config.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from copy import deepcopy
from typing import TYPE_CHECKING

from vllm.logger import init_logger

if TYPE_CHECKING:
    from vllm.config import VllmConfig

logger = init_logger(__name__)


class VerifyAndUpdateConfig:

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        raise NotImplementedError


class GteNewModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "NewConfig"
        assert config.hidden_act == "gelu"

        config.hidden_act = "geglu"

        head_dim = config.hidden_size // config.num_attention_heads
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
            "max_position": config.max_position_embeddings,
            "base": config.rope_theta,
            "rope_scaling": getattr(config, "rope_scaling", None)
        }


class JinaRobertaModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        if config.position_embedding_type == "rotary":
            assert config.__class__.__name__ == "XLMRobertaFlashConfig"

            head_dim = config.hidden_size // config.num_attention_heads
            config.rotary_kwargs = {
                "head_size": head_dim,
                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
                "max_position": config.max_position_embeddings,
                "base": getattr(config, "rope_theta", config.rotary_emb_base),
                "rope_scaling": getattr(config, "rope_scaling", None)
            }


class NomicBertModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "NomicBertConfig"
        assert config.activation_function in ["swiglu", "gelu"]
        config.position_embedding_type = getattr(config,
                                                 "position_embedding_type",
                                                 "rope")

        if config.activation_function == "swiglu":
            config.hidden_act = "silu"
        else:
            config.hidden_act = config.activation_function

        assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
                config.qkv_proj_bias)
        config.bias = config.qkv_proj_bias

        assert config.rotary_emb_scale_base is None
        assert not config.rotary_emb_interleaved

        config.layer_norm_eps = config.layer_norm_epsilon
        config.intermediate_size = config.n_inner
        config.hidden_size = config.n_embd
        config.num_hidden_layers = config.n_layer

        head_dim = config.hidden_size // config.num_attention_heads
        rotary_emb_dim = head_dim * config.rotary_emb_fraction
        max_trained_positions = getattr(config, "max_trained_positions", 2048)
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": rotary_emb_dim,
            "max_position": max_trained_positions,
            "base": getattr(config, "rope_theta", config.rotary_emb_base),
            "rope_scaling": getattr(config, "rope_scaling", None)
        }

        # we ignore config.rotary_scaling_factor so that for datasets shorter
        # than max_trained_positions 2048, the results are consistent
        # with SentenceTransformer.
        # The context extension uses vllm style rope_theta and rope_scaling.
        # See #17785 #18755
        if (not vllm_config.model_config.hf_overrides
                and vllm_config.model_config.original_max_model_len is None):
            # Default
            # Reset max_model_len to max_trained_positions.
            # nomic-embed-text-v2-moe the length is set to 512
            # by sentence_bert_config.json.
            max_model_len_before = vllm_config.model_config.max_model_len
            max_model_len = min(vllm_config.model_config.max_model_len,
                                max_trained_positions)

            vllm_config.recalculate_max_model_len(max_model_len)
            logger.warning(
                "Nomic context extension is disabled. "
                "Changing max_model_len from %s to %s. "
                "To enable context extension, see: "
                "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
                max_model_len_before, vllm_config.model_config.max_model_len)
        else:
            # We need to re-verify max_model_len to avoid lengths
            # greater than position_embedding.
            model_config = vllm_config.model_config
            hf_text_config = model_config.hf_text_config

            if isinstance(model_config.hf_overrides, dict):
                # hf_overrides_kw
                max_model_len = model_config.hf_overrides.get(
                    "max_model_len", vllm_config.model_config.max_model_len)
            else:
                # hf_overrides_fn
                # This might be overridden by sentence_bert_config.json.
                max_model_len = vllm_config.model_config.max_model_len

            # reset hf_text_config for recalculate_max_model_len.
            if hasattr(hf_text_config, "max_model_len"):
                delattr(hf_text_config, "max_model_len")
            hf_text_config.max_position_embeddings = max_trained_positions
            hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]

            # The priority of sentence_bert_config.json is higher
            # than max_position_embeddings
            encoder_config = deepcopy(model_config.encoder_config)
            encoder_config.pop("max_seq_length", None)
            model_config.encoder_config = encoder_config

            vllm_config.recalculate_max_model_len(max_model_len)


class Qwen3ForSequenceClassificationConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        is_original_qwen3_reranker = getattr(config,
                                             "is_original_qwen3_reranker",
                                             False)

        if not is_original_qwen3_reranker:
            return

        tokens = getattr(config, "classifier_from_token", None)
        assert tokens is not None and len(tokens) == 2, \
            ("Try loading the original Qwen3 Reranker?, see: "
             "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py")
        config.num_labels = 1


class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "GteConfig"
        assert config.hidden_act == "gelu"

        config.hidden_act = "geglu"

        head_dim = config.hidden_size // config.num_attention_heads
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
            "max_position": config.max_position_embeddings,
            "base": config.rope_theta,
            "rope_scaling": getattr(config, "rope_scaling", None)
        }


MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
    "GteModel": SnowflakeGteNewModelConfig,
    "GteNewModel": GteNewModelConfig,
    "NomicBertModel": NomicBertModelConfig,
    "Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
    "XLMRobertaModel": JinaRobertaModelConfig,
}