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Move SpeculativeConfig from config/__init__.py to config/speculative.py (#24904)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
GitHub
parent
7ea5c73ad7
commit
0faf3cc3e8
@ -24,7 +24,7 @@ from pydantic import (ConfigDict, SkipValidation, field_validator,
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model_validator)
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from pydantic.dataclasses import dataclass
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from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
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from typing_extensions import Self, assert_never, runtime_checkable
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from typing_extensions import assert_never, runtime_checkable
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import vllm.envs as envs
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from vllm import version
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@ -41,6 +41,7 @@ from vllm.config.multimodal import (MMCacheType, MMEncoderTPMode,
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from vllm.config.parallel import (DistributedExecutorBackend, EPLBConfig,
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ParallelConfig)
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from vllm.config.scheduler import SchedulerConfig, SchedulerPolicy
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from vllm.config.speculative import SpeculativeConfig
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from vllm.config.utils import ConfigType, config
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QuantizationMethods
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@ -1846,528 +1847,6 @@ class DeviceConfig:
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self.device = torch.device(self.device_type)
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SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa",
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"mlp_speculator", "draft_model", "deepseek_mtp",
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"ernie_mtp", "qwen3_next_mtp"]
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@config
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@dataclass
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class SpeculativeConfig:
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"""Configuration for speculative decoding."""
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# General speculative decoding control
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num_speculative_tokens: SkipValidation[int] = None # type: ignore
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"""The number of speculative tokens, if provided. It will default to the
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number in the draft model config if present, otherwise, it is required."""
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model: Optional[str] = None
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"""The name of the draft model, eagle head, or additional weights, if
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provided."""
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method: Optional[SpeculativeMethod] = None
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"""The name of the speculative method to use. If users provide and set the
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`model` param, the speculative method type will be detected automatically
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if possible, if `model` param is not provided, the method name must be
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provided.
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If using `ngram` method, the related configuration `prompt_lookup_max` and
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`prompt_lookup_min` should be considered."""
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draft_tensor_parallel_size: Optional[int] = None
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"""The degree of the tensor parallelism for the draft model. Can only be 1
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or the same as the target model's tensor parallel size."""
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disable_logprobs: bool = True
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"""If set to True, token log probabilities are not returned during
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speculative decoding. If set to False, token log probabilities are returned
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according to the log probability settings in SamplingParams."""
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# Draft model configuration
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quantization: Optional[me_quant.QuantizationMethods] = None
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"""Quantization method that was used to quantize the draft model weights.
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If `None`, we assume the model weights are not quantized. Note that it only
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takes effect when using the draft model-based speculative method."""
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max_model_len: Optional[int] = None
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"""The maximum model length of the draft model. Used when testing the
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ability to skip speculation for some sequences."""
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revision: Optional[str] = None
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"""The specific model version to use for the draft model. It can be a
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branch name, a tag name, or a commit id. If unspecified, will use the
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default version."""
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code_revision: Optional[str] = None
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"""The specific revision to use for the draft model code on Hugging Face
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Hub. It can be a branch name, a tag name, or a commit id. If unspecified,
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will use the default version."""
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# Advanced control
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disable_by_batch_size: Optional[int] = None
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"""Disable speculative decoding for new incoming requests when the number
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of enqueued requests is larger than this value, if provided."""
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# Ngram proposer configuration
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prompt_lookup_max: Optional[int] = None
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"""Maximum size of ngram token window when using Ngram proposer, required
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when method is set to ngram."""
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prompt_lookup_min: Optional[int] = None
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"""Minimum size of ngram token window when using Ngram proposer, if
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provided. Defaults to 1."""
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speculative_token_tree: Optional[str] = None
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"""Specifies the tree structure for speculative token generation.
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"""
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# required configuration params passed from engine
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target_model_config: SkipValidation[ModelConfig] = None # type: ignore
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"""The configuration of the target model."""
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target_parallel_config: SkipValidation[
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ParallelConfig] = None # type: ignore
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"""The parallel configuration for the target model."""
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enable_chunked_prefill: SkipValidation[bool] = None # type: ignore
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"""Whether vLLM is configured to use chunked prefill or not. Used for
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raising an error since it's not yet compatible with speculative decode."""
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disable_log_stats: SkipValidation[bool] = None # type: ignore
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"""Whether to disable the periodic printing of stage times in speculative
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decoding."""
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# params generated in the post-init stage
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draft_model_config: SkipValidation[ModelConfig] = None # type: ignore
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"""The configuration of the draft model initialized internal."""
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draft_parallel_config: SkipValidation[
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ParallelConfig] = None # type: ignore
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"""The parallel configuration for the draft model initialized internal."""
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def compute_hash(self) -> str:
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"""
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WARNING: Whenever a new field is added to this config,
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ensure that it is included in the factors list if
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it affects the computation graph.
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Provide a hash that uniquely identifies all the configs
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that affect the structure of the computation
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graph from input ids/embeddings to the final hidden states,
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excluding anything before input ids/embeddings and after
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the final hidden states.
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"""
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factors: list[Any] = []
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# Eagle3 affects the computation graph because it returns intermediate
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# hidden states in addition to the final hidden state.
