enginex-mthreads-vllm/vllm/transformers_utils/configs/step3_vl.py

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

from transformers.configuration_utils import PretrainedConfig


class Step3VisionEncoderConfig(PretrainedConfig):
    model_type = "step3_vision_encoder"

    def __init__(
        self,
        hidden_size=1792,
        intermediate_size=3072,
        output_hidden_size=4096,
        num_hidden_layers=63,
        num_attention_heads=16,
        num_channels=3,
        image_size=728,
        patch_size=14,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        **kwargs,
    ):
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.output_hidden_size = output_hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.image_size = image_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        super().__init__(**kwargs)


class Step3TextConfig(PretrainedConfig):
    model_type = "step3_text"
    architectures = ["Step3TextForCausalLM"]

    def __init__(
        self,
        hidden_size: int = 7168,
        intermediate_size: int = 18432,
        num_attention_heads: int = 64,
        num_attention_groups: int = 1,
        num_hidden_layers: int = 61,
        max_seq_len: int = 65536,
        vocab_size: int = 128815,
        rms_norm_eps: float = 1e-5,
        moe_intermediate_size: int = 5120,
        moe_num_experts: int = 48,
        moe_top_k: int = 3,
        rope_parameters: dict[str, Any] | None = None,
        max_position_embedding: int = 65536,
        share_expert_dim: int = 5120,
        share_q_dim: int = 2048,
        head_dim: int = 256,
        norm_expert_weight: bool = False,
        moe_layers_enum: tuple[int, ...] = (
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12,
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21,
            22,
            23,
            24,
            25,
            26,
            27,
            28,
            29,
            30,
            31,
            32,
            33,
            34,
            35,
            36,
            37,
            38,
            39,
            40,
            41,
            42,
            43,
            44,
            45,
            46,
            47,
            48,
            49,
            50,
            51,
            52,
            53,
            54,
            55,
            56,
            57,
            58,
            59,
        ),
        **kwargs,
    ) -> None:
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_attention_heads = num_attention_heads
        self.num_attention_groups = num_attention_groups
        self.num_hidden_layers = num_hidden_layers
        self.max_seq_len = max_seq_len
        self.vocab_size = vocab_size
        self.rms_norm_eps = rms_norm_eps
        self.moe_intermediate_size = moe_intermediate_size
        self.moe_num_experts = moe_num_experts
        self.moe_top_k = moe_top_k
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
        rope_theta = kwargs.pop("rope_theta", 500000.0)
        if "rope_theta" not in rope_parameters:
            rope_parameters["rope_theta"] = rope_theta
        self.rope_parameters = rope_parameters
        self.max_position_embedding = max_position_embedding
        self.share_expert_dim = share_expert_dim
        self.share_q_dim = share_q_dim
        self.head_dim = head_dim
        self.norm_expert_weight = norm_expert_weight
        self.moe_layers_enum = moe_layers_enum

        super().__init__(**kwargs)


class Step3VLConfig(PretrainedConfig):
    model_type = "step3_vl"

    def __init__(
        self,
        vision_config: dict | Step3VisionEncoderConfig | None = None,
        text_config: dict | Step3TextConfig | None = None,
        understand_projector_stride: int = 1,
        projector_bias: bool = True,
        image_token_id: int = 128001,
        **kwargs,
    ) -> None:
        if vision_config is None:
            vision_config = Step3VisionEncoderConfig()
        elif isinstance(vision_config, dict):
            vision_config = Step3VisionEncoderConfig(**vision_config)
        self.vision_config = vision_config

        if text_config is None:
            text_config = Step3TextConfig()
        elif isinstance(text_config, dict):
            text_config = Step3TextConfig(**text_config)
        self.text_config = text_config

        self.understand_projector_stride = understand_projector_stride
        self.projector_bias = projector_bias
        self.hidden_size = text_config.hidden_size
        self.image_token_id = image_token_id

        super().__init__(**kwargs)