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d8c6210eea
Signed-off-by: Julien Denize <julien.denize@mistral.ai> Signed-off-by: Julien Denize <40604584+juliendenize@users.noreply.github.com> Signed-off-by: Mickael Seznec <mickael@mistral.ai> Signed-off-by: Roger Wang <hey@rogerw.io> Co-authored-by: Roger Wang <hey@rogerw.io> Co-authored-by: Mickael Seznec <mickael@mistral.ai>
644 lines
28 KiB
Python
644 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import ast
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from typing import TYPE_CHECKING, Any, Literal, get_args
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from pydantic import Field, SkipValidation, model_validator
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from pydantic.dataclasses import dataclass
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from typing_extensions import Self
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from vllm.config.model import ModelConfig
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from vllm.config.parallel import ParallelConfig
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from vllm.config.utils import config
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from vllm.logger import init_logger
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from vllm.utils.hashing import safe_hash
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from vllm.utils.import_utils import LazyLoader, has_arctic_inference
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if TYPE_CHECKING:
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from transformers import PretrainedConfig
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import vllm.model_executor.layers.quantization as me_quant
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else:
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PretrainedConfig = Any
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me_quant = LazyLoader(
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"model_executor", globals(), "vllm.model_executor.layers.quantization"
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)
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logger = init_logger(__name__)
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MTPModelTypes = Literal[
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"deepseek_mtp",
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"mimo_mtp",
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"glm4_moe_mtp",
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"ernie_mtp",
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"qwen3_next_mtp",
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"longcat_flash_mtp",
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"mtp",
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"pangu_ultra_moe_mtp",
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]
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EagleModelTypes = Literal["eagle", "eagle3", MTPModelTypes]
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SpeculativeMethod = Literal[
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"ngram",
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"medusa",
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"mlp_speculator",
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"draft_model",
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"suffix",
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EagleModelTypes,
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]
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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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enforce_eager: bool | None = None
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"""Override the default enforce_eager from model_config"""
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# General speculative decoding control
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num_speculative_tokens: int = Field(default=None, gt=0)
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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: str | None = 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: SpeculativeMethod | None = 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: int | None = Field(default=None, ge=1)
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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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# Draft model configuration
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quantization: me_quant.QuantizationMethods | None = 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: int | None = Field(default=None, ge=1)
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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: str | None = 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: str | None = 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: int | None = Field(default=None, ge=2)
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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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disable_padded_drafter_batch: bool = False
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"""Disable input padding for speculative decoding. If set to True,
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speculative input batches can contain sequences of different lengths,
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which may only be supported by certain attention backends. This currently
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only affects the EAGLE method of speculation."""
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# Ngram proposer configuration
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prompt_lookup_max: int | None = Field(default=None, ge=1)
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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: int | None = Field(default=None, ge=1)
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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: str | None = 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[ParallelConfig] = None # type: ignore
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"""The parallel configuration for the target model."""
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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[ParallelConfig] = None # type: ignore
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"""The parallel configuration for the draft model initialized internal."""
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# Suffix decoding configuration
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suffix_decoding_max_tree_depth: int = 24
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"""The maximum depth of the suffix decoding global and prompt trees. The
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tree depth limits the sum of the prefix match and speculation lengths."""
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suffix_decoding_max_cached_requests: int = 10000
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"""The maximum number of requests to cache in the global suffix tree. If
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exceeded, will trigger eviction in FIFO order. If set to 0, the global
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suffix tree is disabled and past responses are not cached (prompt trees
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are still used)."""
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suffix_decoding_max_spec_factor: float = 1.0
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"""The maximum spec factor for suffix decoding. The spec factor controls
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speculation lengths based on the prefix match length: max_spec_tokens =
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max_spec_factor * prefix_match_length."""
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suffix_decoding_min_token_prob: float = 0.1
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"""The minimum token probability for suffix decoding. Will only speculate
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tokens with estimated probability (based on frequency counts) greater than
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or equal to this value."""
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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 = safe_hash(str(factors).encode(), 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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initial_architecture = hf_config.architectures[0]
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if hf_config.model_type in ("deepseek_v3", "deepseek_v32"):
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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, "architectures": ["DeepSeekMTPModel"]}
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)
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if hf_config.model_type in ("pangu_ultra_moe"):
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hf_config.model_type = "pangu_ultra_moe_mtp"
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if hf_config.model_type == "pangu_ultra_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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{"n_predict": n_predict, "architectures": ["OpenPanguMTPModel"]}
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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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{
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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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)
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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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{
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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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)
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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, "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, "architectures": ["Qwen3NextMTP"]}
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)
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if hf_config.model_type == "longcat_flash":
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hf_config.model_type = "longcat_flash_mtp"
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n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
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hf_config.update(
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{"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]}
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)
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if initial_architecture == "MistralLarge3ForCausalLM":
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hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})
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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.method in get_args(MTPModelTypes) and self.method != "mtp":
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logger.warning(
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"method `%s` is deprecated and replaced with mtp.", self.method
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)
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self.method = "mtp"
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if self.model is None and self.num_speculative_tokens is not None:
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if self.method == "mtp":
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if self.target_model_config is None:
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raise ValueError("target_model_config must be present for mtp")
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if self.target_model_config.hf_text_config.model_type == "deepseek_v32":
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# FIXME(luccafong): cudgraph with v32 MTP is not supported,
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# remove this when the issue is fixed.