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factors.append(self.method == "eagle3")
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hash_str = hashlib.md5(str(factors).encode(),
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usedforsecurity=False).hexdigest()
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return hash_str
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@staticmethod
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def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
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if hf_config.model_type == "deepseek_v3":
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hf_config.model_type = "deepseek_mtp"
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if hf_config.model_type == "deepseek_mtp":
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update({
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"n_predict": n_predict,
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"architectures": ["DeepSeekMTPModel"]
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})
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if hf_config.architectures[0] == "MiMoForCausalLM":
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hf_config.model_type = "mimo_mtp"
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update({
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"num_hidden_layers": 0,
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"n_predict": n_predict,
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"architectures": ["MiMoMTPModel"]
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})
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if hf_config.architectures[0] == "Glm4MoeForCausalLM":
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hf_config.model_type = "glm4_moe_mtp"
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update({
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"num_hidden_layers": 0,
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"n_predict": n_predict,
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"architectures": ["Glm4MoeMTPModel"]
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})
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if hf_config.model_type == "ernie4_5_moe":
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hf_config.model_type = "ernie_mtp"
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if hf_config.model_type == "ernie_mtp":
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update({
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"n_predict": n_predict,
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"architectures": ["ErnieMTPModel"]
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})
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if hf_config.model_type == "qwen3_next":
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hf_config.model_type = "qwen3_next_mtp"
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if hf_config.model_type == "qwen3_next_mtp":
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update({
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"n_predict": n_predict,
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"architectures": ["Qwen3NextMTP"]
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})
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return hf_config
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def __post_init__(self):
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# Note: "method" is a new parameter that helps to extend the
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# configuration of non-model-based proposers, and the "model" parameter
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# will be used to set the draft model, eagle head, or additional weight
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# when needed. If users do not specify "method", the speculative method
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# will be detected automatically if possible. If the speculative method
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# can not be detected, it will be considered as the "draft_model" by
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# default.
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if self.model is None and self.num_speculative_tokens is not None:
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# TODO(Shangming): Refactor mtp configuration logic when supporting
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# mtp acceleration for more models besides deepseek_v3
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if self.target_model_config and \
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(self.target_model_config.hf_text_config.model_type \
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== "deepseek_v3" or
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self.target_model_config.hf_text_config.model_type in
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("mimo","ernie4_5_moe", "qwen3_next")):
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# use the draft model from the same model:
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self.model = self.target_model_config.model
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# Align the quantization of draft model for cases such as
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# --quantization fp8 with a bf16 checkpoint.
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if not self.quantization:
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self.quantization = self.target_model_config.quantization
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elif self.method in ("ngram", "[ngram]"):
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self.model = "ngram"
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else:
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raise ValueError("num_speculative_tokens was provided without "
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"speculative model.")
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# Automatically configure the method for ngram when "model" is used
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# instead of "method"
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if self.method is None and (self.model is not None
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and self.model in ("ngram", "[ngram]")):
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self.method = "ngram"
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if self.method in ("ngram", "[ngram]"):
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# Unified to "ngram" internally
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self.method = "ngram"
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# Set default values if not provided
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if (self.prompt_lookup_min is None
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and self.prompt_lookup_max is None):
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# TODO(woosuk): Tune these values. They are arbitrarily chosen.
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self.prompt_lookup_min = 5
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self.prompt_lookup_max = 5
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elif self.prompt_lookup_min is None:
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assert self.prompt_lookup_max is not None
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self.prompt_lookup_min = self.prompt_lookup_max
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elif self.prompt_lookup_max is None:
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assert self.prompt_lookup_min is not None
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self.prompt_lookup_max = self.prompt_lookup_min
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# Validate values
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if self.prompt_lookup_min < 1:
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raise ValueError(
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f"prompt_lookup_min={self.prompt_lookup_min} must be > 0")
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if self.prompt_lookup_max < 1:
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raise ValueError(
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f"prompt_lookup_max={self.prompt_lookup_max} must be > 0")
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if self.prompt_lookup_min > self.prompt_lookup_max:
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raise ValueError(
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f"prompt_lookup_min={self.prompt_lookup_min} must "
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f"be <= prompt_lookup_max={self.prompt_lookup_max}")
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# TODO: current we still need extract vocab_size from target model
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# config, in future, we may try refactor it out, and set
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# draft related config as None here.
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self.draft_model_config = self.target_model_config
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self.draft_parallel_config = self.target_parallel_config
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else:
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self.prompt_lookup_max = 0
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self.prompt_lookup_min = 0
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if self.model is not None:
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self.draft_model_config = ModelConfig(
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model=self.model,
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runner="draft",
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tokenizer=self.target_model_config.tokenizer,
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tokenizer_mode=self.target_model_config.tokenizer_mode,
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trust_remote_code=self.target_model_config.
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trust_remote_code,
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allowed_local_media_path=self.target_model_config.
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allowed_local_media_path,
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dtype=self.target_model_config.dtype,
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seed=self.target_model_config.seed,
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revision=self.revision,
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code_revision=self.code_revision,
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tokenizer_revision=self.target_model_config.
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tokenizer_revision,
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spec_target_max_model_len=self.target_model_config.
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max_model_len,
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quantization=self.quantization,
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enforce_eager=self.target_model_config.enforce_eager,
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max_seq_len_to_capture=self.target_model_config.
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max_seq_len_to_capture,
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max_logprobs=self.target_model_config.max_logprobs,
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hf_overrides=SpeculativeConfig.hf_config_override,
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)
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# Automatically detect the method
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if self.method in ('eagle', 'eagle3'):
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pass
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# examples:
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# yuhuili/EAGLE-LLaMA3-Instruct-8B
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# yuhuili/EAGLE3-LLaMA3.1-Instruct-8B
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# AngelSlim/Qwen3-8B_eagle3
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elif "eagle-" in self.draft_model_config.model.lower():
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self.method = "eagle"
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elif "eagle3" in self.draft_model_config.model.lower():
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self.method = "eagle3"
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elif self.draft_model_config.hf_config.model_type == "medusa":
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self.method = "medusa"
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elif (self.draft_model_config.hf_config.model_type ==
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"mlp_speculator"):
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self.method = "mlp_speculator"
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elif (self.draft_model_config.hf_config.model_type
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in ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp")):
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self.method = "deepseek_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Deepseek MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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elif (self.draft_model_config.hf_config.model_type ==
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"ernie_mtp"):
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self.method = "ernie_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Ernie MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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elif (self.draft_model_config.hf_config.model_type ==
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"qwen3_next_mtp"):
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self.method = "qwen3_next_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Qwen3Next MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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else:
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self.method = "draft_model"
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raise NotImplementedError(
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"Speculative decoding with draft model is not "
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"supported yet. Please consider using other "
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"speculative decoding methods such as ngram, medusa, "
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"eagle, or deepseek_mtp.")
