wehub-resource-sync
1304 lines
57 KiB
Python
1304 lines
57 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 copy
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import functools
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from collections.abc import Callable
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from typing import TYPE_CHECKING, Any, Literal, get_args
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from pydantic import Field, SkipValidation, field_validator, model_validator
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from typing_extensions import Self
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from vllm.config import LoadConfig
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from vllm.config.kernel import MoEBackend
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from vllm.config.model import HfOverrides, 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.transformers_utils.config import get_hf_text_config
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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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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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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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"mimo_v2_mtp",
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"glm4_moe_mtp",
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"glm4_moe_lite_mtp",
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"glm_ocr_mtp",
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"ernie_mtp",
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"nemotron_h_mtp",
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"exaone_moe_mtp",
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"exaone4_5_mtp",
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"qwen3_next_mtp",
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"qwen3_5_mtp",
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"longcat_flash_mtp",
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"minimax_m3_mtp",
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"bailing_hybrid_mtp",
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"mtp",
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"pangu_ultra_moe_mtp",
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"step3p5_mtp",
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"hy_v3_mtp",
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"gemma4_mtp",
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]
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NgramGPUTypes = Literal["ngram_gpu"]
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DFlashModelTypes = Literal["dflash"]
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DSparkModelTypes = Literal["dspark"]
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EagleModelTypes = Literal[
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"eagle", "eagle3", "extract_hidden_states", MTPModelTypes, DFlashModelTypes
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]
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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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"custom_class",
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EagleModelTypes,
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NgramGPUTypes,
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DSparkModelTypes,
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]
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RejectionSampleMethod = Literal["standard", "synthetic", "block"]
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DraftSampleMethod = Literal["greedy", "probabilistic"]
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@config
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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) # type: ignore[assignment]
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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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tensor_parallel_size: int | None = None
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"""Users should pass "draft_tensor_parallel_size". This parameter's purpose is to
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warn users when they mistakenly provide the wrong argument."""
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# Draft model configuration
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quantization: me_quant.QuantizationMethods | str | 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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moe_backend: MoEBackend | None = None
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"""MoE backend to use for the draft model. When `None`, the draft model
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inherits the target model's `--moe-backend` setting. Useful when the
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drafter and generator require different MoE kernels (e.g. quantized
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generator with unquantized drafter)."""
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attention_backend: AttentionBackendEnum | None = None
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"""Attention backend to use for the draft model. When `None`, the backend is
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automatically selected. Useful when the drafter requires a different attention
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backend (e.g. DFlash needs a non-causal-capable backend like FLASH_ATTN)."""
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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_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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use_local_argmax_reduction: bool = False
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"""Use vocab-parallel local argmax instead of all-gathering full logits
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for draft token generation. Reduces communication from O(vocab_size) to
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O(2 * tp_size) per token. Only applies to greedy draft selection in
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non-tree speculation."""
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use_heterogeneous_vocab: bool = False
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"""Allow draft and target models to use different vocabularies.
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When enabled, builds a token-level intersection at init and constrains
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draft logits to shared tokens only (TLI algorithm). Requires
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method='draft_model'."""
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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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# Alternative drafting strategies
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parallel_drafting: bool = False
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"""Enable parallel drafting, where all speculative tokens are generated
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in parallel rather than sequentially. This can improve performance but
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requires the speculative model be trained to support parallel drafting.
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Only compatible with EAGLE and draft model methods."""
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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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# dynamic speculative decoding control
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num_speculative_tokens_per_batch_size: list[tuple[int, int, int]] | None = None
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"""Batch-size schedule used to dynamically choose speculative-token count.
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Each entry is ``(range_start, range_end, num_speculative_tokens)`` with an
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inclusive batch-size range.
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"""
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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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draft_load_config: LoadConfig | None = None
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"""Load config for the draft model. If not specified, will use the load
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config from the target model."""
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rejection_sample_method: RejectionSampleMethod = "standard"
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"""The rejection sampling method to use. 'standard' uses probabilistic
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rejection sampling (with or without cached draft logits, controlled by
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draft_sample_method). 'synthetic' accepts draft tokens with a decaying
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probability calibrated to synthetic_acceptance_rate. 'block' uses block
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verification (Sun et al.), which jointly verifies the draft tokens as a
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block instead of one at a time."""
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synthetic_acceptance_rates: list[float] | None = None
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"""Per-position *unconditional* acceptance rates for synthetic rejection
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sampling. Position i's entry is the marginal probability that the first
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i+1 draft tokens are all accepted; the list must have length
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num_speculative_tokens, each entry in [0, 1], and be monotonically
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non-increasing. Only valid when rejection_sample_method is 'synthetic'.
