Files
2026-01-19 10:38:50 +08:00

115 lines
4.3 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from typing import Any
from transformers import PretrainedConfig
from vllm.transformers_utils.configs.speculators.algos import (
SUPPORTED_SPECULATORS_TYPES,
)
__all__ = ["SpeculatorsConfig"]
class SpeculatorsConfig(PretrainedConfig):
model_type = "speculators"
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike,
**kwargs,
) -> "SpeculatorsConfig":
"""Load speculators Eagle config and convert to vLLM format."""
config_dict, _ = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
vllm_config = cls.extract_vllm_speculative_config(config_dict)
return cls(**vllm_config)
@classmethod
def extract_vllm_speculative_config(
cls, config_dict: dict[str, Any]
) -> dict[str, Any]:
speculators_model_type = config_dict.get("speculators_model_type")
if speculators_model_type not in SUPPORTED_SPECULATORS_TYPES:
raise ValueError(
f"Expected one of: {SUPPORTED_SPECULATORS_TYPES}. "
"Please ensure you're loading a speculators-format model."
)
# validate fields
# TODO: @dsikka - use speculators pydantic model to validate
cls.validate_speculators_config(config_dict=config_dict)
# Convert from speculators config -> format that can be ingested by vLLM
vllm_config = cls.build_vllm_speculative_config(config_dict=config_dict)
# Apply anything specific to the supported algorithm
algo_updater = SUPPORTED_SPECULATORS_TYPES[speculators_model_type]
algo_updater(config_dict=config_dict, vllm_config=vllm_config)
return vllm_config
@classmethod
def validate_speculators_config(cls, config_dict: dict[str, Any]) -> None:
try:
spec_config = config_dict["speculators_config"]
methods = spec_config["proposal_methods"]
first_method = methods[0]
_ = first_method["speculative_tokens"]
_ = spec_config["verifier"]["name_or_path"]
_ = config_dict["speculators_model_type"]
except (KeyError, IndexError, TypeError) as e:
raise ValueError("Invalid speculators config structure") from e
if "transformer_layer_config" not in config_dict:
raise ValueError("Must provide transformer_layer_config")
if not isinstance(config_dict["transformer_layer_config"], dict):
raise TypeError(
"'transformer_layer_config' must be a dictionary if provided"
)
@classmethod
def build_vllm_speculative_config(
cls, config_dict: dict[str, Any]
) -> dict[str, Any]:
"""
Build vLLM-compatible speculative configuration from speculators format.
This method extracts and transforms speculative configuration from the
speculators format into the structure expected by vLLM.
Args:
config_dict: Configuration dictionary in speculators format
Returns:
Dictionary with vLLM-compatible speculative configuration
"""
# Extract speculators configuration
spec_config = config_dict["speculators_config"]
# Currently we only support one proposal method
proposal_methods = spec_config.get("proposal_methods")
if not proposal_methods:
raise ValueError("No proposal methods found in speculators config")
first_method = proposal_methods[0]
num_speculative_tokens = first_method.get("speculative_tokens")
if num_speculative_tokens is None:
raise ValueError(
f"Missing 'speculative_tokens' in proposal method. Got: {first_method}"
)
# Build base vLLM speculative configuration
vllm_config = {
"method": config_dict.get("speculators_model_type"),
"num_speculative_tokens": num_speculative_tokens,
"target_model": spec_config.get("verifier")["name_or_path"],
}
# Merge transformer layer configuration if present
transformer_config = config_dict.get("transformer_layer_config", {})
vllm_config.update(transformer_config)
return vllm_config