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self.enforce_eager = True
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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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elif self.method == "suffix":
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self.model = "suffix"
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else:
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raise ValueError(
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"num_speculative_tokens was provided but without speculative model."
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)
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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 (
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self.model is not None and self.model in ("ngram", "[ngram]")
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):
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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 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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if self.prompt_lookup_max is None:
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raise ValueError(
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"Either prompt_lookup_max or prompt_lookup_min must be "
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"provided when using the ngram method."
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)
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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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if self.prompt_lookup_min is None:
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raise ValueError(
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"Either prompt_lookup_max or prompt_lookup_min must be "
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"provided when using the ngram method."
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)
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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 > 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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)
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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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elif self.method == "suffix":
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self._validate_suffix_decoding()
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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.trust_remote_code,
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allowed_local_media_path=self.target_model_config.allowed_local_media_path,
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allowed_media_domains=self.target_model_config.allowed_media_domains,
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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.tokenizer_revision,
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spec_target_max_model_len=self.target_model_config.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_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 == "mlp_speculator":
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self.method = "mlp_speculator"
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elif self.draft_model_config.hf_config.model_type in get_args(
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MTPModelTypes
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):
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self.method = "mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"Enabling num_speculative_tokens > 1 will run"
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"multiple times of forward on same MTP layer"
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",which may result in lower acceptance rate"
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)
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elif self.draft_model_config.hf_config.model_type in (
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"longcat_flash_mtp"
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):
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self.method = "longcat_flash_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"LongCat 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 mtp."
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)
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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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from vllm.transformers_utils.configs import SpeculatorsConfig
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from vllm.transformers_utils.configs.eagle import EAGLEConfig
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if isinstance(
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self.draft_model_config.hf_config,
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(EAGLEConfig, SpeculatorsConfig),
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):
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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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)
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self.draft_model_config.hf_config = eagle_config
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if self.num_speculative_tokens is not None and hasattr(
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self.draft_model_config.hf_config, "num_lookahead_tokens"
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):
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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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)
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n_predict = getattr(
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self.draft_model_config.hf_config, "n_predict", None
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)
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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 (
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self.num_speculative_tokens > n_predict
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and self.num_speculative_tokens % n_predict != 0
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):
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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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)
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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,) 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(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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)
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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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)
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|
|
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
|
|
)
|
|
)
|
|
return self
|
|
|
|
def _validate_suffix_decoding(self):
|
|
if not has_arctic_inference():
|
|
raise ImportError(
|
|
"Arctic Inference is required for suffix decoding. "
|
|
"Install via `pip install arctic-inference==0.1.1`."
|
|
)
|
|
if self.num_speculative_tokens is None:
|
|
# Suffix decoding decides the actual number of speculative tokens
|
|
# dynamically and treats num_speculative_tokens as a maximum limit.
|
|
self.num_speculative_tokens = self.suffix_decoding_max_tree_depth
|
|
logger.warning(
|
|
"Defaulted num_speculative_tokens to %s for suffix decoding.",
|
|
self.num_speculative_tokens,
|
|
)
|
|
# Validate values
|
|
if self.suffix_decoding_max_tree_depth < 1:
|
|
raise ValueError(
|
|
f"suffix_decoding_max_tree_depth="
|
|
f"{self.suffix_decoding_max_tree_depth} must be >= 1"
|
|
)
|
|
if self.suffix_decoding_max_cached_requests < 0:
|
|
raise ValueError(
|
|
f"suffix_decoding_max_cached_requests="
|
|
f"{self.suffix_decoding_max_cached_requests} must be >= 0"
|
|
)
|
|
if self.suffix_decoding_max_spec_factor < 0:
|
|
raise ValueError(
|
|
f"suffix_decoding_max_spec_factor="
|
|
f"{self.suffix_decoding_max_spec_factor} must be >= 0"
|
|
)
|
|
if not 0 <= self.suffix_decoding_min_token_prob <= 1:
|
|
raise ValueError(
|
|
f"suffix_decoding_min_token_prob="
|
|
f"{self.suffix_decoding_min_token_prob} must be in [0, 1]"
|
|
)
|
|
|
|
@staticmethod
|
|
def _maybe_override_draft_max_model_len(
|
|
speculative_max_model_len: int | None,
|
|
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: int | None,
|
|
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", "minicpm", "gpt_oss"]
|
|
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
|
|
|
|
def use_eagle(self) -> bool:
|
|
return self.method in ("eagle", "eagle3", "mtp")
|
|
|
|
def __repr__(self) -> str:
|
|
method = self.method
|
|
model = None if method in ("ngram", "suffix") else self.draft_model_config.model
|
|
num_spec_tokens = self.num_speculative_tokens
|
|
return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
|