft worker can have a different parallel strategy, e.g. TP=1.
+        """
+        draft_parallel_config = ParallelConfig(
+            pipeline_parallel_size=target_parallel_config.
+            pipeline_parallel_size,
+            tensor_parallel_size=target_parallel_config.tensor_parallel_size,
+            worker_use_ray=target_parallel_config.worker_use_ray,
+            max_parallel_loading_workers=target_parallel_config.
+            max_parallel_loading_workers,
+            disable_custom_all_reduce=target_parallel_config.
+            disable_custom_all_reduce,
+            tokenizer_pool_config=target_parallel_config.tokenizer_pool_config,
+            ray_workers_use_nsight=target_parallel_config.
+            ray_workers_use_nsight,
+            placement_group=target_parallel_config.placement_group,
+        )
+
+        return draft_parallel_config
+
+    def __init__(
+        self,
+        draft_model_config: ModelConfig,
+        draft_parallel_config: ParallelConfig,
+        num_speculative_tokens: int,
+    ):
+        """Create a SpeculativeConfig object.
+
+        Args:
+            draft_model_config: ModelConfig for the draft model.
+            draft_parallel_config: ParallelConfig for the draft model.
+            num_speculative_tokens: The number of tokens to sample from the
+                draft model before scoring with the target model.
+        """
+        self.draft_model_config = draft_model_config
+        self.draft_parallel_config = draft_parallel_config
+        self.num_speculative_tokens = num_speculative_tokens
+
+        self._verify_args()
+
+    def _verify_args(self) -> None:
+        if self.num_speculative_tokens <= 0:
+            raise ValueError("Expected num_speculative_tokens to be greater "
+                             f"than zero ({self.num_speculative_tokens}).")
+
+        if self.draft_model_config:
+            self.draft_model_config.verify_with_parallel_config(
+                self.draft_parallel_config)
+
+    @property
+    def num_lookahead_slots(self) -> int:
+        """The number of additional slots the scheduler should allocate per
+        step, in addition to the slots allocated for each known token.
+
+        This is equal to the number of speculative tokens, as each speculative
+        token must be scored.
+        """
+        return self.num_speculative_tokens
+
+    def __repr__(self) -> str:
+        draft_model = self.draft_model_config.model
+        num_spec_tokens = self.num_speculative_tokens
+        return f"SpeculativeConfig({draft_model=}, {num_spec_tokens=})"
+
+
 @dataclass
 class LoRAConfig:
     max_lora_rank: int
@@ -838,3 +991,36 @@ def _get_and_verify_max_len(
                 "to incorrect model outputs or CUDA errors. Make sure the "
                 "value is correct and within the model context size.")
     return int(max_model_len)
+
+
+@dataclass(frozen=True)
+class EngineConfig:
+    """Dataclass which contains all engine-related configuration. This
+    simplifies passing around the distinct configurations in the codebase.
+    """
+
+    model_config: ModelConfig
+    cache_config: CacheConfig
+    parallel_config: ParallelConfig
+    scheduler_config: SchedulerConfig
+    device_config: DeviceConfig
+    lora_config: Optional[LoRAConfig]
+    vision_language_config: Optional[VisionLanguageConfig]
+    speculative_config: Optional[SpeculativeConfig]
+
+    def __post_init__(self):
+        """Verify configs are valid & consistent with each other.
+        """
+        self.model_config.verify_with_parallel_config(self.parallel_config)
+        self.cache_config.verify_with_parallel_config(self.parallel_config)
+
+        if self.lora_config:
+            self.lora_config.verify_with_model_config(self.model_config)
+            self.lora_config.verify_with_scheduler_config(
+                self.scheduler_config)
+
+    def to_dict(self):
+        """Return the configs as a dictionary, for use in **kwargs.
+        """
+        return dict(
+            (field.name, getattr(self, field.name)) for field in fields(self))

vllm/engine/arg_utils.py

 import argparse
 import dataclasses
 from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Optional
 