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Mutually exclusive with synthetic_acceptance_length."""
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synthetic_acceptance_length: float | None = None
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"""Target mean acceptance length for synthetic rejection sampling, in
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[1, num_speculative_tokens + 1]. Resolved internally to
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synthetic_acceptance_rates. Only valid when rejection_sample_method is 'synthetic'.
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Mutually exclusive with synthetic_acceptance_rates."""
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@staticmethod
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def _acceptance_length_to_rates(length: float, n: int) -> list[float]:
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"""Mean acceptance length to unconditional per-position rates, using
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the minimum-variance schedule."""
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num_drafts = length - 1 # expected number of accepted draft tokens
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num_full = int(num_drafts)
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return (
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[1.0] * num_full + [num_drafts - num_full] + [0.0] * (n - num_full - 1)
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)[:n]
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@staticmethod
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def _resolve_synthetic_acceptance_rates(
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n: int,
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rates: list[float] | None,
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length: float | None,
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) -> list[float]:
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"""Return per-position unconditional acceptance rates from exactly one
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of `rates` or `length` (validates range, length, and monotonicity)."""
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if (rates is None) == (length is None):
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raise ValueError(
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"rejection_sample_method='synthetic' requires exactly one of "
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"synthetic_acceptance_rates or synthetic_acceptance_length."
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)
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if rates is not None:
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if len(rates) != n:
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raise ValueError(
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f"synthetic_acceptance_rates must have length {n}, got {rates}."
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)
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if not all(0.0 <= r <= 1.0 for r in rates):
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raise ValueError(
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f"synthetic_acceptance_rates entries must be in [0, 1], "
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f"got {rates}."
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)
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if any(rates[i] > rates[i - 1] for i in range(1, n)):
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raise ValueError(
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f"synthetic_acceptance_rates must be non-increasing, got {rates}."
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)
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return list(rates)
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assert length is not None
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if not 1.0 <= length <= float(n + 1):
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raise ValueError(
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f"synthetic_acceptance_length must be in [1, {n + 1}], got {length}."
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)
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return SpeculativeConfig._acceptance_length_to_rates(length, n)
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draft_sample_method: DraftSampleMethod = "greedy"
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"""How the draft model samples tokens. 'greedy' always picks the argmax
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token, and the draft probabilities are treated as one-hot during rejection
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sampling. 'probabilistic' samples stochastically from the draft
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distribution and uses the full draft logits for the probability ratio test
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during rejection sampling. This comes at the cost of additional GPU memory
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usage."""
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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 and extract_hidden_states affect the computation graph because
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# they return intermediate hidden states in addition to the final hidden state.
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uses_aux_hidden_states = self.method in (
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"eagle3",
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"extract_hidden_states",
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"dflash",
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"dspark",
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)
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factors.append(uses_aux_hidden_states)
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if uses_aux_hidden_states and self.draft_model_config is not None:
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factors.append(self.draft_model_config.compute_hash())
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# The specific layers used also affect the computation graph.
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layer_ids = getattr(
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self.draft_model_config.hf_config,
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"eagle_aux_hidden_state_layer_ids",
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None,
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)
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if layer_ids is not None:
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# Convert to tuple to make it hashable
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factors.append(tuple(layer_ids))
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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 (
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"deepseek_v3",
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"deepseek_v32",
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"glm_moe_dsa",
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):
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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 == "deepseek_v4":
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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": ["DeepSeekV4MTPModel"]}
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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 (arch := hf_config.architectures[0]) in (
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"MiMoV2ForCausalLM",
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"MiMoV2OmniForCausalLM",
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):
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from vllm.model_executor.models.mimo_v2_mtp import (
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_MIMO_V2_PRO_NUM_MTP_LAYERS,
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)
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mtp_arch_maps = {
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"MiMoV2ForCausalLM": "MiMoV2MTPModel",
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"MiMoV2OmniForCausalLM": "MiMoV2OmniMTPModel",
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}
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hf_config.model_type = "mimo_v2_mtp"
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# vLLM currently supports only the first MiMo-V2 MTP layer.