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# Replace hf_config for EAGLE draft_model
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if self.method in ("eagle", "eagle3"):
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if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
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raise ValueError(
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"Chunked prefill and EAGLE are not compatible "
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"when using V0.")
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from vllm.transformers_utils.configs import (
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SpeculatorsConfig)
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from vllm.transformers_utils.configs.eagle import (
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EAGLEConfig)
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if isinstance(self.draft_model_config.hf_config,
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(EAGLEConfig, SpeculatorsConfig)):
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pass
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else:
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eagle_config = EAGLEConfig(
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self.draft_model_config.hf_config,
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method=self.method,
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model_type="eagle")
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self.draft_model_config.hf_config = eagle_config
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if (self.num_speculative_tokens is not None
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and hasattr(self.draft_model_config.hf_config,
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"num_lookahead_tokens")):
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self.draft_model_config.hf_config.num_lookahead_tokens = \
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self.num_speculative_tokens
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n_predict = getattr(self.draft_model_config.hf_config,
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"n_predict", None)
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if n_predict is not None:
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if self.num_speculative_tokens is None:
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# Default to max value defined in draft model config.
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self.num_speculative_tokens = n_predict
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elif self.num_speculative_tokens > n_predict and \
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self.num_speculative_tokens % n_predict != 0:
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# Ensure divisibility for MTP module reuse.
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raise ValueError(
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f"num_speculative_tokens:{self.num_speculative_tokens}"
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f" must be divisible by {n_predict=}")
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if self.speculative_token_tree is None:
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# Generate chain of tokens.
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self.speculative_token_tree = str([
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(i + 1) * (0, )
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for i in range(self.num_speculative_tokens)
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])
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else:
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# Sort the token tree breadth-first.
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tree_choices = ast.literal_eval(
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self.speculative_token_tree)
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self.speculative_token_tree = str(
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sorted(tree_choices, key=lambda t: (len(t), t)))
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self.draft_tensor_parallel_size = \
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SpeculativeConfig._verify_and_get_draft_tp(
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self.target_parallel_config,
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self.draft_tensor_parallel_size,
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self.draft_model_config.hf_config
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)
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self.draft_model_config.max_model_len = (
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SpeculativeConfig._maybe_override_draft_max_model_len(
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self.max_model_len,
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self.draft_model_config.max_model_len,
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self.target_model_config.max_model_len,
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))
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self.draft_parallel_config = (
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SpeculativeConfig.create_draft_parallel_config(
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self.target_parallel_config,
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self.draft_tensor_parallel_size))
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@staticmethod
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def _maybe_override_draft_max_model_len(
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speculative_max_model_len: Optional[int],
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draft_max_model_len: int,
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target_max_model_len: int,
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) -> int:
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"""Determine the max sequence len for the draft model. This is usually
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the draft_max_model_len, but may be the target_max_model_len if it is
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less than the draft_max_model_len, or may be speculative_max_model_len
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if it is specified.
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This is necessary so that sequences do not exceed the capacity of the
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draft model or the target model.
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speculative_max_model_len is mainly used for testing that sequences can
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skip speculation.
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"""
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if speculative_max_model_len is not None:
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|
||||
if speculative_max_model_len > draft_max_model_len:
|
||||
raise ValueError(f"{speculative_max_model_len=} cannot be "
|
||||
f"larger than {draft_max_model_len=}")
|
||||
|
||||
if speculative_max_model_len > target_max_model_len:
|
||||
raise ValueError(f"{speculative_max_model_len=} cannot be "
|
||||
f"larger than {target_max_model_len=}")
|
||||
|
||||
return speculative_max_model_len
|
||||
|
||||
return min(
|
||||
draft_max_model_len,
|
||||
target_max_model_len,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _verify_and_get_draft_tp(
|
||||
target_parallel_config: ParallelConfig,
|
||||
speculative_draft_tensor_parallel_size: Optional[int],
|
||||
draft_hf_config: PretrainedConfig) -> int:
|
||||
"""
|
||||
Verifies and adjusts the tensor parallel size for a draft model
|
||||
specified using speculative_draft_tensor_parallel_size.
|
||||
"""
|
||||
# If speculative_draft_tensor_parallel_size is unset then set it
|
||||
# appropriately else verify that it is set correctly.
|
||||
if speculative_draft_tensor_parallel_size is None:
|
||||
if draft_hf_config.model_type == "mlp_speculator":
|
||||
speculative_draft_tensor_parallel_size = 1
|
||||
if target_parallel_config.tensor_parallel_size > 1:
|
||||
logger.warning(
|
||||
"%s cannot currently be run with tp>1; "
|
||||
"setting speculative_draft_tensor_parallel_size=1",
|
||||
draft_hf_config.model_type)
|
||||
else:
|
||||
speculative_draft_tensor_parallel_size = \
|
||||
target_parallel_config.tensor_parallel_size
|
||||
elif speculative_draft_tensor_parallel_size not in (
|
||||
1, target_parallel_config.tensor_parallel_size):
|
||||
raise ValueError(
|
||||
f"{speculative_draft_tensor_parallel_size=} cannot be "
|
||||
f"other value than 1 or target model tensor_parallel_size")
|
||||
return speculative_draft_tensor_parallel_size
|
||||
|
||||
@staticmethod
|
||||
def create_draft_parallel_config(
|
||||
target_parallel_config: ParallelConfig,
|
||||
speculative_draft_tensor_parallel_size: int,
|
||||
) -> ParallelConfig:
|
||||
"""Create a parallel config for use by the draft worker.
