configuration_nllb_clip.py

""" NLLB-CLIP model configuration"""

import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

if TYPE_CHECKING:
    from transformers.processing_utils import ProcessorMixin
    from transformers.utils import TensorType

from transformers import CLIPVisionConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class NLLBCLIPTextConfig(PretrainedConfig):
    model_type = "clip_text_model"
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
    }

    def __init__(
        self,
        vocab_size=128112,
        max_position_embeddings=1024,
        encoder_layers=12,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        encoder_layerdrop=0.05,
        use_cache=True,
        activation_function="relu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.1,
        activation_dropout=0.0,
        init_std=0.02,
        scale_embedding=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        layer_norm_eps=1e-5,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding
        self.layer_norm_eps = layer_norm_eps

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )

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

        # get the vision config dict if we are loading from CLIPConfig
        if config_dict.get("model_type") == "clip":
            config_dict = config_dict["text_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 NLLBCLIPConfig(PretrainedConfig):
    model_type = "clip"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        logit_scale_init_value=2.6592,
        **kwargs,
    ):
        # If `_config_dict` exist, we use them for the backward compatibility.
        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
        # of confusion!).
        text_config_dict = kwargs.pop("text_config_dict", None)
        vision_config_dict = kwargs.pop("vision_config_dict", None)

        super().__init__(**kwargs)

        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
        if text_config_dict is not None:
            if text_config is None:
                text_config = {}

            # This is the complete result when using `text_config_dict`.
            _text_config_dict = NLLBCLIPTextConfig(**text_config_dict).to_dict()

            # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
            for key, value in _text_config_dict.items():
                if (
                    key in text_config
                    and value != text_config[key]
                    and key not in ["transformers_version"]
                ):
                    # If specified in `text_config_dict`
                    if key in text_config_dict:
                        message = (
                            f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
                            f'The value `text_config_dict["{key}"]` will be used instead.'
                        )
                    # If inferred from default argument values (just to be super careful)
                    else:
                        message = (
                            f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The "
                            f'value `text_config["{key}"]` will be overriden.'
                        )
                    logger.warning(message)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
            text_config.update(_text_config_dict)

        if vision_config_dict is not None:
            if vision_config is None:
                vision_config = {}

            # This is the complete result when using `vision_config_dict`.
            _vision_config_dict = CLIPVisionConfig(**vision_config_dict).to_dict()
            # convert keys to string instead of integer
            if "id2label" in _vision_config_dict:
                _vision_config_dict["id2label"] = {
                    str(key): value
                    for key, value in _vision_config_dict["id2label"].items()
                }

            # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
            for key, value in _vision_config_dict.items():
                if (
                    key in vision_config
                    and value != vision_config[key]
                    and key not in ["transformers_version"]
                ):
                    # If specified in `vision_config_dict`
                    if key in vision_config_dict:
                        message = (
                            f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
                            f'values. The value `vision_config_dict["{key}"]` will be used instead.'
                        )
                    # If inferred from default argument values (just to be super careful)
                    else:
                        message = (
                            f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. "
                            f'The value `vision_config["{key}"]` will be overriden.'
                        )
                    logger.warning(message)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
            vision_config.update(_vision_config_dict)

        if text_config is None:
            text_config = {}
            logger.info(
                "`text_config` is `None`. Initializing the `NLLBCLIPTextConfig` with default values."
            )

        if vision_config is None:
            vision_config = {}
            logger.info(
                "`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values."
            )

        self.text_config = NLLBCLIPTextConfig(**text_config)
        self.vision_config = CLIPVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_factor = 1.0

    @classmethod
    def from_text_vision_configs(
        cls, text_config: NLLBCLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs
    ):
        r"""
        Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
        configuration.
        Returns:
            [`CLIPConfig`]: An instance of a configuration object
        """

        return cls(
            text_config=text_config.to_dict(),
            vision_config=vision_config.to_dict(),
            **kwargs,
        )


class CLIPOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
                (
                    "pixel_values",
                    {0: "batch", 1: "num_channels", 2: "height", 3: "width"},
                ),
            ]
        )

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("logits_per_image", {0: "batch"}),
                ("logits_per_text", {0: "batch"}),
                ("text_embeds", {0: "batch"}),
                ("image_embeds", {0: "batch"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4

    def generate_dummy_inputs(
        self,
        processor: "ProcessorMixin",
        batch_size: int = -1,
        seq_length: int = -1,
        framework: Optional["TensorType"] = None,
    ) -> Mapping[str, Any]:
        text_input_dict = super().generate_dummy_inputs(
            processor.tokenizer,
            batch_size=batch_size,
            seq_length=seq_length,
            framework=framework,
        )
        image_input_dict = super().generate_dummy_inputs(
            processor.image_processor, batch_size=batch_size, framework=framework
        )
        return {**text_input_dict, **image_input_dict}

    @property
    def default_onnx_opset(self) -> int:
        return 14