-from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, TokenizerPoolConfig,
+from vllm.config import (CacheConfig, DeviceConfig, EngineConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, TokenizerPoolConfig,
                          VisionLanguageConfig)
 from vllm.utils import str_to_int_tuple
 
@@ -61,9 +62,14 @@ class EngineArgs:
     image_token_id: Optional[int] = None
     image_input_shape: Optional[str] = None
     image_feature_size: Optional[int] = None
+
     scheduler_delay_factor: float = 0.0
     enable_chunked_prefill: bool = False
 
+    # Speculative decoding configuration.
+    speculative_model: Optional[str] = None
+    num_speculative_tokens: Optional[int] = None
+
     def __post_init__(self):
         if self.tokenizer is None:
             self.tokenizer = self.model
@@ -371,6 +377,20 @@ class EngineArgs:
             default=False,
             help='If True, the prefill requests can be chunked based on the '
             'max_num_batched_tokens')
+
+        parser.add_argument(
+            '--speculative-model',
+            type=str,
+            default=None,
+            help=
+            'The name of the draft model to be used in speculative decoding.')
+
+        parser.add_argument(
+            '--num-speculative-tokens',
+            type=int,
+            default=None,
+            help='The number of speculative tokens to sample from '
+            'the draft model in speculative decoding')
         return parser
 
     @classmethod
@@ -381,11 +401,7 @@ class EngineArgs:
         engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
         return engine_args
 
-    def create_engine_configs(
-        self,
-    ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig,
-               DeviceConfig, Optional[LoRAConfig],
-               Optional[VisionLanguageConfig]]:
+    def create_engine_config(self, ) -> EngineConfig:
         device_config = DeviceConfig(self.device)
         model_config = ModelConfig(
             self.model, self.tokenizer, self.tokenizer_mode,
@@ -409,12 +425,23 @@ class EngineArgs:
                 self.tokenizer_pool_type,
                 self.tokenizer_pool_extra_config,
             ), self.ray_workers_use_nsight)
+
+        speculative_config = SpeculativeConfig.maybe_create_spec_config(
+            target_model_config=model_config,
+            target_parallel_config=parallel_config,
+            target_dtype=self.dtype,
+            speculative_model=self.speculative_model,
+            num_speculative_tokens=self.num_speculative_tokens,
+        )
+
         scheduler_config = SchedulerConfig(
             self.max_num_batched_tokens,
             self.max_num_seqs,
             model_config.max_model_len,
             self.use_v2_block_manager,
-            num_lookahead_slots=self.num_lookahead_slots,
+            num_lookahead_slots=(self.num_lookahead_slots
+                                 if speculative_config is None else
+                                 speculative_config.num_lookahead_slots),
             delay_factor=self.scheduler_delay_factor,
             enable_chunked_prefill=self.enable_chunked_prefill,
         )
@@ -442,8 +469,14 @@ class EngineArgs:
         else:
             vision_language_config = None
 
-        return (model_config, cache_config, parallel_config, scheduler_config,
-                device_config, lora_config, vision_language_config)
+        return EngineConfig(model_config=model_config,
+                            cache_config=cache_config,
+                            parallel_config=parallel_config,
+                            scheduler_config=scheduler_config,
+                            device_config=device_config,
+                            lora_config=lora_config,
+                            vision_language_config=vision_language_config,
+                            speculative_config=speculative_config)
 
 
 @dataclass

vllm/engine/async_llm_engine.py

@@ -328,28 +328,27 @@ class AsyncLLMEngine:
     ) -> "AsyncLLMEngine":
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
-        engine_configs = engine_args.create_engine_configs()
-        parallel_config = engine_configs[2]
-        device_config = engine_configs[4]
+        engine_config = engine_args.create_engine_config()
 