|
||||
|
||||
This is mostly a copy of the target parallel config, except the tp_size.
|
||||
"""
|
||||
draft_parallel_config = ParallelConfig(
|
||||
pipeline_parallel_size=target_parallel_config.
|
||||
pipeline_parallel_size,
|
||||
tensor_parallel_size=speculative_draft_tensor_parallel_size,
|
||||
distributed_executor_backend=target_parallel_config.
|
||||
distributed_executor_backend,
|
||||
max_parallel_loading_workers=target_parallel_config.
|
||||
max_parallel_loading_workers,
|
||||
disable_custom_all_reduce=target_parallel_config.
|
||||
disable_custom_all_reduce,
|
||||
ray_workers_use_nsight=target_parallel_config.
|
||||
ray_workers_use_nsight,
|
||||
placement_group=target_parallel_config.placement_group,
|
||||
)
|
||||
|
||||
return draft_parallel_config
|
||||
|
||||
@model_validator(mode='after')
|
||||
def _verify_args(self) -> Self:
|
||||
if self.num_speculative_tokens is None:
|
||||
raise ValueError(
|
||||
"num_speculative_tokens must be provided with "
|
||||
"speculative model unless the draft model config contains an "
|
||||
"n_predict parameter.")
|
||||
|
||||
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)
|
||||
|
||||
if (self.disable_by_batch_size is not None
|
||||
and self.disable_by_batch_size < 2):
|
||||
raise ValueError("Expect the batch size threshold of disabling "
|
||||
"speculative decoding is > 1, but got "
|
||||
f"{self.disable_by_batch_size=}")
|
||||
|
||||
eagle3_target_supported = ["llama", "qwen"]
|
||||
if self.method == "eagle3" and self.target_model_config and not any(
|
||||
supported_model in
|
||||
self.target_model_config.hf_text_config.model_type
|
||||
for supported_model in eagle3_target_supported):
|
||||
raise ValueError(
|
||||
f"Eagle3 is only supported for {eagle3_target_supported} models. " # noqa: E501
|
||||
f"Got {self.target_model_config.hf_text_config.model_type=}")
|
||||
|
||||
return self
|
||||
|
||||
@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 use_eagle(self) -> bool:
|
||||
return self.method in ("eagle", "eagle3", "deepseek_mtp", "ernie_mtp",
|
||||
"qwen3_next_mtp")
|
||||
|
||||
def __repr__(self) -> str:
|
||||
method = self.method
|
||||
model = None if method == "ngram" else self.draft_model_config.model
|
||||
num_spec_tokens = self.num_speculative_tokens
|
||||
return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
|
||||
|
||||
|
||||
@config
|
||||
@dataclass
|
||||
class PoolerConfig:
|
||||
|
||||
554
vllm/config/speculative.py
Normal file
554
vllm/config/speculative.py
Normal file
@ -0,0 +1,554 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import ast
|
||||
import hashlib
|
||||
from typing import TYPE_CHECKING, Any, Literal, Optional
|
||||
|
||||
from pydantic import SkipValidation, model_validator
|
||||
from pydantic.dataclasses import dataclass
|
||||
from typing_extensions import Self
|
||||
|
||||
import vllm.envs as envs
|
||||
from vllm.config.parallel import ParallelConfig
|
||||
from vllm.config.utils import config
|
||||
from vllm.logger import init_logger
|
||||
from vllm.utils import LazyLoader
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
import vllm.model_executor.layers.quantization as me_quant
|
||||
from vllm.config import ModelConfig
|
||||
else:
|
||||
PretrainedConfig = Any
|
||||
ModelConfig = Any
|
||||
|
||||
me_quant = LazyLoader("model_executor", globals(),
|
||||
"vllm.model_executor.layers.quantization")
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa",
|
||||
"mlp_speculator", "draft_model", "deepseek_mtp",
|
||||
"ernie_mtp", "qwen3_next_mtp"]
|
||||
|
||||
|
||||
@config
|
||||
@dataclass
|
||||
class SpeculativeConfig:
|
||||
"""Configuration for speculative decoding."""
|
||||
|
||||
# General speculative decoding control
|
||||
num_speculative_tokens: SkipValidation[int] = None # type: ignore
|
||||
"""The number of speculative tokens, if provided. It will default to the
|
||||
number in the draft model config if present, otherwise, it is required."""
|
||||
model: Optional[str] = None
|
||||
"""The name of the draft model, eagle head, or additional weights, if
|
||||
provided."""
|
||||
method: Optional[SpeculativeMethod] = None
|
||||
"""The name of the speculative method to use. If users provide and set the
|
||||
`model` param, the speculative method type will be detected automatically
|
||||
if possible, if `model` param is not provided, the method name must be
|
||||
provided.
|
||||
|
||||
If using `ngram` method, the related configuration `prompt_lookup_max` and
|
||||
`prompt_lookup_min` should be considered."""
|
||||
draft_tensor_parallel_size: Optional[int] = None
|
||||
"""The degree of the tensor parallelism for the draft model. Can only be 1
|
||||
or the same as the target model's tensor parallel size."""