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n_predict = _MIMO_V2_PRO_NUM_MTP_LAYERS
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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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"num_nextn_predict_layers": n_predict,
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"architectures": [mtp_arch_maps[arch]],
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}
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)
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if hf_config.architectures[0] == "MiMoV2FlashForCausalLM":
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from vllm.model_executor.models.mimo_v2_mtp import (
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_MIMO_V2_FLASH_NUM_MTP_LAYERS,
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)
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hf_config.model_type = "mimo_v2_mtp"
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# vLLM currently supports only the first MiMo-V2 MTP layer.
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n_predict = _MIMO_V2_FLASH_NUM_MTP_LAYERS
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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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"num_nextn_predict_layers": n_predict,
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"architectures": ["MiMoV2MTPModel"],
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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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"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.architectures[0] == "Glm4MoeLiteForCausalLM":
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hf_config.model_type = "glm4_moe_lite_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": ["Glm4MoeLiteMTPModel"],
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}
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)
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if hf_config.architectures[0] == "GlmOcrForConditionalGeneration":
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hf_config.model_type = "glm_ocr_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": ["GlmOcrMTPModel"],
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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.architectures[0] == "NemotronH_Super_Omni_Reasoning_V3":
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# Promote VLM's text_config so MTP detection below fires correctly
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hf_config = hf_config.text_config
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if (
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hf_config.model_type in {"nemotron_h", "nemotron_h_puzzle"}
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and hasattr(hf_config, "num_nextn_predict_layers")
|
|
and hf_config.num_nextn_predict_layers > 0
|
|
):
|
|
# Check if this is an MTP variant
|
|
hf_config.model_type = "nemotron_h_mtp"
|
|
if hf_config.model_type == "nemotron_h_mtp":
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["NemotronHMTPModel"]}
|
|
)
|
|
|
|
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"]}
|
|
)
|
|
|
|
architectures = getattr(hf_config, "architectures", []) or []
|
|
if (
|
|
hf_config.model_type == "bailing_hybrid"
|
|
or "BailingMoeV2_5ForCausalLM" in architectures
|
|
):
|
|
hf_config.model_type = "bailing_hybrid_mtp"
|
|
if hf_config.model_type == "bailing_hybrid_mtp":
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
|
hf_config.update(
|
|
{
|
|
"n_predict": n_predict,
|
|
"architectures": ["BailingMoeV25MTPModel"],
|
|
}
|
|
)
|
|
|
|
if hf_config.model_type == "exaone_moe":
|
|
hf_config.model_type = "exaone_moe_mtp"
|
|
if hf_config.model_type == "exaone_moe_mtp":
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["ExaoneMoeMTP"]}
|
|
)
|
|
if "exaone4_5" in hf_config.model_type:
|
|
hf_config.model_type = "exaone4_5_mtp"
|
|
if hf_config.model_type == "exaone4_5_mtp":
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["Exaone4_5_MTP"]}
|
|
)
|
|
if hf_config.model_type in ("qwen3_5", "qwen3_5_moe"):
|
|
is_moe = hf_config.model_type == "qwen3_5_moe"
|
|
hf_config.model_type = "qwen3_5_mtp"
|
|
n_predict = getattr(hf_config, "mtp_num_hidden_layers", None)
|
|
hf_config.update(
|
|
{
|
|
"n_predict": n_predict,
|
|
"architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
|
|
}
|
|
)
|
|
if hf_config.model_type == "intern_s2_preview":
|
|
text_config = getattr(hf_config, "text_config", None)
|
|
is_moe = getattr(text_config, "model_type", None) == "qwen3_5_moe_text"
|
|
hf_config.model_type = "qwen3_5_mtp"
|
|
n_predict = getattr(text_config, "mtp_num_hidden_layers", None)
|
|
hf_config.update(
|
|
{
|
|
"n_predict": n_predict,
|
|
"architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
|
|
}
|
|
)
|
|
if hf_config.model_type in ("longcat_flash", "longcat_flash_ngram"):
|
|
hf_config.model_type = "longcat_flash_mtp"
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]}
|
|
)
|
|
|
|
if hf_config.model_type in ("step3p5", "step3p7") or hf_config.architectures[
|
|
0
|
|
] in ("Step3p5ForCausalLM", "Step3p7ForConditionalGeneration"):
|
|
quantization_config = getattr(hf_config, "quantization_config", None)
|
|
hf_config = getattr(hf_config, "text_config", hf_config)
|
|
if (
|
|
quantization_config is not None
|
|
and getattr(hf_config, "quantization_config", None) is None
|
|
):
|
|
hf_config.update({"quantization_config": quantization_config})
|
|
hf_config.model_type = "step3p5_mtp"
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
|
|
hf_config.update({"n_predict": n_predict, "architectures": ["Step3p5MTP"]})
|
|
|
|
if initial_architecture == "MistralLarge3ForCausalLM":
|
|
hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})
|
|
|
|
if hf_config.model_type == "hy_v3":
|
|
hf_config.model_type = "hy_v3_mtp"
|
|
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["HYV3MTPModel"]}
|
|
)
|
|
|
|
if hf_config.model_type in ("gemma4_assistant", "gemma4_unified_assistant"):
|
|
hf_config.model_type = "gemma4_mtp"
|
|
text_config = getattr(hf_config, "text_config", hf_config)