-        if device_config.device_type == "neuron":
+        if engine_config.device_config.device_type == "neuron":
             raise NotImplementedError("Neuron is not supported for "
                                       "async engine yet.")
-        elif parallel_config.worker_use_ray or engine_args.engine_use_ray:
-            initialize_ray_cluster(parallel_config)
+        elif (engine_config.parallel_config.worker_use_ray
+              or engine_args.engine_use_ray):
+            initialize_ray_cluster(engine_config.parallel_config)
             from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync
             executor_class = RayGPUExecutorAsync
         else:
-            assert parallel_config.world_size == 1, (
+            assert engine_config.parallel_config.world_size == 1, (
                 "Ray is required if parallel_config.world_size > 1.")
             from vllm.executor.gpu_executor import GPUExecutorAsync
             executor_class = GPUExecutorAsync
         # Create the async LLM engine.
         engine = cls(
-            parallel_config.worker_use_ray,
+            engine_config.parallel_config.worker_use_ray,
             engine_args.engine_use_ray,
-            *engine_configs,
-            executor_class,
+            **engine_config.to_dict(),
+            executor_class=executor_class,
             log_requests=not engine_args.disable_log_requests,
             log_stats=not engine_args.disable_log_stats,
             max_log_len=engine_args.max_log_len,

vllm/engine/llm_engine.py

@@ -5,7 +5,8 @@ from transformers import PreTrainedTokenizer
 
 import vllm
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
 from vllm.core.scheduler import Scheduler, SchedulerOutputs
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.metrics import StatLogger, Stats
@@ -52,6 +53,11 @@ class LLMEngine:
         parallel_config: The configuration related to distributed execution.
         scheduler_config: The configuration related to the request scheduler.
         device_config: The configuration related to the device.
+        lora_config (Optional): The configuration related to serving multi-LoRA.
+        vision_language_config (Optional): The configuration related to vision
+            language models.
+        speculative_config (Optional): The configuration related to speculative
+            decoding.
         executor_class: The model executor class for managing distributed
             execution.
         log_stats: Whether to log statistics.
@@ -66,7 +72,8 @@ class LLMEngine:
         scheduler_config: SchedulerConfig,
         device_config: DeviceConfig,
         lora_config: Optional[LoRAConfig],
-        vision_language_config: Optional["VisionLanguageConfig"],
+        vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
         executor_class: Type[ExecutorBase],
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
@@ -74,6 +81,7 @@ class LLMEngine:
         logger.info(
             f"Initializing an LLM engine (v{vllm.__version__}) with config: "
             f"model={model_config.model!r}, "
+            f"speculative_config={speculative_config!r}, "
             f"tokenizer={model_config.tokenizer!r}, "
             f"tokenizer_mode={model_config.tokenizer_mode}, "
             f"revision={model_config.revision}, "
@@ -100,17 +108,23 @@ class LLMEngine:
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.device_config = device_config
+        self.speculative_config = speculative_config
         self.log_stats = log_stats
-        self._verify_args()
 
         self._init_tokenizer()
         self.detokenizer = Detokenizer(self.tokenizer)
         self.seq_counter = Counter()
 
-        self.model_executor = executor_class(model_config, cache_config,
-                                             parallel_config, scheduler_config,
-                                             device_config, lora_config,
-                                             vision_language_config)
+        self.model_executor = executor_class(
+            model_config=model_config,
+            cache_config=cache_config,
+            parallel_config=parallel_config,
+            scheduler_config=scheduler_config,
+            device_config=device_config,
+            lora_config=lora_config,
+            vision_language_config=vision_language_config,
+            speculative_config=speculative_config,
+        )
 
         # If usage stat is enabled, collect relevant info.
         if is_usage_stats_enabled():
@@ -171,30 +185,28 @@ class LLMEngine:
     ) -> "LLMEngine":
         """Creates an LLM engine from the engine arguments."""
         # Create the engine configs.
-        engine_configs = engine_args.create_engine_configs()
-        parallel_config = engine_configs[2]
-        device_config = engine_configs[4]
+        engine_config = engine_args.create_engine_config()
 