|
||||
disable_logprobs: bool = True
|
||||
"""If set to True, token log probabilities are not returned during
|
||||
speculative decoding. If set to False, token log probabilities are returned
|
||||
according to the log probability settings in SamplingParams."""
|
||||
|
||||
# Draft model configuration
|
||||
quantization: Optional[me_quant.QuantizationMethods] = None
|
||||
"""Quantization method that was used to quantize the draft model weights.
|
||||
If `None`, we assume the model weights are not quantized. Note that it only
|
||||
takes effect when using the draft model-based speculative method."""
|
||||
max_model_len: Optional[int] = None
|
||||
"""The maximum model length of the draft model. Used when testing the
|
||||
ability to skip speculation for some sequences."""
|
||||
revision: Optional[str] = None
|
||||
"""The specific model version to use for the draft model. It can be a
|
||||
branch name, a tag name, or a commit id. If unspecified, will use the
|
||||
default version."""
|
||||
code_revision: Optional[str] = None
|
||||
"""The specific revision to use for the draft model code on Hugging Face
|
||||
Hub. It can be a branch name, a tag name, or a commit id. If unspecified,
|
||||
will use the default version."""
|
||||
|
||||
# Advanced control
|
||||
disable_by_batch_size: Optional[int] = None
|
||||
"""Disable speculative decoding for new incoming requests when the number
|
||||
of enqueued requests is larger than this value, if provided."""
|
||||
|
||||
# Ngram proposer configuration
|
||||
prompt_lookup_max: Optional[int] = None
|
||||
"""Maximum size of ngram token window when using Ngram proposer, required
|
||||
when method is set to ngram."""
|
||||
prompt_lookup_min: Optional[int] = None
|
||||
"""Minimum size of ngram token window when using Ngram proposer, if
|
||||
provided. Defaults to 1."""
|
||||
|
||||
speculative_token_tree: Optional[str] = None
|
||||
"""Specifies the tree structure for speculative token generation.
|
||||
"""
|
||||
# required configuration params passed from engine
|
||||
target_model_config: SkipValidation[ModelConfig] = None # type: ignore
|
||||
"""The configuration of the target model."""
|
||||
target_parallel_config: SkipValidation[
|
||||
ParallelConfig] = None # type: ignore
|
||||
"""The parallel configuration for the target model."""
|
||||
enable_chunked_prefill: SkipValidation[bool] = None # type: ignore
|
||||
"""Whether vLLM is configured to use chunked prefill or not. Used for
|
||||
raising an error since it's not yet compatible with speculative decode."""
|
||||
disable_log_stats: SkipValidation[bool] = None # type: ignore
|
||||
"""Whether to disable the periodic printing of stage times in speculative
|
||||
decoding."""
|
||||
|
||||
# params generated in the post-init stage
|
||||
draft_model_config: SkipValidation[ModelConfig] = None # type: ignore
|
||||
"""The configuration of the draft model initialized internal."""
|
||||
draft_parallel_config: SkipValidation[
|
||||
ParallelConfig] = None # type: ignore
|
||||
"""The parallel configuration for the draft model initialized internal."""
|
||||
|
||||
def compute_hash(self) -> str:
|
||||
"""
|
||||
WARNING: Whenever a new field is added to this config,
|
||||
ensure that it is included in the factors list if
|
||||
it affects the computation graph.
|
||||
|
||||
Provide a hash that uniquely identifies all the configs
|
||||
that affect the structure of the computation
|
||||
graph from input ids/embeddings to the final hidden states,
|
||||
excluding anything before input ids/embeddings and after
|
||||
the final hidden states.
|
||||
"""
|
||||
factors: list[Any] = []
|
||||
# Eagle3 affects the computation graph because it returns intermediate
|
||||
# hidden states in addition to the final hidden state.
|
||||
factors.append(self.method == "eagle3")
|
||||
hash_str = hashlib.md5(str(factors).encode(),
|
||||
usedforsecurity=False).hexdigest()
|
||||
return hash_str
|
||||
|
||||
@staticmethod
|
||||
def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
|
||||
if hf_config.model_type == "deepseek_v3":
|
||||
hf_config.model_type = "deepseek_mtp"
|
||||
if hf_config.model_type == "deepseek_mtp":
|
||||
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
||||
hf_config.update({
|
||||
"n_predict": n_predict,
|
||||
"architectures": ["DeepSeekMTPModel"]
|
||||
})
|
||||
|
||||
if hf_config.architectures[0] == "MiMoForCausalLM":
|
||||
hf_config.model_type = "mimo_mtp"
|
||||
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
||||
hf_config.update({
|
||||
"num_hidden_layers": 0,
|
||||
"n_predict": n_predict,
|
||||
"architectures": ["MiMoMTPModel"]
|
||||
})
|
||||
|
||||
if hf_config.architectures[0] == "Glm4MoeForCausalLM":
|
||||
hf_config.model_type = "glm4_moe_mtp"
|
||||
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
||||
hf_config.update({
|
||||
"num_hidden_layers": 0,
|
||||
"n_predict": n_predict,
|
||||
"architectures": ["Glm4MoeMTPModel"]
|
||||
})
|
||||
|
||||
if hf_config.model_type == "ernie4_5_moe":
|
||||
hf_config.model_type = "ernie_mtp"
|
||||
if hf_config.model_type == "ernie_mtp":
|
||||
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
||||
hf_config.update({
|
||||
"n_predict": n_predict,
|
||||
"architectures": ["ErnieMTPModel"]
|
||||
})
|
||||
|
||||
if hf_config.model_type == "qwen3_next":
|
||||
hf_config.model_type = "qwen3_next_mtp"
|
||||
if hf_config.model_type == "qwen3_next_mtp":
|
||||
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
||||
hf_config.update({
|
||||
"n_predict": n_predict,
|
||||
"architectures": ["Qwen3NextMTP"]
|
||||
})
|
||||
|
||||
return hf_config
|
||||
|
||||
def __post_init__(self):