|
|
# The assistant runs all decoder layers in a single forward
|
|
# call to produce one draft token, so n_predict=1.
|
|
# num_kv_shared_layers must be 0: cross-model KV sharing is
|
|
# set up by the proposer after model construction.
|
|
if hasattr(text_config, "num_kv_shared_layers"):
|
|
text_config.num_kv_shared_layers = 0
|
|
hf_config.update({"n_predict": 1, "architectures": ["Gemma4MTPModel"]})
|
|
|
|
if (
|
|
hf_config.model_type == "minimax_m3_vl"
|
|
or initial_architecture == "MiniMaxM3SparseForConditionalGeneration"
|
|
):
|
|
# MTP modules live on the language model of this VL checkpoint, so
|
|
# promote text_config before rewriting it into an MTP config.
|
|
quantization_config = getattr(hf_config, "quantization_config", None)
|
|
hf_config = getattr(hf_config, "text_config", hf_config)
|
|
if (
|
|
quantization_config is not None
|
|
and getattr(hf_config, "quantization_config", None) is None
|
|
):
|
|
hf_config.update({"quantization_config": quantization_config})
|
|
hf_config.model_type = "minimax_m3_mtp"
|
|
n_predict = getattr(hf_config, "num_mtp_modules", 1)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["MiniMaxM3MTP"]}
|
|
)
|
|
elif (
|
|
hf_config.model_type == "minimax_m3_mtp"
|
|
or initial_architecture == "MiniMaxM3MTP"
|
|
):
|
|
# Standalone MTP checkpoints already use a flat MTP config with no
|
|
# VL wrapper / text_config to promote, so just normalize the
|
|
# architecture and derive n_predict from num_mtp_modules.
|
|
n_predict = getattr(hf_config, "num_mtp_modules", 1)
|
|
hf_config.update(
|
|
{"n_predict": n_predict, "architectures": ["MiniMaxM3MTP"]}
|
|
)
|
|
|
|
return hf_config
|
|
|
|
@staticmethod
|
|
def _apply_composed_hf_override(
|
|
target_hf_overrides: Callable[[PretrainedConfig], PretrainedConfig],
|
|
hf_config: PretrainedConfig,
|
|
) -> PretrainedConfig:
|
|
hf_config = SpeculativeConfig.hf_config_override(hf_config)
|
|
return target_hf_overrides(hf_config)
|
|
|
|
@staticmethod
|
|
def compose_draft_hf_overrides(
|
|
target_hf_overrides: HfOverrides | None,
|
|
) -> Callable[[PretrainedConfig], PretrainedConfig]:
|
|
"""Build the ``hf_overrides`` for the draft ``ModelConfig``.
|
|
|
|
Callable overrides on the target are config-to-config transforms
|
|
(e.g. test harnesses shrinking ``num_hidden_layers``) and must also
|
|
reach the draft config — otherwise a draft belonging to a large
|
|
target is instantiated at full size even when the target is shrunk.
|
|
Dict overrides are target-specific key patches and are not applied
|
|
to the draft.
|
|
|
|
The composed override must stay picklable: the draft ``ModelConfig``
|
|
is sent to spawned engine-core processes, so a local closure would
|
|
fail with ``Can't get local object`` during pickling. Bind the
|
|
target via ``functools.partial`` over a module-referenceable static
|
|
method instead.
|
|
"""
|
|
if not callable(target_hf_overrides):
|
|
return SpeculativeConfig.hf_config_override
|
|
|
|
return functools.partial(
|
|
SpeculativeConfig._apply_composed_hf_override, target_hf_overrides
|
|
)
|
|
|
|
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.
