configuration_imp.py

# Copyright (c) MILVLG team.
# Licensed under the Apache 2.0 license.
#
# Some code here is copied from the project Phi-2 (https://huggingface.co/microsoft/phi-2),
# SigLIP@transformers==4.37.0.dev0 (https://huggingface.co/google/siglip-so400m-patch14-384),
# and Llava (https://github.com/haotian-liu/LLaVA), and modified by 
# Zhenwei Shao (shaozw@hdu.edu.cn) @ MILVLG. We thank them for their great works.
#
# We keep their original copyright statements as follows, which should be inherited:
# ------------------------------- Phi-2 ---------------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# https://huggingface.co/google/siglip-so400m-patch14-384
# 
# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
# Licensed under the BSD 3-Clause License.
# ------------------------------- SigLIP --------------------------------------------
# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------- Llava ---------------------------------------------
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -----------------------------------------------------------------------------------


import os
import math
from typing import Optional, Union

from transformers import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class PhiConfig(PretrainedConfig):
    """Phi configuration."""

    model_type = "phi-msft"
    attribute_map = {
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size: int = 50304,
        n_positions: int = 2048,
        n_embd: int = 1024,
        n_layer: int = 20,
        n_inner: Optional[int] = None,
        n_head: int = 16,
        n_head_kv: Optional[int] = None,
        rotary_dim: Optional[int] = 32,
        activation_function: Optional[str] = "gelu_new",
        flash_attn: bool = False,
        flash_rotary: bool = False,
        fused_dense: bool = False,
        attn_pdrop: float = 0.0,
        embd_pdrop: float = 0.0,
        resid_pdrop: float = 0.0,
        layer_norm_epsilon: float = 1e-5,
        initializer_range: float = 0.02,
        tie_word_embeddings: bool = False,
        pad_vocab_size_multiple: int = 64,
        **kwargs
    ) -> None:
        self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_inner = n_inner
        self.n_head = n_head
        self.n_head_kv = n_head_kv
        self.rotary_dim = min(rotary_dim, n_embd // n_head)
        self.activation_function = activation_function
        self.flash_attn = flash_attn
        self.flash_rotary = flash_rotary
        self.fused_dense = fused_dense
        self.attn_pdrop = attn_pdrop
        self.embd_pdrop = embd_pdrop
        self.resid_pdrop = resid_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range

        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)



class SiglipVisionConfig(PretrainedConfig):

    model_type = "siglip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=16,
        hidden_act="gelu_pytorch_tanh",
        layer_norm_eps=1e-6,
        attention_dropout=0.0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_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.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        # get the vision config dict if we are loading from SiglipConfig
        if config_dict.get("model_type") == "siglip":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class ImpConfig(PhiConfig):
    model_type = "Sparrow"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.image_token_index = getattr(self, "image_token_index", 50296)
        self.image_token = getattr(self, "image_token", "<image>")

        if not hasattr(self, "vision_tower_config") and hasattr(self, "mm_vision_tower"):
            vision_tower_config = SiglipVisionConfig.from_pretrained(self.mm_vision_tower)
            self.vision_tower_config = vision_tower_config.to_diff_dict()
    
    @property
    def vision_tower_cfg(self):
        cfg = SiglipVisionConfig.from_dict(self.vision_tower_config)
        # imp-v1 only supports `patch` feature for now w/o cls token
        # cfg.mm_vision_select_feature = self.mm_vision_select_feature
        cfg.mm_vision_select_layer = self.mm_vision_select_layer
        cfg.mm_vision_tower = self.mm_vision_tower
        return cfg