|
||||
|
||||
# Note: "method" is a new parameter that helps to extend the
|
||||
# configuration of non-model-based proposers, and the "model" parameter
|
||||
# will be used to set the draft model, eagle head, or additional weight
|
||||
# when needed. If users do not specify "method", the speculative method
|
||||
# will be detected automatically if possible. If the speculative method
|
||||
# can not be detected, it will be considered as the "draft_model" by
|
||||
# default.
|
||||
|
||||
if self.model is None and self.num_speculative_tokens is not None:
|
||||
# TODO(Shangming): Refactor mtp configuration logic when supporting
|
||||
# mtp acceleration for more models besides deepseek_v3
|
||||
if self.target_model_config and \
|
||||
(self.target_model_config.hf_text_config.model_type \
|
||||
== "deepseek_v3" or
|
||||
self.target_model_config.hf_text_config.model_type in
|
||||
("mimo","ernie4_5_moe", "qwen3_next")):
|
||||
# use the draft model from the same model:
|
||||
self.model = self.target_model_config.model
|
||||
# Align the quantization of draft model for cases such as
|
||||
# --quantization fp8 with a bf16 checkpoint.
|
||||
if not self.quantization:
|
||||
self.quantization = self.target_model_config.quantization
|
||||
elif self.method in ("ngram", "[ngram]"):
|
||||
self.model = "ngram"
|
||||
else:
|
||||
raise ValueError("num_speculative_tokens was provided without "
|
||||
"speculative model.")
|
||||
|
||||
# Automatically configure the method for ngram when "model" is used
|
||||
# instead of "method"
|
||||
if self.method is None and (self.model is not None
|
||||
and self.model in ("ngram", "[ngram]")):
|
||||
self.method = "ngram"
|
||||
|
||||
if self.method in ("ngram", "[ngram]"):
|
||||
# Unified to "ngram" internally
|
||||
self.method = "ngram"
|
||||
# Set default values if not provided
|
||||
if (self.prompt_lookup_min is None
|
||||
and self.prompt_lookup_max is None):
|
||||
# TODO(woosuk): Tune these values. They are arbitrarily chosen.
|
||||
self.prompt_lookup_min = 5
|
||||
self.prompt_lookup_max = 5
|
||||
elif self.prompt_lookup_min is None:
|
||||
assert self.prompt_lookup_max is not None
|
||||
self.prompt_lookup_min = self.prompt_lookup_max
|
||||
elif self.prompt_lookup_max is None:
|
||||
assert self.prompt_lookup_min is not None
|
||||
self.prompt_lookup_max = self.prompt_lookup_min
|
||||
|
||||
# Validate values
|
||||
if self.prompt_lookup_min < 1:
|
||||
raise ValueError(
|
||||
f"prompt_lookup_min={self.prompt_lookup_min} must be > 0")
|
||||
if self.prompt_lookup_max < 1:
|
||||
raise ValueError(
|
||||
f"prompt_lookup_max={self.prompt_lookup_max} must be > 0")
|
||||
if self.prompt_lookup_min > self.prompt_lookup_max:
|
||||
raise ValueError(
|
||||
f"prompt_lookup_min={self.prompt_lookup_min} must "
|
||||
f"be <= prompt_lookup_max={self.prompt_lookup_max}")
|
||||
|
||||
# TODO: current we still need extract vocab_size from target model
|
||||
# config, in future, we may try refactor it out, and set
|
||||
# draft related config as None here.
|
||||
self.draft_model_config = self.target_model_config
|
||||
self.draft_parallel_config = self.target_parallel_config
|
||||
else:
|
||||
self.prompt_lookup_max = 0
|
||||
self.prompt_lookup_min = 0
|
||||
|
||||
if self.model is not None:
|
||||
# TODO: Move this import to the top once `ModelConfig`
|
||||
# lives in `vllm.config.model`.
|
||||
from vllm.config import ModelConfig
|
||||
self.draft_model_config = ModelConfig(
|
||||
model=self.model,
|
||||
runner="draft",
|
||||
tokenizer=self.target_model_config.tokenizer,
|
||||
tokenizer_mode=self.target_model_config.tokenizer_mode,
|
||||
trust_remote_code=self.target_model_config.
|
||||
trust_remote_code,
|
||||
allowed_local_media_path=self.target_model_config.
|
||||
allowed_local_media_path,
|
||||
dtype=self.target_model_config.dtype,
|
||||
seed=self.target_model_config.seed,
|
||||
revision=self.revision,
|
||||
code_revision=self.code_revision,
|
||||
tokenizer_revision=self.target_model_config.
|
||||
tokenizer_revision,
|
||||
spec_target_max_model_len=self.target_model_config.
|
||||
max_model_len,
|
||||
quantization=self.quantization,
|
||||
enforce_eager=self.target_model_config.enforce_eager,
|
||||
max_seq_len_to_capture=self.target_model_config.