|
|
|
|
# infer method from user args
|
|
# Check if the model field contains a custom module path (e.g., 'pkg.Mod')
|
|
if (
|
|
self.model is not None
|
|
and "." in self.model
|
|
and not self.model.startswith(("http://", "https://", "file://"))
|
|
and "/" not in self.model # not a HuggingFace repo (org/model)
|
|
):
|
|
# Treat as a custom class path
|
|
self.method = "custom_class"
|
|
elif self.method is None:
|
|
if self.model in ("ngram", "[ngram]"):
|
|
self.method = "ngram"
|
|
else:
|
|
self.method = "draft_model"
|
|
|
|
if self.method in get_args(MTPModelTypes) and self.method != "mtp":
|
|
logger.warning(
|
|
"method `%s` is deprecated and replaced with mtp.", self.method
|
|
)
|
|
self.method = "mtp"
|
|
|
|
if self.model is None and self.num_speculative_tokens is not None:
|
|
if self.method == "mtp":
|
|
if self.target_model_config is None:
|
|
raise ValueError("target_model_config must be present for mtp")
|
|
if self.target_model_config.hf_text_config.model_type == "deepseek_v32":
|
|
# FIXME(luccafong): cudagraph with v32 MTP is not supported,
|
|
# remove this when the issue is fixed.
|
|
self.enforce_eager = True
|
|
# 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 == "dspark":
|
|
# DeepSeek DSpark can ship the weights inside the target checkpoint
|
|
if self.target_model_config is None:
|
|
raise ValueError("target_model_config must be present for dspark")
|
|
self.model = self.target_model_config.model
|
|
if not self.quantization:
|
|
self.quantization = self.target_model_config.quantization
|
|
elif self.method in ("ngram", "[ngram]"):
|
|
self.model = "ngram"
|
|
elif self.method == "ngram_gpu":
|
|
self.model = "ngram_gpu"
|
|
elif self.method == "suffix":
|
|
self.model = "suffix"
|
|
elif self.method == "extract_hidden_states":
|
|
self.model = "extract_hidden_states"
|
|
elif self.method == "custom_class":
|
|
# method was set explicitly, but model should already contain the
|
|
# custom module path. If not, this is a configuration error.
|
|
if self.model is None:
|
|
raise ValueError(
|
|
"method='custom_class' requires 'model' to contain the "
|
|
"custom proposer module path (e.g., 'my_module.MyProposer')."
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"num_speculative_tokens was provided but without speculative model."
|
|
)
|
|
|
|
if self.method in ("ngram", "[ngram]"):
|
|
self.method = "ngram"
|
|
|
|
if self.method in ("ngram", "ngram_gpu"):
|
|
# 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:
|
|
if self.prompt_lookup_max is None:
|
|
raise ValueError(
|
|
"Either prompt_lookup_max or prompt_lookup_min must be "
|
|
"provided when using the ngram method."
|
|
)
|
|
self.prompt_lookup_min = self.prompt_lookup_max
|
|
elif self.prompt_lookup_max is None:
|
|
if self.prompt_lookup_min is None:
|
|
raise ValueError(
|
|
"Either prompt_lookup_max or prompt_lookup_min must be "
|
|
"provided when using the ngram method."
|
|
)
|
|
self.prompt_lookup_max = self.prompt_lookup_min
|
|
|
|
# Validate values
|
|
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
|
|
elif self.method == "suffix":
|
|
self._validate_suffix_decoding()
|
|
elif self.method == "custom_class":
|
|
# Custom class proposer does not need a draft model.
|
|
# It will dynamically load the user-provided class at runtime.
|
|
logger.warning_once(
|
|
"Using a custom class-based proposer backend. This is an "
|
|
"experimental feature and the proposer interface is subject to "
|
|
"breaking changes in future vLLM releases."
|
|
)
|
|
self.prompt_lookup_max = 0
|
|
self.prompt_lookup_min = 0
|
|
self.draft_model_config = self.target_model_config
|
|
self.draft_parallel_config = self.target_parallel_config
|
|
elif self.method == "extract_hidden_states":
|
|
from vllm.transformers_utils.configs.extract_hidden_states import (
|
|
ExtractHiddenStatesConfig,
|
|
)
|
|
|
|
# ExtractHiddenStatesModel is instantiated manually in load_model()
|
|
# We just need to store the target model config for KV cache shape info
|
|
self.model = "extract_hidden_states"
|
|
self.prompt_lookup_max = 0
|
|
self.prompt_lookup_min = 0
|
|
|
|
if hasattr(self.draft_model_config, "hf_config"):
|
|
hf_config = self.draft_model_config.hf_config.to_dict()
|
|
elif (
|
|
isinstance(self.draft_model_config, dict)
|
|
and "hf_config" in self.draft_model_config
|
|
):
|
|
hf_config = self.draft_model_config["hf_config"]
|
|
else:
|
|
hf_config = {}
|
|
|
|
self.draft_model_config = copy.copy(self.target_model_config)
|
|
self.draft_model_config.hf_config = ExtractHiddenStatesConfig(
|
|
self.draft_model_config.hf_config, **hf_config
|
|
)
|
|
self.update_arch_()
|
|
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:
|
|
# Old-format Medusa checkpoints (e.g. FasterDecoding/medusa-*)
|
|
# lack a model_type key in config.json, so AutoConfig cannot
|
|
# detect them. When the method is explicitly "medusa", inject
|
|
# model_type so MedusaConfig.from_pretrained is used instead.
|
|
draft_hf_overrides: HfOverrides
|
|
if self.method == "medusa":
|
|
draft_hf_overrides = {"model_type": "medusa"}
|
|
else:
|
|
# Compose any callable hf_overrides set on the target so the
|
|
# draft config receives the same transform (e.g. the test
|
|
# shrink). Dict overrides stay target-only.
|
|
draft_hf_overrides = SpeculativeConfig.compose_draft_hf_overrides(
|
|
self.target_model_config.hf_overrides
|
|
)
|
|
self.draft_model_config = ModelConfig(
|
|
model=self.model,
|
|
runner="draft",
|
|
tokenizer=(
|
|
self.model
|
|
if self.use_heterogeneous_vocab
|
|
else 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,
|
|
allowed_media_domains=self.target_model_config.allowed_media_domains,
|
|
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,
|
|
max_model_len=self.max_model_len, # type: ignore[arg-type]
|
|
spec_target_max_model_len=self.target_model_config.max_model_len,
|
|
quantization=self.quantization,
|
|
enforce_eager=self.target_model_config.enforce_eager,
|
|
max_logprobs=self.target_model_config.max_logprobs,
|
|
hf_overrides=draft_hf_overrides,
|
|
config_format=self.target_model_config.config_format,
|
|
)
|
|
|
|
# Old-format Medusa checkpoints (e.g. FasterDecoding/medusa-*)
|
|
# omit vocab_size in config.json, so MedusaConfig falls back to
|
|
# its default (32001). Align with the target model's vocab size
|
|
# to avoid shape mismatches when loading LM-head weights.
|
|
if self.method == "medusa":
|
|
target_vocab = self.target_model_config.hf_config.vocab_size
|
|
draft_hf = self.draft_model_config.hf_config
|
|
if draft_hf.vocab_size != target_vocab:
|
|
draft_hf.vocab_size = target_vocab
|
|
draft_hf.truncated_vocab_size = target_vocab
|
|
|
|
# Automatically detect the method
|
|
if self.method in ("eagle", "eagle3", "dflash", "dspark"):
|
|
pass
|
|
# examples:
|
|
# yuhuili/EAGLE-LLaMA3-Instruct-8B
|
|
# yuhuili/EAGLE3-LLaMA3.1-Instruct-8B
|
|
# AngelSlim/Qwen3-8B_eagle3
|
|
# deepseek-ai/dspark_qwen3_8b_block7
|
|
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 "dflash" in self.draft_model_config.model.lower():
|
|
self.method = "dflash"
|
|
elif (
|
|
"dspark" in self.draft_model_config.model.lower()
|
|
or "Qwen3DSparkModel" in self.draft_model_config.architectures
|
|
):
|
|
self.method = "dspark"
|
|
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 get_args(
|
|
MTPModelTypes
|
|
):
|
|
self.method = "mtp"
|
|
if (
|
|
self.num_speculative_tokens > 1
|
|
and self.draft_model_config.hf_config.model_type
|
|
!= "step3p5_mtp"
|
|
):
|
|
logger.warning(
|
|
"Enabling num_speculative_tokens > 1 will run "
|
|
"multiple times of forward on same MTP layer"
|
|
",which may result in lower acceptance rate"
|
|
)
|
|
elif self.method == "draft_model":
|
|
pass
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Unsupported speculative method: '{self.method}'"
|
|
)
|
|
|
|
# Replace hf_config for EAGLE draft_model
|
|
if self.method in ("eagle", "eagle3", "dflash"):
|
|
from vllm.transformers_utils.configs.eagle import EAGLEConfig
|
|
from vllm.transformers_utils.configs.speculators import (
|
|
SpeculatorsConfig,
|
|
)
|
|
|
|
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
|
|
self.update_arch_()
|
|
|
|
if self.method == "dspark" and (
|
|
"Qwen3DSparkModel" not in self.draft_model_config.architectures
|
|
):