         # Initialize the cluster and specify the executor class.
-        if device_config.device_type == "neuron":
+        if engine_config.device_config.device_type == "neuron":
             from vllm.executor.neuron_executor import NeuronExecutor
             executor_class = NeuronExecutor
-        elif device_config.device_type == "cpu":
+        elif engine_config.device_config.device_type == "cpu":
             from vllm.executor.cpu_executor import CPUExecutor
             executor_class = CPUExecutor
-        elif parallel_config.worker_use_ray:
-            initialize_ray_cluster(parallel_config)
+        elif engine_config.parallel_config.worker_use_ray:
+            initialize_ray_cluster(engine_config.parallel_config)
             from vllm.executor.ray_gpu_executor import RayGPUExecutor
             executor_class = RayGPUExecutor
         else:
-            assert parallel_config.world_size == 1, (
+            assert engine_config.parallel_config.world_size == 1, (
                 "Ray is required if parallel_config.world_size > 1.")
             from vllm.executor.gpu_executor import GPUExecutor
             executor_class = GPUExecutor
 
         # Create the LLM engine.
         engine = cls(
-            *engine_configs,
+            **engine_config.to_dict(),
             executor_class=executor_class,
             log_stats=not engine_args.disable_log_stats,
             usage_context=usage_context,

vllm/executor/executor_base.py

@@ -2,7 +2,8 @@ from abc import ABC, abstractmethod
 from typing import Dict, List, Optional
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
 from vllm.lora.request import LoRARequest
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 
@@ -25,6 +26,7 @@ class ExecutorBase(ABC):
         device_config: DeviceConfig,
         lora_config: Optional[LoRAConfig],
         vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
     ) -> None:
         raise NotImplementedError
 

vllm/executor/gpu_executor.py

 from typing import Dict, List, Optional
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.executor.utils import check_block_size_valid
 from vllm.logger import init_logger
@@ -24,6 +25,7 @@ class GPUExecutor(ExecutorBase):
         device_config: DeviceConfig,
         lora_config: Optional[LoRAConfig],
         vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
     ) -> None:
         self.model_config = model_config
         self.cache_config = cache_config
@@ -33,6 +35,9 @@ class GPUExecutor(ExecutorBase):
         self.device_config = device_config
         self.vision_language_config = vision_language_config
 
+        assert (not speculative_config
+                ), "Speculative decoding not yet supported for GPU backend"
+
         # Instantiate the worker and load the model to GPU.
         self._init_worker()
 

vllm/executor/neuron_executor.py

 from typing import Dict, List, Optional
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -21,6 +22,7 @@ class NeuronExecutor(ExecutorBase):
         device_config: DeviceConfig,
         lora_config: Optional[LoRAConfig],
         vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
     ) -> None:
         self.model_config = model_config
         self.cache_config = cache_config
@@ -28,6 +30,8 @@ class NeuronExecutor(ExecutorBase):
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.device_config = device_config
+        assert (not speculative_config
+                ), "Speculative decoding not yet supported for Neuron backend."
 
         # Set the number of GPU blocks to be the same as the maximum number of
         # sequences that can be processed in a single batch. This is equivalent

vllm/executor/ray_gpu_executor.py

@@ -6,7 +6,8 @@ from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
 from vllm.engine.ray_utils import RayWorkerVllm, ray
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.executor.utils import check_block_size_valid
@@ -41,6 +42,7 @@ class RayGPUExecutor(ExecutorBase):
         device_config: DeviceConfig,
         lora_config: Optional[LoRAConfig],
         vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
     ) -> None:
         self.model_config = model_config
         self.cache_config = cache_config
@@ -49,6 +51,8 @@ class RayGPUExecutor(ExecutorBase):
         self.scheduler_config = scheduler_config
         self.device_config = device_config
         self.vision_language_config = vision_language_config
+        assert (not speculative_config
+                ), "Speculative decoding not yet supported for RayGPU backend."
 
         assert self.parallel_config.worker_use_ray
         placement_group = self.parallel_config.placement_group