|
||||
max_seq_len_to_capture,
|
||||
max_logprobs=self.target_model_config.max_logprobs,
|
||||
hf_overrides=SpeculativeConfig.hf_config_override,
|
||||
)
|
||||
|
||||
# Automatically detect the method
|
||||
if self.method in ('eagle', 'eagle3'):
|
||||
pass
|
||||
# examples:
|
||||
# yuhuili/EAGLE-LLaMA3-Instruct-8B
|
||||
# yuhuili/EAGLE3-LLaMA3.1-Instruct-8B
|
||||
# AngelSlim/Qwen3-8B_eagle3
|
||||
elif "eagle-" in self.draft_model_config.model.lower():
|
||||
self.method = "eagle"
|
||||
elif "eagle3" in self.draft_model_config.model.lower():
|
||||
self.method = "eagle3"
|
||||
elif self.draft_model_config.hf_config.model_type == "medusa":
|
||||
self.method = "medusa"
|
||||
elif (self.draft_model_config.hf_config.model_type ==
|
||||
"mlp_speculator"):
|
||||
self.method = "mlp_speculator"
|
||||
elif (self.draft_model_config.hf_config.model_type
|
||||
in ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp")):
|
||||
self.method = "deepseek_mtp"
|
||||
if self.num_speculative_tokens > 1:
|
||||
logger.warning(
|
||||
"All Deepseek MTP models only have " \
|
||||
"one layer. Might need some code changes " \
|
||||
"to support multiple layers."
|
||||
)
|
||||
elif (self.draft_model_config.hf_config.model_type ==
|
||||
"ernie_mtp"):
|
||||
self.method = "ernie_mtp"
|
||||
if self.num_speculative_tokens > 1:
|
||||
logger.warning(
|
||||
"All Ernie MTP models only have " \
|
||||
"one layer. Might need some code changes " \
|
||||
"to support multiple layers."
|
||||
)
|
||||
elif (self.draft_model_config.hf_config.model_type ==
|
||||
"qwen3_next_mtp"):
|
||||
self.method = "qwen3_next_mtp"
|
||||
if self.num_speculative_tokens > 1:
|
||||
logger.warning(
|
||||
"All Qwen3Next MTP models only have " \
|
||||
"one layer. Might need some code changes " \
|
||||
"to support multiple layers."
|
||||
)
|
||||
else:
|
||||
self.method = "draft_model"
|
||||
raise NotImplementedError(
|
||||
"Speculative decoding with draft model is not "
|
||||
"supported yet. Please consider using other "
|
||||
"speculative decoding methods such as ngram, medusa, "
|
||||
"eagle, or deepseek_mtp.")
|
||||
|
||||
# Replace hf_config for EAGLE draft_model
|
||||
if self.method in ("eagle", "eagle3"):
|
||||
if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
|
||||
raise ValueError(
|
||||
"Chunked prefill and EAGLE are not compatible "
|
||||
"when using V0.")
|
||||
|
||||
from vllm.transformers_utils.configs import (
|
||||
SpeculatorsConfig)
|
||||
from vllm.transformers_utils.configs.eagle import (
|
||||
EAGLEConfig)
|
||||
|
||||
if isinstance(self.draft_model_config.hf_config,
|
||||
(EAGLEConfig, SpeculatorsConfig)):
|
||||
pass
|
||||
else:
|
||||
eagle_config = EAGLEConfig(
|
||||
self.draft_model_config.hf_config,
|
||||
method=self.method,
|
||||
model_type="eagle")
|
||||
self.draft_model_config.hf_config = eagle_config
|
||||
|
||||
if (self.num_speculative_tokens is not None
|
||||
and hasattr(self.draft_model_config.hf_config,
|
||||
"num_lookahead_tokens")):
|
||||
self.draft_model_config.hf_config.num_lookahead_tokens = \
|
||||
self.num_speculative_tokens
|
||||
|
||||
n_predict = getattr(self.draft_model_config.hf_config,
|
||||
"n_predict", None)
|
||||
if n_predict is not None:
|
||||
if self.num_speculative_tokens is None:
|
||||
# Default to max value defined in draft model config.
|
||||
self.num_speculative_tokens = n_predict
|
||||
elif self.num_speculative_tokens > n_predict and \
|
||||
self.num_speculative_tokens % n_predict != 0:
|
||||
# Ensure divisibility for MTP module reuse.
|
||||
raise ValueError(
|
||||
f"num_speculative_tokens:{self.num_speculative_tokens}"
|
||||
f" must be divisible by {n_predict=}")
|
||||
|
||||
if self.speculative_token_tree is None:
|
||||
# Generate chain of tokens.
|
||||
self.speculative_token_tree = str([
|
||||
(i + 1) * (0, )
|
||||
for i in range(self.num_speculative_tokens)
|
||||
])
|
||||
else:
|
||||
# Sort the token tree breadth-first.
|
||||
tree_choices = ast.literal_eval(
|
||||
self.speculative_token_tree)
|
||||
self.speculative_token_tree = str(
|
||||
sorted(tree_choices, key=lambda t: (len(t), t)))
|
||||
|
||||
self.draft_tensor_parallel_size = \
|
||||
SpeculativeConfig._verify_and_get_draft_tp(
|
||||
self.target_parallel_config,
|
||||
self.draft_tensor_parallel_size,
|
||||
self.draft_model_config.hf_config
|
||||
)
|
||||
|
||||
self.draft_model_config.max_model_len = (
|
||||
SpeculativeConfig._maybe_override_draft_max_model_len(
|
||||
self.max_model_len,
|
||||
self.draft_model_config.max_model_len,
|
||||
self.target_model_config.max_model_len,
|
||||
))
|
||||
|
||||
self.draft_parallel_config = (
|
||||
SpeculativeConfig.create_draft_parallel_config(
|
||||
self.target_parallel_config,
|
||||
self.draft_tensor_parallel_size))
|
||||
|
||||
@staticmethod
|
||||
def _maybe_override_draft_max_model_len(
|
||||
speculative_max_model_len: Optional[int],
|
||||
draft_max_model_len: int,
|
||||
target_max_model_len: int,
|
||||
) -> int:
|
||||
"""Determine the max sequence len for the draft model. This is usually
|
||||
the draft_max_model_len, but may be the target_max_model_len if it is
|
||||
less than the draft_max_model_len, or may be speculative_max_model_len
|
||||
if it is specified.