|
|
# DeepSeek-V4 DSpark reuses the full DeepSeek-V4 config
|
|
# and its weights ship in the target checkpoint.
|
|
self.draft_model_config.hf_config.model_type = "deepseek_v4"
|
|
self.draft_model_config.hf_config.architectures = [
|
|
"DSparkDraftModel"
|
|
]
|
|
self.update_arch_()
|
|
|
|
if self.method in ("dflash", "dspark"):
|
|
self.parallel_drafting = True
|
|
|
|
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.num_speculative_tokens is None:
|
|
raise ValueError(
|
|
"A speculative model was provided, but "
|
|
"`num_speculative_tokens` was not provided"
|
|
)
|
|
|
|
if self.method == "dspark":
|
|
# DSpark is a semi-autoregressive *block* drafter. A
|
|
# speculative length smaller than the checkpoint's block
|
|
# feeds the block / Markov-head machinery an unsupported
|
|
# layout and yields incorrect (garbled) output rather than
|
|
# merely lower acceptance. Require num_speculative_tokens to
|
|
# be at least the block size (e.g. 5 or 7 for DeepSeek-V4).
|
|
dspark_block_size = getattr(
|
|
self.draft_model_config.hf_config,
|
|
"dspark_block_size",
|
|
None,
|
|
)
|
|
if (
|
|
dspark_block_size is not None
|
|
and self.num_speculative_tokens < dspark_block_size
|
|
):
|
|
raise ValueError(
|
|
"DSpark requires num_speculative_tokens >= "
|
|
f"dspark_block_size ({dspark_block_size}); got "
|
|
f"{self.num_speculative_tokens}. Smaller values "
|
|
"produce incorrect output. Use "
|
|
f"num_speculative_tokens={dspark_block_size} or "
|
|
"larger (e.g. 7)."
|
|
)
|
|
|
|
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
|
|
)
|
|
)
|
|
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
|
|
|
|
result = min(
|
|
draft_max_model_len,
|
|
target_max_model_len,
|
|
)
|
|
if result != draft_max_model_len:
|
|
logger.info(
|
|
"Overriding draft model max model len from %d to %d",
|
|
draft_max_model_len,
|
|
result,
|
|
)
|
|
return result
|
|
|
|
@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
|
|
|
|
def update_arch_(self):
|
|
"""
|
|
EagleConfig and ExtractHiddenStatesConfig update architectures, so update all
|
|
architectures-related fields in self.draft_model_config
|
|
"""
|
|
self.draft_model_config.hf_text_config = get_hf_text_config(
|
|
self.draft_model_config.hf_config
|
|
)
|
|
self.draft_model_config.model_arch_config = (
|
|
self.draft_model_config.get_model_arch_config()
|
|
)
|
|
model_info, arch = self.draft_model_config.registry.inspect_model_cls(
|
|
self.draft_model_config.architectures,
|
|
self.draft_model_config,
|
|
)
|
|
self.draft_model_config._model_info = model_info
|
|
self.draft_model_config._architecture = arch
|
|
|
|
@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
|
|
|
|
@field_validator("attention_backend", mode="before")
|
|
@classmethod
|
|
def _parse_attention_backend(cls, value: Any) -> Any:
|
|
if isinstance(value, str):
|
|
if value.lower() == "auto":
|
|
return None
|
|
return AttentionBackendEnum[value.upper()]
|
|
return value
|
|
|
|
@model_validator(mode="after")
|
|
def _verify_args(self) -> Self:
|
|
if self.tensor_parallel_size is not None:
|
|
raise ValueError(
|
|
"'tensor_parallel_size' is not a valid argument in the "
|
|
"speculative_config. Please pass 'draft_tensor_parallel_size' instead."
|
|
)
|
|
|
|
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.rejection_sample_method == "synthetic":
|
|
# Consolidate to per-position rates
|
|
self.synthetic_acceptance_rates = self._resolve_synthetic_acceptance_rates(
|
|
self.num_speculative_tokens,
|
|
self.synthetic_acceptance_rates,
|
|
self.synthetic_acceptance_length,
|
|
)
|
|
self.synthetic_acceptance_length = None
|
|
elif (
|
|
self.synthetic_acceptance_rates is not None
|
|
or self.synthetic_acceptance_length is not None
|
|
):
|
|
raise ValueError(
|
|
"synthetic_acceptance_rates / synthetic_acceptance_length "
|
|
"are only valid with rejection_sample_method='synthetic'."
|
|
)
|
|
|
|
if self.draft_model_config:
|
|
self.draft_model_config.verify_with_parallel_config(
|
|
self.draft_parallel_config
|
|
)
|
|
|
|
if self.use_heterogeneous_vocab and not self.uses_draft_model():
|
|
raise ValueError(
|
|
"use_heterogeneous_vocab only works with method='draft_model'"
|
|
)
|
|
|
|
if self.use_heterogeneous_vocab and self.draft_sample_method != "greedy":
|
|
raise ValueError(
|
|
"use_heterogeneous_vocab currently only supports greedy draft "
|
|
"sampling. Set draft_sample_method='greedy' (the default) or "
|
|
"omit it."
|
|
)
|
|
|
|
if not self.use_heterogeneous_vocab:
|
|
self.verify_equal_vocab_size_if_draft_model()
|
|
return self
|
|
|
|
def verify_equal_vocab_size_if_draft_model(self):
|
|
if (
|
|
self.method == "draft_model"
|
|
and self.target_model_config is not None
|
|
and self.draft_model_config is not None
|
|
):
|
|
target_vocab_size = self.target_model_config.get_vocab_size()
|
|
draft_vocab_size = self.draft_model_config.get_vocab_size()
|
|
if target_vocab_size != draft_vocab_size:
|
|
raise ValueError(
|
|
f"Target and draft model should have the same vocabulary size. "
|
|
f"Target model vocab_size={target_vocab_size}. "
|
|
f"Draft model vocab_size={draft_vocab_size}. "
|
|
f"Using models with different tokenizers can cause out-of-bounds "
|
|
f"errors during speculative decoding."
|
|
)
|
|
|
|
@property
|
|
def max_num_new_slots_for_drafting(self) -> int:
|
|
"""
|
|
Calculate the maximum number of new slots that might be added to the batch
|
|
when drafting.
|
|
"""
|
|
slots_per_req = 0 # for serial non-draft-model methods, no change needed
|
|
if self.parallel_drafting:
|
|
# For parallel drafting, we need one new slot per 'masked' token
|
|
slots_per_req = self.num_speculative_tokens - 1
|
|
if self.uses_draft_model():
|
|
# For draft model-based speculation, we need one new slot per request
|
|
# Since we do not slice the draft tokens
|
|
slots_per_req += 1
|
|
return slots_per_req
|
|
|
|
def use_gemma4_mtp(self) -> bool:
|
|
return (
|
|
self.method == "mtp"
|
|
and self.draft_model_config is not None
|
|
and getattr(self.draft_model_config.hf_config, "model_type", None)
|
|
== "gemma4_mtp"
|
|
)
|
|
|
|
def use_step3p5_mtp(self) -> bool:
|
|
return (
|
|
self.method == "mtp"
|
|
and self.draft_model_config is not None
|
|
and getattr(self.draft_model_config.hf_config, "model_type", None)
|
|
== "step3p5_mtp"
|
|
)
|
|
|
|
def use_eagle(self) -> bool:
|
|
# NOTE: This method is usually a stand-in for "speculative decoding using
|
|
# target model hidden states"
|
|
# TODO(ben): Refactor this so the naming is clearer
|
|
return self.method in ("eagle", "eagle3", "mtp", "dflash", "dspark")
|
|
|
|
def use_dflash(self) -> bool:
|
|
return self.method == "dflash"
|
|
|
|
def use_dspark(self) -> bool:
|
|
return self.method == "dspark"
|
|
|
|
def uses_dynamic_speculative_decoding(self) -> bool:
|
|
return self.num_speculative_tokens_per_batch_size is not None
|
|
|
|
def uses_draft_model(self) -> bool:
|
|
return self.method == "draft_model"
|
|
|
|
def uses_extract_hidden_states(self) -> bool:
|
|
return self.method == "extract_hidden_states"
|
|
|
|
def use_ngram_gpu(self) -> bool:
|
|
return self.method == "ngram_gpu"
|
|
|
|
def __repr__(self) -> str:
|
|
method = self.method
|
|
model = (
|
|
None
|
|
if method
|
|
in (
|
|
"ngram",
|
|
"suffix",
|
|
"extract_hidden_states",
|
|
"custom_class",
|
|
)
|
|
else self.draft_model_config.model
|
|
)
|
|
num_spec_tokens = self.num_speculative_tokens
|
|
return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
|