|
||||
|
||||
This is necessary so that sequences do not exceed the capacity of the
|
||||
draft model or the target model.
|
||||
|
||||
speculative_max_model_len is mainly used for testing that sequences can
|
||||
skip speculation.
|
||||
"""
|
||||
|
||||
if speculative_max_model_len is not None:
|
||||
|
||||
if speculative_max_model_len > draft_max_model_len:
|
||||
raise ValueError(f"{speculative_max_model_len=} cannot be "
|
||||
f"larger than {draft_max_model_len=}")
|
||||
|
||||
if speculative_max_model_len > target_max_model_len:
|
||||
raise ValueError(f"{speculative_max_model_len=} cannot be "
|
||||
f"larger than {target_max_model_len=}")
|
||||
|
||||
return speculative_max_model_len
|
||||
|
||||
return min(
|
||||
draft_max_model_len,
|
||||
target_max_model_len,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _verify_and_get_draft_tp(
|
||||
target_parallel_config: ParallelConfig,
|
||||
speculative_draft_tensor_parallel_size: Optional[int],
|
||||
draft_hf_config: PretrainedConfig) -> int:
|
||||
"""
|
||||
Verifies and adjusts the tensor parallel size for a draft model
|
||||
specified using speculative_draft_tensor_parallel_size.
|
||||
"""
|
||||
# If speculative_draft_tensor_parallel_size is unset then set it
|
||||
# appropriately else verify that it is set correctly.
|
||||
if speculative_draft_tensor_parallel_size is None:
|
||||
if draft_hf_config.model_type == "mlp_speculator":
|
||||
speculative_draft_tensor_parallel_size = 1
|
||||
if target_parallel_config.tensor_parallel_size > 1:
|
||||
logger.warning(
|
||||
"%s cannot currently be run with tp>1; "
|
||||
"setting speculative_draft_tensor_parallel_size=1",
|
||||
draft_hf_config.model_type)
|
||||
else:
|
||||
speculative_draft_tensor_parallel_size = \
|
||||
target_parallel_config.tensor_parallel_size
|
||||
elif speculative_draft_tensor_parallel_size not in (
|
||||
1, target_parallel_config.tensor_parallel_size):
|
||||
raise ValueError(
|
||||
f"{speculative_draft_tensor_parallel_size=} cannot be "
|
||||
f"other value than 1 or target model tensor_parallel_size")
|
||||
return speculative_draft_tensor_parallel_size
|
||||
|
||||
@staticmethod
|
||||
def create_draft_parallel_config(
|
||||
target_parallel_config: ParallelConfig,
|
||||
speculative_draft_tensor_parallel_size: int,
|
||||
) -> ParallelConfig:
|
||||
"""Create a parallel config for use by the draft worker.
|
||||
|
||||
This is mostly a copy of the target parallel config, except the tp_size.
|
||||
"""
|
||||
draft_parallel_config = ParallelConfig(
|
||||
pipeline_parallel_size=target_parallel_config.
|
||||
pipeline_parallel_size,
|
||||
tensor_parallel_size=speculative_draft_tensor_parallel_size,
|
||||
distributed_executor_backend=target_parallel_config.
|
||||
distributed_executor_backend,
|
||||
max_parallel_loading_workers=target_parallel_config.
|
||||
max_parallel_loading_workers,
|
||||
disable_custom_all_reduce=target_parallel_config.
|
||||
disable_custom_all_reduce,
|
||||
ray_workers_use_nsight=target_parallel_config.
|
||||
ray_workers_use_nsight,
|
||||
placement_group=target_parallel_config.placement_group,
|
||||
)
|
||||
|
||||
return draft_parallel_config
|
||||
|
||||
@model_validator(mode='after')
|
||||
def _verify_args(self) -> Self:
|
||||
if self.num_speculative_tokens is None:
|
||||
raise ValueError(
|
||||
"num_speculative_tokens must be provided with "
|
||||
"speculative model unless the draft model config contains an "
|
||||
"n_predict parameter.")
|
||||
|
||||
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)
|
||||
|
||||
if (self.disable_by_batch_size is not None
|
||||
and self.disable_by_batch_size < 2):
|
||||
raise ValueError("Expect the batch size threshold of disabling "
|
||||
"speculative decoding is > 1, but got "
|
||||
f"{self.disable_by_batch_size=}")
|
||||
|
||||
eagle3_target_supported = ["llama", "qwen"]
|
||||
if self.method == "eagle3" and self.target_model_config and not any(
|
||||
supported_model in
|
||||
self.target_model_config.hf_text_config.model_type
|
||||
for supported_model in eagle3_target_supported):
|
||||
raise ValueError(
|
||||
f"Eagle3 is only supported for {eagle3_target_supported} models. " # noqa: E501
|
||||
f"Got {self.target_model_config.hf_text_config.model_type=}")
|
||||
|
||||
return self
|
||||
|
||||
@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 use_eagle(self) -> bool:
|
||||
return self.method in ("eagle", "eagle3", "deepseek_mtp", "ernie_mtp",
|
||||
"qwen3_next_mtp")
|
||||
|
||||
def __repr__(self) -> str:
|
||||
method = self.method
|
||||
model = None if method == "ngram" else self.draft_model_config.model
|
||||
num_spec_tokens = self.num_speculative_tokens
|
||||
return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
|
||||
Loading…
x
Reference in New Issue
Block a user